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Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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Shona McCombes

25 Types of Research Designs

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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Research design refers to the strategies and methods researchers employ to carry out their research and reach valid and reliable results.

It can refer to the collection, interpretation, and analysis of the dataset.

While various sources claim there are between 4 and 5 types of research design (each list, it seems, differs in its arguments), under each type are sub-types, representing the diversity of ways of going about conducting research.

For example, Jalil (2015) identified five types: descriptive, correlational, experimental, and meta-analytic. But the farther we broaden our scope to include the wide array of fields of study in academic research, the more we can incorporate – for example, in cultural studies, thematic content analysis is a very common, albeit somewhat alternative, way of designing a study of empirical data.

So, below, I present 25 potential forms of research design that can be employed in an academic empirical study.

Types of Research Designs

1. experimental research design.

The experimental research design involves manipulating one variable to determine if changes in one variable lead to changes in another variable.

An experimental research design tends to split research participants into two groups, known as the control group and experimental group(Abbott & McKinney, 2013). The control group receives nothing, or, a placebo (e.g. sugar pill), while the experimental group is provided the dependant variable (e.g. a new medication).

Participants are typically assigned to groups at random in order to control for any extraneous variables that could influence the results. Furthermore, the study may occur in a controlled environment where extraneous variables can be controlled and minimized, allowing for the analysis of cause-and-effect.

Example of Experimental Research Design In a study exploring the effects of sleep deprivation on cognitive performance, the researcher might take two groups of people. One group is deprived of sleep for 24 hours (experimental group), while the other group is allowed a full night’s sleep (control group). The researcher then measures the cognitive performance of both groups. If the sleep-deprived group performs significantly worse, it could be inferred that sleep deprivation negatively affects cognitive performance.

See Also: Experimental vs Observational Research Design

2. Causal Research Design

Causal research design is used when the goal is to find a cause-and-effect relationship between two variables – an independent vs dependent variable.

This design is used to determine whether one variable influences another variable (Ortiz & Greene, 2007).

Causal research involves conducting experiments where one or more variables are manipulated and the effects are measured.

It seeks to isolate cause-and-effect relationships by holding all factors constant except for the one under investigation (the independent variable). Researchers then observe if changes to the manipulated variables cause changes to the variable they are measuring (the dependent variable).

There are three criteria that must be met to determine causality in a causal research design:

• Temporal Precedence: This means the cause (independent variable) must occur before the effect (dependent variable). For example, if you are studying the impact of studying on test scores, the studying must occur before the test.
• Covariation of the Cause and Effect: Observing that a change in the independent variable is accompanied by a change in the dependent variable. For example, decreased class sizes (cause) might lead to improved test scores (effect), which we could plot on a chart.
• No Plausible Alternative Explanations: The researcher must be able to rule out other factors or variables that might be causing the observed effect. This is often the most challenging criteria to meet and is typically addressed through the use of control groups and random assignment in experimental designs (Ortiz & Greene, 2007)..

Example of Causal Research Design Consider a study that aims to investigate the impact of classroom size on academic achievement. The researchers choose a causal research design, where they collect data on the size of each classroom (independent variable) and then compare that to the average academic performance of each class group (dependent variable). They would then be bale to determine whether students in smaller classes perform at any different rate, on average, compared to larger class groups. If there is a difference, they may be able to demonstrate a causal relationship between classroom size and academic performance.

3. Correlational Research Design

A correlational research design is used when researchers want to determine if there is a relationship between two variables, but it does not necessarily mean that one variable causes changes in the other (Marczyk, DeMatteo & Festinger, 2010).

The primary goal is to identify whether two variables are related and if they move together, i.e., change in one variable is associated with the change in another variable (Abbott & McKinney, 2013; Marczyk, DeMatteo & Festinger, 2010). This relationship can be positive (both variables increase or decrease together), negative (one variable increases while the other decreases), or nonexistent (no connection between the variables).

However, unlike causal research design that we looked at above, correlation does not imply causation. Just because two variables correlate doesn’t mean that changing one variable will change the other.

Example of Correlational Research Design For example, researchers could be interested in finding out if there is a relationship between the amount of time spent on homework (variable one) and academic performance (variable two). If students who spend more time on homework tend to have better academic performance, then there is a positive correlation between these two variables. However, they may not be able to determine that this correlation implies causation. Other factors could be at play. To make it causal design, they may need to employ control and experimental groups in the study.

Also See: 15 Examples of Random Assignment

4. Diagnostic Research Design

Diagnostic research is a type of research that is conducted to identify and understand the nature of a phenomenon or to develop a profile of characteristics related to a certain issue (Abbott & McKinney, 2013; Leavy, 2022).

It is more precise and focused than exploratory research and goes further to provide additional insights about the specifics of the problem.

In the context of medical or psychological research, diagnostic research often involves detailed examinations or tests to identify the nature of a disease or disorder, its causes, symptoms, and effects. The objective of this research is to gain a deep understanding of the problem in order to provide a diagnosis or create an intervention (Leavy, 2022).

In non-clinical research, diagnostic research still focuses on understanding a particular issue or phenomenon in depth. Researchers collect data and investigate to determine the source of particular problems, behaviors, attitudes, or market trends. This could involve conducting detailed interviews, observations, surveys, or reviewing existing records.

Example of Diagnostic Research Design Suppose a teacher is curious about why students in her class are struggling with reading comprehension. She may conduct a diagnostic study where she individually assesses each student’s reading skills , looking for patterns of common difficulties. She may find that many of the students struggle with vocabulary, identifying main ideas, or making inferences. This insight can then guide her teaching strategies to improve students’ reading comprehension.

5. Exploratory research design

Exploratory research is a type of research conducted to clarify ambiguous problems or discover ideas that can be potential research topics.

This type of research is usually conducted when a problem is not clearly defined. It is the preliminary stage of research and helps to define the problem statement, understand the underlying phenomena, or set the stage for further research (Abbott & McKinney, 2013).

Exploratory research design does not aim to provide conclusive results or decide a course of action. Instead, it focuses on gaining insights and familiarity with the subject.

It’s typically characterized by its flexibility, as it allows researchers to shift their focus as new data and insights are collected. The main methods of data collection for exploratory research are survey research, qualitative research , literature reviews , case studies, and focus groups.

Exploratory Research Example Design Consider a business that is noticing a decline in its customer retention rates. They are not sure of the cause, so they decide to conduct exploratory research. They may start with open-ended surveys or interviews with their customers to understand their needs and challenges. Based on the initial feedback, they might find several possible causes – poor customer service, outdated product features, or increased competition. These insights can help define further research to fully understand and address the identified issues.

6. Observational research design

Observational research, as the name suggests, involves observing subjects in their natural environment without any manipulation or control by the researcher.

This can be done in a number of ways including direct observation, participant observation , unobtrusive observation, and structured observation (Marczyk, DeMatteo & Festinger, 2010; Ortiz & Greene, 2007).

Observational research is particularly valuable when researchers want to study behavior as it naturally occurs, without interference or intervention. It can provide a high degree of ecological validity , which means the behavior is likely a reflection of real life because it’s observed in a natural setting. However, observational research may be influenced by observer bias and can be time-consuming and difficult to replicate.

Example of Observational Research Design  A child psychologist may want to study the impact of playground design on children’s social interactions. Using observational research, they could spend time watching children play in different playground environments, recording their interactions and behaviors. This could reveal patterns such as more cooperative play on playgrounds with particular features, which could inform future playground design.

7. Descriptive research design

Descriptive research is a form of research design aims to accurately and systematically describe a situation, problem, phenomenon, service, or program, or provides information about, say, the living conditions of a community, or describes attitudes towards an issue (Abbott & McKinney, 2013;).

It provides a snapshot of the variables included in the study at a particular point in time.

Descriptive research does not fit neatly into the definition of either quantitative or qualitative research methodologies , but instead, it can utilize elements of both, often within the same study.

The descriptive function of research relies on instrumentation for measurement and observations. The descriptive research results in our ability to carefully describe the phenomena, events, or case under study.

Example of Descriptive Research A market research company is hired to understand the types of customers frequenting a new shopping mall. They may conduct descriptive research using methods such as surveys, interviews, and observations. This could result in a detailed description of customer demographics, preferences, and behaviors. The information could then be used by the mall’s management to make strategic business decisions.

8. Case study

Case study research is a design that involves studying a specific phenomenon, person, or group of people in a specific context (Bennett, 2004).

This allows you to go into depth in the study, gaining strong insights into a specific instance.

Case studies tend to be qualitative, not quantitative. The knowledge that can be generated via a case study project can reveal high-quality insights, but is not generalizable because there is not sufficient breadth of subjects or contexts in order to get a good grasp of whether the case study is representative of a broader experience.

Example of a Case Study A researcher conducts a case study in one classroom, examining a new teaching method that the teachers have implemented. The study focuses on how the teacher and students adapt to the new method, conducting semi-structured interviews with the teachers and students. While the study provides specific and detailed insights of the teaching method in that classroom, it cannot be generalized to other educational settings, as statistical significance has not been established to achieve generalization.

See Also: Case Study Advantages and Disadvantages

9. Action research design

Action research is a research design that involves using the scientific method to study professional practice in the workplace and improve upon it.

The defining purpose of action research is to improve workplace practice. In this sense, it’s extremely practical, designed to achieve tangible results for a specific practitioner in a specific setting.

Gillis and Jackson (2002) offer a very concise definition of action research:

“systematic collection and analysis of data for the purpose of taking action and making change” (p.264).

Action research is often participatory, meaning the practitioner is both a participant in the research and the person studying the phenomenon (Macdonald, 2012).

This design is often cyclical, meaning the practitioner implements a change, studies it, then uses the feedback to implement another change, and so forth, until substantive change is made.

Example of Action Research Design I supervised one research student, Mark, who completed an action research study in his own classroom under my supervision. He implemented a digital game-based approach to literacy teaching with boys and interviewed his students to see if the use of games as stimuli for storytelling helped draw them into the learning experience. You can read his study here (Ellison & Drew, 2019).

10. Cross-sectional research design

A cross-sectional research design involves collecting data on a sample of individuals at one specific point in time (Levin, 2006).

Unlike longitudinal studies, which examine variables across a time horizon, a cross-sectional design will only collect data at one point in time.

The researchers will generally collect various datapoints at the one time to study how they are interrelated, the predominance of some other others, and so on.

A cross-sectional research is descriptive only, painting a picture of a sub-population being analyzed, but cannot determine cause and effect .

Cross-Sectional Research Example Psychologists could collect data on people’s socioeconomic status (for example, their current income levels, education, and occupation). During the study, they may also gather data on self-reported mental health status using validated Likert scales. Based on this dataset, the researchers then explored the relationship between socioeconomic status and profession and mental health. While this provided excellent descriptive insights about which professions and SES groups tend to have higher mental health concerns, the researchers could not determine causal factors through the cross-sectional study alone.

11. Sequential research design

Sequential research design is a method that combines both quantitative and qualitative research approaches, in a sequence, to gain a broader understanding of a research problem (Abbott & McKinney, 2013; Leavy, 2022).

This approach allows the researcher to take the benefits of both methods, using one method to enhance or inform the other.

It may take the form of:

• QUAN→QUAL: This design involves conducting quantitative analysis first, then supplementing it with a qualitative study.
• QUAL→QUAN: This design goes in the other direction, starting with qualitative analysis and ending with quantitative analysis.

This type of research design allows for flexibility and is particularly effective when the researcher doesn’t have a clear idea of the problems that will arise during the research.

It also allows the researcher to adapt the study according to the emerging results, which can lead to a more nuanced and informed understanding of a research problem. However, this research design can be time-consuming and requires substantial resources, as it involves two phases of research.

Sequential Research Example  A researcher interested in understanding the effectiveness of a new teaching method could first conduct quantitative research, such as a survey, to measure the overall student performance. Then, in the second phase, the researcher could conduct qualitative research, such as focus group discussions or interviews, to understand the students’ experiences with the new teaching method.

12. Cohort research design

Cohort research is a form of longitudinal study design that observes a defined group, or cohort, over a period of time.

The cohort can be defined by a common characteristic or set of characteristics. Cohort studies are often used in life sciences, social sciences , and health research (Marczyk, DeMatteo & Festinger, 2010; Ortiz & Greene, 2007).

Cohort research allows for the analysis of sequences and patterns in life events. It can be retrospective (observing historical data) or prospective (collecting data forward in time).

The major advantage of cohort research is its ability to study causation, i.e., to make definitive statements about cause-and-effect relationships. However, it can be time-consuming and expensive to conduct.

Cohort Research Example A health researcher could study a cohort of smokers and non-smokers over a period of 20 years to understand the long-term effects of smoking on lung health. The researcher could gather data at regular intervals, tracking changes in the participants’ health over time.

13. Historical research design

Historical research design involves studying the past to draw conclusions that are relevant to the present or the future (Danto, 2008).

This research method involves a deep dive into historical data to gain a clear understanding of past events, contexts, or phenomena.

Historical research helps us understand how past events inform current circumstances. It can include the examination of records, documents, artifacts, and other archival material (Danto, 2008).

However, the reliability of historical research is often challenged due to the accuracy of past records, potential bias in recorded histories, and the interpretive nature of the analysis.

Historical Research Example  A historian might conduct research on the economic impact of the Great Depression on the United States. They would likely analyze data from that era, such as economic indicators, governmental policies, and personal accounts to form a comprehensive understanding of the economic climate of the time.

14. Field research design

Field research is a qualitative method of research concerned with understanding and interpreting the social interactions, behaviors, and perceptions within a specific social or environmental setting.

It involves collecting data ‘in the field’, i.e., in a natural or social setting, and often involves direct and prolonged contact with participants.

Field research can include observations, interviews, and document review. The goal is to gain insights into a group’s practices, behaviors, and culture by observing and interacting with them in their natural environment. This method can provide rich, contextual data but is also time-intensive and requires significant planning to ensure representative sampling and accurate recording of data.

Field Research Example An anthropologist studying the social practices of a remote indigenous tribe may live with the tribe for several months, participating in their daily activities, observing, and documenting their practices and rituals. Through this field research, they can understand the tribe’s social structure, beliefs, and customs in

15. Systematic review

A systematic review is a type of research design that involves a comprehensive and structured overview of existing literature on a specific topic (Jalil, 2015).

This research method aims to collate all empirical evidence that fits pre-specified eligibility criteria to answer a specific research question.

The systematic review follows a transparent and replicable methodology to minimize bias and ensure reliability.

It involves identifying, evaluating, and interpreting all available research relevant to the research question.

However, it can be time-consuming and resource-intensive and relies heavily on the availability and quality of existing studies.

Systematic Review Example A health researcher interested in the impact of a plant-based diet on heart disease might conduct a systematic review of all published studies on the topic. They would gather, analyze, and synthesize data from these studies to draw a comprehensive understanding of the current evidence base on this issue.

A survey research design involves gathering information from a sample of individuals using a standardized questionnaire or interview format (Fowler, 2013).

Surveys can be used to describe, compare, or explain individual and societal phenomena. Surveys allow for data collection from a large population, in a cost-effective and efficient manner (Fowler, 2013).

They can be delivered in various formats, such as online, telephone, mail, or in-person.

However, the reliability of survey data can be affected by several factors, such as response bias and sample representativeness.

Survey Example A market research company might use a survey to understand consumer preferences for a new product. They could distribute the survey to a representative sample of their target market, asking questions about preferences, behaviors, and demographics to inform the product’s development and marketing strategy.

17. Meta-analysis research design

A meta-analysis is a type of research design that involves looking over the current literature on a topic and assessing its quality, trends, and collective insights (Borenstein et al., 2021).

Meta-analysis doesn’t involve collecting first-hand data, but rather using secondary data in the form of the results of other peoples’ studies.

It then analyzes the quality and findings of each study in-depth, comparing and contrasting each study, and synthesizing the data from the collective studies deemed of sufficient quality, to see what collective knowledge these studies can provide (Borenstein et al., 2021).

Meta-analyses are considered some of the most valuable and respected research designs because they can demonstrate that there is sufficient data from the scientific community for an authoritative scientific account of a phenomenon or topic.

Meta-Analysis Example In the early 2000s, a few small studies arguing that vaccines caused autism caused moral panic in the media. In response, several meta-analyses emerged that combined the collective data from the scientific community. These meta-analyses demonstrated that, across the scientifically rigorous studies, overwhelming consensus showed there was no correlation between vaccines and autism (see: Taylor, Swerdfeger & Eslick, 2014).

18. Mixed-method research design

Mixed-method research design is a method that combines both quantitative (numerical data) and qualitative (non-numerical data) research techniques, methods, approaches, concepts or language into a single study.

This approach to research allows for the capturing of a more complete, holistic picture of the phenomena being studied (Leavy, 2022; Marczyk, DeMatteo & Festinger, 2010).

Mixed-method research can provide a more in-depth understanding of a research problem or question. It allows the researcher to explore complex phenomena and validate the findings.

However, it requires a thorough understanding of both quantitative and qualitative research methods and can be time-consuming.

Mixed-Methods Example An education researcher interested in student motivation might use a mixed-method approach. They could distribute a survey (quantitative method) to measure levels of motivation, and then conduct interviews ( qualitative method ) to gain a deeper understanding of factors influencing student motivation.

19. Longitudinal research design

Longitudinal studies take place over a long period of time to explore changes to the research subjects or variables over time (Neale, 2020).

This sort of study is often valuable in detecting correlations between variables over the course of an intervention.

By examining multiple data points at different period, it’s possible to record continuous changes within things like consumer behavior or demographics of a society (Vogl, 2023).

This makes a detailed analysis of change possible.

For example, a national census, conducted every 5 years, can be considered longitudinal. It gathers comparative demographic data that can show how the demographics of an area have changed over time.

Longitudinal Study Example The famous Minnesota Twins study examined identical twins who were raised in separate environments to examine whether behavioral and personality traits were genetic or environmental. The study by Thomas J Bouchard, which took place between 1979 to 1990, argued that identical twins who grew up separate and in different environments did not display any greater chances of being different from each other than twins that were raised together in the same house. The study indicated that similarities in personality and behavior between twins are likely genetic rather than environmental in nature, giving sway to the argument that nature is more powerful than nurture (Bouchard et. al., 1990).

20. Philosophical research design

Philosophical research is a research design that uses philosophical methods to address broad questions about issues such as reality, morality, existence, truth, justice, and freedom (Novikov & Novikov, 2013).

This type of research often involves broad, abstract thinking and deep contemplation on the fundamental nature of human existence.

Philosophical research often relies on the critical analysis of texts , argumentation, and the formulation of theories. It requires abstract thinking and logical reasoning, but it doesn’t usually involve empirical studies.

However, it’s invaluable for underpinning other research methods and for informing our understanding of fundamental principles and theories.

Philosophical Research Example A researcher studying ethics might use a philosophical research design to explore the concept of ‘justice’ in various societies. They would likely examine a variety of texts, historical contexts , and moral frameworks, before formulating a comprehensive theory of justice.

21. Grounded Theory

Grounded theory is characterized by a research study where no hypothesis is being tested. Instead, a hypothesis or ‘theory’ emerges out of the study (Tracy, 2019).

This goes against most research designs, where a researcher starts with a hypothesis and then they create a study to test the hypothesis. Then, they would usually come to a result affirming or debunking the study.

But in grounded theory, we start with a phenomenon, and then we go about studying it to identify themes and insights that emerge from the data. At the end of the study, the researchers would come up with a theory or hypothesis.

This has the strength of remaining open-minded about the possible outcomes of the study, and not being restricted to only studying a specifically noted hypothesis from the beginning.

Grounded Theory Example Developing a Leadership Identity   by Komives et al (2005) employs a grounded theory approach to develop a thesis based on the data rather than testing a hypothesis. The researchers studied the leadership identity of 13 college students taking on leadership roles. Based on their interviews, the researchers theorized that the students’ leadership identities shifted from a hierarchical view of leadership to one that embraced leadership as a collaborative concept.

22. Ethnographic Research Design

Ethnographic research is a qualitative research design that aims to explore and understand the culture, social interactions, behaviors, and perceptions of a group of people (Stokes & Wall, 2017).

The methodology is derived from the field of anthropology where researchers immerse themselves in the culture they’re studying to gather in-depth insights.

An ethnographic study is usually conducted over an extended period of time and involves observing and interacting with the participants in their natural setting  (Stokes & Wall, 2017).

This method can provide rich, detailed, and nuanced data. However, it is time-consuming, and its success heavily relies on the skill and sensitivity of the researcher to understand and interpret the cultural nuances of the group.

Ethnographic Research Example A researcher interested in understanding the impact of digital technology on the daily life of a remote indigenous tribe might spend several months living with the tribe. The researcher would observe and participate in their daily activities, conduct informal interviews, and take detailed field notes to capture the changes and influences brought about by digital technology.

23. Quasi-Experimental Research Design

A quasi-experimental research design resembles an experimental design but lacks the element of random assignment to treatment or control (Abbott & McKinney, 2013; Leavy, 2022).

Instead, subjects are assigned to groups based on non-random criteria. Quasi-experiments are often used in social sciences where it’s difficult or ethically problematic to manipulate independent variables and randomly assign participants (Ortiz & Greene, 2007).

While quasi-experimental designs help establish causal relationships, they can be subject to confounding variables, which may impact the validity of the results. Also, the lack of random assignment can result in selection bias .

Quasi-Experimental Design Example A researcher studying the impact of an educational program on students’ performance might compare the test scores of students who chose to participate in the program (the treatment group) with those who did not (the control group). The researcher could control for factors such as gender, age, and previous performance, but without random assignment, there could be other differences between the groups that impact the results.

24. Comparative Research Design

Comparative research is a research design that involves comparing two or more groups, cultures, variables, or phenomena to identify similarities and differences (Abbott & McKinney, 2013).

The comparison can be cross-sectional (comparing at a single point in time) or longitudinal (comparing over time).

Comparative research can provide insight into the effects of different variables and contribute to understanding social, economic, political, or cultural issues across different contexts. However, ensuring comparability can be challenging as factors influencing the variables being studied can vary widely between contexts.

Comparative Research Design Example A social scientist studying gender inequality might compare the wage gap, educational attainment, and political representation in several countries. The researcher would collect data from each country and conduct a comparative analysis to identify patterns, trends, and differences, contributing to a broader understanding of gender inequality globally.

25. Thematic Content Analysis

Content analysis  has a range of sub-designs, such as semiotic analysis,  multimodal analysis , and  discourse analysis . But overall, this design focuses on the analysis of texts and language.

A content analysis will involve systematic and objective coding and interpreting of text or media to identify patterns, biases , themes, ideologies, and so on (Schweigert, 2021).

They may focus on newspapers, movies, films, political speeches, and other types of ‘content’ contain narratives and biases.

The design is often thematic, involving deductive or inductive coding , whereby researchers look through the data for ‘codes’ such as word choice, word repetition, and other meaning-making elements which, combined, can give insights into themes that emerge throughout the texts.

Content Analysis Example Poorebrahim and Zarei (2013) employ a popular type of content analysis called critical discourse analysis (common in  poststructuralist  and  critical theory research ) to study newspapers in their study titled How is Islam Portrayed in Western Media? . This study combs through a group of media texts to explore the language and symbolism that is used in relation to Islam and Muslims. The study demonstrates how media content has the capacity to stereotype Muslims, representing anti-Islam bias or failure to understand the Islamic world.

Abbott, M. L., & McKinney, J. (2013).  Understanding and applying research design . John Wiley & Sons.

Bennett, A. (2004). Case study methods: Design, use, and comparative advantages.  Models, numbers, and cases: Methods for studying international relations ,  2 (1), 19-55.

Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2021).  Introduction to meta-analysis . John Wiley & Sons.

Danto, E. A. (2008).  Historical research . Oxford University Press.

Fowler Jr, F. J. (2013).  Survey research methods . London: Sage publications.

Gillis, A., & Jackson, W. (2002).  Research Methods for Nurses: Methods and Interpretation . Philadelphia: F.A. Davis Company.

Jalil, M. M. (2013). Practical guidelines for conducting research-Summarising good research practice in line with the DCED standard.  Available at SSRN 2591803 .

Leavy, P. (2022).  Research design: Quantitative, qualitative, mixed methods, arts-based, and community-based participatory research approaches . Guilford Publications.

Levin, K. A. (2006). Study design III: Cross-sectional studies.  Evidence-based Dentistry ,  7 (1), 24-25.

Macdonald, C. (2012). Understanding participatory action research: A qualitative research methodology option.  Canadian Journal of Action Research, 13 , 34-50.  https://doi.org/10.33524/cjar.v13i2.37  Mertler, C. A. (2008).  Action Research: Teachers as Researchers in the Classroom . London: Sage.

Marczyk, G. R., DeMatteo, D., & Festinger, D. (2010).  Essentials of research design and methodology  (Vol. 2). John Wiley & Sons.

Neale, B. (2020).  Qualitative longitudinal research: Research methods . Bloomsbury Publishing.

Novikov, A. M., & Novikov, D. A. (2013).  Research methodology: From philosophy of science to research design  (Vol. 2). CRC Press.

Ortiz, D., & Greene, J. (2007). Research design: qualitative, quantitative, and mixed methods approaches.  Qualitative Research Journal ,  6 (2), 205-208.

Stokes, P., & Wall, T. (2017).  Research methods . New York: Bloomsbury Publishing.

Taylor, L. E., Swerdfeger, A. L., & Eslick, G. D. (2014). Vaccines are not associated with autism: an evidence-based meta-analysis of case-control and cohort studies.  Vaccine ,  32 (29), 3623-3629.

Tracy, S. J. (2019).  Qualitative research methods: Collecting evidence, crafting analysis, communicating impact . London: John Wiley & Sons.

Vogl, S. (2023). Mixed methods longitudinal research. In  Forum Qualitative Sozialforschung/Forum: Qualitative Social Research  (Vol. 24, No. 1).

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Basics of Research Design: A Guide to selecting appropriate research design

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Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy and analytical approach that you have chosen in order to integrate, in a coherent and logical way, the different components of the study, thus ensuring that the research problem will be thoroughly investigated. It constitutes the blueprint for the collection, measurement, and interpretation of information and data. Note that the research problem determines the type of design you choose, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base. 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test the underlying assumptions of a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing the research design in your paper can vary considerably, but any well-developed description will achieve the following :

• Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
• Review and synthesize previously published literature associated with the research problem,
• Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
• Effectively describe the information and/or data which will be necessary for an adequate testing of the hypotheses and explain how such information and/or data will be obtained, and
• Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction of your paper . You can obtain an overall sense of what to do by reviewing studies that have utilized the same research design [e.g., using a case study approach]. This can help you develop an outline to follow for your own paper.

NOTE: Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Action Research Design

Definition and Purpose

The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of interventionary strategy. Then the intervention is carried out [the "action" in action research] during which time, pertinent observations are collected in various forms. The new interventional strategies are carried out, and this cyclic process repeats, continuing until a sufficient understanding of [or a valid implementation solution for] the problem is achieved. The protocol is iterative or cyclical in nature and is intended to foster deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.

What do these studies tell you ?

• This is a collaborative and adaptive research design that lends itself to use in work or community situations.
• Design focuses on pragmatic and solution-driven research outcomes rather than testing theories.
• When practitioners use action research, it has the potential to increase the amount they learn consciously from their experience; the action research cycle can be regarded as a learning cycle.
• Action research studies often have direct and obvious relevance to improving practice and advocating for change.
• There are no hidden controls or preemption of direction by the researcher.

What these studies don't tell you ?

• It is harder to do than conducting conventional research because the researcher takes on responsibilities of advocating for change as well as for researching the topic.
• Action research is much harder to write up because it is less likely that you can use a standard format to report your findings effectively [i.e., data is often in the form of stories or observation].
• Personal over-involvement of the researcher may bias research results.
• The cyclic nature of action research to achieve its twin outcomes of action [e.g. change] and research [e.g. understanding] is time-consuming and complex to conduct.
• Advocating for change usually requires buy-in from study participants.

Coghlan, David and Mary Brydon-Miller. The Sage Encyclopedia of Action Research . Thousand Oaks, CA:  Sage, 2014; Efron, Sara Efrat and Ruth Ravid. Action Research in Education: A Practical Guide . New York: Guilford, 2013; Gall, Meredith. Educational Research: An Introduction . Chapter 18, Action Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Kemmis, Stephen and Robin McTaggart. “Participatory Action Research.” In Handbook of Qualitative Research . Norman Denzin and Yvonna S. Lincoln, eds. 2nd ed. (Thousand Oaks, CA: SAGE, 2000), pp. 567-605; McNiff, Jean. Writing and Doing Action Research . London: Sage, 2014; Reason, Peter and Hilary Bradbury. Handbook of Action Research: Participative Inquiry and Practice . Thousand Oaks, CA: SAGE, 2001.

Case Study Design

A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey or comprehensive comparative inquiry. It is often used to narrow down a very broad field of research into one or a few easily researchable examples. The case study research design is also useful for testing whether a specific theory and model actually applies to phenomena in the real world. It is a useful design when not much is known about an issue or phenomenon.

• Approach excels at bringing us to an understanding of a complex issue through detailed contextual analysis of a limited number of events or conditions and their relationships.
• A researcher using a case study design can apply a variety of methodologies and rely on a variety of sources to investigate a research problem.
• Design can extend experience or add strength to what is already known through previous research.
• Social scientists, in particular, make wide use of this research design to examine contemporary real-life situations and provide the basis for the application of concepts and theories and the extension of methodologies.
• The design can provide detailed descriptions of specific and rare cases.
• A single or small number of cases offers little basis for establishing reliability or to generalize the findings to a wider population of people, places, or things.
• Intense exposure to the study of a case may bias a researcher's interpretation of the findings.
• Design does not facilitate assessment of cause and effect relationships.
• Vital information may be missing, making the case hard to interpret.
• The case may not be representative or typical of the larger problem being investigated.
• If the criteria for selecting a case is because it represents a very unusual or unique phenomenon or problem for study, then your interpretation of the findings can only apply to that particular case.

Case Studies. Writing@CSU. Colorado State University; Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 4, Flexible Methods: Case Study Design. 2nd ed. New York: Columbia University Press, 1999; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Greenhalgh, Trisha, editor. Case Study Evaluation: Past, Present and Future Challenges . Bingley, UK: Emerald Group Publishing, 2015; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Stake, Robert E. The Art of Case Study Research . Thousand Oaks, CA: SAGE, 1995; Yin, Robert K. Case Study Research: Design and Theory . Applied Social Research Methods Series, no. 5. 3rd ed. Thousand Oaks, CA: SAGE, 2003.

Causal Design

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

• Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
• Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
• Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.
• Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
• Replication is possible.
• There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.
• Not all relationships are causal! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
• Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
• If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the  actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base. 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, rather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

• Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
• Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
• The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
• Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
• Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
• Either original data or secondary data can be used in this design.
• In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
• Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
• Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101. Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study. Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

• Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
• Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
• Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
• Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
• Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
• Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
• Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
• Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
• Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
• Studies cannot be utilized to establish cause and effect relationships.
• This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
• There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In  The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design Application, Strengths and Weaknesses of Cross-Sectional Studies. Healthknowledge, 2009. Cross-Sectional Study. Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

• The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
• Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
• If the limitations are understood, they can be a useful tool in developing a more focused study.
• Descriptive studies can yield rich data that lead to important recommendations in practice.
• Appoach collects a large amount of data for detailed analysis.
• The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
• Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
• The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies. Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design, September 26, 2008; Erickson, G. Scott. "Descriptive Research Design." In New Methods of Market Research and Analysis . (Northampton, MA: Edward Elgar Publishing, 2017), pp. 51-77; Sahin, Sagufta, and Jayanta Mete. "A Brief Study on Descriptive Research: Its Nature and Application in Social Science." International Journal of Research and Analysis in Humanities 1 (2021): 11; K. Swatzell and P. Jennings. “Descriptive Research: The Nuts and Bolts.” Journal of the American Academy of Physician Assistants 20 (2007), pp. 55-56; Kane, E. Doing Your Own Research: Basic Descriptive Research in the Social Sciences and Humanities . London: Marion Boyars, 1985.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

• Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
• Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
• Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
• Approach provides the highest level of evidence for single studies.
• The design is artificial, and results may not generalize well to the real world.
• The artificial settings of experiments may alter the behaviors or responses of participants.
• Experimental designs can be costly if special equipment or facilities are needed.
• Some research problems cannot be studied using an experiment because of ethical or technical reasons.
• Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs. School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design. Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research. Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

• Familiarity with basic details, settings, and concerns.
• Well grounded picture of the situation being developed.
• Generation of new ideas and assumptions.
• Development of tentative theories or hypotheses.
• Determination about whether a study is feasible in the future.
• Issues get refined for more systematic investigation and formulation of new research questions.
• Direction for future research and techniques get developed.
• Design is a useful approach for gaining background information on a particular topic.
• Exploratory research is flexible and can address research questions of all types (what, why, how).
• Provides an opportunity to define new terms and clarify existing concepts.
• Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
• In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
• Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
• The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
• The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
• Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research. Wikipedia.

Field Research Design

Sometimes referred to as ethnography or participant observation, designs around field research encompass a variety of interpretative procedures [e.g., observation and interviews] rooted in qualitative approaches to studying people individually or in groups while inhabiting their natural environment as opposed to using survey instruments or other forms of impersonal methods of data gathering. Information acquired from observational research takes the form of “ field notes ” that involves documenting what the researcher actually sees and hears while in the field. Findings do not consist of conclusive statements derived from numbers and statistics because field research involves analysis of words and observations of behavior. Conclusions, therefore, are developed from an interpretation of findings that reveal overriding themes, concepts, and ideas. More information can be found HERE .

• Field research is often necessary to fill gaps in understanding the research problem applied to local conditions or to specific groups of people that cannot be ascertained from existing data.
• The research helps contextualize already known information about a research problem, thereby facilitating ways to assess the origins, scope, and scale of a problem and to gage the causes, consequences, and means to resolve an issue based on deliberate interaction with people in their natural inhabited spaces.
• Enables the researcher to corroborate or confirm data by gathering additional information that supports or refutes findings reported in prior studies of the topic.
• Because the researcher in embedded in the field, they are better able to make observations or ask questions that reflect the specific cultural context of the setting being investigated.
• Observing the local reality offers the opportunity to gain new perspectives or obtain unique data that challenges existing theoretical propositions or long-standing assumptions found in the literature.

What these studies don't tell you

• A field research study requires extensive time and resources to carry out the multiple steps involved with preparing for the gathering of information, including for example, examining background information about the study site, obtaining permission to access the study site, and building trust and rapport with subjects.
• Requires a commitment to staying engaged in the field to ensure that you can adequately document events and behaviors as they unfold.
• The unpredictable nature of fieldwork means that researchers can never fully control the process of data gathering. They must maintain a flexible approach to studying the setting because events and circumstances can change quickly or unexpectedly.
• Findings can be difficult to interpret and verify without access to documents and other source materials that help to enhance the credibility of information obtained from the field  [i.e., the act of triangulating the data].
• Linking the research problem to the selection of study participants inhabiting their natural environment is critical. However, this specificity limits the ability to generalize findings to different situations or in other contexts or to infer courses of action applied to other settings or groups of people.
• The reporting of findings must take into account how the researcher themselves may have inadvertently affected respondents and their behaviors.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

• The historical research design is unobtrusive; the act of research does not affect the results of the study.
• The historical approach is well suited for trend analysis.
• Historical records can add important contextual background required to more fully understand and interpret a research problem.
• There is often no possibility of researcher-subject interaction that could affect the findings.
• Historical sources can be used over and over to study different research problems or to replicate a previous study.
• The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
• Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
• Interpreting historical sources can be very time consuming.
• The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
• Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
• Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
• It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58;  Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

• Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
• Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
• The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
• Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
• The data collection method may change over time.
• Maintaining the integrity of the original sample can be difficult over an extended period of time.
• It can be difficult to show more than one variable at a time.
• This design often needs qualitative research data to explain fluctuations in the results.
• A longitudinal research design assumes present trends will continue unchanged.
• It can take a long period of time to gather results.
• There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study. Wikipedia.

Meta-Analysis Design

Meta-analysis is an analytical methodology designed to systematically evaluate and summarize the results from a number of individual studies, thereby, increasing the overall sample size and the ability of the researcher to study effects of interest. The purpose is to not simply summarize existing knowledge, but to develop a new understanding of a research problem using synoptic reasoning. The main objectives of meta-analysis include analyzing differences in the results among studies and increasing the precision by which effects are estimated. A well-designed meta-analysis depends upon strict adherence to the criteria used for selecting studies and the availability of information in each study to properly analyze their findings. Lack of information can severely limit the type of analyzes and conclusions that can be reached. In addition, the more dissimilarity there is in the results among individual studies [heterogeneity], the more difficult it is to justify interpretations that govern a valid synopsis of results. A meta-analysis needs to fulfill the following requirements to ensure the validity of your findings:

• Clearly defined description of objectives, including precise definitions of the variables and outcomes that are being evaluated;
• A well-reasoned and well-documented justification for identification and selection of the studies;
• Assessment and explicit acknowledgment of any researcher bias in the identification and selection of those studies;
• Description and evaluation of the degree of heterogeneity among the sample size of studies reviewed; and,
• Justification of the techniques used to evaluate the studies.
• Can be an effective strategy for determining gaps in the literature.
• Provides a means of reviewing research published about a particular topic over an extended period of time and from a variety of sources.
• Is useful in clarifying what policy or programmatic actions can be justified on the basis of analyzing research results from multiple studies.
• Provides a method for overcoming small sample sizes in individual studies that previously may have had little relationship to each other.
• Can be used to generate new hypotheses or highlight research problems for future studies.
• Small violations in defining the criteria used for content analysis can lead to difficult to interpret and/or meaningless findings.
• A large sample size can yield reliable, but not necessarily valid, results.
• A lack of uniformity regarding, for example, the type of literature reviewed, how methods are applied, and how findings are measured within the sample of studies you are analyzing, can make the process of synthesis difficult to perform.
• Depending on the sample size, the process of reviewing and synthesizing multiple studies can be very time consuming.

Beck, Lewis W. "The Synoptic Method." The Journal of Philosophy 36 (1939): 337-345; Cooper, Harris, Larry V. Hedges, and Jeffrey C. Valentine, eds. The Handbook of Research Synthesis and Meta-Analysis . 2nd edition. New York: Russell Sage Foundation, 2009; Guzzo, Richard A., Susan E. Jackson and Raymond A. Katzell. “Meta-Analysis Analysis.” In Research in Organizational Behavior , Volume 9. (Greenwich, CT: JAI Press, 1987), pp 407-442; Lipsey, Mark W. and David B. Wilson. Practical Meta-Analysis . Thousand Oaks, CA: Sage Publications, 2001; Study Design 101. Meta-Analysis. The Himmelfarb Health Sciences Library, George Washington University; Timulak, Ladislav. “Qualitative Meta-Analysis.” In The SAGE Handbook of Qualitative Data Analysis . Uwe Flick, editor. (Los Angeles, CA: Sage, 2013), pp. 481-495; Walker, Esteban, Adrian V. Hernandez, and Micheal W. Kattan. "Meta-Analysis: It's Strengths and Limitations." Cleveland Clinic Journal of Medicine 75 (June 2008): 431-439.

Mixed-Method Design

• Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
• Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
• A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
• The strengths of one method can be used to overcome the inherent weaknesses of another method.
• Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
• May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
• Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
• A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
• Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
• Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
• Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
• Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
• Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

• Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
• The researcher is able to collect in-depth information about a particular behavior.
• Can reveal interrelationships among multifaceted dimensions of group interactions.
• You can generalize your results to real life situations.
• Observational research is useful for discovering what variables may be important before applying other methods like experiments.
• Observation research designs account for the complexity of group behaviors.
• Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
• In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
• There can be problems with bias as the researcher may only "see what they want to see."
• There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
• Sources or subjects may not all be equally credible.
• Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

Philosophical Design

Understood more as an broad approach to examining a research problem than a methodological design, philosophical analysis and argumentation is intended to challenge deeply embedded, often intractable, assumptions underpinning an area of study. This approach uses the tools of argumentation derived from philosophical traditions, concepts, models, and theories to critically explore and challenge, for example, the relevance of logic and evidence in academic debates, to analyze arguments about fundamental issues, or to discuss the root of existing discourse about a research problem. These overarching tools of analysis can be framed in three ways:

• Ontology -- the study that describes the nature of reality; for example, what is real and what is not, what is fundamental and what is derivative?
• Epistemology -- the study that explores the nature of knowledge; for example, by what means does knowledge and understanding depend upon and how can we be certain of what we know?
• Axiology -- the study of values; for example, what values does an individual or group hold and why? How are values related to interest, desire, will, experience, and means-to-end? And, what is the difference between a matter of fact and a matter of value?
• Can provide a basis for applying ethical decision-making to practice.
• Functions as a means of gaining greater self-understanding and self-knowledge about the purposes of research.
• Brings clarity to general guiding practices and principles of an individual or group.
• Philosophy informs methodology.
• Refine concepts and theories that are invoked in relatively unreflective modes of thought and discourse.
• Beyond methodology, philosophy also informs critical thinking about epistemology and the structure of reality (metaphysics).
• Offers clarity and definition to the practical and theoretical uses of terms, concepts, and ideas.
• Limited application to specific research problems [answering the "So What?" question in social science research].
• Analysis can be abstract, argumentative, and limited in its practical application to real-life issues.
• While a philosophical analysis may render problematic that which was once simple or taken-for-granted, the writing can be dense and subject to unnecessary jargon, overstatement, and/or excessive quotation and documentation.
• There are limitations in the use of metaphor as a vehicle of philosophical analysis.
• There can be analytical difficulties in moving from philosophy to advocacy and between abstract thought and application to the phenomenal world.

Burton, Dawn. "Part I, Philosophy of the Social Sciences." In Research Training for Social Scientists . (London, England: Sage, 2000), pp. 1-5; Chapter 4, Research Methodology and Design. Unisa Institutional Repository (UnisaIR), University of South Africa; Jarvie, Ian C., and Jesús Zamora-Bonilla, editors. The SAGE Handbook of the Philosophy of Social Sciences . London: Sage, 2011; Labaree, Robert V. and Ross Scimeca. “The Philosophical Problem of Truth in Librarianship.” The Library Quarterly 78 (January 2008): 43-70; Maykut, Pamela S. Beginning Qualitative Research: A Philosophic and Practical Guide . Washington, DC: Falmer Press, 1994; McLaughlin, Hugh. "The Philosophy of Social Research." In Understanding Social Work Research . 2nd edition. (London: SAGE Publications Ltd., 2012), pp. 24-47; Stanford Encyclopedia of Philosophy . Metaphysics Research Lab, CSLI, Stanford University, 2013.

Sequential Design

• The researcher has a limitless option when it comes to sample size and the sampling schedule.
• Due to the repetitive nature of this research design, minor changes and adjustments can be done during the initial parts of the study to correct and hone the research method.
• This is a useful design for exploratory studies.
• There is very little effort on the part of the researcher when performing this technique. It is generally not expensive, time consuming, or workforce intensive.
• Because the study is conducted serially, the results of one sample are known before the next sample is taken and analyzed. This provides opportunities for continuous improvement of sampling and methods of analysis.
• The sampling method is not representative of the entire population. The only possibility of approaching representativeness is when the researcher chooses to use a very large sample size significant enough to represent a significant portion of the entire population. In this case, moving on to study a second or more specific sample can be difficult.
• The design cannot be used to create conclusions and interpretations that pertain to an entire population because the sampling technique is not randomized. Generalizability from findings is, therefore, limited.
• Difficult to account for and interpret variation from one sample to another over time, particularly when using qualitative methods of data collection.

Betensky, Rebecca. Harvard University, Course Lecture Note slides; Bovaird, James A. and Kevin A. Kupzyk. "Sequential Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 1347-1352; Cresswell, John W. Et al. “Advanced Mixed-Methods Research Designs.” In Handbook of Mixed Methods in Social and Behavioral Research . Abbas Tashakkori and Charles Teddle, eds. (Thousand Oaks, CA: Sage, 2003), pp. 209-240; Henry, Gary T. "Sequential Sampling." In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman and Tim Futing Liao, editors. (Thousand Oaks, CA: Sage, 2004), pp. 1027-1028; Nataliya V. Ivankova. “Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice.” Field Methods 18 (February 2006): 3-20; Bovaird, James A. and Kevin A. Kupzyk. “Sequential Design.” In Encyclopedia of Research Design . Neil J. Salkind, ed. Thousand Oaks, CA: Sage, 2010; Sequential Analysis. Wikipedia.

Systematic Review

• A systematic review synthesizes the findings of multiple studies related to each other by incorporating strategies of analysis and interpretation intended to reduce biases and random errors.
• The application of critical exploration, evaluation, and synthesis methods separates insignificant, unsound, or redundant research from the most salient and relevant studies worthy of reflection.
• They can be use to identify, justify, and refine hypotheses, recognize and avoid hidden problems in prior studies, and explain data inconsistencies and conflicts in data.
• Systematic reviews can be used to help policy makers formulate evidence-based guidelines and regulations.
• The use of strict, explicit, and pre-determined methods of synthesis, when applied appropriately, provide reliable estimates about the effects of interventions, evaluations, and effects related to the overarching research problem investigated by each study under review.
• Systematic reviews illuminate where knowledge or thorough understanding of a research problem is lacking and, therefore, can then be used to guide future research.
• The accepted inclusion of unpublished studies [i.e., grey literature] ensures the broadest possible way to analyze and interpret research on a topic.
• Results of the synthesis can be generalized and the findings extrapolated into the general population with more validity than most other types of studies .
• Systematic reviews do not create new knowledge per se; they are a method for synthesizing existing studies about a research problem in order to gain new insights and determine gaps in the literature.
• The way researchers have carried out their investigations [e.g., the period of time covered, number of participants, sources of data analyzed, etc.] can make it difficult to effectively synthesize studies.
• The inclusion of unpublished studies can introduce bias into the review because they may not have undergone a rigorous peer-review process prior to publication. Examples may include conference presentations or proceedings, publications from government agencies, white papers, working papers, and internal documents from organizations, and doctoral dissertations and Master's theses.

Denyer, David and David Tranfield. "Producing a Systematic Review." In The Sage Handbook of Organizational Research Methods .  David A. Buchanan and Alan Bryman, editors. ( Thousand Oaks, CA: Sage Publications, 2009), pp. 671-689; Foster, Margaret J. and Sarah T. Jewell, editors. Assembling the Pieces of a Systematic Review: A Guide for Librarians . Lanham, MD: Rowman and Littlefield, 2017; Gough, David, Sandy Oliver, James Thomas, editors. Introduction to Systematic Reviews . 2nd edition. Los Angeles, CA: Sage Publications, 2017; Gopalakrishnan, S. and P. Ganeshkumar. “Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare.” Journal of Family Medicine and Primary Care 2 (2013): 9-14; Gough, David, James Thomas, and Sandy Oliver. "Clarifying Differences between Review Designs and Methods." Systematic Reviews 1 (2012): 1-9; Khan, Khalid S., Regina Kunz, Jos Kleijnen, and Gerd Antes. “Five Steps to Conducting a Systematic Review.” Journal of the Royal Society of Medicine 96 (2003): 118-121; Mulrow, C. D. “Systematic Reviews: Rationale for Systematic Reviews.” BMJ 309:597 (September 1994); O'Dwyer, Linda C., and Q. Eileen Wafford. "Addressing Challenges with Systematic Review Teams through Effective Communication: A Case Report." Journal of the Medical Library Association 109 (October 2021): 643-647; Okoli, Chitu, and Kira Schabram. "A Guide to Conducting a Systematic Literature Review of Information Systems Research."  Sprouts: Working Papers on Information Systems 10 (2010); Siddaway, Andy P., Alex M. Wood, and Larry V. Hedges. "How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-analyses, and Meta-syntheses." Annual Review of Psychology 70 (2019): 747-770; Torgerson, Carole J. “Publication Bias: The Achilles’ Heel of Systematic Reviews?” British Journal of Educational Studies 54 (March 2006): 89-102; Torgerson, Carole. Systematic Reviews . New York: Continuum, 2003.

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What is Research Design? Understand Types of Research Design, with Examples

Have you been wondering “ what is research design ?” or “what are some research design examples ?” Are you unsure about the research design elements or which of the different types of research design best suit your study? Don’t worry! In this article, we’ve got you covered!   

Table of Contents

What is research design?  

Have you been wondering “ what is research design ?” or “what are some research design examples ?” Don’t worry! In this article, we’ve got you covered!  

A research design is the plan or framework used to conduct a research study. It involves outlining the overall approach and methods that will be used to collect and analyze data in order to answer research questions or test hypotheses. A well-designed research study should have a clear and well-defined research question, a detailed plan for collecting data, and a method for analyzing and interpreting the results. A well-thought-out research design addresses all these features.  

Research design elements  

Research design elements include the following:  

• Clear purpose: The research question or hypothesis must be clearly defined and focused.  
• Sampling: This includes decisions about sample size, sampling method, and criteria for inclusion or exclusion. The approach varies for different research design types .  
• Data collection: This research design element involves the process of gathering data or information from the study participants or sources. It includes decisions about what data to collect, how to collect it, and the tools or instruments that will be used.  
• Data analysis: All research design types require analysis and interpretation of the data collected. This research design element includes decisions about the statistical tests or methods that will be used to analyze the data, as well as any potential confounding variables or biases that may need to be addressed.  
• Type of research methodology: This includes decisions about the overall approach for the study.  
• Time frame: An important research design element is the time frame, which includes decisions about the duration of the study, the timeline for data collection and analysis, and follow-up periods.  
• Ethical considerations: The research design must include decisions about ethical considerations such as informed consent, confidentiality, and participant protection.  
• Resources: A good research design takes into account decisions about the budget, staffing, and other resources needed to carry out the study.  

The elements of research design should be carefully planned and executed to ensure the validity and reliability of the study findings. Let’s go deeper into the concepts of research design .    

Characteristics of research design  

Some basic characteristics of research design are common to different research design types . These characteristics of research design are as follows:  

• Neutrality : Right from the study assumptions to setting up the study, a neutral stance must be maintained, free of pre-conceived notions. The researcher’s expectations or beliefs should not color the findings or interpretation of the findings. Accordingly, a good research design should address potential sources of bias and confounding factors to be able to yield unbiased and neutral results.   
•   Reliability : Reliability is one of the characteristics of research design that refers to consistency in measurement over repeated measures and fewer random errors. A reliable research design must allow for results to be consistent, with few errors due to chance.   
•   Validity : Validity refers to the minimization of nonrandom (systematic) errors. A good research design must employ measurement tools that ensure validity of the results.  
•   Generalizability: The outcome of the research design should be applicable to a larger population and not just a small sample . A generalized method means the study can be conducted on any part of a population with similar accuracy.   
•   Flexibility: A research design should allow for changes to be made to the research plan as needed, based on the data collected and the outcomes of the study  

A well-planned research design is critical for conducting a scientifically rigorous study that will generate neutral, reliable, valid, and generalizable results. At the same time, it should allow some level of flexibility.  

Different types of research design  

A research design is essential to systematically investigate, understand, and interpret phenomena of interest. Let’s look at different types of research design and research design examples .  

Broadly, research design types can be divided into qualitative and quantitative research.  

Qualitative research is subjective and exploratory. It determines relationships between collected data and observations. It is usually carried out through interviews with open-ended questions, observations that are described in words, etc.  

Quantitative research is objective and employs statistical approaches. It establishes the cause-and-effect relationship among variables using different statistical and computational methods. This type of research is usually done using surveys and experiments.  

Qualitative research vs. Quantitative research  

Qualitative research design types and qualitative research design examples  

The following will familiarize you with the research design categories in qualitative research:  

• Grounded theory: This design is used to investigate research questions that have not previously been studied in depth. Also referred to as exploratory design , it creates sequential guidelines, offers strategies for inquiry, and makes data collection and analysis more efficient in qualitative research.   

Example: A researcher wants to study how people adopt a certain app. The researcher collects data through interviews and then analyzes the data to look for patterns. These patterns are used to develop a theory about how people adopt that app.  

•   Thematic analysis: This design is used to compare the data collected in past research to find similar themes in qualitative research.  

Example: A researcher examines an interview transcript to identify common themes, say, topics or patterns emerging repeatedly.  

• Discourse analysis : This research design deals with language or social contexts used in data gathering in qualitative research.   

Example: Identifying ideological frameworks and viewpoints of writers of a series of policies.  

Quantitative research design types and quantitative research design examples  

Note the following research design categories in quantitative research:  

• Descriptive research design : This quantitative research design is applied where the aim is to identify characteristics, frequencies, trends, and categories. It may not often begin with a hypothesis. The basis of this research type is a description of an identified variable. This research design type describes the “what,” “when,” “where,” or “how” of phenomena (but not the “why”).   

Example: A study on the different income levels of people who use nutritional supplements regularly.  

• Correlational research design : Correlation reflects the strength and/or direction of the relationship among variables. The direction of a correlation can be positive or negative. Correlational research design helps researchers establish a relationship between two variables without the researcher controlling any of them.  

Example : An example of correlational research design could be studying the correlation between time spent watching crime shows and aggressive behavior in teenagers.  

•   Diagnostic research design : In diagnostic design, the researcher aims to understand the underlying cause of a specific topic or phenomenon (usually an area of improvement) and find the most effective solution. In simpler terms, a researcher seeks an accurate “diagnosis” of a problem and identifies a solution.  

Example : A researcher analyzing customer feedback and reviews to identify areas where an app can be improved.    

• Explanatory research design : In explanatory research design , a researcher uses their ideas and thoughts on a topic to explore their theories in more depth. This design is used to explore a phenomenon when limited information is available. It can help increase current understanding of unexplored aspects of a subject. It is thus a kind of “starting point” for future research.  

Example : Formulating hypotheses to guide future studies on delaying school start times for better mental health in teenagers.  

•   Causal research design : This can be considered a type of explanatory research. Causal research design seeks to define a cause and effect in its data. The researcher does not use a randomly chosen control group but naturally or pre-existing groupings. Importantly, the researcher does not manipulate the independent variable.   

Example : Comparing school dropout levels and possible bullying events.  

•   Experimental research design : This research design is used to study causal relationships . One or more independent variables are manipulated, and their effect on one or more dependent variables is measured.  

Example: Determining the efficacy of a new vaccine plan for influenza.  

Benefits of research design  

 T here are numerous benefits of research design . These are as follows:  

• Clear direction: Among the benefits of research design , the main one is providing direction to the research and guiding the choice of clear objectives, which help the researcher to focus on the specific research questions or hypotheses they want to investigate.  
• Control: Through a proper research design , researchers can control variables, identify potential confounding factors, and use randomization to minimize bias and increase the reliability of their findings.
• Replication: Research designs provide the opportunity for replication. This helps to confirm the findings of a study and ensures that the results are not due to chance or other factors. Thus, a well-chosen research design also eliminates bias and errors.  
• Validity: A research design ensures the validity of the research, i.e., whether the results truly reflect the phenomenon being investigated.  
• Reliability: Benefits of research design also include reducing inaccuracies and ensuring the reliability of the research (i.e., consistency of the research results over time, across different samples, and under different conditions).  
• Efficiency: A strong research design helps increase the efficiency of the research process. Researchers can use a variety of designs to investigate their research questions, choose the most appropriate research design for their study, and use statistical analysis to make the most of their data. By effectively describing the data necessary for an adequate test of the hypotheses and explaining how such data will be obtained, research design saves a researcher’s time.   

Overall, an appropriately chosen and executed research design helps researchers to conduct high-quality research, draw meaningful conclusions, and contribute to the advancement of knowledge in their field.

Frequently Asked Questions (FAQ) on Research Design

Q: What are th e main types of research design?

Broadly speaking there are two basic types of research design –

qualitative and quantitative research. Qualitative research is subjective and exploratory; it determines relationships between collected data and observations. It is usually carried out through interviews with open-ended questions, observations that are described in words, etc. Quantitative research , on the other hand, is more objective and employs statistical approaches. It establishes the cause-and-effect relationship among variables using different statistical and computational methods. This type of research design is usually done using surveys and experiments.

Q: How do I choose the appropriate research design for my study?

Choosing the appropriate research design for your study requires careful consideration of various factors. Start by clarifying your research objectives and the type of data you need to collect. Determine whether your study is exploratory, descriptive, or experimental in nature. Consider the availability of resources, time constraints, and the feasibility of implementing the different research designs. Review existing literature to identify similar studies and their research designs, which can serve as a guide. Ultimately, the chosen research design should align with your research questions, provide the necessary data to answer them, and be feasible given your own specific requirements/constraints.

Q: Can research design be modified during the course of a study?

Yes, research design can be modified during the course of a study based on emerging insights, practical constraints, or unforeseen circumstances. Research is an iterative process and, as new data is collected and analyzed, it may become necessary to adjust or refine the research design. However, any modifications should be made judiciously and with careful consideration of their impact on the study’s integrity and validity. It is advisable to document any changes made to the research design, along with a clear rationale for the modifications, in order to maintain transparency and allow for proper interpretation of the results.

Q: How can I ensure the validity and reliability of my research design?

Validity refers to the accuracy and meaningfulness of your study’s findings, while reliability relates to the consistency and stability of the measurements or observations. To enhance validity, carefully define your research variables, use established measurement scales or protocols, and collect data through appropriate methods. Consider conducting a pilot study to identify and address any potential issues before full implementation. To enhance reliability, use standardized procedures, conduct inter-rater or test-retest reliability checks, and employ appropriate statistical techniques for data analysis. It is also essential to document and report your methodology clearly, allowing for replication and scrutiny by other researchers.

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Research Design: What it is, Elements & Types

Can you imagine doing research without a plan? Probably not. When we discuss a strategy to collect, study, and evaluate data, we talk about research design. This design addresses problems and creates a consistent and logical model for data analysis. Let’s learn more about it.

What is Research Design?

Research design is the framework of research methods and techniques chosen by a researcher to conduct a study. The design allows researchers to sharpen the research methods suitable for the subject matter and set up their studies for success.

Creating a research topic explains the type of research (experimental,  survey research ,  correlational , semi-experimental, review) and its sub-type (experimental design, research problem , descriptive case-study). 

There are three main types of designs for research:

• Data collection
• Measurement
• Data Analysis

The research problem an organization faces will determine the design, not vice-versa. The design phase of a study determines which tools to use and how they are used.

The Process of Research Design

The research design process is a systematic and structured approach to conducting research. The process is essential to ensure that the study is valid, reliable, and produces meaningful results.

• Consider your aims and approaches: Determine the research questions and objectives, and identify the theoretical framework and methodology for the study.
• Choose a type of Research Design: Select the appropriate research design, such as experimental, correlational, survey, case study, or ethnographic, based on the research questions and objectives.
• Identify your population and sampling method: Determine the target population and sample size, and choose the sampling method, such as random , stratified random sampling , or convenience sampling.
• Choose your data collection methods: Decide on the data collection methods , such as surveys, interviews, observations, or experiments, and select the appropriate instruments or tools for collecting data.
• Plan your data collection procedures: Develop a plan for data collection, including the timeframe, location, and personnel involved, and ensure ethical considerations.
• Decide on your data analysis strategies: Select the appropriate data analysis techniques, such as statistical analysis , content analysis, or discourse analysis, and plan how to interpret the results.

The process of research design is a critical step in conducting research. By following the steps of research design, researchers can ensure that their study is well-planned, ethical, and rigorous.

Research Design Elements

Impactful research usually creates a minimum bias in data and increases trust in the accuracy of collected data. A design that produces the slightest margin of error in experimental research is generally considered the desired outcome. The essential elements are:

• Accurate purpose statement
• Techniques to be implemented for collecting and analyzing research
• The method applied for analyzing collected details
• Type of research methodology
• Probable objections to research
• Settings for the research study
• Measurement of analysis

Characteristics of Research Design

A proper design sets your study up for success. Successful research studies provide insights that are accurate and unbiased. You’ll need to create a survey that meets all of the main characteristics of a design. There are four key characteristics:

• Neutrality: When you set up your study, you may have to make assumptions about the data you expect to collect. The results projected in the research should be free from research bias and neutral. Understand opinions about the final evaluated scores and conclusions from multiple individuals and consider those who agree with the results.
• Reliability: With regularly conducted research, the researcher expects similar results every time. You’ll only be able to reach the desired results if your design is reliable. Your plan should indicate how to form research questions to ensure the standard of results.
• Validity: There are multiple measuring tools available. However, the only correct measuring tools are those which help a researcher in gauging results according to the objective of the research. The  questionnaire  developed from this design will then be valid.
• Generalization:  The outcome of your design should apply to a population and not just a restricted sample . A generalized method implies that your survey can be conducted on any part of a population with similar accuracy.

The above factors affect how respondents answer the research questions, so they should balance all the above characteristics in a good design. If you want, you can also learn about Selection Bias through our blog.

Research Design Types

A researcher must clearly understand the various types to select which model to implement for a study. Like the research itself, the design of your analysis can be broadly classified into quantitative and qualitative.

Qualitative research

Qualitative research determines relationships between collected data and observations based on mathematical calculations. Statistical methods can prove or disprove theories related to a naturally existing phenomenon. Researchers rely on qualitative observation research methods that conclude “why” a particular theory exists and “what” respondents have to say about it.

Quantitative research

Quantitative research is for cases where statistical conclusions to collect actionable insights are essential. Numbers provide a better perspective for making critical business decisions. Quantitative research methods are necessary for the growth of any organization. Insights drawn from complex numerical data and analysis prove to be highly effective when making decisions about the business’s future.

Qualitative Research vs Quantitative Research

Here is a chart that highlights the major differences between qualitative and quantitative research:

In summary or analysis , the step of qualitative research is more exploratory and focuses on understanding the subjective experiences of individuals, while quantitative research is more focused on objective data and statistical analysis.

You can further break down the types of research design into five categories:

1. Descriptive: In a descriptive composition, a researcher is solely interested in describing the situation or case under their research study. It is a theory-based design method created by gathering, analyzing, and presenting collected data. This allows a researcher to provide insights into the why and how of research. Descriptive design helps others better understand the need for the research. If the problem statement is not clear, you can conduct exploratory research. 

2. Experimental: Experimental research establishes a relationship between the cause and effect of a situation. It is a causal research design where one observes the impact caused by the independent variable on the dependent variable. For example, one monitors the influence of an independent variable such as a price on a dependent variable such as customer satisfaction or brand loyalty. It is an efficient research method as it contributes to solving a problem.

The independent variables are manipulated to monitor the change it has on the dependent variable. Social sciences often use it to observe human behavior by analyzing two groups. Researchers can have participants change their actions and study how the people around them react to understand social psychology better.

3. Correlational research: Correlational research  is a non-experimental research technique. It helps researchers establish a relationship between two closely connected variables. There is no assumption while evaluating a relationship between two other variables, and statistical analysis techniques calculate the relationship between them. This type of research requires two different groups.

A correlation coefficient determines the correlation between two variables whose values range between -1 and +1. If the correlation coefficient is towards +1, it indicates a positive relationship between the variables, and -1 means a negative relationship between the two variables. 

4. Diagnostic research: In diagnostic design, the researcher is looking to evaluate the underlying cause of a specific topic or phenomenon. This method helps one learn more about the factors that create troublesome situations. 

This design has three parts of the research:

• Inception of the issue
• Diagnosis of the issue
• Solution for the issue

5. Explanatory research : Explanatory design uses a researcher’s ideas and thoughts on a subject to further explore their theories. The study explains unexplored aspects of a subject and details the research questions’ what, how, and why.

Benefits of Research Design

There are several benefits of having a well-designed research plan. Including:

• Clarity of research objectives: Research design provides a clear understanding of the research objectives and the desired outcomes.
• Increased validity and reliability: To ensure the validity and reliability of results, research design help to minimize the risk of bias and helps to control extraneous variables.
• Improved data collection: Research design helps to ensure that the proper data is collected and data is collected systematically and consistently.
• Better data analysis: Research design helps ensure that the collected data can be analyzed effectively, providing meaningful insights and conclusions.
• Improved communication: A well-designed research helps ensure the results are clean and influential within the research team and external stakeholders.
• Efficient use of resources: reducing the risk of waste and maximizing the impact of the research, research design helps to ensure that resources are used efficiently.

A well-designed research plan is essential for successful research, providing clear and meaningful insights and ensuring that resources are practical.

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How to Write a Research Design – Guide with Examples

Published by Alaxendra Bets at August 14th, 2021 , Revised On June 24, 2024

A research design is a structure that combines different components of research. It involves the use of different data collection and data analysis techniques logically to answer the  research questions .

It would be best to make some decisions about addressing the research questions adequately before starting the research process, which is achieved with the help of the research design.

Below are the key aspects of the decision-making process:

• Data type required for research
• Research resources
• Participants required for research
• Hypothesis based upon research question(s)
• Data analysis  methodologies
• Variables (Independent, dependent, and confounding)
• The location and timescale for conducting the data
• The time period required for research

The research design provides the strategy of investigation for your project. Furthermore, it defines the parameters and criteria to compile the data to evaluate results and conclude.

Your project’s validity depends on the data collection and  interpretation techniques.  A strong research design reflects a strong  dissertation , scientific paper, or research proposal .

Step 1: Establish Priorities for Research Design

Before conducting any research study, you must address an important question: “how to create a research design.”

The research design depends on the researcher’s priorities and choices because every research has different priorities. For a complex research study involving multiple methods, you may choose to have more than one research design.

Multimethodology or multimethod research includes using more than one data collection method or research in a research study or set of related studies.

If one research design is weak in one area, then another research design can cover that weakness. For instance, a  dissertation analyzing different situations or cases will have more than one research design.

For example:

• Experimental research involves experimental investigation and laboratory experience, but it does not accurately investigate the real world.
• Quantitative research is good for the  statistical part of the project, but it may not provide an in-depth understanding of the  topic .
• Also, correlational research will not provide experimental results because it is a technique that assesses the statistical relationship between two variables.

While scientific considerations are a fundamental aspect of the research design, It is equally important that the researcher think practically before deciding on its structure. Here are some questions that you should think of;

• Do you have enough time to gather data and complete the write-up?
• Will you be able to collect the necessary data by interviewing a specific person or visiting a specific location?
• Do you have in-depth knowledge about the  different statistical analysis and data collection techniques to address the research questions  or test the  hypothesis ?

If you think that the chosen research design cannot answer the research questions properly, you can refine your research questions to gain better insight.

Step 2: Data Type you Need for Research

Decide on the type of data you need for your research. The type of data you need to collect depends on your research questions or research hypothesis. Two types of research data can be used to answer the research questions:

Primary Data Vs. Secondary Data

Qualitative Vs. Quantitative Data

Also, see; Research methods, design, and analysis .

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Step 3: Data Collection Techniques

Once you have selected the type of research to answer your research question, you need to decide where and how to collect the data.

It is time to determine your research method to address the  research problem . Research methods involve procedures, techniques, materials, and tools used for the study.

For instance, a dissertation research design includes the different resources and data collection techniques and helps establish your  dissertation’s structure .

The following table shows the characteristics of the most popularly employed research methods.

Research Methods

Step 4: Procedure of Data Analysis

Use of the  correct data and statistical analysis technique is necessary for the validity of your research. Therefore, you need to be certain about the data type that would best address the research problem. Choosing an appropriate analysis method is the final step for the research design. It can be split into two main categories;

Quantitative Data Analysis

The quantitative data analysis technique involves analyzing the numerical data with the help of different applications such as; SPSS, STATA, Excel, origin lab, etc.

This data analysis strategy tests different variables such as spectrum, frequencies, averages, and more. The research question and the hypothesis must be established to identify the variables for testing.

Qualitative Data Analysis

Qualitative data analysis of figures, themes, and words allows for flexibility and the researcher’s subjective opinions. This means that the researcher’s primary focus will be interpreting patterns, tendencies, and accounts and understanding the implications and social framework.

You should be clear about your research objectives before starting to analyze the data. For example, you should ask yourself whether you need to explain respondents’ experiences and insights or do you also need to evaluate their responses with reference to a certain social framework.

Step 5: Write your Research Proposal

The research design is an important component of a research proposal because it plans the project’s execution. You can share it with the supervisor, who would evaluate the feasibility and capacity of the results  and  conclusion .

Read our guidelines to write a research proposal  if you have already formulated your research design. The research proposal is written in the future tense because you are writing your proposal before conducting research.

The  research methodology  or research design, on the other hand, is generally written in the past tense.

How to Write a Research Design – Conclusion

A research design is the plan, structure, strategy of investigation conceived to answer the research question and test the hypothesis. The dissertation research design can be classified based on the type of data and the type of analysis.

Above mentioned five steps are the answer to how to write a research design. So, follow these steps to  formulate the perfect research design for your dissertation .

ResearchProspect writers have years of experience creating research designs that align with the dissertation’s aim and objectives. If you are struggling with your dissertation methodology chapter, you might want to look at our dissertation part-writing service.

Our dissertation writers can also help you with the full dissertation paper . No matter how urgent or complex your need may be, ResearchProspect can help. We also offer PhD level research paper writing services.

Frequently Asked Questions

What is research design.

Research design is a systematic plan that guides the research process, outlining the methodology and procedures for collecting and analysing data. It determines the structure of the study, ensuring the research question is answered effectively, reliably, and validly. It serves as the blueprint for the entire research project.

How to write a research design?

To write a research design, define your research question, identify the research method (qualitative, quantitative, or mixed), choose data collection techniques (e.g., surveys, interviews), determine the sample size and sampling method, outline data analysis procedures, and highlight potential limitations and ethical considerations for the study.

How to write the design section of a research paper?

In the design section of a research paper, describe the research methodology chosen and justify its selection. Outline the data collection methods, participants or samples, instruments used, and procedures followed. Detail any experimental controls, if applicable. Ensure clarity and precision to enable replication of the study by other researchers.

How to write a research design in methodology?

To write a research design in methodology, clearly outline the research strategy (e.g., experimental, survey, case study). Describe the sampling technique, participants, and data collection methods. Detail the procedures for data collection and analysis. Justify choices by linking them to research objectives, addressing reliability and validity.

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Quantitative vs. Qualitative Research: The Differences Explained

From Scribbr 

Empirical Research

What is empirical research.

"Empirical research is research that is based on observation and measurement of phenomena, as directly experienced by the researcher. The data thus gathered may be compared against a theory or hypothesis, but the results are still based on real life experience. The data gathered is all primary data, although secondary data from a literature review may form the theoretical background."

Characteristics of Empirical Research

Emerald Publishing's  guide to conducting empirical research  identifies a number of common elements to empirical research: 

A  research question , which will determine research objectives.

A particular and planned  design  for the research, which will depend on the question and which will find ways of answering it with appropriate use of resources.

The gathering of  primary data , which is then analysed.

A particular  methodology  for collecting and analysing the data, such as an experiment or survey.

The limitation of the data to a particular group, area or time scale, known as a  sample  [emphasis added]: for example, a specific number of employees of a particular company type, or all users of a library over a given time scale. The sample should be somehow representative of a wider population.

The ability to  recreate  the study and test the results. This is known as  reliability .

The ability to  generalize  from the findings to a larger sample and to other situations.

If you see these elements in a research article, you can feel confident that you have found empirical research. Emerald's guide goes into more detail on each element. 

Emerald Publishing (n.d.). How to... conduct empirical research. https://www.emeraldgrouppublishing.com/how-to/research-methods/conduct-empirical-research-l 

• Quantitative vs. Qualitative
• Data Collection Methods
• Analyzing Data

When collecting and analyzing data,  quantitative research  deals with numbers and statistics, while  qualitative research  deals with words and meanings. Both are important for gaining different kinds of knowledge.

Quantitative research

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Qualitative research

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Streefkerk, R. (2022, February 7). Qualitative vs. quantitative research: Differences, examples & methods. Scibbr. https://www.scribbr.com/methodology/qualitative-quantitative-research/ 

Quantitative and qualitative data can be collected using various methods. It is important to use a  data collection  method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observations or  case studies , your data can be represented as numbers (e.g. using rating scales or counting frequencies) or as words (e.g. with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a  sample  (online, in person, or over the phone).
• Experiments :  Situation in which  variables  are controlled and manipulated to establish cause-and-effect relationships.
• Observations:  Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups:  Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review :  Survey of published works by other authors.

When to use qualitative vs. quantitative research

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to  confirm or test something  (a theory or hypothesis)
• Use qualitative research if you want to  understand something  (concepts, thoughts, experiences)

For most  research topics  you can choose a qualitative, quantitative or  mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an  inductive vs. deductive research approach ; your  research question(s) ; whether you’re doing  experimental ,  correlational , or  descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Streefkerk, R. (2022, February 7).  Qualitative vs. quantitative research: Differences, examples & methods.  Scibbr. https://www.scribbr.com/methodology/qualitative-quantitative-research/ 

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced  statistical analysis  is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores
• The number of times a particular answer was given
• The  correlation or causation  between two or more variables
• The  reliability and validity  of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

Comparison of Research Processes

Creswell, J. W., & Creswell, J. D. (2018).  Research design : qualitative, quantitative, and mixed methods approaches  (Fifth). SAGE Publications.

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FAQ: Research Design & Method

What is the difference between Research Design and Research Method?

Research design is a plan to answer your research question.  A research method is a strategy used to implement that plan.  Research design and methods are different but closely related, because good research design ensures that the data you obtain will help you answer your research question more effectively.

Which research method should I choose ?

It depends on your research goal.  It depends on what subjects (and who) you want to study.  Let's say you are interested in studying what makes people happy, or why some students are more conscious about recycling on campus.  To answer these questions, you need to make a decision about how to collect your data.  Most frequently used methods include:

• Observation / Participant Observation
• Focus Groups
• Experiments
• Secondary Data Analysis / Archival Study
• Mixed Methods (combination of some of the above)

One particular method could be better suited to your research goal than others, because the data you collect from different methods will be different in quality and quantity.   For instance, surveys are usually designed to produce relatively short answers, rather than the extensive responses expected in qualitative interviews.

What other factors should I consider when choosing one method over another?

Time for data collection and analysis is something you want to consider.  An observation or interview method, so-called qualitative approach, helps you collect richer information, but it takes time.  Using a survey helps you collect more data quickly, yet it may lack details.  So, you will need to consider the time you have for research and the balance between strengths and weaknesses associated with each method (e.g., qualitative vs. quantitative).

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1000+ FREE Research Topics & Title Ideas

Select your area of interest to view a collection of potential research topics and ideas.

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PS – You can also check out our free topic ideation webinar for more ideas

How To Find A Research Topic

If you’re struggling to get started, this step-by-step video tutorial will help you find the perfect research topic.

Research Topic FAQs

What (exactly) is a research topic.

A research topic is the subject of a research project or study – for example, a dissertation or thesis. A research topic typically takes the form of a problem to be solved, or a question to be answered.

A good research topic should be specific enough to allow for focused research and analysis. For example, if you are interested in studying the effects of climate change on agriculture, your research topic could focus on how rising temperatures have impacted crop yields in certain regions over time.

To learn more about the basics of developing a research topic, consider our free research topic ideation webinar.

What constitutes a good research topic?

A strong research topic comprises three important qualities : originality, value and feasibility.

• Originality – a good topic explores an original area or takes a novel angle on an existing area of study.
• Value – a strong research topic provides value and makes a contribution, either academically or practically.
• Feasibility – a good research topic needs to be practical and manageable, given the resource constraints you face.

To learn more about what makes for a high-quality research topic, check out this post .

What's the difference between a research topic and research problem?

A research topic and a research problem are two distinct concepts that are often confused. A research topic is a broader label that indicates the focus of the study , while a research problem is an issue or gap in knowledge within the broader field that needs to be addressed.

To illustrate this distinction, consider a student who has chosen “teenage pregnancy in the United Kingdom” as their research topic. This research topic could encompass any number of issues related to teenage pregnancy such as causes, prevention strategies, health outcomes for mothers and babies, etc.

Within this broad category (the research topic) lies potential areas of inquiry that can be explored further – these become the research problems . For example:

• What factors contribute to higher rates of teenage pregnancy in certain communities?
• How do different types of parenting styles affect teen pregnancy rates?
• What interventions have been successful in reducing teenage pregnancies?

Simply put, a key difference between a research topic and a research problem is scope ; the research topic provides an umbrella under which multiple questions can be asked, while the research problem focuses on one specific question or set of questions within that larger context.

How can I find potential research topics for my project?

There are many steps involved in the process of finding and choosing a high-quality research topic for a dissertation or thesis. We cover these steps in detail in this video (also accessible below).

How can I find quality sources for my research topic?

Finding quality sources is an essential step in the topic ideation process. To do this, you should start by researching scholarly journals, books, and other academic publications related to your topic. These sources can provide reliable information on a wide range of topics. Additionally, they may contain data or statistics that can help support your argument or conclusions.

Identifying Relevant Sources

When searching for relevant sources, it’s important to look beyond just published material; try using online databases such as Google Scholar or JSTOR to find articles from reputable journals that have been peer-reviewed by experts in the field.

You can also use search engines like Google or Bing to locate websites with useful information about your topic. However, be sure to evaluate any website before citing it as a source—look for evidence of authorship (such as an “About Us” page) and make sure the content is up-to-date and accurate before relying on it.

Evaluating Sources

Once you’ve identified potential sources for your research project, take some time to evaluate them thoroughly before deciding which ones will best serve your purpose. Consider factors such as author credibility (are they an expert in their field?), publication date (is the source current?), objectivity (does the author present both sides of an issue?) and relevance (how closely does this source relate to my specific topic?).

By researching the current literature on your topic, you can identify potential sources that will help to provide quality information. Once you’ve identified these sources, it’s time to look for a gap in the research and determine what new knowledge could be gained from further study.

How can I find a good research gap?

Finding a strong gap in the literature is an essential step when looking for potential research topics. We explain what research gaps are and how to find them in this post.

How should I evaluate potential research topics/ideas?

When evaluating potential research topics, it is important to consider the factors that make for a strong topic (we discussed these earlier). Specifically:

• Originality
• Feasibility

So, when you have a list of potential topics or ideas, assess each of them in terms of these three criteria. A good topic should take a unique angle, provide value (either to academia or practitioners), and be practical enough for you to pull off, given your limited resources.

Finally, you should also assess whether this project could lead to potential career opportunities such as internships or job offers down the line. Make sure that you are researching something that is relevant enough so that it can benefit your professional development in some way. Additionally, consider how each research topic aligns with your career goals and interests; researching something that you are passionate about can help keep motivation high throughout the process.

How can I assess the feasibility of a research topic?

When evaluating the feasibility and practicality of a research topic, it is important to consider several factors.

First, you should assess whether or not the research topic is within your area of competence. Of course, when you start out, you are not expected to be the world’s leading expert, but do should at least have some foundational knowledge.

Time commitment

When considering a research topic, you should think about how much time will be required for completion. Depending on your field of study, some topics may require more time than others due to their complexity or scope.

Additionally, if you plan on collaborating with other researchers or institutions in order to complete your project, additional considerations must be taken into account such as coordinating schedules and ensuring that all parties involved have adequate resources available.

Resources needed

It’s also critically important to consider what type of resources are necessary in order to conduct the research successfully. This includes physical materials such as lab equipment and chemicals but can also include intangible items like access to certain databases or software programs which may be necessary depending on the nature of your work. Additionally, if there are costs associated with obtaining these materials then this must also be factored into your evaluation process.

Potential risks

It’s important to consider the inherent potential risks for each potential research topic. These can include ethical risks (challenges getting ethical approval), data risks (not being able to access the data you’ll need), technical risks relating to the equipment you’ll use and funding risks (not securing the necessary financial back to undertake the research).

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Organizing Your Social Sciences Research Paper: Types of Research Designs

• Purpose of Guide
• Writing a Research Proposal
• Design Flaws to Avoid
• Independent and Dependent Variables
• Narrowing a Topic Idea
• Broadening a Topic Idea
• The Research Problem/Question
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• The C.A.R.S. Model
• Background Information
• Theoretical Framework
• Citation Tracking
• Evaluating Sources
• Reading Research Effectively
• Primary Sources
• Secondary Sources
• What Is Scholarly vs. Popular?
• Is it Peer-Reviewed?
• Qualitative Methods
• Quantitative Methods
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism [linked guide]
• Annotated Bibliography
• Grading Someone Else's Paper

Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy that you choose to integrate the different components of the study in a coherent and logical way, thereby, ensuring you will effectively address the research problem; it constitutes the blueprint for the collection, measurement, and analysis of data. Note that your research problem determines the type of design you should use, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base . 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations far too early, before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing research designs in your paper can vary considerably, but any well-developed design will achieve the following :

• Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
• Review and synthesize previously published literature associated with the research problem,
• Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
• Effectively describe the data which will be necessary for an adequate testing of the hypotheses and explain how such data will be obtained, and
• Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction and varies in length depending on the type of design you are using. However, you can get a sense of what to do by reviewing the literature of studies that have utilized the same research design. This can provide an outline to follow for your own paper.

NOTE : Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Videos in Business and Management , Criminology and Criminal Justice , Education , and Media, Communication and Cultural Studies specifically created for use in higher education.

A literature review tool that highlights the most influential works in Business & Management, Education, Politics & International Relations, Psychology and Sociology. Does not contain full text of the cited works. Dates vary.

Encyclopedias, handbooks, ebooks, and videos published by Sage and CQ Press. 2000 to present

Causal Design

Definition and Purpose

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

• Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
• Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
• Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.

What do these studies tell you ?

• Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
• Replication is possible.
• There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.

What these studies don't tell you ?

• Not all relationships are casual! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
• Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
• If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the  actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation ; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base . 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, r ather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

• Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
• Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
• The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
• Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
• Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
• Either original data or secondary data can be used in this design.
• In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
• Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
• Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101 . Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study . Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

• Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
• Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
• Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
• Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
• Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
• Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
• Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
• Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
• Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
• Studies cannot be utilized to establish cause and effect relationships.
• This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
• There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In  The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design, Application, Strengths and Weaknesses of Cross-Sectional Studies . Healthknowledge, 2009. Cross-Sectional Study . Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

• The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
• Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
• If the limitations are understood, they can be a useful tool in developing a more focused study.
• Descriptive studies can yield rich data that lead to important recommendations in practice.
• Appoach collects a large amount of data for detailed analysis.
• The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
• Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
• The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies . Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design , September 26, 2008. Explorable.com website.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

• Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
• Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
• Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
• Approach provides the highest level of evidence for single studies.
• The design is artificial, and results may not generalize well to the real world.
• The artificial settings of experiments may alter the behaviors or responses of participants.
• Experimental designs can be costly if special equipment or facilities are needed.
• Some research problems cannot be studied using an experiment because of ethical or technical reasons.
• Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs . School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research . Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design . Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research . Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

• Familiarity with basic details, settings, and concerns.
• Well grounded picture of the situation being developed.
• Generation of new ideas and assumptions.
• Development of tentative theories or hypotheses.
• Determination about whether a study is feasible in the future.
• Issues get refined for more systematic investigation and formulation of new research questions.
• Direction for future research and techniques get developed.
• Design is a useful approach for gaining background information on a particular topic.
• Exploratory research is flexible and can address research questions of all types (what, why, how).
• Provides an opportunity to define new terms and clarify existing concepts.
• Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
• In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
• Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
• The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
• The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
• Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research . Wikipedia.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

• The historical research design is unobtrusive; the act of research does not affect the results of the study.
• The historical approach is well suited for trend analysis.
• Historical records can add important contextual background required to more fully understand and interpret a research problem.
• There is often no possibility of researcher-subject interaction that could affect the findings.
• Historical sources can be used over and over to study different research problems or to replicate a previous study.
• The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
• Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
• Interpreting historical sources can be very time consuming.
• The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
• Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
• Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
• It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58;  Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

• Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
• Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
• The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
• Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
• The data collection method may change over time.
• Maintaining the integrity of the original sample can be difficult over an extended period of time.
• It can be difficult to show more than one variable at a time.
• This design often needs qualitative research data to explain fluctuations in the results.
• A longitudinal research design assumes present trends will continue unchanged.
• It can take a long period of time to gather results.
• There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study . Wikipedia.

Mixed-Method Design

• Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
• Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
• A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
• The strengths of one method can be used to overcome the inherent weaknesses of another method.
• Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
• May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
• Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
• A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
• Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
• Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
• Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
• Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
• Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

• Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
• The researcher is able to collect in-depth information about a particular behavior.
• Can reveal interrelationships among multifaceted dimensions of group interactions.
• You can generalize your results to real life situations.
• Observational research is useful for discovering what variables may be important before applying other methods like experiments.
• Observation research designs account for the complexity of group behaviors.
• Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
• In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
• There can be problems with bias as the researcher may only "see what they want to see."
• There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
• Sources or subjects may not all be equally credible.
• Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research . Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

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• 10 Research Question Examples to Guide Your Research Project

10 Research Question Examples to Guide your Research Project

Published on October 30, 2022 by Shona McCombes . Revised on October 19, 2023.

The research question is one of the most important parts of your research paper , thesis or dissertation . It’s important to spend some time assessing and refining your question before you get started.

The exact form of your question will depend on a few things, such as the length of your project, the type of research you’re conducting, the topic , and the research problem . However, all research questions should be focused, specific, and relevant to a timely social or scholarly issue.

Once you’ve read our guide on how to write a research question , you can use these examples to craft your own.

Note that the design of your research question can depend on what method you are pursuing. Here are a few options for qualitative, quantitative, and statistical research questions.

Other interesting articles

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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Research Design: Definition, Types, Characteristics & Study Examples

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A research design is the blueprint for any study. It's the plan that outlines how the research will be carried out. A study design usually includes the methods of data collection, the type of data to be gathered, and how it will be analyzed. Research designs help ensure the study is reliable, valid, and can answer the research question.

Behind every groundbreaking discovery and innovation lies a well-designed research. Whether you're investigating a new technology or exploring a social phenomenon, a solid research design is key to achieving reliable results. But what exactly does it means, and how do you create an effective one? Stay with our paper writers and find out:

• Detailed definition
• Types of research study designs
• How to write a research design
• Useful examples.

Whether you're a seasoned researcher or just getting started, understanding the core principles will help you conduct better studies and make more meaningful contributions.

What Is a Research Design: Definition

Research design is an overall study plan outlining a specific approach to investigating a research question . It covers particular methods and strategies for collecting, measuring and analyzing data. Students  are required to build a study design either as an individual task or as a separate chapter in a research paper , thesis or dissertation .

Before designing a research project, you need to consider a series aspects of your future study:

• Research aims What research objectives do you want to accomplish with your study? What approach will you take to get there? Will you use a quantitative, qualitative, or mixed methods approach?
• Type of data Will you gather new data (primary research), or rely on existing data (secondary research) to answer your research question?
• Sampling methods How will you pick participants? What criteria will you use to ensure your sample is representative of the population?
• Data collection methods What tools or instruments will you use to gather data (e.g., conducting a survey , interview, or observation)?
• Measurement  What metrics will you use to capture and quantify data?
• Data analysis  What statistical or qualitative techniques will you use to make sense of your findings?

By using a well-designed research plan, you can make sure your findings are solid and can be generalized to a larger group.

Research design example

What Makes a Good Study Design? 

To design a research study that works, you need to carefully think things through. Make sure your strategy is tailored to your research topic and watch out for potential biases. Your procedures should be flexible enough to accommodate changes that may arise during the course of research. 

A good research design should be:

• Clear and methodologically sound
• Feasible and realistic
• Knowledge-driven.

By following these guidelines, you'll set yourself up for success and be able to produce reliable results.

Research Study Design Structure

A structured research design provides a clear and organized plan for carrying out a study. It helps researchers to stay on track and ensure that the study stays within the bounds of acceptable time, resources, and funding.

A typical design includes 5 main components:

• Research question(s): Central research topic(s) or issue(s).
• Sampling strategy: Method for selecting participants or subjects.
• Data collection techniques: Tools or instruments for retrieving data.
• Data analysis approaches: Techniques for interpreting and scrutinizing assembled data.
• Ethical considerations: Principles for protecting human subjects (e.g., obtaining a written consent, ensuring confidentiality guarantees).

Research Design Essential Characteristics

Creating a research design warrants a firm foundation for your exploration. The cost of making a mistake is too high. This is not something scholars can afford, especially if financial resources or a considerable amount of time is invested. Choose the wrong strategy, and you risk undermining your whole study and wasting resources. 

To avoid any unpleasant surprises, make sure your study conforms to the key characteristics. Here are some core features of research designs:

• Reliability   Reliability is stability of your measures or instruments over time. A reliable research design is one that can be reproduced in the same way and deliver consistent outcomes. It should also nurture accurate representations of actual conditions and guarantee data quality.
• Validity For a study to be valid , it must measure what it claims to measure. This means that methodological approaches should be carefully considered and aligned to the main research question(s).
• Generalizability Generalizability means that your insights can be practiced outside of the scope of a study. When making inferences, researchers must take into account determinants such as sample size, sampling technique, and context.
• Neutrality A study model should be free from personal or cognitive biases to ensure an impartial investigation of a research topic. Steer clear of highlighting any particular group or achievement.

Key Concepts in Research Design

Now let’s discuss the fundamental principles that underpin study designs in research. This will help you develop a strong framework and make sure all the puzzles fit together.

Primary concepts

Types of Approaches to Research Design

Study frameworks can fall into 2 major categories depending on the approach to compiling data you opt for. The 2 main types of study designs in research are qualitative and quantitative research. Both approaches have their unique strengths and weaknesses, and can be utilized based on the nature of information you are dealing with. 

Quantitative Research  

Quantitative study is focused on establishing empirical relationships between variables and collecting numerical data. It involves using statistics, surveys, and experiments to measure the effects of certain phenomena. This research design type looks at hard evidence and provides measurements that can be analyzed using statistical techniques. 

Qualitative Research 

Qualitative approach is used to examine the behavior, attitudes, and perceptions of individuals in a given environment. This type of study design relies on unstructured data retrieved through interviews, open-ended questions and observational methods. 

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Types of Research Designs & Examples

Choosing a research design may be tough especially for the first-timers. One of the great ways to get started is to pick the right design that will best fit your objectives. There are 4 different types of research designs you can opt for to carry out your investigation:

• Experimental
• Correlational
• Descriptive
• Diagnostic/explanatory.

Below we will go through each type and offer you examples of study designs to assist you with selection.

1. Experimental

In experimental research design , scientists manipulate one or more independent variables and control other factors in order to observe their effect on a dependent variable. This type of research design is used for experiments where the goal is to determine a causal relationship. 

Its core characteristics include:

• Randomization
• Manipulation
• Replication.

2. Correlational

Correlational study is used to examine the existing relationships between variables. In this type of design, you don’t need to manipulate other variables. Here, researchers just focus on observing and measuring the naturally occurring relationship.

Correlational studies encompass such features: 

• Data collection from natural settings
• No intervention by the researcher
• Observation over time.

3. Descriptive 

Descriptive research design is all about describing a particular population or phenomenon without any interruption. This study design is especially helpful when we're not sure about something and want to understand it better.

Descriptive studies are characterized by such features:

• Random and convenience sampling
• Observation
• No intervention.

4. Diagnostic

Diagnostic or explanatory research is used to determine the cause of an existing problem or a chronic symptom. Unlike other types of design, here scientists try to understand why something is happening. 

Among essential hallmarks of explanatory studies are: 

• Testing hypotheses and theories
• Examining existing data
• Comparative analysis.

How to Design a Research Study: Step-by-Step Process

When designing your research don't just jump into it. It's important to take the time and do things right in order to attain accurate findings. Follow these simple steps on how to design a study to get the most out of your project.

1. Determine Your Aims 

The first step in the research design process is figuring out what you want to achieve. This involves identifying your research question, goals and specific objectives you want to accomplish. Think whether you want to explore a specific issue or develop a new theory? Setting your aims from the get-go will help you stay focused and ensure that your study is driven by purpose. 

Once  you are clear with your goals, you need to decide on the main approach. Will you use qualitative or quantitative methods? Or perhaps a mixture of both?

2. Select a Type of Research Design

Choosing a suitable design requires considering multiple factors, such as your research question, data collection methods, and resources. There are various research design types, each with its own advantages and limitations. Think about the kind of data that would be most useful to address your questions. Ultimately, a well-devised strategy should help you gather accurate data to achieve your objectives.

3. Define Your Population and Sampling Methods

To design a research project, it is essential to establish your target population and parameters for selecting participants. First, identify a cohort of individuals who share common characteristics and possess relevant experiences. 

With your population in mind, you can now choose an optimal sampling method. Sampling is basically the process of narrowing down your target group to only those individuals who will participate in your study. At this point, you need to decide on whether you want to randomly choose the participants (probability sampling) or set out any selection criteria (non-probability sampling). 

4. Decide on Your Data Collection Methods

When devising your study, it is also important to consider how you will retrieve data.  Depending on the type of design you are using, you may deploy diverse methods. Below you can see various data collection techniques suited for different research designs. 

Data collection methods in various studies

Additionally, if you plan on integrating existing data sources like medical records or publicly available datasets, you want to mention this as well. 

5. Arrange Your Data Collection Process

Your data collection process should also be meticulously thought out. This stage involves scheduling interviews, arranging questionnaires and preparing all the necessary tools for collecting information from participants. Detail how long your study will take and what procedures will be followed for recording and analyzing the data. 

State which variables will be studied and what measures or scales will be used when assessing each variable.

Measures and scales 

Measures and scales are tools used to quantify variables in research. A measure is any method used to collect data on a variable, while a scale is a set of items or questions used to measure a particular construct or concept. Different types of scales include nominal, ordinal, interval, or ratio , each of which has distinct properties

Operationalization 

When working with abstract information that needs to be quantified, researchers often operationalize the variable by defining it in concrete terms that can be measured or observed. This allows the abstract concept to be studied systematically and rigorously. 

Operationalization in study design example

Remember that research design should be flexible enough to adjust for any unforeseen developments. Even with rigorous preparation, you may still face unexpected challenges during your project. That’s why you need to work out contingency plans when designing research.

6. Choose Data Analysis Techniques

It’s impossible to design research without mentioning how you are going to scrutinize data. To select a proper method, take into account the type of data you are dealing with and how many variables you need to analyze. 

Qualitative data may require thematic analysis or content analysis.

Quantitative data, on the other hand, could be processed with more sophisticated statistical analysis approaches such as regression analysis, factor analysis or descriptive statistics.

Finally, don’t forget about ethical considerations. Opt for those methods that minimize harm to participants and protect their rights.

Research Design Checklist

Having a checklist in front of you will help you design your research flawlessly.

Bottom Line on Research Design & Study Types

Designing a research project involves making countless decisions that can affect the quality of your work. By planning out each step and selecting the best methods for data collection and analysis, you can ensure that your project is conducted professionally.

We hope this article has helped you to better understand the research design process. If you have any questions or comments, ping us in the comments section below.

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• checkbox I clearly defined my research question and its significance.
• checkbox I considered crucial factors such as the nature of my study, type of required data and available resources to choose a suitable design.
• checkbox A sample size is sufficient to provide statistically significant results.
• checkbox My data collection methods are reliable and valid.
• checkbox Analysis methods are appropriate for the type of data I will be gathering.
• checkbox My research design protects the rights and privacy of my participants.
• checkbox I created a realistic timeline for research, including deadlines for data collection, analysis, and write-up.
• checkbox I considered funding sources and potential limitations.

You are going to investigate the effectiveness of a mindfulness-based intervention for reducing stress and anxiety among college students. You decide to organize an experiment to explore the impact. Participants should be randomly assigned to either an intervention group or a control group. You need to conduct pre- and post-intervention using self-report measures of stress and anxiety.

A pharmaceutical company wants to test a new drug to investigate its effectiveness in treating a specific medical condition. Researchers would randomly assign participants to either a control group (receiving a placebo) or an experimental group (receiving the new drug). They would rigorously control all variables (e.g, age, medical history) and manipulate them to get reliable results.

A research team wants to examine the relationship between academic performance and extracurricular activities. They would observe students' performance in courses and measure how much time they spend engaging in extracurricular activities.

A psychologist wants to understand how parents' behavior affects their child's self-concept. They would observe the interaction between children and their parents in a natural setting. Gathered information will help her get an overview of this situation and recognize some patterns.

A public health specialist wants to identify the cause of an outbreak of water-borne disease in a certain area. They would inspect water samples and records to compare them with similar outbreaks in other areas. This will help to uncover reasons behind this accident.

For instance, if you are researching the impact of social media on mental health, your population could be young adults aged 18-25 who use social media frequently.

To examine the influence of social media on mental well-being, we will divide a whole population into smaller subgroups using stratified random sampling . Then, we will randomly pick participants from each subcategory to make sure that findings are also true for a broader group of young adults.

If studying the concept of happiness, researchers might operationalize it by using a scale that measures positive affect or life satisfaction. This allows us to quantify happiness and inspect its relationship with other variables, such as income or social support.

FAQ About Research Study Designs

1. what is a study design.

Study design, or else called research design, is the overall plan for a project, including its purpose, methodology, data collection and analysis techniques. A good design ensures that your project is conducted in an organized and ethical manner. It also provides clear guidelines for replicating or extending a study in the future.

2. What is the purpose of a research design?

The purpose of a research design is to provide a structure and framework for your project. By outlining your methodology, data collection techniques, and analysis methods in advance, you can ensure that your project will be conducted effectively.

3. What is the importance of research designs?

Research designs are critical to the success of any research project for several reasons. Specifically, study designs grant:

• Clear direction for all stages of a study
• Validity and reliability of findings
• Roadmap for replication or further extension
• Accurate results by controlling for potential bias
• Comparison between studies by providing consistent guidelines.

By following an established plan, researchers can be sure that their projects are organized, ethical, and reliable.

4. What are the 4 types of study designs?

There are generally 4 types of study designs commonly used in research:

• Experimental studies: investigate cause-and-effect relationships by manipulating the independent variable.
• Correlational studies: examine relationships between 2 or more variables without intruding them.
• Descriptive studies: describe the characteristics of a population or phenomenon without making any inferences about cause and effect.
• Explanatory studies: intended to explain causal relationships.

For more advanced studies, you can even combine several types. Mixed-methods research may come in handy when exploring complex phenomena that cannot be adequately captured by one method alone.

How to Start a Research Project: Choosing a Topic

• Choosing a Topic

Beginning Your Research Project

You have an assignment coming up in class. You need to write a research paper, create an annotated bibliography, or make a presentation. These are just some research projects you may need to do.

This guide will show you different ways to start a research project. When following this guide, please consider 3 concepts:

• Center your personal research interests - What are you interested in?
• Take as long on each step as you would like.
• Skip steps and repeat steps as you need.

Starting from Nothing: The Mind Map

A mind map is a visual way of building a topic into a research question .

A topic is the basic idea that interests you. This is the idea that sparks your research. A topic could be "barbeque," "The Cold War," "flightless birds," or "the common cold." If you are having trouble choosing a topic , review the class syllabus or canvas modules. Find a topic covered in class that you can see yourself spending time with.

A research question is the focus of your research project. It is the thesis of your paper or the point of your presentation.

Work with us through the mind map steps to build your own research question .

To create a mind map , you will need to be able to write or type text, and the text must also be rearrangeable.

• Start with an idea like "Kitchen Design". Place your idea in the center.

• Surround your central idea with related concepts. I wrote all the kinds of kitchens I could think of. I could have also chosen to list appliances or design themes instead.

• Out of the kitchen-types, I was most drawn to "Hospital Kitchens". I then added concepts around "Hospital Kitchens". These concepts can be moved to also combined with other ideas.

• I also thought more about "Home Kitchens". I combined, "Kitchen Safety", "Consumer Preferences", and "Advertisements."

• My final version of my mind map example is very small. Don't worry if you have many more ideas and need more time rearranging your cards and planning.

I have identified two different starting research questions by combining my concepts:

• How could hospital managers design hospital kitchens to be safer for employees?
• How do kitchen appliance manufacturers advertise the safety of their products to consumers?

Research Questions

A research question is the focus of your research project. It is the thesis of your paper or the point of your presentation. Here are some requirements of a good research question:

• Research questions cannot be answered with "yes" or "no".
• Research questions can be researched.
• A small research paper shouldn't have a research question with a giant scope: How does preventative healthcare get planned?
• A small research paper should have a research question with a manageable scope: How do preventative care programs for type II diabetes in Alabaman clinics get advertised?

In this example, we narrowed the scope of our initial research question in a few ways:

• Type: "Preventative care" was limited to - "type II diabetes"
• Place: We had no initial location limit. We limited ourselves to "Alabaman clinics"
• Action: "Planned" was defined as "advertised"

Sometimes, research questions need to change slightly after you have done some research. If you were not able to find any useful resources for the example research question, then you could try changing the scope. If you cannot find anything specific to Alabaman clinics, then you could change that part of your research question to "United States clinics" or "Alabaman healthcare providers."

Still stuck? Please check Monash University's Developing Research Questions guide .

Turning your Research Question into a Search

Useful links.

• Purdue OWL: Choosing a Topic This handout provides detailed information about how to write research papers including discussing research papers as a genre, choosing topics, and finding sources.
• UNC: Brainstorming This handout discusses techniques that will help you start writing a paper and continue writing through the challenges of the revising process. Brainstorming can help you choose a topic, develop an approach to a topic, or deepen your understanding of the topic’s potential.
• University Writing Center Schedule a session with a tutor at the University Writing Center.
• Next: JSTOR >>
• Last Updated: Aug 6, 2024 12:46 PM
• URL: https://libguides.southalabama.edu/start-research

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Home » Research Topics – Ideas and Examples

Research Topics – Ideas and Examples

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Research Topic

Definition:

Research topic is a specific subject or area of interest that a researcher wants to investigate or explore in-depth through research. It is the overarching theme or question that guides a research project and helps to focus the research activities towards a clear objective.

How to Choose Research Topic

You can Choose a Research Topic by following the below guide:

Identify your Interests

One of the most important factors to consider when choosing a research topic is your personal interest. This is because you will be spending a considerable amount of time researching and writing about the topic, so it’s essential that you are genuinely interested and passionate about it. Start by brainstorming a list of potential research topics based on your interests, hobbies, or areas of expertise. You can also consider the courses that you’ve enjoyed the most or the topics that have stood out to you in your readings.

Review the Literature

Before deciding on a research topic, you need to understand what has already been written about it. Conducting a preliminary review of the existing literature in your field can help you identify gaps in knowledge, inconsistencies in findings, or unanswered questions that you can explore further. You can do this by reading academic articles, books, and other relevant sources in your field. Make notes of the themes or topics that emerge and use this information to guide your research question.

Consult with your Advisor

Your academic advisor or a mentor in your field can provide you with valuable insights and guidance on choosing a research topic. They can help you identify areas of interest, suggest potential research questions, and provide feedback on the feasibility of your research proposal. They can also direct you towards relevant literature and resources that can help you develop your research further.

Consider the Scope and Feasibility

The research topic you choose should be manageable within the time and resource constraints of your project. Be mindful of the scope of your research and ensure that you are not trying to tackle a topic that is too broad or too narrow. If your topic is too broad, you may find it challenging to conduct a comprehensive analysis, while if it’s too narrow, you may struggle to find enough material to support your research.

Brainstorm with Peers

Discussing potential research topics with your peers or colleagues can help you generate new ideas and perspectives. They may have insights or expertise that you haven’t considered, and their feedback can help you refine your research question. You can also join academic groups or attend conferences in your field to network with other researchers and get inspiration for your research.

Consider the Relevance

Choose a research topic that is relevant to your field of study and has the potential to contribute to the existing knowledge. You can consider the latest trends and emerging issues in your field to identify topics that are both relevant and interesting. Conducting research on a topic that is timely and relevant can also increase the likelihood of getting published or presenting your research at conferences.

Keep an Open Mind

While it’s essential to choose a research topic that aligns with your interests and expertise, you should also be open to exploring new ideas or topics that may be outside of your comfort zone. Consider researching a topic that challenges your assumptions or introduces new perspectives that you haven’t considered before. You may discover new insights or perspectives that can enrich your research and contribute to your growth as a researcher.

Components of Research Topic

A research topic typically consists of several components that help to define and clarify the subject matter of the research project. These components include:

• Research problem or question: This is the central issue or inquiry that the research seeks to address. It should be well-defined and focused, with clear boundaries that limit the scope of the research.
• Background and context: This component provides the necessary background information and context for the research topic. It explains why the research problem or question is important, relevant, and timely. It may also include a literature review that summarizes the existing research on the topic.
• Objectives or goals : This component outlines the specific objectives or goals that the research seeks to achieve. It should be clear and concise, and should align with the research problem or question.
• Methodology : This component describes the research methods and techniques that will be used to collect and analyze data. It should be detailed enough to provide a clear understanding of how the research will be conducted, including the sampling method, data collection tools, and statistical analyses.
• Significance or contribution : This component explains the significance or contribution of the research topic. It should demonstrate how the research will add to the existing knowledge in the field, and how it will benefit practitioners, policymakers, or society at large.
• Limitations: This component outlines the limitations of the research, including any potential biases, assumptions, or constraints. It should be transparent and honest about the potential shortcomings of the research, and how these limitations will be addressed.
• Expected outcomes or findings : This component provides an overview of the expected outcomes or findings of the research project. It should be realistic and based on the research objectives and methodology.

Purpose of Research Topic

The purpose of a research topic is to identify a specific area of inquiry that the researcher wants to explore and investigate. A research topic is typically a broad area of interest that requires further exploration and refinement through the research process. It provides a clear focus and direction for the research project, and helps to define the research questions and objectives. A well-defined research topic also helps to ensure that the research is relevant and useful, and can contribute to the existing body of knowledge in the field. Ultimately, the purpose of a research topic is to generate new insights, knowledge, and understanding about a particular phenomenon, issue, or problem.

Characteristics of Research Topic

some common characteristics of a well-defined research topic include:

• Relevance : A research topic should be relevant and significant to the field of study and address a current issue, problem, or gap in knowledge.
• Specificity : A research topic should be specific enough to allow for a focused investigation and clear understanding of the research question.
• Feasibility : A research topic should be feasible, meaning it should be possible to carry out the research within the given constraints of time, resources, and expertise.
• Novelty : A research topic should add to the existing body of knowledge by introducing new ideas, concepts, or theories.
• Clarity : A research topic should be clearly articulated and easy to understand, both for the researcher and for potential readers of the research.
• Importance : A research topic should be important and have practical implications for the field or society as a whole.
• Significance : A research topic should be significant and have the potential to generate new insights and understanding in the field.

Examples of Research Topics

Here are some examples of research topics that are currently relevant and in-demand in various fields:

• The impact of social media on mental health: With the rise of social media use, this topic has gained significant attention in recent years. Researchers could investigate how social media affects self-esteem, body image, and other mental health concerns.
• The use of artificial intelligence in healthcare: As healthcare becomes increasingly digitalized, researchers could explore the use of AI algorithms to predict and prevent disease, optimize treatment plans, and improve patient outcomes.
• Renewable energy and sustainable development: As the world seeks to reduce its carbon footprint, researchers could investigate the potential of renewable energy sources such as wind and solar power, and how these technologies can be integrated into existing infrastructure.
• The impact of workplace diversity and inclusion on employee productivity: With an increasing focus on diversity and inclusion in the workplace, researchers could investigate how these factors affect employee morale, productivity, and retention.
• Cybersecurity and data privacy: As data breaches and cyber attacks become more common, researchers could explore new methods of protecting sensitive information and preventing malicious attacks.
• T he impact of mindfulness and meditation on stress reduction: As stress-related health issues become more prevalent, researchers could investigate the effectiveness of mindfulness and meditation practices on reducing stress and improving overall well-being.

Research Topics Ideas

Here are some Research Topics Ideas from different fields:

• The impact of social media on mental health and well-being.
• The effectiveness of various teaching methods in improving academic performance in high schools.
• The role of AI and machine learning in healthcare: current applications and future potentials.
• The impact of climate change on wildlife habitats and conservation efforts.
• The effects of video game violence on aggressive behavior in young adults.
• The effectiveness of mindfulness-based stress reduction techniques in reducing anxiety and depression.
• The impact of technology on human relationships and social interactions.
• The role of exercise in promoting physical and mental health in older adults.
• The causes and consequences of income inequality in developed and developing countries.
• The effects of cultural diversity in the workplace on job satisfaction and productivity.
• The impact of remote work on employee productivity and work-life balance.
• The relationship between sleep patterns and cognitive functioning.
• The effectiveness of online learning versus traditional classroom learning.
• The role of government policies in promoting renewable energy adoption.
• The effects of childhood trauma on mental health in adulthood.
• The impact of social media on political participation and civic engagement.
• The effectiveness of cognitive-behavioral therapy in treating anxiety disorders.
• The relationship between nutrition and cognitive functioning.
• The impact of gentrification on urban communities.
• The effects of music on mood and emotional regulation.
• The impact of microplastics on marine ecosystems and food webs.
• The role of artificial intelligence in detecting and preventing cyberattacks.
• The effectiveness of mindfulness-based interventions in managing chronic pain.
• The relationship between personality traits and job satisfaction.
• The effects of social isolation on mental and physical health in older adults.
• The impact of cultural and linguistic diversity on healthcare access and outcomes.
• The effectiveness of psychotherapy in treating depression and anxiety in adolescents.
• The relationship between exercise and cognitive aging.
• The effects of social media on body image and self-esteem.
• The role of corporate social responsibility in promoting sustainable business practices.
• The impact of mindfulness meditation on attention and focus in children.
• The relationship between political polarization and media consumption habits.
• The effects of urbanization on mental health and well-being.
• The role of social support in managing chronic illness.
• The impact of social media on romantic relationships and dating behaviors.
• The effectiveness of behavioral interventions in promoting physical activity in sedentary adults.
• The relationship between sleep quality and immune function.
• The effects of workplace diversity and inclusion programs on employee retention.
• The impact of climate change on global food security.
• The role of music therapy in improving communication and social skills in individuals with autism spectrum disorder.
• The impact of cultural values on the development of mental health stigma.
• The effectiveness of mindfulness-based stress reduction techniques in reducing burnout in healthcare professionals.
• The relationship between social media use and body dissatisfaction among adolescents.
• The effects of nature exposure on cognitive functioning and well-being.
• The role of peer mentoring in promoting academic success in underrepresented student populations.
• The impact of neighborhood characteristics on physical activity and obesity.
• The effectiveness of cognitive rehabilitation interventions in improving cognitive functioning in individuals with traumatic brain injury.
• The relationship between organizational culture and employee job satisfaction.
• The effects of cultural immersion experiences on intercultural competence development.
• The role of assistive technology in promoting independence and quality of life for individuals with disabilities.
• The impact of workplace design on employee productivity and well-being.
• The impact of digital technologies on the music industry and artist revenues.
• The effectiveness of cognitive behavioral therapy in treating insomnia.
• The relationship between social media use and body weight perception among young adults.
• The effects of green spaces on mental health and well-being in urban areas.
• The role of mindfulness-based interventions in reducing substance use disorders.
• The impact of workplace bullying on employee turnover and job satisfaction.
• The effectiveness of animal-assisted therapy in treating mental health disorders.
• The relationship between teacher-student relationships and academic achievement.
• The effects of social support on resilience in individuals experiencing adversity.
• The role of cognitive aging in driving safety and mobility.
• The effectiveness of psychotherapy in treating post-traumatic stress disorder (PTSD).
• The relationship between social media use and sleep quality.
• The effects of cultural competency training on healthcare providers’ attitudes and behaviors towards diverse patient populations.
• The role of exercise in preventing chronic diseases such as type 2 diabetes and cardiovascular disease.
• The impact of the gig economy on job security and worker rights.
• The effectiveness of art therapy in promoting emotional regulation and coping skills in children and adolescents.
• The relationship between parenting styles and child academic achievement.
• The effects of social comparison on well-being and self-esteem.
• The role of nutrition in promoting healthy aging and longevity.
• The impact of gender diversity in leadership on organizational performance.
• The effectiveness of family-based interventions in treating eating disorders.
• The relationship between social media use and perceived loneliness among older adults.
• The effects of mindfulness-based interventions on pain management in chronic pain patients.
• The role of physical activity in preventing and treating depression.
• The impact of cultural differences on communication and conflict resolution in international business.
• The effectiveness of eye movement desensitization and reprocessing (EMDR) in treating anxiety disorders.
• The relationship between student engagement and academic success in higher education.
• The effects of discrimination on mental health outcomes in minority populations.
• The role of virtual reality in enhancing learning experiences.
• The impact of social media influencers on consumer behavior and brand loyalty.
• The effectiveness of acceptance and commitment therapy (ACT) in treating chronic pain.
• The relationship between social media use and body image dissatisfaction among men.
• The effects of exposure to nature on cognitive functioning and creativity.
• The role of spirituality in coping with illness and disability.
• The impact of automation on employment and job displacement.
• The effectiveness of dialectical behavior therapy (DBT) in treating borderline personality disorder.
• The relationship between teacher-student relationships and school attendance.
• The effects of mindfulness-based interventions on workplace stress and burnout.
• The role of exercise in promoting cognitive functioning and brain health in older adults.
• The impact of diversity and inclusion initiatives on organizational innovation and creativity.
• The effectiveness of cognitive remediation therapy in treating schizophrenia.
• The relationship between social media use and body dissatisfaction among women.
• The effects of exposure to natural light on mood and sleep quality.
• The role of spirituality in enhancing well-being and resilience in military personnel.
• The impact of artificial intelligence on job training and skill development.
• The effectiveness of interpersonal therapy (IPT) in treating depression.
• The relationship between parental involvement and academic achievement among low-income students.
• The effects of mindfulness-based interventions on emotional regulation and coping skills in trauma survivors.
• The role of nutrition in preventing and treating mental health disorders.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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2025 Nissan Frontier Receives Design Refresh and Utility Updates

August 9, 2024 • By Staff and News Reports • Bookmark +

The refreshed 2025 Nissan Frontier will reach Nissan dealerships later this summer. Pricing will be announced closer to the on-sale date.

Photo: Nissan

The 2025 Nissan Frontier features a new design, enhanced utility, and updated technology. Noticeable changes can be seen with the 6 ft. bed for all trims, an increased maximum tow rating, and a larger available infotainment system.

The refreshed 2025 Nissan Frontier will reach Nissan dealerships later this summer. Pricing and range will be announced closer to the on-sale date.

The new MY Frontier comes with a larger infotainment system, with SV grades and higher featuring a 12.3-in. touchscreen system.

Specs, Trims, and Driver Assistance Capabilities

The Frontier comes equipped with a direct-injected 3.8-liter V6 engine and 9-speed automatic transmission. The engine features idle stop/start and is rated for 310 hp and 281 lb.-ft. of torque.

Choosing the Right Vehicle for Tall Fleet Drivers

Active Brake Limited Slip is standard on all grades, with Pro-4X upgrading to an electronic locking differential. The Pro-X and Pro-4X feature all-terrain tires , Bilstein off-road suspension components, over-fenders, and Lava Red tow hooks.

The Frontier offers a Utili-Track system for its bed with adjustable tie-down cleats, a spray-in bedliner, and an available 120V power outlet.

The Frontier SL includes leather-appointed front and rear seating, a heated leather-wrapped steering wheel, LED exterior lighting, and a Fender Premium Audio system with 10 speakers.

Numerous driver assistance and safety technologies are now standard across all grades, from S through SL, including Lane Departure Warning, Blind Spot Warning, Rear Cross Traffic Alert, Rear Parking Sensors, High Beam Assist, and Intelligent Cruise Control. The Frontier SL also features Traffic Sign Recognition.

The 2025 Frontier offers a 6 ft. bed on all trims and a maximum trailering capacity of up to 7,150 lbs.

Truck Bed Updates and Max Towing Increase

The 2025 Frontier expands the availability of the Crew Cab long-wheelbase configuration from only SV to SV, Pro-4X, and SL grades. The Frontier's 6 ft. bed could benefit fleets hauling large loads and cargo to job sites.

The vehicle also boasts an increased maximum trailering capacity of up to 7,150 lbs. this year. Towing capacities increase by around 500 lbs. on all grades and configurations compared to the 2024 Frontier.

The Frontier Pro-4X includes an Intelligent Around View Monitor. The Off-Road Mode viewing function can now be used while driving up to 12 mph.

The 2025 Frontier's new look has a revised front fascia, grille, and bumper design, with a new Afterburn Orange paint option.

Refreshed Design and Function

The 2025 Frontier's new look has a revised front fascia, grille, and bumper design, a new tailgate finisher for the Pro-X and Pro-4X, redesigned dashboard trim, a new 17-in. alloy wheel design, and the introduction of a new Afterburn Orange paint.

Based on customer feedback, Nissan made multiple functional interior revisions, such as a telescopic-adjusting steering wheel standard for all grades. A four-way power passenger seat also becomes standard on the Pro-X, Pro-4X, and SL grades. The six-way power driver's seat now adds two-way power lumbar on SV grades and higher.

SV grades and higher add a 12.3-in. touchscreen infotainment system. The system includes wireless Android Auto and Apple CarPlay connectivity, enabling compatible smartphone mirroring without plugging in a USB cord.

All Frontier grades now have a standard sliding rear window and a sunglasses holder in the overhead console. SV models now have standard 17-in. alloy wheels. In SL grades, drivers can find a wireless phone charger, an Intelligent Around View Monitor, and a four-way power passenger seat that has also become standard.

Keep Reading:  Nissan Enhancing Headlight Technology to Protect Drivers

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Workers crave autonomy and flexibility. Here are ways to achieve that balance

Respecting personal boundaries, reducing work stress, and having predictable work schedules are among the steps both employees and employers can take to achieve work-life harmony

• Healthy Workplaces
• Managing Human Capital

Today’s employees want control over where they work—whether fully in-person, fully remote, or a hybrid approach—and they’re more satisfied when they are given the chance to decide, according to APA’s 2024 Work in America survey.  

Still, 1 in 3 workers (33%) said they do not have enough flexibility at work to keep their professional life and personal life in balance, the survey found. Employers and employees can take steps to increase workers’ autonomy and flexibility, which research has found improves work-life balance.

Here are tips from industrial-organizational psychologists and workplace experts Tammy Allen, PhD, a psychology professor at the University of South Florida in Tampa, and Mindy Shoss, PhD, a psychology professor at the University of Central Florida in Orlando.

How can employers offer more job autonomy? Mindy Shoss: Employers can give employees greater control over how tasks are performed, the scope and pace of work, the scheduling and location of work, and more. But it’s important that  job autonomy be provided  within the context of a supportive supervisor-employee relationship. That way, the expectations and goals of both the organization and worker can be openly discussed and navigated.

How can workers negotiate policies that are better for work-life harmony? Tammy Allen: Employees can advocate for themselves by discussing their needs with supervisors; they should emphasize how addressing these needs benefit the employee, the organization, and the bottom line. They can provide evidence that the support or accommodation, such as working remotely several days a week, will not harm productivity or team dynamics. If the supervisor is hesitant, ask for a trial period.

Only 25% of workers in manual labor jobs say their employers offer a culture where time off is respected, compared with 48% of office workers. How can employers address this issue with blue-collar workers? Mindy Shoss: Schedules should be predictable and flexible. Work schedules should be planned far enough in advance for any worker to coordinate other aspects of their life.  Income should be relatively stable . Organizations can also look for ways to help employees fulfill personal needs at work. For example,  Rosen Hotels and Resorts  offers an advanced medical center for its employees and other initiatives on-site so that workers can more easily address their health, fitness, and well-being needs.

What can employees do if their supervisor expects them to check email on their personal time and not take vacation when they want to? Tammy Allen: Expectations should be aligned with the nature of the job and based on a mutual understanding between the employee and the boss. If the understanding is violated (for example, you’ve agreed on no emails on Sundays but the boss consistently expects emails to be answered on Sundays), then a discussion about realignment may be needed. You can discuss the  importance of establishing boundaries  and the importance of detachment from work to enable  recovery from work for your health and well-being . That allows you to be at your best for the organization.

What kind of boundaries can employers set to better respect workers’ free time? Mindy Shoss: Working with employees to find solutions that best fit the  individual’s, team’s, and leader’s needs  can result in new norms around when, and for what purpose, it’s acceptable to send emails or text messages, for example. As with most things, open, respectful, and bidirectional communication , and creative problem solving are key.

What mental health strategies can employees use to help minimize work stress overall? Tammy Allen:   Mindfulness-based stress reduction practices such as yoga, meditation, and deep breathing have been shown to help reduce employee work stress and enhance employee health.

Further reading

Work-family research: A review and next steps Allen, T. D., & French, K. A., Personnel Psychology , 2022

Clarifying work–family intervention processes: The roles of work–family conflict and family-supportive supervisor behaviors Hammer, L. B., et al. Journal of Applied Psychology , 2011

Precarious work schedules as a source of economic insecurity and institutional distrust Lambert, S. J., et al.  RSF: The Russell Sage Foundation Journal of the Social Sciences , 2019

Recommended Reading

Work in america 2024.

• U.S. workers adjust to the changing nature of employment
• Psychological safety in the changing workplace

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Bibliometrics & citations, view options, recommendations, configuring alliance portfolios for digital innovation.

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This book presents the latest research that shows how design thinking, making, and acting contribute to the co-designing and development of products, spaces, and services with people living with dementia. We know that there is currently no cure for the 130+ kinds of dementia that millions of people live with all over the world, but the designed interventions such as the products, spaces, and services described in this book can address stigma, isolation, loss of confidence, and raise awareness and greater understanding of dementia. This book showcases a range of innovative and creative design interventions that have been developed to break the cycle of well-established opinions, strategies, mindsets, and ways of doing that tend to remain unchallenged in the health and social care of people living with dementia. The book will be of interest to scholars working in product design, service design, experience design, architecture, design research, information design, user-centred design, and design for health.

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Paul A. Rodgers is a Professor of Design at the University of Strathclyde and is the Arts and Humanities Research Council (AHRC) Design Leadership Fellow.

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