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Are teams better than individuals at getting work done, october 12, 2021 • 9 min listen updated: april 26, 2024.

Whether teams or individuals are better at accomplishing tasks depends on the complexity of the work, according to a new study co-authored by Wharton’s Duncan Watts.

Wharton’s Duncan Watts talks with Wharton Business Daily on SiriusXM about his research on whether teams or individuals are better at accomplishing tasks.

When it comes to getting work done, two heads are better than one. Except when they aren’t.

A new study from Wharton professor of operations, information and decisions Duncan Watts digs into the question of whether it’s better for employees to work in teams or alone — and the answer may be surprising for managers trying to figure out the best way to assign tasks.

In their research, Watts and his co-authors found that the answer depends on the complexity: Simple tasks are best accomplished by individuals, while difficult ones are more efficiently completed by a group.

“Groups are as fast as the fastest individual and more efficient than the most efficient individual when the task is complex but not when the task is simple,” the researchers wrote in their paper titled, “ Task Complexity Moderates Group Synergy ,” which was published last month in the Proceedings of the National Academy of Sciences.

The co-authors are Abdullah Almaatouq , information technology professor at the MIT Sloan School of Management; Mohammed Alsobay , doctorate student at the MIT Sloan School of Management; and Ming Yin , computer science assistant professor at Purdue University.

Watts, who is a Penn Integrates Knowledge Professor and director of the Computational Social Science Lab at Penn , said the study is unique because it’s the first to make an “apples to apples” comparison in a lab setting. The scholars created an experiment that allowed them to manipulate the complexity of the same task, rather than simply giving the participants different kinds of tasks, as most previous studies have done.

“A manager is kind of stuck a little bit because they don’t really know how to evaluate the complexity of the task that they’re looking at. In this research, we got around that by identifying a class of tasks where we could vary complexity in a nice, systematic, principled way without changing anything else,” Watts said during an interview with Wharton Business Daily on SiriusXM. (Listen to the podcast above.)

Team Efficiency: Group Work vs. Individual Work

In their experiment, participants — both individuals and groups — were given a real-world problem of assigning students to dorm rooms. What started out as an easy job became more complicated as the researchers added constraints such as more students, fewer rooms, students who could not be neighbors or live in the same room, and students who must be neighbors or live in the same room.

At the end of the experiment, it became clear that all participants needed more time as the work became more difficult. But groups were ultimately more efficient at getting it done, even if they arrived at the same result as the individual.

“Interestingly, what we found is that where teams really shine is in terms of efficiency,” Watts said. “Teams for a complex task could do almost as well as the very best individual, but they were able to do it much quicker. That’s because they were much faster, they generated more solutions, they generated faster solutions, and they explored the space of possibilities more broadly.”

“Interestingly, what we found is that where teams really shine is in terms of efficiency.” –Duncan Watts

That’s not to say that groups don’t suffer from certain negative dynamics.

“When you get together in a group, you waste time, you compete with each other, you fall into bad habits like groupthink,” Watts said. “So, there are quite good reasons why you might take seriously that individuals can [be better than] a team.”

One way for managers to circumvent negative group dynamics is to assign a group leader who can keep everyone moving in the right direction, the co-authors noted. They also said managers may want to store the best group solutions so they can be “reloaded and potentially modified in subsequent steps,” much like what happens in personal productivity software.

Teams are elevated in today’s workplaces, but the study shows that managers shouldn’t assume that teams are the optimal solution for every problem. Sometimes, Watts said, a single employee can be just as effective.

“Depending on whether your task is simple or complex, and depending on whether what you care about is getting the absolute best possible score or getting something that’s pretty close to the best possible score but getting it efficiently, you’re going to make a different decision as a manager,” he said.

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• Research article
• Open access
• Published: 14 October 2015

A method for measuring individual research productivity in hospitals: development and feasibility

• Caterina Caminiti 1 ,
• Elisa Iezzi 1 ,
• Caterina Ghetti 2 ,
• Gianluigi De’ Angelis 3 &
• Carlo Ferrari 4  

BMC Health Services Research volume  15 , Article number:  468 ( 2015 ) Cite this article

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Research capacity is a prerequisite for any health care institution intending to provide high-quality care, yet, few clinicians engage in research, and their work is rarely recognized. To make research an institutional activity, it could be helpful to measure health care professionals’ research performance. However, a comprehensive approach to do this is lacking.

We conducted a literature analysis to determine how best to assess research performance. Our method was not restricted to bibliometric and citation parameters, as is usually the case, but also including “hidden” activities, generally not considered in research performance evaluations.

A set of 12 easily retrievable indicators was used and corresponding points assigned according to a weighting system intended to reflect the effort estimated to perform each activity. We observed a highly skewed score distribution, with a minority of health care professionals performing well across the indicators. The highest score was recorded for scientific papers (768/1098 points, 70 %). Twenty percent of researchers at our institution generated 50 % of points.

Conclusions

We develop a simple method for measuring research performance, which could be rapidly implemented in health care institutions. It is hoped that the proposed method might be useful for promoting research and guiding resource allocation, although further evaluations are needed to confirm the method’s utility.

Peer Review reports

It is widely accepted that research plays an essential role in developing new health care services and improving healthcare quality. Research provides new knowledge that can be transferred into practice, helps create advanced care environments, that attract the best physicians contributes to learning among young Health Care Professionals (HCPs) and ensures continuous education among established professional. In fact, hospitals engaged in research have been recognized to as providing better patient care. Therfore, adequate research capacity is a prerequisite for any public health care system striving to provide high-quality care [ 1 ].

The goal of evidence-based practice increasingly requires research to be embedded within the health care setting, making clinician participation an essential component of its success. In fact, clinicians are well-placed to identify relevant research ideas, design and conduct innovative projects, ensure translation of research into improved health outcomes, and solicit patient enrollment in experimental trials. Nevertheless, the international literature shows that only a minority of clinicians participate in research aproblem common to many countries [ 2 – 5 ].

This issue is particularly relevant in teaching hospitals, which have a responsibility to provide leadership in conducting, supporting and supervising research [ 5 ]. There is no standardized method for measuring HCP’s research efforts and their results; this is a key obstacle to the incorporation of research in hospitals as an institutional activity [ 6 ]. Such a method would, among other benefits, inform resource allocation decisions, encourage research participation among increasingly busy clinicians, and create accountability to the community for research projects.

To this end, we developed a mechanism that attempts to measure as objectively as possible research productivity, and tested it at our institution to determine its feasibility and utility. In this study, research productivity is defined as the product of research activities. The terms “research productivity”, “output” and “performance”, are used interchangeably.

The University Hospital of Parma is a large health care facility located in Emilia-Romagna, a region in northern Italy with a population of 5 million served by four university hospitals, four health care research institutes and 12 community hospitals.

Since 2004, regional legislation formally identifies research as a fundamental institutional activity, equal to patient care and continuous training. This policy underlies several funding initiatives aimed to promote research, with special attention to young professionals [ 7 ]. Regional hospitals are required proactively support their researchers, through clinical governance actions aiming to track research activities already underway, identify priority areas for resource allocation and infrastructure, and provide adequate tracking and recognition of researcher efforts. However, no method for measuring research has been devised and implemented across health care institutions in this region, where hospital productivity is currently only being measured in terms of patient care activity.

This work pursues the following objectives:

develop a simple method to measure individual research productivity and analyze hospital department performance

determine its feasibility and describe its potential usefulness in a large University Hospital

Choice of indicator variables

Literature analysis was conducted to determine how best to assess research performance. The terms “research output”, “research productivity”, and “research performance” were used to retrieve potentially relevant studies published over the last 5 years. Articles that only analyzed bibliometric indices and those that did not report on an empirical setting were not considered.

Although the evaluation of research productivity would ideally include the assessment of impact, in practice this is extremely difficult to achieve, because the multifaceted nature of evaluation, the lack of standard terminology, and the heterogeneity of empirical experiences make it hard to identify a preferred model of impact measurement [ 8 ]. For this reason, the measure of research output is often used as a proxy for impact. A wide range of indicators and metrics are available for this purpose, and their choice depends on considerations of their strengths and limitations [ 8 ]. The most widely used research output indicators are bibliometric and citation parameters (e.g. number of publications in peer-reviewed journals, impact factor and H-index) [ 9 ]. These are very simple to calculate, but also exhibit various limitations that have been extensively described [ 9 – 11 ].

Wootton [ 12 ] recently proposed a simple method to measure research output, defining an indicator simple enough to be calculated and generalizable to other settings, but still able to capture the complexity of research productivity. The indicator, inspired by the analysis of 12 reports on research productivity, is constructed on the following three domains, based on data relating to individual researchers: (i) research grant income, (ii) peer-reviewed publications and (iii) PhD student supervision. Activity in each domain is converted to points, which are used to calculate a score for research output that allows comparisons: (a) within an organizational unit, for example from year to year, or (b) in the same year between organizational units (e.g. research teams, wards, departments, and hospitals). The proposed score was arbitrary, because no validated and widely accepted metrics exists, but it was compared with an independent assessment made by a group of expert researchers, which yielded a significant correlation of 71 %.

This indicator, however, neglects a range of “hidden” activities that are also relevant to research output assessment. There are described in a well-known editorial by former BMJ Editor Richard Smith [ 13 ]. These include, for example, participation in the preparation of guidelines, teaching activities in the field of research, and peer-reviewing.

In another interesting work- by Mezrich et al. [ 14 ] reported the development of a more complex system for the assessment of the productivity of a ward/academic department, which assigns points to research activities by considering the estimated effort required to perform each activity and its attributed academic value.

Development of the method

The approach we defined is an adaptation of the model proposed by Wootton [ 12 ], integrated with other types of activities indicated by Smith [ 13 ] and inspired by the metrics used by Mezrich et al. [ 14 ]. The choice of research activities was determined by the availability of required information. The weighting system was constructed considering the hypothesized effort for all indicators,. For some indicators, specific criteria were also applied.

For each HCP, a set of information easily retrievable from existing administrative sources (mostly the Parma Ethics Committee’s archive) and from bibliographic databases (e.g.,ISI Web of Knowledge) was collected. These includedcompetitive research funding, publications, students/collaborators supervised, commissioned studies and patent filing. Additionally, some information usually not recorded was gathered by means of a simple questionnaire adapted from the literature, a tool used by German researchers for the measurement of the effects of a training program designed to improve HCPs’ research skills [ 15 ]. This adapted questionnaire (see Additional file 1 ) has been employed at our institution since 2012 to gather data on self-reported participation in research activities [ 16 ] and contains the remaining seven indicators used in this study. To be included in the final score, research activities indicated in the questionnaire had have been previously documented.

The set of proposed indicators, the weighting system and the number of possible points assigned to each are depicted in Table  1 .

For the first indicator, concerning grants acquired by the Principal Investigator in competitive research funding programs, one point is assigned for every €24.000 awarded; this is the lowest award for a standard Italian research grant (Decree of the Italian Ministry of Education, Universities and Research, no. 102, 9 March 2011). This solution is suggested by Wootton as a scaling factor to facilitate comparisons between countries. Thus because on average the annual cost of a resident physician is three times greater than that of a research grant, one point is assumed to reflect about 1/3 of an HCP’s annual work (approximately 4 months). This assumption was used to assign scores taking -into account the estimated time required to carry out a given research activity relative to others.

For the publication indicator, each paper received a score weighted by the Normalized journal Impact Factor (NIF) and by author position (Table  2 ). The NIF is an adjusted method for calculating the impact factor that takes into account the diversity of citing behavior in different disciplines and is inteded intended to assess the relative position of journals, potential employers, and researchers within each field [ 17 ]. Weighting criteria for the publication score are based on the method developed by Tscharntke [ 18 ], whereby the first author is awarded the highest value, but the second and last authors also receive a higher score than the other coauthors.

For the remaining indicators, score assignment was straightforward, as shown in Table  1 . The calculation performed for this set of indicators allows us to obtain (for each HCP) a combined score resulting from the sum of non-dimensional values, which permits spatial and temporal comparisons.

Implementation

Overall, the time needed to create a single database, process indicators for each HCP and analyze data was about 9 weeks of work by one person. The analysis was performed using SAS version 8.2. Time for data collection and analysis may be significantly reduced with the use of web-based software into which pertinent data may be entered by HCPs themselves.

To allow for comparisons with other institutions, wards were grouped into the following six areas, which represent relatively homogeneous research activities: Surgery units; Diagnostic Services; Emergency Medicine; General Medicine, Geriatrics and Rehabilitation; Specialized Medicine; Pediatrics and Gynecology. To reduce variability (Coefficient of Variation = 38 %) due the different numbers of HCPs in each area, estimates were corrected by direct standardization. For each area, along with the sum and the weighted sum, the mean score (per capita output) and corresponding range are also provided.

Intra- and inter comparisons

Tables  3 and 4 summarize respectively raw data and calculated values for research activity relating to the year 2013, subdivided for each indicator. The most relevant findings with respect to this work’s objectives are the following:

When no score is assigned, prevailing activities are publications (597/1165, 51 %), projects not funded by competitive programs (108/1165, 9 %) and research proposals submitted to competitive programs but non awarded (89/1165, 8 %).

The highest score was recorded for scientific papers (768/1098 points, 70 %), followed by research grant income (15 %) and peer-reviewing (5 %). Together, these three items account for 90 % of the research output at our institution.

The area of specialized medicine exhibited the highest research productivity, even after standardization (111/222). This means that a mere 20 % of HCPs at our institution produced 50 % of points

The annual mean per-capita output score was 1.2 points, ranging from 0.4 to 2.2 points (indicated the the highest-scoring researchers were nearly six times more productive than lowest scoring researchers).

Figure  1 shows the research output for individual HCPs belonging to each area, for the following indicators: grant income, scientific publications, PhD students/collaborators supervised, and other activities. Our analysis indicated:

a (Research output for each HCP belonging to the area) – SURGERY UNITS. b (Research output for each HCP belonging to the area) – DIAGNOSTIC SERVICES. c (Research output for each HCP belonging to the area) – EMERGENCY MEDICINE. d (Research output for each HCP belonging to the area) – GENERAL MEDICINE, GERIATRICS AND REHABILITATION. e (Research output for each HCP belonging to the area) – SPECIALIZED MEDICINE. f (Research output for each HCP belonging to the area) – PEDIATRICS AND GYNECOLOGY

Within all areas, few individuals obtained high scores, whereas the majority received low scores or zero points

Only a few individuals performed well across multiple indicators, whereas for the majority, output mainly consisted of publications.

Figure  2 summarizes the individual score distribution for score classes, which shows even more clearly that high research productivity was only achieved by a small group of HCPs.

Score frequency distribution

We present a novel method for measuring research performance. The results obtained by our method’s implementation at a large Italian University Hospital highlight the simplicity of its implementation and describe its potential uses.

To our knowledge, this is the first comprehensive approach to measuring individual research output in hospitals that also includes “hidden” research activities, which are essential to ensure high-quality patient care, such as participation in the definition of guidelines, submission of research proposals to competitive funding programs regardless of funding acquisition, and teaching activities concerning one’s own research. This system exhibits many potential strengths and possible applications: it enables identification of identify which HCPs are highly productive in research, reveals of areas potentially in need of improvement, and provides indications for resource allocation.

Our work differs from Wootton’s study in many respects. First other things, implementation lasted 1 year and involved the entire institution, whereas for Wootton’s study lased 5 years and concerned two departments. Still, the two studies are similar enough to make a direct comparison of results. In both studies, score distribution is considerably skewed, and most points are earned by a small number of HCPs, mostly performing well on the publication indicator.

The chosen indicators and attributed scores still remain to be validated and widely shared. In fact, as evident in Tables  3 and 4 , the chosen weighting system leads to the dominance of publication output and grant income. Other hospitals may feel that a more balanced scorecard would be preferable. However, validation was not the aim of this work, also because a precise use of results has not yet been defined. In fact, as Mezrich et al. pointed out by [ 14 ], for some purposes, such as measuring change in activity or productivity from one year to the next or the relative productivity of individuals performing similar activities in a single division or at different institutions, the values chosen would not matter, as long as they were consistent for all HCPs. A validated weighting system may instead be used as a tool to guide and promote research. For instance, more points may be assigned to strategic research activities (e.g., supervision of young PhD students and research collaborators), or rankings may be used (e.g., reviewers for prestigious international journals could be awarded higher scores). However, such systems should be applied with caution, as pointed out in a recent systematic review [ 19 ] on the effects of strategies introduced in academic medical centers to assess productivity as part of compensation schemes. The results of the 9 study review demonstrate that these strategies improve research output and help to achieve the department’s mission, but may have unintended negative consequences; for instance, HCPs may assume that items not included in the evaluation are less important and may thus neglect them.

It must be emphasized that this study is based on secondary data not collected for the purpose of this research, which may have led to an underestimation of the score, particularly concerning “hidden” activities, which had to have been previously documented by HCPs.

Although further evaluations is needed, this work suggests that the proposed method may is feasible and may be useful to achieve different purposes, such as:

Guiding funding of health care facilities, as is done with patient care (for instance through Diagnosis-Related Groups - DRGs)

Including research activity in the assessment of a ward’s productivity, in the analysis of the workload and in subsequent allocation of necessary resources

Overcoming the current disparity observed in Italian university hospitals, where recognition for research activities is ensured to HCPs employed by the university but not to those employed by the hospital, though both groups work in the same institution

Highlighting the most productive and authoritative research centers, which may be qualified as centers of excellence for research worth being supported and enhanced

Providing information that could form the basis for a regional research network, according to a Hub and Spoke model, to increase research capacity in facilities that do not have research as their mission and to prevent study duplication and consequent waste of resources.

Abbreviations

Health-Care Professionals

Normalized journal impact factor

Diagnosis-Related Groups

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Acknowledgements

We thank Prof. Marco Vitale of the University of Parma, and Luca Sircana, Managing Director of the Parma University Hospital, for supporting this project, and for believing in the importance of promoting a culture of scientific research. We also thank Francesca Diodati for her help with the translation and editing of the manuscript.

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Caterina Caminiti & Elisa Iezzi

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Caterina Ghetti

Gastroenterology Unit, University Hospital of Parma, Parma, Italy

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Authors’ contributions

CC was responsible for the design of the study, participated in data interpretation and was in charge of drafting the manuscript. EI was involved with data collection and analysis, and contributed to drafting the manuscript. CG conducted the review of policy documents used to plan the study, and critically revised the manuniscript. GDA participated in the design of the study and critically revised the manuscript. CF conceived the study, participated in its design, and contributed to drafting the manuscript. All authors read and approved the final manuscript.

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Additional file 1:.

Questionnaire to record participation in resarch activities. (DOC 32 kb)

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Caminiti, C., Iezzi, E., Ghetti, C. et al. A method for measuring individual research productivity in hospitals: development and feasibility. BMC Health Serv Res 15 , 468 (2015). https://doi.org/10.1186/s12913-015-1130-7

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Published : 14 October 2015

DOI : https://doi.org/10.1186/s12913-015-1130-7

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Individual or collaborative projects? Considerations influencing the preferences of students with high reasoning ability and others their age

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Conditions influencing 328 students’ (Grades 6-8) preferences for collaborating or working alone on challenging projects were investigated, as well as their potential interactions with ability, grade and sex. Each student completed the Cognitive Abilities Test (Form 7) and Project Context Survey. No overall preference for individual or collaborative projects was found. Students’ preferences were sensitive to features of the context (subject, nature of the task and social dynamics). Individual projects were preferred in art and shared projects in science and social studies. Students with high ability and boys preferred individual projects in Math. Principal components analyses revealed three contextual considerations influenced students’ desire to work on projects alone (enjoyment, optimizing the outcome, and risk management) and five influenced the appeal of collaborating (inclusiveness and trust, access to the strengths of others, their perceived need for support, familiarity, and fair assessment). High ability students were more concerned with the efficiency and quality of their work, and their grades while others their age were more influenced by the potential for fun. Grade 8 students were more concerned with risk management and the assessment process than younger students. If the safe, supportive, fair conditions they sought for collaborating were not available, students’ default preference was to work alone on a challenging project.

• High ability
• Collaborative

Theorists’ and practitioners’ enthusiasm for collaborative learning and its potential benefits are at an all-time high, however students’ perspectives on those experiences have always been mixed. While Jang, Reeve, and Halusic ( Citation 2016 ) indicated that teaching students in the ways they prefer can play a significant role in students’ learning, the considerations influencing their preferences for working with others and individually have received less attention. One’s preferences for working alone and with others have been found to be sensitive to the nature of the task and conditions surrounding the task (Cantwell & Andrews, Citation 2002 ; French, Walker, & Shore, Citation 2011 ; Kanevsky, Citation 2011 ; Koutrouba, Kariotaki, & Christopoulos, Citation 2012 ; Walker & Shore, Citation 2015 ; Wismath & Orr, Citation 2015 ), as well as one’s cognitive ability (French et al., Citation 2011 ; Pyryt, Sandals, & Begoray, Citation 1998 ; Ramsay & Richards, Citation 1997 ; Ricca, Citation 1984 ; Samardzija & Peterson, Citation 2015 ; Sears & Reagin, Citation 2013 ; Walker & Shore, Citation 2015 ).

In the past, a student’s preference for learning alone or with others was conceptualized as a global, dichotomous, stable trait, a “learning style” (Pyryt, Citation 1991 ), however an accumulating body of research and critique has challenged the concept of fixed learning styles (e.g., Burns, Johnson, & Gable, Citation 1998 ; Cuevas, Citation 2015 ; Curry, Citation 1990 ; Pashler, McDaniel, Rohrer, & Bjork, Citation 2008 ; Reynolds, Citation 1997 ; Sadler-Smith, Citation 1997 ). It argues that one’s preferences are context-specific and has broadened the discussion from viewing them narrowly as a trait (a characteristic of the individual) to seeing them as a state that results from complex, dynamic interactions between a learner and the learning context (Curry, Citation 1990 ; Riding, Citation 1997 ; Walker, Shore, & French, Citation 2011 ).

Researchers sharing a context-sensitive orientation to learning preferences have found that students’ desire to learn with or without others varies depending on what is to be learned, how, with whom, and other considerations (e.g., French et al., Citation 2011 ; Kanevsky, Citation 2011 , Citation 2015 ; Koutrouba et al., Citation 2012 ; Samardzija & Peterson, Citation 2015 ; Walker & Shore, Citation 2015 ). For example, more students prefer to work with others when the conditions for working in a group include working with others they have chosen. Alternatively, most students prefer working alone when they feel collaborating may result in potential conflict, unfair workload, or a poor grade (Kanevsky, Citation 2011 , Citation 2015 ; Samardzija & Peterson, Citation 2015 ; Walker & Shore, Citation 2015 ). This study further investigates the ways in which interactions among learner characteristics (reasoning ability, age and sex) and features of the context influence the appeal of working alone or with others.

“Contexts can consist of subject-matter domains (e.g., science), specific tasks/problems (e.g., a textbook problem to solve), social interactions (e.g., caretaking routines between a parent and child), and situational/physical settings (e.g., home, classrooms, museums, labs)” (APA Coalition for Psychology in Schools and Education, Citation 2015 , p. 10). With this definition in mind, we held the second element of context, the task, constant rather than studying preferences broadly, across tasks. We sought a better understanding of the ways in which known or new combinations of these features of the context influence learners’ task-specific preferences for individual and group work when they are engaged in a frequently assigned task, a challenging project. Projects were identified as the target task because: (a) they can be undertaken alone or as a shared endeavor, (b) they are an essential element of many forms of inquiry-based learning that were strongly recommended and frequently implemented in schools participants attended, (c) they often offer students opportunities for deeper learning than smaller academic tasks, (d) all students had experienced individual and group projects by the time they reached middle school (Grades 6, 7, and 8), and (e) students in our focus groups considered them complex, “high stakes” assignments that played a significant role in their grades. The popularity of complex, shared assignments makes it increasingly important to provide educators with the evidence-based guidance they need to create contexts that are not only effective but attractive to learners.

Academic and social outcomes of learning alone versus with others

Numerous meta-analyses have examined the academic and social outcomes of group work as well as differences in the outcomes of collaborative and individual learning activities (e.g., Abrami, Lou, Chambers, Poulsen, & Spence, Citation 2000 ; Kulik & Kulik, Citation 1987 ; Lou, Abrami, & d’Apollonia, Citation 2001 ; Lou, Abrami, & Spence, Citation 2000 ; Lou et al., Citation 1996 ; Pai, Sears, & Maeda, Citation 2015 ; Roseth, Johnson, & Johnson, Citation 2008 ; Slavin, Citation 1987 , Citation 1990 ). For example, Pai et al. ( Citation 2015 ) reported an estimated overall effect size of 0.30 in favor of small group learning over individual after analyzing the results of 24 studies that had compared students’ performance on transfer tasks. Roseth et al. ( Citation 2008 ) also found “higher achievement and more positive peer relationships were associated with cooperative rather than competitive or individualistic goal structures” (p. 223) in their meta-analysis of 148 studies involving middle school students. These authors repeatedly reminded readers to consider the variability evident in the findings of individual studies included in their meta-analyses. Many attributed it to differences in the nature of the individual, cooperative, and collaborative learning contexts involved (e.g., Lou et al., Citation 1996 ).

Contextual considerations related to working alone and with others

The influences of the four aspects of the context on individual and group work have been extensively investigated. This brief review of the literature was limited to one setting: school. In classrooms, students can share tasks with one or more peers. These activities are often described as either cooperative or collaborative. In practice, distinctions between the two blur. This became evident in the first author’s debriefing sessions with participants while field-testing the data collection instrument. All had worked on projects alone and with one or more classmates. Although their informal accounts included many features of collaborative learning, none were aware of the distinction made between it and cooperative learning in the literature. They mentioned that they had chosen workmates and had co-constructed projects and assessment criteria with their teacher. Members of their group worked without differentiated roles or responsibilities (Gillespie & Richardson, Citation 2011 ) and occasionally collaborated with other groups. Power and responsibility for learning were redistributed from the teacher to students (Abrami et al., Citation 1995 ). These experiences are more often associated with collaborative than cooperative learning, therefore the word “collaborative” will be used to refer to activities in which students work with another or others as it more accurately captures the meaning it had for students.

Surprisingly few studies have compared students’ preferences in different school subjects. This is likely because students’ preferences had been believed to be stable “styles” that were consistent across content areas. Pyryt’s ( Citation 1991 ) survey data revealed no differences in gifted students’ preferences across math, English, social studies, and science. However, Li and Adamson ( Citation 1992 ) found differences related to subject and sex. In math, their gifted secondary students preferred to learn alone. And in science, boys preferred individual work more than girls while girls preferred it more in English. More recently, Cowan ( Citation 2014 ) reported 75% of his high school students preferred group work in social studies so they could share the workload, be more creative and social, and have more fun. Lou et al. ( Citation 1996 ) found the effects of within-class grouping (vs. no grouping) on academic performance were positive in all school subjects; however, they were greater in math and science than in reading, language arts, and other courses. As mentioned above, they suggested this finding may have reflected differences in the types and difficulty of the tasks students associated with each subject rather than the subject. In other words, the tasks in math and science were considered more challenging or better suited to groups than those offered in reading and language arts.

Nature of the task

The structure and features of a task also affect what is learned and how learning occurs (Cohen, Citation 1994 ; Walker & Shore, Citation 2015 ). Relatively simple, factual, well-defined tasks involving specific procedures have been found to be more appropriate for individuals than groups while complex, ill-defined, difficult activities requiring a variety of roles, skills, and knowledge are better for dyads or groups (Fuchs, Fuchs, Hamlett, & Karns, Citation 1998 ; Gillies, Citation 2014 ; Lou et al., Citation 2001 ). “A group task is a task that requires resources (information, knowledge, heuristic problem-solving strategies, materials, and skills) that no single individual possesses so that no single individual is likely to solve the problem or accomplish the task objective without at least some input from others” (Cohen, Citation 1994 , p. 8). For example, Diezmann and Watters’ ( Citation 2001 ) found increasing task difficulty played an essential role in gifted students’ (11–12 years old) collaborations with peers when solving a series of challenging math problems. “If a task is sufficiently difficult, students tend to seek interaction with someone who can provide either cognitive or affective support” (Diezmann & Watters, p. 26). Additional evidence can be found in Sears and Reagin’s (2013) comparison of individuals and dyads solving problems in “traditional” (mixed ability) and “accelerated” (high ability) math classes. Pairs performed better than individuals in the traditional classes while the opposite was true in accelerated classes. “In other words, for students who were able to solve the problem successfully alone, collaboration was more of a hindrance than a benefit to performance” (p. 1167). This may reflect differences their expectations regarding their effort in group work. Cera Guy, Williams, and Shore ( Citation 2019 ) found that while high-achieving students expected to work as hard alone as they did in a group while others expected to work less in a group.

Social interactions

Interpersonal dynamics generate most of the challenges and benefits students associate with group work. Their concerns regarding group composition, fair distribution of workload (Orbell & Dawes, Citation 1981 ; Robinson, Citation 1990 ; Salomon & Globerson, Citation 1989 ), and status differentials (Cohen, Citation 1994 ) often underlie their desire to work alone. When these concerns are not resolved, they can result in a sense of injustice, hurt feelings, and conflicts that erode motivation, learning, and relationships. Orbell and Dawes ( Citation 1981 ) felt these “social dilemmas” (p. 39) were often the result of a lack of individual accountability within groups, such as all members of a group receiving the same grade regardless of the nature or extent of their contribution (Neber, Finsterwald, & Urban, Citation 2001 ). Students have reported frustration associated with social loafing (when a peer exerts less effort in a group than they would if they worked alone), the free-rider effect (when a peer relies on others to do her or his share of the work [Salomon & Globerson, Citation 1989 ]), the sucker effect (when the group expects the most capable student to do most of the work [Orbell & Dawes, Citation 1981 ]), status differentials (“when higher status members dominate group activity” [Salomon & Globerson, Citation 1989 , p. 95]), and ganging-up effects (when “the whole group adopts a work-avoidance tendency, spending as little effort as possible” [Neber et al., Citation 2001 , p. 201]). Although some enjoyed peer tutoring (Ristow, Edeburn, & Ristow, Citation 1985 ), others resented being treated as a “junior teacher” (Coleman, Gallagher, & Nelson, Citation 1993 ; Robinson, Citation 1990 , Citation 2003 ). Gifted students’ concerns about group work were greatest in mixed-ability (heterogeneous) groups (e.g., Clinkenbeard, Citation 1991 ; French et al., Citation 2011 ; Neber et al., Citation 2001 ; Robinson, Citation 1990 , Citation 1991 ; Schmitt & Goebel, Citation 2015 ; Willard-Holt, Weber, Morrison, & Horgan, Citation 2013 ), however, “When members contributed equally, students preferred working in groups to working alone” (Samardzija & Peterson, Citation 2015 , p. 245). Differences mentioned earlier in expectations students have for the effort involved in individual and group work are likely to contribute to high-achievers’ frustrations when others expect to work less in groups than they do (Cera Guy et al., Citation 2019 )

Same- or mixed-ability groups

Grouping students with others of similar ability, in homogeneous groups, or heterogeneously, in mixed-ability groups, may be the most contentious aspect of group work. Fuchs et al.’s ( Citation 1998 ) investigation of same- versus mixed-ability dyads working on complex, challenging tasks found “homogeneous pairs worked more collaboratively and with greater cognitive conflict and resolution, focus on interacting, and helpfulness and cooperation” (p. 247); however “heterogeneous groups may be used appropriately with less complex tasks” (p. 251). Other researchers were concerned that in mixed-ability classes, homogeneously grouping students may enhance the efficiency of high ability students’ learning at the expense of lower ability peers’ because high ability students were not there to support their growth (e.g., Hooper & Hannafin, Citation 1991 ).

Recent studies have found high ability students, and often their peers, preferred group to individual work when they were able to work with others of similar ability (Schmitt & Goebel, Citation 2015 ; Walker & Shore, Citation 2015 ) or those who learned at the same pace (Kanevsky, Citation 2011 ). Neber et al.’s ( Citation 2001 ) meta-analysis of research on ability grouping gifted students during cooperative learning supports these findings. They concluded “high achievers’ performances improve if they learn in homogeneous groups with other high-achieving students. Lower performances result if these students either learn individually or together with lower achieving students in heterogeneous groups” (p. 210, emphasis in original). “Often when gifted students are faced with working in a mixed ability group of agemates, they tend to express a desire to work alone” (Rayneri, Gerber, & Wiley, Citation 2006 , p. 115).

Walker and Shore ( Citation 2015 ) explored interactions among ability, task characteristics, and preferences via a questionnaire and interviews. When asked if the impact a task had on their grade would influence their desire to collaborate, students in high performing classes, more than those in mixed-ability classes, wanted to work alone on “high stakes” assignments, such as projects. This result, as well as others from their study, provide evidence of the complex relationships among preferences, characteristics of learners and the contexts in which they learn.

Working with friends and choosing workmates

The opportunity to work with friends has been one of the reasons students prefer and enjoy group work more than working alone (Cowan, Citation 2014 ; Fisher & Frey, Citation 2012 ). Myers ( Citation 2012 ) found “students who self-select their classroom work group members do report higher levels of commitment, trust, and relational satisfaction, as well as more affective learning and more cognitive learning, than students who are randomly assigned to classroom work groups” (p. 50). Collaborating was more attractive when students were able to work with a friend or choose their partner or members of their group (French & Shore, Citation 2009 ; Kanevsky, Citation 2011 , Citation 2015 ; Walker & Shore, Citation 2015 ). When choosing their workmates, Kanevsky ( Citation 2011 ) found more than 83% of her sample (Grades 3 to 8, gifted and nongifted) wanted to work with a partner or in a group. More than half disliked being assigned to dyads or groups by their teacher. Similarly, most of the twice-exceptional learners in Willard-Holt et al.’s ( Citation 2013 ) study also wanted to choose their workmates.

It is intriguing that students became less enamored with choosing their groupmates over time in high school science classes (Mitchell, Reilly, Bramwell, Solnosky, & Lilly, Citation 2004 ). Students felt obligated to choose friends in group tasks. They reported spending more time socializing than working in student-selected groups and felt the social consequences could be detrimental beyond the science class. Many did not want to take responsibility for their choices if the group did not work well and came to appreciate their teacher’s group-building expertise. The short- and long-term academic, social, and emotional consequences of choosing group members and working with friends on learning and behavior are not clear at this point and in need of further studies (Hanham & McCormick, Citation 2018 ).

Studies of students’ preferred group size have also yielded inconsistent results however they favor small groups. Riding and Read ( Citation 1996 ) found students preferred dyads and groups to working alone, as did Kanevsky ( Citation 2011 ), however Kanevsky’s participants’ preferences were dependent upon having a choice of workmates. Working with a partner or in a small group were also favored by the nine to 16 year-old high- and otherachieving students (Cera Guy et al., Citation 2019 ).

Most studies comparing individual students’ academic, affective and social outcomes when learning in groups of different sizes have concluded groups have better outcomes than individuals, and smaller groups (e.g., two to four students) are better than large groups (e.g., five or more students) most of the time (Wilkinson & Fung, Citation 2002 ). Students working in pairs learned more than groups or individuals (e.g., Lou et al., Citation 2001 ; Wilkinson & Fung, Citation 2002 ) and developed higher levels of social self-esteem (e.g., Bertucci, Conte, Johnson, & Johnson, Citation 2010 ). Both were superior to individual learning. Similarly, Fuchs et al. ( Citation 2000 ) found pairs of 3rd and 4th graders solving complex math problems “earned higher scores than small groups on participation, helpfulness, cooperation, quality of talk, and PA [performance assessment] work” (p. 183). It appeared that students not only liked but benefited most from working with up to three peers.

Learner characteristics

Inconsistent results have also emerged from efforts to associate preferences with learner characteristics such as ability, sex, and age or grade.

Academic or cognitive ability

For many years, textbooks on the education of gifted individuals portrayed them as “loners” who preferred to learn independently and indicated this distinguished them from their nongifted peers. Research has not provided consistent support for this claim. Although some found they preferred independent study (e.g., Boultinghouse, Citation 1984 ; Chan, Citation 2001 ; Li & Bourque, Citation 1987 ; Ricca, Citation 1984 ; Ristow et al., Citation 1985 ; Stewart, Citation 1981 ) and learning alone (Ewing & Yong, Citation 1993 ; Griggs & Dunn, Citation 1984 ; Griggs & Price, Citation 1980a , Citation 1980b ; Li & Adamson, Citation 1992 ; Pyryt et al., Citation 1998 ), others did not (e.g., Burns et al., Citation 1998 ; Dunn & Price, Citation 1980 ; Ewing & Yong, Citation 1992 ; Kanevsky, Citation 2011 , Citation 2015 ; Rayneri et al., Citation 2006 ; Walker & Shore, Citation 2015 ). Based on the findings of their survey data from 247 gifted, high achieving and nonidentified students in Grades 4 through 12, French et al. ( Citation 2011 ) concluded “Some gifted students prefer to work alone some of the time” (p. 155) and this desire grows stronger when the social conditions work against them. “Gifted students who felt that their work was appreciated by teachers and fellow students reported the strongest preference to work with others” (p. 145).

These conditional preferences for individual and group work were also evident in Kanevsky’s ( Citation 2011 ) findings. The 416 gifted and nongifted students’ (Grades 3–8) responses to a survey of their preferences for different types of differentiation shed further light on ability-related differences. With respect to working alone or with others, she found only two conditions of 18 related to collaboration distinguished the preferences of individuals who had been identified as gifted from those who had not. A larger majority of gifted students wanted to work with others some of the time and alone at other times and a smaller proportion disliked sitting alone. The reasons for keeping their options open became apparent in related results. A majority of all participants wanted to work with others when they were able to work with their choice of workmates, when their workmates worked at the same pace as they did, and when they sat in clusters. They did not want to be taught by peers or assigned to groups by their teacher. Similarly, students in Walker and Shore’s ( Citation 2015 ) study also preferred group work to working alone when it was with “the right” workmates. More than their peers, high performing students wanted to work with others when it would have little impact on their grades but alone on those that did.

Age or grade

The findings of studies investigating age-related trends in preferences have also varied. Some found the desire to work alone increased with age or grade (e.g., French et al., Citation 2011 ; Kanevsky, Citation 2015 ) while others did not (e.g., Pyryt et al., Citation 1998 ). Methodological differences (e.g., instrumentation, ranges of ages and ability of participants) make it difficult to distill any reliable patterns or insights from their findings.

Sex may play a role in learners’ preferences however, again, the evidence is not clear. Some researchers found boys preferred learning with peers more than girls (French et al., Citation 2011 ; Pyryt et al., Citation 1998 ; Ramsay & Richards, Citation 1997 ), others the opposite (Johnson & Engelhard, Citation 1992 ; Li & Adamson, Citation 1992 ), or they found no difference (Kanevsky, Citation 2015 ). French et al. suggested sex was likely to interact with other learner characteristics, such as ability, and may also be influenced by social roles. They found gifted girls preferred to learn alone more than nongifted boys and thought this might be due to being gifted rather than being female. These girls also enjoyed teaching peers more than gifted boys. Relationships between sex and other personal characteristics with learning preferences also deserve further study.

Theoretical value of learning preferences

Most studies of students’ preferences for working alone or with others have either had no theoretical orientation or claim social-constructivism frames the work (e.g., French et al., Citation 2011 ). Self-determination theory offers a complementary perspective on the relationship between learning and preferences that focuses on student motivation. It accounts for the effect of accommodating students’ preferences on their engagement and motivation to learn (Jang et al., Citation 2016 ). In order for students’ motivation to flourish, their basic needs for autonomy, competence, and sense of belonging must be met (Reeve, Deci, & Ryan, Citation 2004 ; Ryan & Deci, Citation 2000 ). Engagement can be increased by facilitating autonomy support, students’ feelings of competence, and relationships among class members. Learning in ways they prefer has been found to have these benefits as well as enhancing conceptual outcomes (Jang et al., Citation 2016 ). Providing students with opportunities to choose or control organizational, procedural, or cognitive aspects of their learning can also achieve these goals (Patall, Cooper, & Robinson, Citation 2008 ; Reeve, Jang, Carrell, Jeon, & Barch, Citation 2004 ; Stefanou, Perencevich, DiCintio, & Turner, Citation 2004 ). Allowing them to choose their workmates is an example of organizational autonomy support (Stefanou et al., Citation 2004 ). Of course, enhancing engagement is not that simple. Jang, Reeve, and Deci ( Citation 2010 ) found that autonomy support and structure provided by the teacher (e.g., clear directions and guidance, keeping students on task, managing behavior) both contributed to student’s observed engagement, however “only autonomy support was a unique predictor of students’ self-reported engagement” (p. 588).

When grouping students and creating activities for those groups, educators expect students to collaborate and share their resources so their group’s work will reflect the participation and contributions of all group members. They also expect students’ relationships and interpersonal competence will be enhanced by opportunities to explore each other’s “reasoning and viewpoints in order to construct a shared understanding of the task, … propose and defend their own ideas, and to ask their peers to clarify and justify any ideas they do not understand” (Goos, Galbraith, & Renshaw, Citation 2002 , pp. 196–197). By the time North American students reach middle school (10–14 years old) most have had many experiences with group work, some in which those outcomes were actualized and others in which they were not. For example, Chichekian and Shore ( Citation 2017 ) found academically high performing adolescents appreciated others who, like themselves, “held firm” to their perspectives when they differed from peers’. As a result, they developed evidence-based preferences for a variety of conditions associated with individual and group work.

In sum, studies investigating conditions influencing students’ eagerness to work alone and with others indicate their preferences likely involve interactions among characteristics of the learner and context. We sought to improve understandings of the context-specific interactions that influence students’ desire to work on projects alone and with others on a challenging project, a complex, popular task that can be assigned to individuals, partners or groups. Although studies using academic performance or program placement have often been used to distinguish high achieving or high ability learners from their peers, we used scores on a standardized measure of cognitive reasoning ability to provide greater precision than other means of operationally defining ability.

Research questions

Do middle school students generally prefer to work alone or with others on projects, or are their preferences context-specific?

Do students’ desires to work alone or with others on projects vary across school subjects? And do these subject-specific preferences vary with students’ cognitive ability, grade, and sex?

What contextual considerations underlie middle school students’ desire to work alone or with others on projects? In addition, do cognitive ability, grade, and sex contribute to students’ preferences?

Participants

Table 1. summary of participants by sex, grade, and cognitive ability.

In this school district, most middle school students had one teacher for their core academic subjects (mathematics, language arts, science, social studies and often for French and physical education as well). Students also rotate through an additional block of elective studies with different teachers three or four times in a year. These electives include drama, digital literacy, music, art, and other options.

Pedagogies involving collaborative learning were strongly encouraged by the school district however the nature and extent of their implementation varied. Many students in the focus and field test groups reported they had worked with classmates on many of their assignments in recent years while others in the same school told us this was only occasionally the case in their classes. All had extensive experience with projects, challenging or not, from Kindergarten until the data were collected.

Instruments

The Cognitive Abilities Test, Form 7 (CogAT7; Lohman, Citation 2011 ) was used to assess academic reasoning ability and a Project Context Survey (PCS) which was developed to assess the influence of variety of contextual considerations (features of the context) on a student’s desire to work alone or with others on a challenging project.

The CogAT7 (Lohman, Citation 2011 ) was used to provide an individualized, standardized assessment of ability. It is a group-administered assessment of learned “reasoning abilities in the three symbol systems most closely related to success in school: verbal reasoning, quantitative reasoning, and nonverbal reasoning” (Lohman, Citation 2012a , p. 1). Its nine scales are clustered in three batteries: Verbal (Verbal Analogies, Sentence Completion, and Verbal Classification), Quantitative (Number Analogies, Number Puzzles, and Number Series), and Nonverbal (Figure Matrices, Paper Folding, and Figure Classification). Composite scores were used to form the two ability groups. Reliability coefficients for composite scores computed using the part-test method were .96 for Grades 5 to 8, indicating this measure has high internal consistency (Lohman, Citation 2012b ). The .76 correlation between CogAT7 composite scores and Full Scale WISC IV IQ scores provides evidence of its concurrent validity (Lohman, Citation 2012b ).

Project context survey (PCS)

The PCS consisted of two subscales: Working Alone (PCS-A, eight unique items specifically related to working alone) and Working with Others (PCS-O, 19 unique items specifically related to working with others). In additional to the unique items, each subscale included five parallel, subject-specific items. They assessed students’ desire to work alone (PCS-A) or with others (PCS-O) on projects in five school subjects: art, language arts, mathematics, social studies, and science. Because students’ preferences were not presumed to be dichotomous, the matched (but independent) items on the two subscales enabled students to express preferences for working with others or alone on projects in each subject that were not dichotomous. Following the subscales, students responded to a final multiple-choice item asking if they had a general preference for working alone or with others on projects.

The survey began with demographic questions (sex, grade, home language, etc.), followed by the PCS-A subscale, the PCS-O subscale, and ended with the general preference item. Within the PCS-A and PCS-O subscales, the five subject-specific items were answered first, then the eight and 19 unique items respectively. Other than the final item, all items were formatted on a Likert-type 5-point scale: “Strongly Disagree,” “Disagree,” “Neutral,” “Agree,” and “Strongly Agree.” Items on the PCS-A began with the stem “I prefer to work on a challenging project on my own,” followed by the item content, such as “when I feel my grade will be higher than if I work with others.” Similarly, items on the PCS-O began with the stem “I prefer to work with others on a challenging project when,” followed by the item content, such as “when all group members’ ideas are included to create the final product.” The five subject-focused items followed the same response format, e.g., “I prefer to work on a challenging project on my own when … it is in Art” on the PCS-A, and “I prefer to work with others on a challenging project when … it is in Art” on the PCS-O. Again, students indicated the strength of their agreement with each item.

Development of the PCS

An initial draft of the PCS was based on a review of research investigating conditions influencing students’ preferences for individual and group work. Two focus groups of students in Grades 6 to 8 were recruited to complete the draft survey items, provide feedback on their relevance and clarity, and suggest additional items. Revisions were based on input from the focus group, a research assistant, and a subject matter expert (SME) in gifted education. This process was repeated with the revised instrument and the same students. Another round of revisions was made before field-testing the survey in two mixed-ability classes.

The data collection instrument included the demographic items, the two subscales (PCS-A had 15 unique items and PCS-O had 40), and the general preference item. The number of items on the survey was approximately three times the number retained. This is consistent with Standards set by the American Educational Research Association, the American Psychological Association, and the National Council on Measurement in Education ( Citation 2014 ). Two other SMEs, one in gifted education and the other in measurement, conducted item analyses after the data was collected. Ambiguous items and those with intercorrelations greater than .90 were excluded. As a result, eight and 19 unique items from the PCS-A and PCS-O subscales respectively were retained and included in the principal component analyses.

Participants completed the PCS and the grade-appropriate level of the CogAT7 in three or more sessions led by the first and third authors. Most sessions took place in participants’ classrooms. Some were held in the school’s cafeteria or library when students were drawn from two or more classrooms at the same time. Sessions varied in duration from 25–60 minutes depending on students’ availability.

General or context-specific project preferences for working alone or with others

Participants indicated their general preference for working alone or collaborating on challenging projects in one item by choosing one of five options: “always alone,” “always with a partner,” “always in a group of three or more,” “sometimes alone and sometimes with others,” and “don’t care.” A significant majority, 69% ( n = 210) indicated their preferences varied by choosing the flexible option, “sometimes alone and sometimes with others.” In contrast, 20% ( n = 61) always wanted to work with others on projects and only 7% ( n = 21) always wanted to work on them alone. The remaining 5% ( n = 15) reported no preference. The large proportion selecting the conditional “sometimes” option indicated most students’ preference for working alone or with others on a challenging project was sensitive to the context.

Subject-specific project preferences and their interactions with cognitive ability, grade, and sex

As mentioned earlier, items on each subscale asked students to rate the extent to which they wanted to learn alone (on the PCS-A) or with others (on the PCS-O) in five school subjects (art, language arts, mathematics, science and social studies). These items were rated separately on each subscale so participants were able to like or dislike both working alone and working with others on a project in each subject. Bivariate correlations were computed between all participants’ ratings for each school subject on the two subscales to see if their desire to work alone or collaboratively on projects was stronger in some subjects than others. They were −.65. −.55, −.58, −.50, and −.55, respectively. All were statistically significant ( p < .001) and were considered large effect sizes. The negative direction of these correlations indicated these students preferred either working alone or collaborating on projects in all five subjects but not which one. The next set of analyses were undertaken to find out.

Subject preference scores (SPS; the difference between a student’s rating for each subject on the PCS-A and PCS-O) indicated whether they preferred working alone or with others on projects in a particular school subject. The range for each response was −2 to +2. For example, if a student strongly agreed (+2) with working alone on projects in math and strongly disagreed with collaborating on them (−2), their SPS would be +4 (2 – [−2] = 4). Another student might agree with both working alone and with others on math projects which would result in a SPS of zero (1–1 = 0), indicating no subject-specific project preference in math. Using this formula, the SPS could range from +4 (strong desire to work alone in a specific school subject) to −4 (strong desire to work with others in that subject). SPS were used in this initial analysis of subject-specific preferences, and later in the ability-, grade-, and sex-related comparisons.

Table 2. Summary of group comparisons of subject-related project preferences

Overall project preferences related to each school subject.

The single-sample t -tests of SPS revealed that students preferred to work alone on a challenging project in art ( M = 1.33, t [327] = −11.34, p < .001, Cohen’s d = 0.62), and with others in science ( M = −0.82, t [327] = 7.80, p < .001, Cohen’s d = 0.44) and social studies ( M = −.079, t [327] = 7.47, p < .001, Cohen’s d = 0.42). A moderate effect size was observed in art while small effect sizes were found in science and social studies (Cohen, Citation 1992 ). No statistically significant differences were found in language arts and math which indicated the students’ preferences were diverse in those subjects.

Subject-specific project preferences and cognitive ability

To address the unequal group sizes and skew introduced by the 90 th percentile boundary between the ability groups, Mann-Whitney tests (nonparametric t -tests) were used to compare the median SPS of the CHA and GC groups. This revealed a statistically significant difference with a small effect size in math (CHA Mdn  = 0.5, GC Mdn  = 0, U = 9951, p = .006, Hedges’ g = 0.17) that showed students with high cognitive ability had a slight preference for working alone while students in the comparison group had no preference. No ability-related differences in project preferences were found in the other subjects.

Subject-specific project preferences and grade

One-way ANOVAs comparing the SPS of students in Grades 6, 7, and 8 resulted in a statistically significant age difference with a small effect size in science ( F [2,325] = 4.73, p = .009, η 2 p  = .03). Only one of the post hoc comparisons of the means using the Tukey HSD test achieved significance. It suggested that in science, Grade 8 students ( M = −0.35, SD  = 1.97) were less inclined to collaborate on projects than the Grade 7 students ( M = −1.10, SD  = 2.07).

Subject-specific project preferences and sex

Independent sample t -tests of SPS revealed statistically significant differences between boys and girls in two subjects: art and math. In art, while both boys and girls preferred to work on projects alone, girls indicated a stronger inclination to do so (Boys’ Mean = .98, Girls’ Mean = 1.61, t [326] = −2.62, p = .009). In math, boys indicated a slight preference for individual projects whereas girls indicated a slight preference for working with others (Boys’ Mean = .33, Girls’ Mean = −0.39, t [326] = 2.92, p = .004). Girls’ and boys’ preferences did not differ the other three academic subjects.

Contextual considerations underlying students’ project preferences

Separate principal component analyses with Varimax rotation were conducted on 328 students’ ratings for the conditions described in the eight and 19 unique items on the PCS-A and PCS-O subscales, respectively. They reduced the number of items to a few distinct dimensions within each subscale. These extracted dimensions represent themes that were interpreted as contextual considerations that influenced students’ preference for working individually or collaboratively on a challenging project within the parameters of this survey. Bartlett’s test of sphericity indicated satisfactory interdependence among the items in the principal component analysis of the PCS-A ( χ 2 [28] = 426.61, p < .001) and PCS-O ( χ 2 [171] = 1264.65, p < .001) subscales. The Kaiser-Meyer-Olkin measure of sampling adequacy was .73 for PCS-A and .79 for PCS-O, which were sufficient for principal component analyses.

Table 3. Principal component analysis summary and themes for projects alone (PCS-A)

Table 4. principal component analysis summary and themes for projects with others (pcs-o), contextual considerations influencing students’ desire to work alone.

As shown in Table 3 , the three themes related to working alone on a difficult project were: (a) Enjoyment (the pleasure they associated with engaging in the topic and creating a product they enjoy while having the support of their teacher when it becomes difficult), (b) Optimizing the Outcome (working better or faster, and receiving a higher grade), and (c) and Risk Management (the desire to reduce the perceived risks to their writing, relationships and grade by completing a difficult project alone). The three themes explained 21%, 20%, and 20% of the total variance, respectively, thus 61% of the total variance was explained by the model. Reliability estimates (Cronbach’s alphas) for the three themes were .59, .73, and .56, respectively, with an overall alpha of .71 for the PCS-A subscale. None of the items in any of these themes, when deleted, resulted in a higher alpha, suggesting a preliminary fit in the component in which they were included.

Contextual considerations influencing students’ desire to work with others

The five components influencing the desire to work with others are presented in Table 4 : (a) Inclusiveness (wanting an atmosphere involving mutual trust in which ideas can be shared safely and understood), (b) access to the Strengths of Others (being able to rely on the contributions of workmates who may be smarter and will maintain their focus on the project), (c) the Support provided by peers when they need help completing a difficult project and want a better grade than they could earn alone, (d) Familiarity (working on a project with a partner they have chosen who is a friend or someone they know well), and (e) fair Assessment (receiving an individual grade for their contributions to the project and having the opportunity to self-assess it as well). The variances explained by each of the five themes were 15% by Inclusiveness, 11% by Strengths of Others, 10% by Support, 9% by Familiarity, and 8% by Assessment. They accounted for 53% of the total variance. Their reliability estimates (Cronbach’s alphas) were .75, .61, .58, .61 and .38, respectively, with an alpha of .73 for the PCS-O overall. Again, deleting items did not result in a higher alpha for any component, suggesting a preliminary fit between each item and the component in which it was included.

The alphas for both subscales were acceptable (i.e., greater than .70; Kline, Citation 2013 ), however the alphas for four of the five components were not. They indicate a lack of internal consistency among the items within them. This is likely due to the small number of items (three or four) included in the four themes with alphas less than .70. Although they were not as strong as desired, moderate and low alphas were tolerated to avoid construct under-representation and because this project is the first to examine the instrument’s psychometric properties. Although only two items loaded on it, the “Optimizing the Outcome” theme found in the PCS-A analysis was retained for the same reasons.

Differences in contextual considerations related to cognitive ability, grade, and sex

A theme score (a standardized weighted average by component loading) was computed for each participant for each component. Interactions were investigated between these theme scores and cognitive ability (using Mann-Whitney U tests), grade (using one-way ANOVAs), and sex (using independent t -tests with equal variance assumed).

Cognitive ability

Table 5. group comparisons of pcs-a themes scores related to working alone on a challenging project, table 6. group comparisons of pcs-o themes scores related to working with others on a challenging project.

When thinking about a collaborative project, the Support of others was more important to the CHA than GC when they expected it to be difficult and that they would need help to achieve the grade they wanted (CHA Mdn  = 0.55, GC Mdn  = −0.006, U = 5253, p < .001, Hedges’ g = 0.48). The CHA group’s greater pragmatism was also evident in the differences found in the PCS-A theme scores above for “Optimizing the Outcome.” It seems that the CHA group’s preferences for working alone and with others was significantly influenced by the likelihood they would need and benefit from other’s contributions, or if they felt they would do better on their own.

A statistically significant difference with a small effect size was found only among the mean theme scores of students in Grades 6, 7, and 8 for students’ concerns regarding Assessment when collaborating on difficult projects (PCS-O; F [2, 325] = 6.32, p < .002, η 2 p  = 0.04). Post hoc comparisons of the grade level means for Assessment showed the Grade 8 students’ mean (M = 0.31, SD = 1.02) was significantly higher than the means for students in Grade 6 (M = −0.12, SD = 1.00) and Grade 7 (M = −0.12, SD = 0.92). Receiving recognition for their individual contribution to a difficult group project and self-assessment appeared to be more important to students in Grade 8 than their younger schoolmates.

Independent t -tests revealed no statistically significant differences in the contextual considerations related to students’ sex. This tells us their influence on the preferences of girls and boys were similar.

The task-specific findings reported here provide additional evidence that the appeal of individual or shared projects depended on the school subject and a number of other contextual considerations. Some arose in the findings of earlier investigations; some were familiar but nuanced.

It was not surprising that a large majority of these middle school students preferred neither working alone nor with others on a challenging project. Their preferences were dynamic and varied in response to features of the context. Arguments and evidence supporting the context-sensitivity of learning preferences related to the desire to work individually or collaboratively have accumulated to the point that they can be considered trustworthy (Cantwell & Andrews, Citation 2002 ; French & Shore, Citation 2009 ; French et al., Citation 2011 ; Kanevsky, Citation 2011 , Citation 2015 ; Koutrouba et al., Citation 2012 ; Samardzija & Peterson, Citation 2015 ; Walker & Shore, Citation 2015 ; Walker et al., Citation 2011 ; Wismath & Orr, Citation 2015 ). It appears students’ preferences result from weighing the risks and benefits associated with the context.

Subject-specific project preferences

Sometimes the school subject involved in the project was a consideration. Like Cowan ( Citation 2014 ), we found group projects were popular in social studies. Our middle school students also found them attractive in science but not in art. Individual art projects were even more desirable to girls than boys. We suspect the opportunity to express ideas freely and creatively may be more important than in art than in other subjects and this resulted in the popularity of individual projects. Further, working alone is a way to avoid having to compromise or engage in uncomfortable conversations regarding differences of opinion. The appeal of collaborative projects in science differs from Li and Adamson’s ( Citation 1992 ) whose high IQ (WISC-R IQ > 120) secondary students preferred to work individually in science. This may be due to differences in students’ experiences, age and ability, or all of the above.

In math, group differences in project preferences related to ability and sex emerged. Like the high IQ students in Li and Adamson ( Citation 1992 ) study, the CHA students reported a greater desire to work on projects alone in math. This may reflect a greater concern regarding potential free-riders, social loafing, sucker effects, and status differentials that might arise when collaborating because they would be the most capable member of the group (Neber et al., Citation 2001 ; Orbell & Dawes, Citation 1981 ; Robinson, Citation 2003 ; Salomon & Globerson, Citation 1989 ). If working alone, their grade and the quality of their experience would not suffer if others did not understand the material or care about it or their grade as much as they did. This concern might also contribute to boys’ slight preference for individual projects in math. On the other hand, girls’ slight preference for collaborative math projects may reflect having lower confidence in their ability to complete a challenging math project without the support of others, or perhaps a desire to help others, or both. Socialization into sex-related roles may also have contributed to these results.

Overall, themes from the principal component analyses revealed interaction among features of the context. Collectively, they suggested students’ default “preference” was to work alone when they felt their ideas, feelings, relationships, or their grade might be at in peril. Some of their concerns (e.g., potential conflict, unfair assessment) have appeared consistently in 30 years of research on this topic and studies of cooperative and collaborative learning (e.g., Hooper & Hannafin, Citation 1991 ; Neber et al., Citation 2001 ; Orbell & Dawes, Citation 1981 ; Salomon & Globerson, Citation 1989 ). Unfortunately, their presence in these findings indicate they persist in students’ experiences and continue to influence their preferences.

Although students may feel working alone was socially and academically safer, they preferred collaborative projects when the conditionsfelt right. These conditions were: when they could work with a friend or familiar classmates, when they felt members of their group trusted each other and would include others’ ideas, when they could rely on them because they were smarter and would maintain the focus, when their contributions resulted in a better grade, and when the project’s assessment recognized the group’s accomplishments as well as individual contributions. The opportunity to choose workmates has consistently made collaboration attractive to students in this age group (Fisher & Frey, Citation 2012 ; Kanevsky, Citation 2011 ; Koutrouba et al., Citation 2012 ; Walker & Shore, Citation 2015 ), however secondary students became less enthusiastic as result of tensions between peer pressure and concern for their grade (Mitchell et al., Citation 2004 ).

Our findings and others indicate high ability learners enjoyed working alone more than peers when the project or task is challenging, difficult, interesting and complex (Diezmann & Watters, Citation 2001 ; Kanevsky, Citation 1992 ; Walker & Shore, Citation 2015 ). In contrast, all students, regardless of ability, ranked “fun” assignments as most preferred group work in Walker and Shore’s study. They attributed the difference in preferences on “difficult” or “difficult but interesting” and fun assignments to differences in the impact students perceived each type of task would have on their grade. Difficult tasks were expected to have greater consequences so students preferred to do them alone. This preference was believed to be stronger for high performers because their academic orientation was stronger (Schapiro, Schneider, Shore, Margison, & Udvari, Citation 2009 ). Fun assignments were expected to have fewer consequences so working with others was more attractive because the “stakes” were lower. Students in our study likely felt a “challenging project” would also be a difficult assignment so these findings align with Walker and Shore’s but with an additional nuance. The enjoyment these high ability participants associated with working alone on a difficult project also involved having their teacher available to help if needed. Just knowing that assistance is available may provide a safety net that protects or enhances a greater sense of “flow” (Nakamura & Csikszentmihalyi, Citation 2005 ) or accomplishment (Kanevsky, Citation 1992 ) learners with high ability derive from challenges involving content and a topic they enjoy.

The preferences of the CHA students were also more sensitive to the pragmatism evident in their desire to “optimize the outcome” by working alone on a challenging project and also access the support provided by workmates when collaborating on a difficult project. Both of these findings may be related to gifted students’ stronger academic task-orientation (Schapiro et al., Citation 2009 ) as mentioned above. French et al. ( Citation 2011 ) also found the support of others enhanced the appeal of working with others more for high achieving and gifted learners than others. The findings of our studies complement each other but differ significantly in their meaning. In French et al., “support” meant the feeling “that people around you (for example, parents, teachers, or classmates) appreciate your work (think your work is valuable or important)” (p. 158) In our results, “support” represented the types of assistance and expertise provided by collaborators. It made working with others more attractive to CHA students’ but not GCs. Differences in the support sought or valued by students who differ in academic ability deserves further investigation.

We found concerns regarding the assessment of joint projects increased with students’ age as they have elsewhere (French et al., Citation 2011 ; Kanevsky, Citation 2015 ). Older students were more interested in collaborating if they assessed their own work and received a group grade as well as a separate grade from a teacher for their contribution. It may be that negative experiences with group dynamics and grading accumulate over time so older students are more skeptical of group work than younger.

Implications

Theoretical.

Although the items on the PCS were not based on self-determination theory, the contextual considerations emerging from the data resonated with the basic psychological needs to feel competent, autonomous, and connected to others identified in it (Reeve et al., Citation 2004 ; Ryan & Deci, Citation 2000 ). Individual projects were preferred when students felt their potential workmates would be less competent, when the lack of individual accountability in a group project would result in a low grade that did not accurately reflect their individual efforts and competence, when relationships were weak (they lacked trust and acceptance), unreliable, or uncomfortable (they expected disagreements), or when they wanted to protect their autonomy. In contrast, working with others was attractive and enhanced their competence (and grade) when they knew their workmates well and felt safe with them, when they felt they were trustworthy and reliable, and when they were able to exercise their autonomy (e.g., they could choose workmates and felt they had a voice in the project’s assessment). This alignment with self-determination theory suggests it holds promise for explaining and investigating the benefits and challenges associated with the development of intrinsic motivation by offering students opportunities to find and enact their project preferences. In addition, the repertoire of evidence-based practices self-determination theory has inspired may assist researchers and educators efforts to understand and address students’ preferences.

Educational

The appeal of an individual or collaborative assignment depends on students’ thoughts and feelings about dimensions of the context, i.e., the subject, task, social dynamics, and setting). Educators may find these highlights from our findings helpful when planning for project work.

In art by most students and by high ability learners in math,

When they expect working alone on a project will be more enjoyable, result in a better experience and outcome, and is a way to manage the risks associated with collaborating,

By high ability learners more than others because enjoying and optimizing the project are more important to them.

In social studies and science,

When students feel included and trusted, when they will need others knowledge and support, when they know or choose their workmate(s) and their work is assessed fairly,

By high ability learners more than others in the class when they feel they will need the knowledge and support provided by collaborators to complete the project to their satisfaction.

In light of the fluidity of students’ preferences, we highly recommend teachers consult their students to accurately determine their preferences in their contexts.

The persistence and prevalence of students’ concerns regarding interpersonal dynamics and individual accountability in collaborative projects indicate they are still problematic (Clark, Citation 2017 ; Cohen, Citation 1994 ; Wismath & Orr, Citation 2015 ). Weak learning communities and relying solely on a “group grade” diminishes academic benefits, social relationships, and intrinsic motivation. The development of the safe learning environment that is prerequisite to cooperative and collaborative learning involves preparatory training and community-building activities in which students become familiar with and trusting each other (see Abrami et al., Citation 1995 ; Allen, Citation 2012 ; Gillies, Citation 2007 ; Johnson & Johnson, Citation 1998 ; Kagan & Kagan, Citation 2009 ). Professional development for educators is needed to prepare them to develop those activities and assignments that are most appropriate for individual and group work.

Future research

Many questions remain regarding students’ preference for working alone and with others. Given their sensitivity to context, research needs to be undertaken in the contexts in which learners’ preferences develop. Intentionally varying the four aspects of the context and monitoring their interactions in the experiences of diverse students and teachers over time would create opportunities explore their complex dynamics as they influence and are influenced by learning, motivation and relationships among those involved. Rigorously designed implementation studies are needed to determine short- and long-term consequences of activities that are and are not matched with students’ preferences (Pashler et al., Citation 2008 ). Students’ expectations of learning alone and with others as well as definitions for key words students use to describe the context and support should be considered as well (Cera Guy et al., Citation 2019 ; Williams, Cera Guy, & Shore, Citation 2019 ). These words include difficulty, interest, fun, support and collaboration. It will also be valuable to collect descriptions of the nature and extent of their experiences working alone and with others on assignments that vary in size, duration, roles, responsibilities, and impact on their grades and relationships.

Limitations

Our findings are specific to one academic task, a challenging project, and thus are limited to that assignment. The nature of the sample also constrains the results. The restricted age range (Grades 6, 7, and 8) likely explains the small number of age-related differences. Further, although the sample included students from diverse cultures, it was predominantly Anglophone Canadian and was drawn from one school district. As a result, our findings are limited to students with similar project-related experiences, in the same grades, and in instructional contexts similar to those involved in this study. Finally, CHA and GC groups were formed using students’ scores on an assessment of reasoning abilities, the CogAT7. This should be considered when interpreting findings related to ability. Despite the psychometric shortcomings of the PCS, the themes emerging from students’ responses were relatively consistent with the findings of earlier studies and offer a few nuanced insights.

The tight focus on a single challenging task and the use of CogAT7 reasoning scores to define ability groups adds precision to the findings of previous efforts to untangle the complexities of learners’ preferences for working alone and with others. The results of this study affirm those indicating the general belief that all learners with high ability prefer to work alone all of the time is false, however it appears true for difficult math activities. Across school subjects, the considerations influencing students’ preferences aligned well with previous findings while the items within some themes introduced new insights. Those that influenced the preferences of students with high ability more than their agemates included some that highlighted their greater concern for the quality of their experience, project and grade. More than others their age, access to a teacher’s help contributed to the enjoyment students with high ability associated with working alone on a challenging project. They were also more conscientious, perhaps pragmatic, preferring to work alone when they felt the process and their grade would be better and collaborating when others would be needed to complete the project well. No matter their ability, students’ concerns for fairness and safety in collaborative projects played significant roles in their appeal. Interdependent social, emotional, ethical and academic aspects of the context collide in a student’s preference to work alone or with others on a particular assignment. Additional research is needed to continue to elucidate the ways in which they contribute students’ preferences, motivation and their roles in achieving the benefits of challenging individual and collaborative learning experiences.

Acknowledgement

The authors are grateful for the assistance and support provided by the following: School District #43 (Coquitlam, BC), Mrs. Louise Malfesi (Coquitlam’s District Coordinator of Gifted Education), and the students and teachers who participated in the study; Zahra Rajan for her data collection and management; and the contributions of Shun Fu Hu (UBC) and Ian Bercovitz (SFU) to the analysis and interpretation of the data.

No potential conflict of interest was reported by the author(s).

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• How to Write a Research Proposal | Examples & Templates

How to Write a Research Proposal | Examples & Templates

Published on October 12, 2022 by Shona McCombes and Tegan George. Revised on September 5, 2024.

A research proposal describes what you will investigate, why it’s important, and how you will conduct your research.

The format of a research proposal varies between fields, but most proposals will contain at least these elements:

Introduction

Literature review.

• Research design

Reference list

While the sections may vary, the overall objective is always the same. A research proposal serves as a blueprint and guide for your research plan, helping you get organized and feel confident in the path forward you choose to take.

Table of contents

Research proposal purpose, research proposal examples, research design and methods, contribution to knowledge, research schedule, other interesting articles, frequently asked questions about research proposals.

Academics often have to write research proposals to get funding for their projects. As a student, you might have to write a research proposal as part of a grad school application , or prior to starting your thesis or dissertation .

In addition to helping you figure out what your research can look like, a proposal can also serve to demonstrate why your project is worth pursuing to a funder, educational institution, or supervisor.

Research proposal length

The length of a research proposal can vary quite a bit. A bachelor’s or master’s thesis proposal can be just a few pages, while proposals for PhD dissertations or research funding are usually much longer and more detailed. Your supervisor can help you determine the best length for your work.

One trick to get started is to think of your proposal’s structure as a shorter version of your thesis or dissertation , only without the results , conclusion and discussion sections.

Download our research proposal template

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Writing a research proposal can be quite challenging, but a good starting point could be to look at some examples. We’ve included a few for you below.

• Example research proposal #1: “A Conceptual Framework for Scheduling Constraint Management”
• Example research proposal #2: “Medical Students as Mediators of Change in Tobacco Use”

Like your dissertation or thesis, the proposal will usually have a title page that includes:

• The proposed title of your project
• Your supervisor’s name
• Your institution and department

The first part of your proposal is the initial pitch for your project. Make sure it succinctly explains what you want to do and why.

Your introduction should:

• Introduce your topic
• Give necessary background and context
• Outline your  problem statement  and research questions

To guide your introduction , include information about:

• Who could have an interest in the topic (e.g., scientists, policymakers)
• How much is already known about the topic
• What is missing from this current knowledge
• What new insights your research will contribute
• Why you believe this research is worth doing

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
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As you get started, it’s important to demonstrate that you’re familiar with the most important research on your topic. A strong literature review  shows your reader that your project has a solid foundation in existing knowledge or theory. It also shows that you’re not simply repeating what other people have already done or said, but rather using existing research as a jumping-off point for your own.

In this section, share exactly how your project will contribute to ongoing conversations in the field by:

• Comparing and contrasting the main theories, methods, and debates
• Examining the strengths and weaknesses of different approaches
• Explaining how will you build on, challenge, or synthesize prior scholarship

Following the literature review, restate your main  objectives . This brings the focus back to your own project. Next, your research design or methodology section will describe your overall approach, and the practical steps you will take to answer your research questions.

To finish your proposal on a strong note, explore the potential implications of your research for your field. Emphasize again what you aim to contribute and why it matters.

For example, your results might have implications for:

• Improving best practices
• Informing policymaking decisions
• Strengthening a theory or model
• Challenging popular or scientific beliefs
• Creating a basis for future research

Last but not least, your research proposal must include correct citations for every source you have used, compiled in a reference list . To create citations quickly and easily, you can use our free APA citation generator .

Some institutions or funders require a detailed timeline of the project, asking you to forecast what you will do at each stage and how long it may take. While not always required, be sure to check the requirements of your project.

Here’s an example schedule to help you get started. You can also download a template at the button below.

Download our research schedule template

If you are applying for research funding, chances are you will have to include a detailed budget. This shows your estimates of how much each part of your project will cost.

Make sure to check what type of costs the funding body will agree to cover. For each item, include:

• Cost : exactly how much money do you need?
• Justification : why is this cost necessary to complete the research?
• Source : how did you calculate the amount?

To determine your budget, think about:

• Travel costs : do you need to go somewhere to collect your data? How will you get there, and how much time will you need? What will you do there (e.g., interviews, archival research)?
• Materials : do you need access to any tools or technologies?
• Help : do you need to hire any research assistants for the project? What will they do, and how much will you pay them?

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

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement .

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement , before your research objectives.

Research objectives are more specific than your research aim. They indicate the specific ways you’ll address the overarching aim.

A PhD, which is short for philosophiae doctor (doctor of philosophy in Latin), is the highest university degree that can be obtained. In a PhD, students spend 3–5 years writing a dissertation , which aims to make a significant, original contribution to current knowledge.

A PhD is intended to prepare students for a career as a researcher, whether that be in academia, the public sector, or the private sector.

A master’s is a 1- or 2-year graduate degree that can prepare you for a variety of careers.

All master’s involve graduate-level coursework. Some are research-intensive and intend to prepare students for further study in a PhD; these usually require their students to write a master’s thesis . Others focus on professional training for a specific career.

Critical thinking refers to the ability to evaluate information and to be aware of biases or assumptions, including your own.

Like information literacy , it involves evaluating arguments, identifying and solving problems in an objective and systematic way, and clearly communicating your ideas.

The best way to remember the difference between a research plan and a research proposal is that they have fundamentally different audiences. A research plan helps you, the researcher, organize your thoughts. On the other hand, a dissertation proposal or research proposal aims to convince others (e.g., a supervisor, a funding body, or a dissertation committee) that your research topic is relevant and worthy of being conducted.

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National Research Council (US) and Institute of Medicine (US) Committee on Assessing Integrity in Research Environments. Integrity in Scientific Research: Creating an Environment That Promotes Responsible Conduct. Washington (DC): National Academies Press (US); 2002.

Integrity in Scientific Research: Creating an Environment That Promotes Responsible Conduct.

• Hardcopy Version at National Academies Press

2 Integrity in Research

The pursuit and dissemination of knowledge enjoy a place of distinction in American culture, and the public expects to reap considerable benefit from the creative and innovative contributions of scientists. As science becomes increasingly intertwined with major social, philosophical, economic, and political issues, scientists become more accountable to the larger society of which they are a part. As a consequence, it is more important than ever that individual scientists and their institutions periodically reassess the values and professional practices that guide their research as well as their efforts to perform their work with integrity.

Society's confidence in and support of research rest in large part on public trust in the integrities of individual researchers and their supporting institutions. The National Academies' report On Being a Scientist states: “The level of trust that has characterized science and its relationship with society has contributed to a period of unparalleled scientific productivity. But this trust will endure only if the scientific community devotes itself to exemplifying and transmitting the values associated with ethical scientific conduct” (NAS, 1995, preface). It is therefore incumbent on all scientists and scientific institutions to create and nurture a research environment that promotes high ethical standards, contributes to ongoing professional development, and preserves public confidence in the scientific enterprise (Grinnell, 1999; IOM, 2001; Resnik, 1998; Yarborough and Sharp, 2002).

Government oversight of scientific research is important, but such oversight, often in the form of administrative rules, typically stipulates what cannot be done; it rarely prescribes what should be done (see Chapter 4 for further discussion of the strengths and limitations of a regulatory approach). In essence, government rules define the floor of expected behavior. More, however, should be expected from scientists when it comes to the responsible conduct of research. By appealing to the conscience of individual scientists, the scientific community as a whole should seek to evoke the highest possible standard of research behavior. When institutions committed to promoting integrity in research support those standards, the likelihood of creating an environment that advances responsible research practices is greatly enhanced. It is essential that institutions foster a culture of integrity in which students and trainees, as well as senior researchers and administrators, have an understanding of and commitment to integrity in research.

The committee's task was to define integrity for the particular activity of research as conducted within contemporary society. Integrity has two general senses. The first sense concerns wholeness; the second, soundness of moral principle ( Oxford English Dictionary , 1989). Plato and subsequent philosophers have argued that leading the good life depends on a person's success in integrating moral, religious, and philosophical convictions. In conversations with experts in ethics and others, the committee found no consensus regarding whether a person could exhibit high integrity in research but not in other aspects of his life. Consequently, the committee decided to focus on the second aspect of integrity—namely, soundness of moral principle in the specific context of research practice.

• INTEGRITY IN RESEARCH

Integrity characterizes both individual researchers and the institutions in which they work. For individuals, it is an aspect of moral character and experience. 1 For institutions, it is a matter of creating an environment that promotes responsible conduct by embracing standards of excellence, trustworthiness, and lawfulness that inform institutional practices.

For the individual scientist, integrity embodies above all a commitment to intellectual honesty and personal responsibility for one's actions and to a range of practices that characterize responsible research conduct. These practices include:

• intellectual honesty in proposing, performing, and reporting research;
• accuracy in representing contributions to research proposals and reports;
• fairness in peer review;
• collegiality in scientific interactions, including communications and sharing of resources;
• transparency in conflicts of interest or potential conflicts of interest;
• protection of human subjects in the conduct of research;
• humane care of animals in the conduct of research; and
• adherence to the mutual responsibilities between investigators and their research teams.

Individual scientists work within complex organizational structures. (These structures and their interactions are described in detail in Chapter 3 .) Factors that promote responsible conduct can exert their influences at the level of the individual; at the level of the work group (e.g., the research group); and at the level of the research institution itself. These different organizational levels are interdependent in the conduct of research. Institutions seeking to create an environment that promotes responsible conduct by individual scientists and that fosters integrity must establish and continuously monitor structures, processes, policies, and procedures that:

• provide leadership in support of responsible conduct of research;
• encourage respect for everyone involved in the research enterprise;
• promote productive interactions between trainees and mentors;
• advocate adherence to the rules regarding all aspects of the conduct of research, especially research involving human subjects and animals;
• anticipate, reveal, and manage individual and institutional conflicts of interest;
• arrange timely and thorough inquiries and investigations of allegations of scientific misconduct and apply appropriate administrative sanctions;
• offer educational opportunities pertaining to integrity in the conduct of research; and
• monitor and evaluate the institutional environment supporting integrity in the conduct of research and use this knowledge for continuous quality improvement.

Leadership by individuals of high personal integrity helps to foster an environment in which scientists can openly discuss responsible research practices in the face of conflicting pressures. All those involved in the research enterprise should acknowledge that integrity is a key dimension of the essence of being a scientist and not a set of externally imposed regulatory constraints.

• INTEGRITY OF THE INDIVIDUAL SCIENTIST

As noted above, the committee has identified a range of key practices that pertain to the responsible conduct of research by individual scientists. The following sections elucidate the practices. 2

Intellectual Honesty in Proposing, Performing, and Reporting Research

Intellectual honesty in proposing, performing, and reporting research refers to honesty with respect to the meaning of one's research. It is expected that researchers present proposals and data honestly and communicate their best understanding of the work in writing and verbally. The descriptions of an individual's work found in such communications frequently present selected data from the work organized into frameworks that emphasize conceptual understanding rather than the chronology of the discovery process. Clear and accurate research records must underlie these descriptions, however. Researchers must be advocates for their research conclusions in the face of collegial skepticism and must acknowledge errors.

Accuracy in Representing Contributions to Research Proposals and Reports

Accuracy in representing one's contributions to research proposals and reports requires the assignment of credit. It is expected that researchers will not report the work of others as if it were their own. This is plagiarism. Furthermore, they should be honest with respect to the contributions of colleagues and collaborators. Decisions regarding authorship are best anticipated at the outset of projects rather than at their completion. In publications, it should be possible in principle to specify each author's contribution to the work. It also is expected that researchers honestly acknowledge the precedents on which their research is based.

Fairness in Peer Review

Fairness in peer review means that researchers should agree to be peer reviewers only when they can be impartial in their judgments and only when have revealed their conflicts of interest. Peer review functions to maintain the excellence of published scientific work and ensure a merit-based system of support for research. A delicate balance pervades the peer-review system, because the best reviewers are precisely those individuals who have the most to gain from “insider information”: they are doing similar work and they will be unable to “strike” from memory and thought what they learn through the review process. Investigators serving as peer reviewers should treat submitted manuscripts and grant applications fairly and confidentially and avoid using them inappropriately.

Collegiality in Scientific Interactions, Including Communications and Sharing of Resources

Collegiality in scientific interactions, including communications and sharing of resources requires that investigators report research findings to the scientific community in a full, open, and timely fashion. At the same time, it should be recognized that the scientific community is highly competitive. The investigator who first reports new and important findings gets credited with the discovery.

It is not obvious that rapid reporting is the approach that is always the most conducive to progress. Intellectual property provisions and secrecy allow for patents and licensure and encourage private investment in research. Furthermore, even for publicly funded research, a degree of discretion may permit a research group to move ahead more efficiently. Conversely, an investigator who delays reporting important new findings risks having others publish similar results first and receiving little recognition for the discovery. Knowing when and how much to tell will always remain a challenge in scientific communication.

Once scientific work is published, researchers are expected to share unique materials with other scientists in a reasonable fashion to facilitate confirmation of their results. (The committee recognizes that there are limits to sharing, especially when doing so requires a time or cost commitment that interferes with the function of the research group.) When materials are developed through public funding, the requirement for sharing is even greater. Public funding is based on the principle that the public good is advanced by science conducted in the interest of humanity. This commitment to the public good implies a responsibility to share materials with others to demonstrate reproducibility and to facilitate the replication and validation of one's work by responding constructively to inquiries from other scientists, particularly regarding methodologies.

Collegiality and sharing of resources is also an important aspect of the interaction between trainees and their graduate or postdoctoral advisers. Students and fellows will ultimately depart the research team, and discussion of and planning for departure should occur over the course of their education. Expectations about such issues as who inherits intellectual property rights to a project or to the project itself upon the trainee's departure should be discussed when the trainee first joins the research group and should be revisited periodically over the course of the project (NAS, 2000).

Transparency in Conflicts of Interest or Potential Conflicts of Interest

A conflict of interest in research exists when the individual has interests in the outcome of the research that may lead to a personal advantage and that might therefore, in actuality or appearance, compromise the integrity of the research. The most compelling example is competition between financial reward and the integrity of the research process. Religious, political, or social beliefs can also be undisclosed sources of research bias.

Many scientific advances that reach the public often involve extensive collaboration between academia and industry (Blumenthal et al., 1996; Campbell et al., 1998; Cho et al., 2000). Such collaborations involve consulting and advisory services as well as the development of specific inventions, and they can result in direct financial benefit to both individuals and institutions. Conflicts of interest reside in a situation itself, not in any behavior of members of a research team. Thus, researchers should disclose all conflicts of interest to their institutions so that the researchers and their work can be properly managed. They should also voluntarily disclose conflicts of interest in all publications and presentations resulting from the research. The committee believes that scientific institutions, including universities, research institutes, professional societies, and professional and lay journals, should embrace disclosure of conflicts of interest as an essential component of integrity in research.

Protection of Human Subjects in the Conduct of Research

The protection of individuals who volunteer to participate in research is essential to integrity in research. The ethical principles underlying such research have been elaborated on in international codes and have been integrated into national regulatory frameworks (in the United States, 45 C.F.R. § 46, 2001). Elements included in such frameworks pertain to the quality and importance of the science, its risks and benefits, fairness in the selection of subjects, and, above all, the voluntary participation and informed consent of subjects. To ensure the conformance of research efforts with these goals, institutions carry out extensive research subject protection programs. To be successful, such programs require high-level, functioning institutional review boards, knowledgeable investigators, ongoing performance assessment through monitoring and feedback, and educational programs (IOM, 2001). The IOM report Preserving Public Trust (IOM, 2001) focuses specifically on the important topic of research involving human subjects, and further discussion is not included here.

Humane Care of Animals in the Conduct of Research

The humane care of animals is essential for producing sound science and its social benefits. Researchers have a responsibility to engage in the humane care of animals in the conduct of research. This means evaluating the need for animals in any particular protocol, ensuring that research animals' basic needs for life are met prior to research, and carefully considering the benefits of the research to society or to animals versus the likely harms to any animals included as part of the research protocol. Procedures that minimize animal pain, suffering, and distress should be implemented. Research protocols involving animals must be reviewed and approved by properly constituted bodies, as required by law (Animal Welfare Act of 1966 [PL 89-544], inclusive of amendments passed in 1970 [PL 91-579], 1976 [PL 94-279], 1985 [PL 99-198], and 1990 [PL 101-624] and subsequent amendments) and professional standards (AAALAC, 2001; NRC, 1996).

Adherence to the Mutual Responsibilities Between Investigators and Their Research Teams

Adherence to the mutual responsibilities between investigators and members of their research teams refers to both scientific and interpersonal interactions. The research team might include other faculty members, colleagues (including coinvestigators), and trainees (undergraduate students, graduate and medical students, postdoctoral fellows), as well as employed staff (e.g., technicians, statisticians, study coordinators, nurses, animal handlers, and administrative personnel). The head of the research team should encourage all members of the team to achieve their career goals. The interpersonal interactions should reflect mutual respect among members of the team, fairness in assignment of responsibilities and effort, open and frequent communication, and accountability. In this regard, scientists should also conduct disputes professionally (Gunsalus, 1998). (The American Association of University Professors (AAUP) guidelines on academic freedom and professional ethics articulate the obligation of members of the academic community to root their statements in fact and to respect the opinions of others [AAUP, 1987, 1999].)

Mentoring and Advising

Mentor is often used interchangeably with faculty adviser . However, a mentor is more than a supervisor or an adviser (Bird, 2001; Swazey and Anderson, 1998). 3 An investigator or research adviser may or may not be a mentor. Some advisers may be accomplished researchers but do not have the time, training, or ability to be good mentors (NAS, 2000). For a trainee, “a mentoring relationship is a close, individualized relationship that develops over time between a graduate student (or other trainee) and a faculty member (or others) that includes both caring and guidance” (University of Michigan, 1999, p. 5). A successful mentoring relationship is based on mutual respect, trust, understanding, and empathy (NAS, 1997). Mentoring relationships can extend throughout all phases of a science career, and, as such, they are sometimes referred to as mentor-protégé or mentor-apprentice relationships, rather than mentor-trainee relationships.

The committee believes that mentor should be the dominant and usual role of the laboratory director or research advisor in regard to his or her trainee. With regard to such mentor-trainee relationships, responsibilities include a commitment to continuous education and guidance of trainees, appropriate delegation of responsibility, regular review and constructive appraisal of trainees, fair attribution of accomplishment and authorship, and career guidance, as well as help in creating opportunities for employment and funding. For the trainee, essential elements include respect for the mentor, loyalty to the research group, a strong commitment to science, dedication to the project, careful performance of experiments, precise and complete record keeping, accurate reporting of results, and a commitment to oral and written presentations and publication. It should be noted that most academic research institutions play a dual role. On the one hand, they are concerned with producing original research; on the other, with educating students. The two goals are compatible, but when they come in conflict, it is important that the educational needs of the students not be forgotten. If students are exploited, then they will learn by example that such behavior is acceptable.

• SUPPORT OF INTEGRITY BY THE RESEARCH INSTITUTION

The individual investigator and the laboratory or research unit carry out their functions in institutions that are responsible for the management and support of the research carried out within their domains. The institutions, in turn, are regulated by governmental and other bodies that impose rules and responsibilities (see Chapter 3 for further discussion). The vigor, resources, and attitudes with which institutions carry out their responsibilities will influence investigators' commitment and adherence to responsible research practices.

Provide Leadership in Support of Responsible Conduct of Research

It takes the leadership of an institution to promulgate a culture of responsible research. This involves the development of a vision for the research enterprise and a strategic plan. It is the responsibility of the institution leadership to develop programs to orient new researchers to institutional policies, rules, and guidelines; to sponsor opportunities for dialogue about new and emerging issues; and to sponsor continuing education about new policies and regulations as they are developed. Furthermore, institutional leaders have the responsibility to ensure that such programs are carried out, with appropriate delegation of responsibility and accountability and with adequate resources.

The observed actions of institutions in problem situations communicate as strongly (or perhaps more strongly) about responsible conduct as do any policies or programs. Institutional leaders (e.g., chancellor, president, dean, CEO) set the tone for the institutions with their own actions. Research leaders should set an example not only in their own research practices but also in their willingness to engage in dialogue about ethical questions that arise (Sigma Xi, 1999). McCabe and Pavela note that “faculty members who seek to instill a sense of social obligation without affirming personal virtues are planting trees without roots” (McCabe and Pavela, 1998, p.101).

Encourage Respect for Everyone Involved in the Research Enterprise

An environment that fosters competence and honest interactions among all participants in the investigative process supports the integrity of research. Institutions have many legally mandated policies that foster mutual respect and trust—for example, policies concerning harassment, occupational health and safety, fair employment practices, pay and benefits, protection of research subjects, exposure to ionizing radiation, and due process regarding allegations of research misconduct. State and local policies and guidelines governing research may be in effect as well. It is anticipated that through a process of self-assessment, institutions can identify issues and develop programs that further integrity in research (see Chapter 6 for further discussion). Fair enforcement of all institutional policies is a critical element of the institutional commitment to integrity in research. That is not enough, however.

Support Systems

Within the research institution, there can be multiple smaller units (e.g., departments, divisions within a department, research groups within a division). Within these institutional subunits, there will always be power differences between members of the group. Consequently, research institutions require support mechanisms—for example, ombudspersons—that research team members can turn to for help when they feel they are being treated unfairly. Institutions need to provide guidance and recourse to anyone with concerns about research integrity (e.g., a student who observes a lack of responsible conduct by a senior faculty member). Support systems should be accessible (multiple entry points for those with questions) and have a record of reaching objective, fact-based decisions untainted by personal bias or conflicts of interest (Gunsalus, 1993). Lack of recourse within the institution for those individuals who have concerns about possible misconduct will undermine efforts to foster a climate of integrity. Equally important to having support systems in place is the dissemination of information on how and where individuals may seek such support.

The ultimate goal for institutions should be to create a culture within which all persons on a research team can work effectively and realize their full potential.

Promote Productive Interactions Between Trainees and Mentors

Mentors play a special role in the development of new scientists. A mentor must consider the student's core interests and needs in preference to his or her own. Trainees and mentors are codependent and, at times, competitive. Trainees depend on their mentors for scientific education and training, for support, and, eventually, for career guidance and references. Mentors tend to be role models as well. Mentors depend on trainees for performing work and bringing fresh ideas and approaches to the research group. They can enhance the mentor's reputation as a teacher and as an investigator. Institutions should establish programs that foster productive relations between mentors and trainees, including training in mentoring and advising for faculty. Moreover, institutions should work to ensure that trainees are properly paid, receive reasonable benefits (including health insurance), and are protected from exploitation. Written guidelines, ombudspersons, and mutual evaluations can help to reduce problems and identify situations requiring remediation. As mentioned earlier in this chapter, the dual role academic research institutions play in both producing original research and educating students can be balanced, but when they come in conflict, educational interests of the student should take precedence.

Advocate Adherence to the Rules Regarding All Aspects of the Conduct of Research, Especially Research Involving Human Subjects and Animals

Effective advocacy by an institution of the rules involving the use of human subjects and animals in research involves much more than simply posting the relevant federal, state, and local regulations and providing “damage control” and formal sanctions when irregularities are discovered. At all levels of the institution, including the level of the dean, department chair, research group leader, and individual research group member, regular affirmation of the guiding principles underlying the rules is essential. The goal is to create an institutional climate such that anyone who violates these guiding principles through words or deeds is immediately made aware of the behavior and, when indicated, appropriately sanctioned.

Anticipate, Reveal, and Manage Individual and Institutional Conflicts of Interest

Research institutions must conduct their work in a manner that earns public trust. To do so, they must be sensitive to any conflict of interest that might affect or appear to affect their decisions and behavior in ways that could compromise their roles as trustworthy sources of information and policy advice or their obligations to ensure the protection of human research subjects. As research partnerships between industry and academic institutions continue to expand, with the promise of considerable public benefit, the management of real or perceived conflicts of interest in research requires that institutions have a written policy on such conflicts. The policy should apply to both institutions and individual investigators.

Institutional Conflicts of Interest

Institutions should have clearly stated policies and procedures by which they will guard against compromise by external influences. As with individual conflicts of interest, institutional leadership is not in the best position to determine whether a particular arrangement represents an unacceptable or manageable conflict of interest. Institutions should draw on independent reviews by external bodies and should have appropriate procedures for such reviews. Factors of concern include not only direct influences on institutional policy but also indirect influences on the use of resources, educational balance, and hiring of faculty, for example (AAU, 2001).

Institutional Responsibility for Investigator Conflicts of Interest

The policy on conflicts of interest should apply to individuals who are directly involved in the conduct, design, and review of research, including faculty, trainees, students, and administrators, and should clearly state their disclosure responsibilities. The policy should define conflicts of interest and should have means to convey an understanding of the term to the parties involved. It should delineate the activities and the levels and kinds of research-related financial interests that are and are not permissible, as well as those that require review and approval. The special circumstances associated with research involving human subjects should be specifically addressed. Beyond meeting their responsibility to ensure the dissemination and understanding of their policies, institutions should develop means to monitor compliance equitably. Detailed descriptions of institutional responsibilities in this area were recently reported by the Association of American Universities (AAU, 2001) and the Association of American Medical Colleges (AAMC, 2001), as described in Box 2-1 .

Definition of Institutional Conflict of Interest.

Arrange Timely and Thorough Inquiries and Investigations of Allegations of Scientific Misconduct and Apply Appropriate Sanctions

Every institution receiving federal funds for research and related activities must have in place policies and procedures for responding to allegations of research misconduct (42 C.F.R. § 50, §§ A, 1989; 45 C.F.R. § 689, 1996). Although the federal government imposes these requirements, the institutions must implement them. Their effectiveness depends on investigation of allegations of misconduct with vigor and fairness. The institution should embrace the notion that it is important to the quality and integrity of science that individuals report possible research misconduct. Means of protecting any individual who reports possible misconduct in good faith must be instituted.

In carrying out their responsibilities, institutions must ensure that faculty, students, and staff are properly informed of their rights and responsibilities. Those likely to receive allegations—for example, administrators, department chairs, and research group chiefs—must be fully informed of institutional provisions and trained in dealing with issues related to research conduct or misconduct. Mechanisms must be in place to protect the public's interest in the research record, the research subjects' health, and the financial interests of the institution, as well as to ensure notification of appropriate authorities. Clear lines of authority for management of the institution's response must exist, and, where indicated, appropriate sanctions should be applied or efforts should be made to protect or restore the reputations of innocent parties.

Offer Educational Opportunities Pertaining to Integrity in the Conduct of Research

Research institutions should provide students, faculty, and staff with educational opportunities related to the responsible conduct of research. These are mandatory for those involved in clinical research (NIH, 2000) and for recipients of Public Health Service training grants (NIH, 1989). These offerings should encourage open discussion of the values at stake and the ethical standards that promote responsible research practices. The core objective of such education is to increase participants' knowledge and sensitivity to the issues associated with integrity in research and to improve their ability to make ethical choices. It should give them an appreciation for the diversity of views that may be brought to bear on issues, inform them about the institutional rules and government regulations that apply to research, and instill in them the scientific community's expectations regarding proper research practice. Educational offerings should be flexible in their approach and be cognizant of normative differences among disciplines. Such programs should offer opportunities for the participants to explore the underlying values that shape the research enterprise and to analyze how those values are manifested in behaviors in different research environments

It is expected that effective educational programs will empower individual researchers, students, and staff in making responsible choices in the course of their research. Regular evaluation and improvement of the educational and behavioral effectiveness of these educational offerings should be a part of an institutional assessment. (See Chapter 5 for further discussion of education in the responsible conduct of research.)

Monitor and Evaluate the Institutional Environment Supporting Integrity in the Conduct of Research and Use This Knowledge for Continuous Quality Improvement

The main thrust of this report reflects the need for continuing attention toward sustaining and improving a culture of integrity in research. This requires diligent oversight by institutional management to ensure that the practices associated with integrity described above are carried out. It also requires examination of the policy-making process, the policies themselves, their execution, and the degree to which they are understood and adhered to by those affected. If researchers and administrators believe that the rules are excellent and that the institution applies them equitably, then the institutional commitment to integrity will be clear. Chapter 6 addresses ways to help identify those elements critical to establishment of the perception of moral commitment and determination of whether such commitments have been made.

The committee believes that integrity in research is essential for maintaining scientific excellence and keeping the public's trust. The concept of integrity in research cannot, however, be reduced to a one-line definition. For a scientist, integrity embodies above all the individual's commitment to intellectual honesty and personal responsibility. It is an aspect of moral character and experience. For an institution, it is a commitment to creating an environment that promotes responsible conduct by embracing standards of excellence, trustworthiness, and lawfulness and then assessing whether researchers and administrators perceive that an environment with high levels of integrity has been created. This chapter has described multiple practices that are most likely to promote responsible conduct. Individuals and institutions should use these practices with the goal of fostering a culture in which high ethical standards are the norm, ongoing professional development is encouraged, and public confidence in the scientific enterprise is preserved.

• AAALAC (Association for Assessment and Accreditation of Laboratory Animal Care). 2001. AAALAC International Rules of Accreditation . [Online]. Available: http://www.aaalac. org/rules.htm [Accessed January 31, 2002].
• AAMC (Association of American Medical Colleges). 2001. Protecting Subjects, Preserving Trust, Promoting Progress . [Online] Available: http://www ​.aamc.org/coitf [Accessed December 18, 2001].
• AAU (Association of American Universities). 2001. Report on Individual and Institutional Con flict of Interest . [Online] Available: http://www ​.aau.edu/research/conflict ​.html [Accessed January 31, 2002].
• AAUP (American Association of University Professors). 1987. Statement on Professional Eth ics . [Online]. Available: http://www ​.aaup.org/statements ​/Redbook/Rbethics.htm [Accessed May 14, 2002].
• AAUP. 1999. Recommended Institutional Regulations on Academic Freedom and Tenure . [Online]. Available: http://www aaup.org/statements/Redbook/Rbrir.htm [Accessed May 14, 2002].
• Bird SJ. 2001. Mentors, advisors and supervisors: Their role in teaching responsible research conduct. Science and Engineering Ethics7:455–468. [ PubMed : 11697001 ]
• Blumenthal D, Causino N, Campbell E, Seashore Louis K. 1996. Relationships between academic institutions and industry in the life sciences: An industry survey. New En gland Journal of Medicine334:368–373. [ PubMed : 8538709 ]
• Campbell EG, Seashore Louis K, Blumenthal D. 1998. Looking a gift horse in the mouth. Corporate gifts supporting life sciences research. Journal of the American Medical Asso ciation279:995–999. [ PubMed : 9533497 ]
• Cho MK, Shohara R, Schissel A, Rennie D. 2000. Policies on faculty conflicts of interest at U.S. universities. Journal of the American Medical Association284:2203–2208. [ PubMed : 11056591 ]
• Grinnell F. 1999. Ambiguity, trust, and responsible conduct of research. Science and Engi neering Ethics5:205–214. [ PubMed : 11657858 ]
• Gunsalus CK. 1993. Institutional structure to ensure research integrity. Academic Medicine68:S33–S38. [ PubMed : 8373489 ]
• Gunsalus CK. 1998. How to blow the whistle and still have a career afterwards. Science and Engineering Ethics4:51–64.
• IOM (Institute of Medicine). 2001. Preserving Public Trust . Washington, DC: National Academy Press.
• McCabe DL, Pavela GM. 1998. The effect of institutional policies and procedures on academic integrity. In: Burnett DD, Rudolph L, Clifford KO, eds. Academic Integrity Mat ters . Washington, DC: National Association of Student Personnel Administrators, Inc. Pp.93–108.
• NAS (National Academy of Sciences). 1995. On Being a Scientist , 2nd ed. Washington, DC: National Academy Press.
• NAS. 1997. Advisor, Teacher, Role Model, Friend: On Being a Mentor to Students in Science and Engineering . Washington, DC: National Academy Press.
• NAS. 2000. Enhancing the Postdoctoral Experience for Scientists and Engineers . Washington, DC: National Academy Press.
• NIH (National Institutes of Health). 1989. Requirement for programs on the responsible conduct of research in National Research Service Award Institutional Training Programs, p. 1 . In: NIH Guide for Grants and Contracts, Vol. 18:1 , December 22, 1989. Rockville, MD: NIH.
• NIH. 2000. Required Education in the Protection of Human Research Participants NIH Guide for Grants and Contracts , June 5, 2000 (Revised August 25, 2000). [Online]. Available: http: //grants.nih.gov/grants/guide/notice-files/NOT-OD-00-039.html [Accessed December 10, 2001].
• NRC (National Research Council). 1996. Guide for the Care and Use of Laboratory Animals . Washington, DC: National Academy Press.
• Oxford English Dictionary , 2nd ed. 1989. Oxford: Oxford University Press.
• Resnik DB. 1998. The Ethics of Science: An Introduction . New York: Routledge.
• Sigma Xi. 1999. The Responsible Researcher: Paths and Pitfalls . Research Triangle Park, NC: Sigma Xi, the Scientific Research Society.
• Swazey JP, Anderson MS. 1998. Mentors, advisors, and role models in graduate and professional education. In: Rubin ER, ed. Mission Management . Washington, DC: Association of Academic Health Centers. Pp.165–185.
• University of Michigan. 1999. How to Get the Mentoring You Want: A Guide for Graduate Students at a Diverse University . [Online] Available: http://www.rackham.umich.edu/ StudentInfo/Publications/StudentMentoring/mentoring.pdf [Accessed March 15, 2002].
• Yarborough M, Sharp RR. 2002. Restoring and preserving trust in biomedical research. Academic Medicine77:8–14. [ PubMed : 11788317 ]

Further discussion of moral character and behavior and the development of abilities that give rise to responsible conduct can be found in Chapter 5 .

See the section of Appendix D entitled Responsible Scientific Conduct for resources with case studies that can be used in a teaching setting to further illustrate the topics discussed here.

A special issue of Science and Engineering Ethics (7:451–640, 2001) is devoted to the relationship between mentoring and responsible conduct.

• Cite this Page National Research Council (US) and Institute of Medicine (US) Committee on Assessing Integrity in Research Environments. Integrity in Scientific Research: Creating an Environment That Promotes Responsible Conduct. Washington (DC): National Academies Press (US); 2002. 2, Integrity in Research.
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Who is Hispanic?

Debates over who is Hispanic have often fueled conversations about identity among Americans who trace their heritage to Latin America or Spain .

So, who is considered Hispanic in the United States today? How exactly do the federal government and others count the Hispanic population? And what role does race play in deciding who counts as Hispanic?

We’ll answer these and other common questions here.

To answer the question of who is Hispanic, this analysis draws on about five decades of U.S. Census Bureau data and about two decades of Pew Research Center surveys of Hispanic adults in the United States.

National counts of the Latino population come from the Census Bureau’s decennial census (this includes P.L. 94-171 census data ) and official population estimates . The bureau’s American Community Survey (ACS) provides demographic details such as race, country of origin and intermarriage rates. Some ACS data was accessed through IPUMS USA from the University of Minnesota.

Views of Hispanic identity draw on the Center’s National Survey of Latinos (NSL), which is fielded in English and Spanish. The survey has been conducted online since 2019, primarily through the Center’s American Trends Panel (ATP), which is recruited through national, random sampling of residential addresses. This way nearly all adults have a chance of selection. The survey is weighted to be representative of the U.S. Hispanic adult population by gender, Hispanic origin, partisan affiliation, education and other categories. Read more about the ATP’s methodology . The NSL was conducted by phone from 2002 to 2018.

Read further details on how the Census Bureau asked about race and ethnicity and coded responses in the 2020 census. Here is a full list of origin groups that were coded as Hispanic in the 2020 census.

How many Hispanics are in the U.S. today?

The Census Bureau estimates there were 65.2 million Hispanics in the U.S. as of July 1, 2023, a new high. They made up more than 19% of the nation’s population .

How are Hispanics identified and counted in government surveys, public opinion polls and other studies?

Before diving into the details, keep in mind that some surveys ask about Hispanic origin and race separately, following current Census Bureau practices – though these are soon to change.

One way to count Hispanics is to include those who say they are Hispanic, with no exceptions – that is, you are Hispanic if you say you are. Pew Research Center uses this approach in our surveys, as do other polling firms such as Gallup and voter exit polls .

The Census Bureau largely counts Hispanics this way, too, but with some exceptions. If respondents select only the “Other Hispanic” category and write in only non-Hispanic responses such as “Irish,” the Census Bureau recodes the response as non-Hispanic.

However, beginning in 2020 , the bureau widened the lens to include a relatively small number of people who did not check a Hispanic box on the census form but answered the race question in a way that implied a Hispanic background. As a result, someone who answered the race question by saying that they are “Mexican” or “Argentinean” was counted as Hispanic, even if they did not check the Hispanic box.

From the available data, the exact number of respondents affected by this change is difficult to determine. But it appears to be about 1% of Hispanics or fewer, according to a Pew Research Center analysis of U.S. Census Bureau data.

How do Hispanics identify their race in Census Bureau surveys?

In the eyes of the Census Bureau, Hispanics can be of any race, because “Hispanic” is an ethnicity and not a race. However, this distinction is subject to debate . A 2015 Center survey found that 17% of Hispanic adults said being Hispanic is mainly a matter of race, while 29% said it is mainly a matter of ancestry. Another 42% said it is mainly a matter of culture.

Nonetheless, the Census Bureau’s 2022 American Community Survey (ACS) provides the self-reported racial identity of Hispanics: 22.5 million single-race Hispanics identified only as “some other race.” This group mostly includes those who wrote in a Hispanic origin or nationality as their race. Another 10.7 million identified as White. Fewer Hispanics identified as American Indian (1.5 million), Black (1.0 million) or Asian (300,000).

Multiracial Hispanics

Another roughly 27.5 million Hispanics identified as more than one race in 2022, up from just 3 million in 2010.

Growth in the number of multiracial Hispanics comes primarily from those who identify as White and “some other race.” That population grew from 1.6 million to 24.9 million between 2010 and 2022. The number of Hispanics who identify as White and no other race declined from 26.7 million to 10.7 million.

The sharp increase in multiracial Hispanics could be due to several factors, including changes to the census form introduced in 2020 that added more space for written responses to the race question and growing racial diversity among Hispanics. This explanation is supported by the fact that almost 25 million of the Hispanics who identified as two or more races in 2022 were coded as “some other race” (and wrote in a response) and one of the specific races (such as Black or White). About 2.6 million Hispanics identified with two or more of the five major races offered in the census.

Changes for the 2030 census

The 2030 census will combine the race and ethnicity questions , a change that other federal surveys will implement in coming years. The new question will add checkboxes for “Hispanic or Latino” and “Middle Eastern or North African” among other race groups long captured in Census Bureau surveys.

Officials hope the changes will reduce the number of Americans who choose the “Some other race” category, especially among Hispanics . However, it’s worth noting that public feedback has raised a variety of concerns, including that combining the race and ethnicity questions could lead to an undercount of the nation’s Afro-Latino population .

Is there an official definition of Hispanic or Latino?

In 1976, Congress passed a law that required the government to collect and analyze data for a specific ethnic group: “Americans of Spanish origin or descent.” That legislation defined this group as “Americans [who] identify themselves as being of Spanish-speaking background and trace their origin or descent from Mexico, Puerto Rico, Cuba, Central and South America, and other Spanish-speaking countries.” This includes around 20 Spanish-speaking nations from Latin America and Spain itself, but not Portugal or Portuguese-speaking Brazil.

To implement this law, the U.S. Office of Management and Budget (OMB) developed Statistical Policy Directive No. 15 (SPD 15) in 1977, then revised it in 1997 and again in March 2024. In the most recent revision, OMB updated racial and ethnic definitions when it announced the combined race and ethnicity question. The current definition of “ Hispanic or Latino ” is “individuals of Mexican, Puerto Rican, Salvadoran, Cuban, Dominican, Guatemalan, and other Central or South American or Spanish culture or origin.”

The Census Bureau first asked everybody in the U.S. about Hispanic ethnicity in 1980. But it made some efforts before then to count people who today would be considered Hispanic. The Census Bureau also has a long history of changing labels and shifting categories . In the 1930 census, for example, the race question had a category for “Mexican.”

The first major attempt to estimate the size of the nation’s Hispanic population came in 1970 and prompted widespread concerns among Hispanic organizations about an undercount. A portion of the U.S. population (5%) was asked if their origin or descent was from the following categories: “Mexican, Puerto Rican, Cuban, Central or South American, Other Spanish” or “No, none of these.”

This approach indeed undercounted about 1 million Hispanics. Many second-generation Hispanics did not select one of the Hispanic groups because the question did not include terms like “Mexican American.” The question wording also resulted in hundreds of thousands of people living in the Central or Southern regions of the U.S. being mistakenly included in the “Central or South American” category.

By 1980, the current approach – in which someone is asked if they are Hispanic – had taken hold, with some changes to the question and response categories since then. In 2000, for example, the term “Latino” was added to make the question read, “Is this person Spanish/Hispanic/Latino?”

What’s the difference between Hispanic and Latino?

“Hispanic” and “Latino” are pan-ethnic terms meant to describe – and summarize – the population of people of that ethnic background living in the U.S. In practice, the Census Bureau often uses the term “Hispanic” or “Hispanic or Latino.”

Some people have drawn sharp distinctions between these two terms . For example, some say that Hispanics are from Spain or from Spanish-speaking countries in Latin America, which matches the federal definition, and Latinos are people from Latin America, regardless of language. In this definition, Latinos would include people from Brazil (where Portuguese is the official language) but not Spain or Portugal.

Pan-ethnic labels like Hispanic and Latino, though widely used, are not universally embraced by the population being labeled. Our 2023 National Survey of Latinos shows a preference for other terms to describe identity: 52% of respondents most often described themselves by their family’s country of origin, while 30% used the terms Hispanic, Latino, Latinx or Latine, and 17% most often described themselves as American.

The 2023 survey also finds varying preferences for pan-ethnic labels: 52% of Hispanics prefer to describe themselves as Hispanic, 29% prefer Latino, 2% prefer Latinx, 1% prefer Latine and 15% have no preference.

What is ‘Latinx’ and who uses it?

Latinx is a pan-ethnic identity term that has emerged in recent years as an alternative to Hispanic and Latino. Some news and entertainment outlets, corporations , local governments and universities use it to describe the nation’s Hispanic population.

However, its popularity has brought increased scrutiny in the U.S. and abroad . Some critics say it ignores the gendered forms of Spanish language, while others see Latinx as a gender- and LGBTQ+-inclusive term . Adding to the debate, some state lawmakers favor banning the use of the term entirely in government documents; Arkansas has done so already .

A 2023 survey found that awareness of Latinx has doubled among U.S. Hispanics since 2019, with growth across all major demographic subgroups. Still, the share of Hispanic adults who use Latinx to describe themselves is statistically unchanged: In 2023, 4% said they use it, compared with 3% in 2019.

Latinx is also broadly unpopular among Latinos who know the term. Three-in-four Latino adults who are aware of Latinx say the term should not be used to describe Hispanics or Latinos.

The emergence of Latinx coincides with a global movement to introduce gender-neutral nouns and pronouns into many languages that have traditionally used male or female constructions. In the U.S., Latinx first appeared more than a decade ago, and it was added to a widely used English dictionary in 2018.

What is ‘Latine’ and who uses it?

Latine is another pan-ethnic term that has emerged in recent years. Our 2023 survey found that 18% of U.S. Hispanics have heard of the term.

Similar to familiarity with Latinx, awareness of Latine varies by age, education and sexual orientation. Among Latinos, awareness of Latine is highest among those ages 18 to 29 (22%), college graduates (24%) and lesbian, gay and bisexual adults (32%).

How do factors like language, parental background and last name affect whether someone is considered Hispanic?

Many U.S. Hispanics have an inclusive view of what it means to be Hispanic:

• 78% of Hispanic adults said in a 2022 Center survey that speaking Spanish is not required to be considered Hispanic. English-dominant Hispanics were more likely than Spanish-dominant Hispanics to say so (93% vs. 64%).
• 33% of Hispanic adults said in a 2019 survey that having two Hispanic parents is not an essential part of what being Hispanic means to them. Another 34% said it was important but not essential and 32% said it was essential.
• 84% of Hispanic adults said in a 2015 survey that having a Spanish last name is not required.

Views of Hispanic identity may change in the coming decades as broad societal changes, such as rising intermarriage rates, produce an increasingly diverse and multiracial U.S. population .

Today, many Hispanic families include people who are not Hispanic:

Spouses: Among all married Hispanics in 2022, 22% had a spouse who is not Hispanic. And in a 2023 Center survey , 27% of Hispanics with a spouse or partner said their spouse or partner is not Hispanic.

Newlyweds: In 2022, 30% of Hispanic newlyweds married someone who is not Hispanic. Among them, 41% of those born in the U.S. married someone who is not Hispanic, compared with 11% of immigrant newlyweds, according to an analysis of ACS data.

Parents: Our 2015 survey found that 15% of U.S. Hispanic adults had at least one parent who is not Hispanic. This share rose to 29% among the U.S. born and 48% among the third or higher generation – those born in the U.S. to parents who were also U.S. born.

What role does skin color play in whether someone is Hispanic?

In surveys like those from the Census Bureau, skin color does not play a role in determining who is Hispanic or not. However, as with race, Latinos can have many different skin tones. A 2021 Center survey of Latino adults showed respondents a palette of 10 skin colors and asked them to choose which one most closely resembled their own.

Latinos reported having a variety of skin tones, reflecting the diversity within the group. Eight-in-ten Latinos selected one of the four lightest skin colors. By contrast, only 3% selected one of the four darkest skin colors.

A majority of Latino adults (57%) say skin color shapes their daily life experiences at least somewhat. Similar shares say having a lighter skin color helps Latinos get ahead in the U.S. (59%) and that having a darker skin color hurts Latinos’ ability to get ahead (62%).

Are Afro-Latinos Hispanic?

Afro-Latino identity is distinct from and can exist alongside a person’s Hispanic identity. Afro-Latinos’ life experiences are shaped by race, skin tone and other factors in ways that differ from other Hispanics. While most Afro-Latinos identify as Hispanic or Latino, not all do, according to our estimates based on a survey of U.S. adults conducted in 2019 and 2020.

In 2020, about 6 million Afro-Latino adults lived in the U.S., making up about 2% of the U.S. adult population and 12% of the adult Latino population. About one-in-seven Afro-Latinos – an estimated 800,000 adults – do not identify as Hispanic.

Are Brazilians, Portuguese, Belizeans and Filipinos considered Hispanic?

Officially, Brazilians are not considered Hispanic or Latino because the federal government’s definition applies only to those of “Spanish culture or origin.” In most cases, people who report their Hispanic or Latino ethnicity as Brazilian in Census Bureau surveys are later recategorized – or “back coded” – as not Hispanic or Latino . The same is true for people with origins in Belize, the Philippines and Portugal.

An error in how the Census Bureau processed data from a 2020 national survey omitted some of this coding and provided a rare window into how Brazilians (and other groups) living in the U.S. view their identity.

In 2020, at least 416,000 Brazilians — more than two-thirds of Brazilians in the U.S. — described themselves as Hispanic or Latino on the ACS and were mistakenly counted that way. Only 14,000 Brazilians were counted as Hispanic in 2019, and 16,000 were in 2021.

The large number of Brazilians who self-identified as Hispanic or Latino highlights how their view of their own identity does not necessarily align with official government definitions. It also underscores that being Hispanic or Latino means different things to different people .

How many people with Hispanic ancestry do not identify as Hispanic?

Of the 42.7 million adults with Hispanic ancestry living in the U.S. in 2015, an estimated 5 million people, or 11%, said they do not identify as Hispanic or Latino , according to a 2015-16 Center survey. These people aren’t counted as Hispanic in our surveys.

Notably, Hispanic self-identification varies across immigrant generations. Among immigrants from Latin America, nearly all identify as Hispanic. But by the fourth generation, only half of people with Hispanic heritage in the U.S. identify as Hispanic.

Note: This is an update of a post originally published on May 28, 2009.

• Hispanic/Latino Identity
• Racial & Ethnic Identity

Mark Hugo Lopez is director of race and ethnicity research at Pew Research Center .

Jens Manuel Krogstad is a senior writer and editor at Pew Research Center .

Jeffrey S. Passel is a senior demographer at Pew Research Center .

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