quantitative research history

An Introduction to Quantitative Research Methods in History

University of Plymouth

[email protected]

The workshop outlined below introduced students of history to the basic skills required by all historians to evaluate and present quantitative data in summary statistical and graphical form using the SPSS statistical package to manipulate, analyse and present British general election data 1945-2001. The workshop aimed to introduce students to some elementary techniques of quantitative history as an essential and necessary skill for those interested in the past and to equip students of history with transferable skills appropriate for the modern job market. The pedagogic aims included student awareness of interdisciplinary research, increased understanding and engagement with political science sources, and the development of confidence to handle quantitative historical evidence.

.01 Engaging Students in Quantitative Research

"All those interested in studying society, past or present, need to take charge of quantitative data: to command it rather than be the slave of a seeming authority of numbers emerging from documents or the writings of a small body of numerically inclined researchers" (Hudson 2000:xvii). As Hudson points up, most historians and history students have to accept uncritically the research findings that underpin many historical arguments because they lack the skills necessary to evaluate quantitative evidence. Students of history especially need basic quantitative research skills to enable them to access the treasure-trove of social, economic and political data that has been amassed in recent decades. Indeed, as projects, for example, as those funded by the Leverhulme Trust, such as the building of a substantial collection of computerised nineteenth century census data, come to fruition and the use of the Geographical Information System which allows data to be spatially mapped, historians and students of history need to learn the skills to access, manipulate, analyse and present quantitative data and thereby widen the scope of the evidence base that supports the particular argument they present, whether it be in journal articles or student essays and dissertations. Furthermore, the vast majority of students of history will not become historians. However, no matter which profession they choose, it will certainly involve the manipulation, analysis and effective display of both numeric and textual data.

.02 The Workshop in Qualitative Research Methods in History

For all the above reasons it is important that students of history engage in quantitative research methods and that those teaching history integrate multi-media technology into the undergraduate history curriculum. With this pedagogic aim in mind the workshop "Presentations of History: An Introduction to Quantitative Research Methods in History" has been introduced as a part of a Presentation of History module for stage one history students at the University of Plymouth. The workshop evolved out of my PhD research which employed a multi-disciplinary approach to research into post-war British electoral behaviour and melded the quantitative research methods of the political scientist and the traditional textual based research methods of the historian in an attempt to provide more nuanced explanations of political behaviour than individually the disciplines of history or political science have so far provided.

Course Aims and Objectives

The overall aims of the workshop were to introduce first year history students to the basic skills of presenting quantitative data in summary statistical, graphical and tabular form. The workshop aimed to enable history students to combine quantitative evidence, that has been gathered and produced using a science based approach to research methods favoured by political scientists, social scientists and, indeed increasingly in some branches of history, with the text-based interpretative evidence of the historian. In short, the workshop is an exercise in multi-disciplinary research, taught by a combination of lectures and hands-on computer laboratory work. However, there are important issues that the history student needed to confront concerning what is acceptable as knowledge. An explanation in one discipline is not necessarily accepted as warrantable knowledge in another discipline. Thus, the theory that underpins the political science approach to the study of electoral behaviour was outlined and the students were introduced to the concepts of ontology, epistemology and the consequent methodological differences between how historians and political scientists go about their work and produce knowledge about political behaviour. This divergence was explained in terms of the interpretative, subjective, impressionistic, value-laden and non-generalisable explanations and theories that some positivists accuse historians of producing. This was then contrasted with the ostensibly objective, precisely measured, accurately and unambiguously defined, value free and generalisable explanations, theories, predictions and universal laws of cause and effect that political scientist aspire to, albeit using what some historians would consider often fragmentary, distorted or biased data.

The upshot of the argument presented to the students was that each approach has its particular strengths and weaknesses. Nevertheless, each can tell us something about a political phenomenon. Indeed, in the study of electoral behaviour political scientists increasingly recognise that quantitative approaches can and should be complemented by qualitative techniques as used by the historian in order to explain contextual effects that are intrinsically difficult to measure. Likewise, in the discipline of history, especially political history, there is a recognition of the need not only to be able to analyse and present succinctly trends in electoral behaviour, but also the need to be able to engage more meaningfully in scholarly argument and debate with political scientists (see Dunleavy 1990, Kavanagh 1991, Ramsden 1992, Devine 1994,Rallings and Thrasher 1997, Bale 1999).

No prior knowledge of computers, statistics or social research methods on the part of the students was assumed or required for participation in the workshop. The workshop aimed to enable students to evaluate quantitative evidence, analyse and display raw quantitative data, integrate quantitative and qualitative approaches mindful of their respective strengths and weaknesses, and to equip students with the skills necessary to mine the multiplicity of political, social and economic data that remains inaccessible without such skills. The intended learning outcomes of the workshop included increased student awareness of inter-disciplinary research and an enhanced ability to engage with political science sources. The module aimed to improve a history student's ability to evaluate and present quantitative evidence, and to combine written work that is clearly structured and based upon wide reading of political history sources with quantitative evidence presented in appropriate graphical, tabular and statistical form. Thereby the confidence of history students in the handling quantitative historical data will be improved. The assessed skills element of the workshop was based on the ability to meld quantitative and qualitative evidence appropriately and effectively in an essay in response to a specific question on post-war voting behaviour in Britain. The delivery of the workshop was over six, two-hour sessions, in the form of lectures that preceded supervised hands-on computer laboratory sessions accompanied by step-by-step guides to data entry.

Course Schedule

The first lecture entailed an overview of positivism and the quantitative research methodology that informs the methods used by political scientists in their studies of electoral behaviour. The concepts of ontology and epistemology and what they mean in terms of acceptable methods of producing warrantable knowledge in the social sciences was contrasted with the interpretative approach of the political historian. Students were made aware of the seeming incommensurability of the quantitative and qualitative perspectives. An argument was then presented that each approach has its strengths and weaknesses and that each can tell us something about political behaviour. The main points of the lecture and a bibliography of texts that dealt with the quantitative/qualitative debate, were outlined in a student handout. The emphasis of the lecture then turned to the presentation of history and how numbers are used and can be used by historians as historical evidence. Students were made aware of the power of numbers and how they can be selected, reconstituted, redefined, reordered and displayed to suit the purposes of those that gather and use them.

In the following lecture the students were introduced to descriptive statistics and some elementary statistical techniques that arrange and display quantitative data so that basic questions can immediately be asked of the data. It became increasingly evident to the students that a table or a figure that represented the character of a mass of electoral data was extremely useful, and that by some elementary processing of figures using a statistical programme on the computer, simple measures of average or typical experience gave some notion of the range of variation in voting behaviour over time and space. It was shown that at its simplest level, quantification brought to history the ability to summarise large bodies of data, to display such data effectively and to express typical measures and values. Students were made aware of the variety of types of data and types of numbers and what this meant in terms of the kind of meaningful analysis they can be subjected to. This was followed by an overview of the growth of quantitative history, its advantages and disadvantages, and the uses of quantitative methods in the academic discipline of history were exemplified.

The third lecture had as its focus the different types of 'average' that are used to summarise information from a larger set of numbers, in this case electoral data. Given that the main purpose of statistics is to describe sets of numbers briefly and accurately it was brought to the students' attention that the so-called average can be misleading and that there can be a large departure from it. Indeed, how, for example, the aggregation of electoral data can disguise significant variations in actual voting behaviour, and that the mere indication of the central point of a distribution of numbers only allows a partial view, only an indication of typical patterns of electoral behaviour. From the pros and cons of these measures of central tendency the lecture then turned to measures of dispersion and how these descriptive statistical techniques allowed the researcher to gain a broader picture of the data and facilitated the description of any variation, i.e. the atypical so often of primary interest. This lecture ended with a recap of descriptive statistics and a very brief overview of what inferential statistics are and what they can and cannot do.

The following two lectures looked at electoral change in Britain since 1945. First, how historians have interpreted voting patterns as an expression of underlying social forces and thereby attributed developments in modern British political history to fundamental shifts in the social structure and social attitudes, and how political history is characterised by a sociological approach with electoral behaviour regarded as a barometer of social change. Studies of voting behaviour at British general elections in the 1945-1970 period were then reviewed and an era of two-party dominance, electoral stability, strong party identification, and class and party alignment was presented. In the following lecture changes in the voting behaviour of the British electorate and how these changes have been measured, evidenced and explained by political historians and political scientists was discussed. The lecture explored the decline in support for the two major British political parties and the concomitant rise of the minor parties, increased regional variations in the distribution of each party's share of the vote, increased electoral volatility and the debates that accompany these political phenomenon.

In the fifth and final lecture the requirements of the workshop's essay assignment were outlined. The general format and presentation of the essay, what was required in terms of citation and referencing of quantitative and qualitative sources of information and data. Indeed, how to cite and reference data from various sources including electronic, how to cite sources of data used in tables and charts, and how to compile a list of tables and charts and there contents. The essay assignment required the students to meld written work based on research of textual sources with quantitative evidence. The students were required to answer a question on electoral behaviour in Britain at post-war general elections and to integrate appropriate charts, graphs and tables into the text in order to support their central argument. Handouts accompanied the lectures and summarised each particular lecture and highlighted recommended reading. A workshop descriptor was given to each student in which the aims, contents, and requirements of the course were outlined, and a bibliography and glossary of terms included.

At the last of the six weekly sessions the students had an opportunity to present preliminary drafts of their work and to resolve any difficulties they may have regarding the assignment, and of course to complete any unfinished graphs, tables, charts and editing of output they had been working on during the supervised data processing sessions on the computer.

In the hands-on computer sessions the students were provided with a step-by—step guide and close supervision where necessary, that enabled students without any prior knowledge of computing to enter and analyse the electoral data provided and to create a number of summary statistical charts, figures and tables. At the end of the six one-hour computer sessions most students had completed many of the charts etc. required and had only to write up the essay and integrate the quantitative evidence appropriately.

Course Themes and Output

The principal themes of the computer sessions included the assembly and handling of data sets, the analysis of data and the presentation of findings. Students used the SPSS statistical package to manipulate and analyse British general electoral data 1945-2001. At the end of the workshop the students had gained the basic skills needed to assemble data sets by having entered their own data, had prepared data by assigning names and value labels to variables, and analysed data using a variety of elementary but nonetheless very useful statistical methods. The students had learnt how to log-on and open an SPSS data file, to enter data, create, define and label variables, to run Frequency Analysis and obtain descriptive statistical information about variables, to create new index variables, to create charts and graphs, to customise charts and place them in a word document, to print selections from a data set, to select cases and split files, to exit from SPSS and to save and retrieve the data set.

The output generated by the students included; a multiple-line graph that depicted trends in each party's share of the vote at British general elections, and a line graph that depicted changes over time in the two-party share of the vote i.e. the sum of the two major British parties, Labour and Conservative. The students used the electoral data to create an index variable that measured the level of net electoral volatility at each successive general election and presented the results in the form of a bar graph, similarly they created an index variable to measure trends in class voting at British general elections. Changes in the support for the Liberal Party were charted in a line graph that depicted the party's percentage share of the vote at successive elections and contrasted in a further chart with the percentage seats with which the first past-the-post system rewards minor parties. The students also produced appropriate tables to accompany the charts and graphs. These were the minimum requirements of the workshop in order for students to be able to visually present quantitative data in summary statistical, tabular and graphical form and adequately evidence their response to an essay question on British voting behaviour in the 1945-2001 period.

Course Evaluation

On completion of the module students received module evaluation forms that gave them the opportunity to anonymously express their views on the quality of the module. The completed forms, returned to the faculty office by the students, requested that they circle the appropriate number for each of the following questions using a scale : 1 = unsatisfactory, 2 = below average, 3 = satisfactory, 4 = good, 5 = very good/excellent.

• Q 1. Were the aims and objectives/learning outcomes of the module presented clearly?
• Q 2. Were the assessment requirements made clear and fully discussed?
• Q 3. Was the library provision adequate for the module?
• Q 4. On a week-by-week basis, was the module well-organised and effectively run?
• Q 5. Was the module taught in a stimulating way?
• Q 6. If known, was your written work returned punctually, with adequate feedback?
• Q 7. Did the module deliver what it promised, in terms of content, aims, skills etc?
• Q 8. All things considered, what is your verdict on this module?

The evaluation form also invited students to comment upon what they particularly liked or disliked about the module, and to make suggestions for future improvements. Feedback from the students, (sixteen out of twenty-seven returned their module evaluation forms) was encouraging. As can be seen in the statistics outlined in the Table 1, the indicators reflect a positive experience by the students. Library provision apart, the means for six of the eight indicators were equal to or more than 4, categorised as good on the scale. More specifically, in terms of how the module delivered on its promised content, aims and skill development, 31% of the students reported satisfactory, 50% good, and the remainder excellent (Table 8). Furthermore, many students commented upon the improvement to their IT skills that the module had made. Less pleasing was that 56% of students thought that library provision of core module texts was only below average to satisfactory (Table 4), a point reiterated in the comments made by students and one which will be addressed. Although the majority of the students reported that the module was taught in a stimulating way and was well organised and effectively run (Tables 4 and 5), there were nonetheless comments made by some students about the limited number of computers with the SPSS program available for their use on campus and the difficulty for those students who lived off campus in that few had computers at home let alone ones with the SPSS program. In the main students were able to complete the data analysis requirements of the module during supervised laboratory sessions and had only to integrate their charts, tables and figures into the text of their essays in their own time. Clearly, for slower students and especially those living off campus without access to a computer completion of the module meant longer hours at the university and in some cases extra costs in travel and time. Nevertheless, these are problems that can be overcome by an increased proportion of teaching time allocated to hands on supervised computer laboratory and a corresponding decrease in that allocated to lectures. These problems apart, the statistics outlined in Tables 1-9 , and the general tone of the comments reflected a very positive experience by the students.

Assessment of the module was determined by the student's ability to meld quantitative and qualitative evidence appropriately and effectively in answer to a specific question on post-war voting behaviour in Britain at parliamentary elections. The assignment required students to answer one of a choice of questions in no more than one thousand words plus charts, graphs and tables that they considered appropriate to substantiate their argument. The essays had to be supported with footnotes/endnotes where appropriate and a bibliography in all cases. Citation of all sources of data used in tables and charts was also required. The number of students who completed the module was twenty-two, the remaining five students failed to present work for assessment. The minimum mark awarded was 41% and the maximum mark 70%. The mean mark achieved was 55.6% and the standard deviation of the marks awarded 8.2%. Every one of the twenty-two students who completed the module surpassed the minimum pass rate of 40%. One student scored a first class grade of 70%+, seven students achieved upper-second grades of 60-69%, eight students lower second grades 50-59%, and six students third class grades 40-49%.

.03 Using Quantitative Skills in Subsequent Courses

These same students are now half way through the second year of their BA History degree course and have submitted essays that show judicious use of their newly acquired quantitative skills. When and where appropriate tables, charts and graphs have been incorporated to evidence arguments and illustrate points that hitherto required extensive explication. For example, the second year module, Ordinary Lives; Themes from the Social History of Early Modern England, required students to investigate through primary source material aspects of everyday life in a particular Devon village/parish and examine the structure of society and the variety of institutional frameworks which supported that community. The patterns and trends in birth, death, marriage, work, religious affiliation, indeed the gamut of demographic, social and economic data in parish records etc. have been exploited in a way hitherto denied to history students without quantitative skills. The students have been enabled to compare and contrast the national picture, to and with, the particular trends and patterns they have discovered, and thereby have been stimulated to investigate and explain the atypical, or confirm accepted orthodoxies. The ability to use basic descriptive statistical analysis and summary presentation of data has provided them with a source of historical evidence largely denied to them in the past and enhanced the quality of their historical research skills. The essays submitted have added weight to the assertion that the "primary business of the historian is to explain how the particular occurred, and [that] to deny the use of statistics in this quest is to dismiss a useful explanatory tool" ( Nossiter 1996:326).

.04 Additional Motivation for Teaching Quantitative Research

A number of the factors that had motivated the introduction of this module into the history curriculum have been expounded above. In addition, there had been for some time encouragement for increased synergy between the History and the Politics departments. Under the umbrella of the Politics Department, the University of Plymouth has a nationally and internationally renowned Local Government Chronicle Election Centre that compiles, analyses and publishes information relating to all aspects of electoral politics in Britain. Among the centre's currently funded projects are; a project on local democracy, a role as the British partner in a multi-national study of electoral participation in the European Union, and the development of a database of post-war local election results. The centre's research methodology is naturally predominantly quantitative data analysis of aggregate voting data, however, the qualitative approach of the historian in the analysis of electoral behaviour has had an increasingly important role to play in some areas of its research. Collaborative research, whether between a history department and those of sociology, economics or politics, necessitates post-graduate historians with quantitative skills and the promotion and nurture of this has in part motivated the introduction of this module to the history curriculum.

An equally important motivation was that the combination of subjects that deal with very similar material and which attempt to resolve very similar problems, albeit from different intellectual perspectives is generally accepted as being beneficial to both subjects. In the case of the disciplines of history and that of politics there is much in the study of each that complements the other, not least the fact that their combination in this module has brought to the student's attention the significance of theory and concepts in the study of modern British political history. Indeed, the combination of these approaches by the module has enhanced the links between empirical and analytical studies, between political history and political theory and thereby has widened a student's understanding of the historical and political themes that shape modern Britain.

The motivation for the introduction of the module was also influenced by planned structural change at the University of Plymouth whereby departments such as history, which is situated on a satellite campus is to be relocated to the main campus in order that the scope of combined honours courses may be expanded. This development is a product of the increasing need for universities to engage in inter-disciplinary research and thereby attract funding, and also to enable the university to remain attractive to potential students by offering innovative courses and modules that develop skills relevant to the modern economy.

Quantification and the use of computers in historical analysis is well established in many areas of historical research however there is still much prejudice and antipathy towards quantification by many historians. Pat Hudson delivers a timely counterblast when she writes:

It is perhaps surprising, given the greater opportunities which quantification presents for writing histories of the mass of the population, that so many historians of popular culture and society feel so negative about it. Personal papers and official records leave the historian with more information on the elites than on the working classes, on adult males than on women and children, on settled natives rather than on the migrant or ethnic minorities and on political and social activists rather than on the more passive majority of the population. Greater quantification can help to make best use of the documentation from the past particularly where that documentation deals with large numbers and with ordinary people (Hudson 2000:7).

Clearly, no such prejudices are prevalent within either the History Department or the Politics Department at the University of Plymouth, whose respective heads of department have encouraged and enabled this workshop in quantitative history to come to fruition. The introduction of this module, as evidenced above, has enhanced the research skills of these history students, widened the evidential base of their essays, encouraged wider reading and consideration of sources and materials from associated disciplines, and brought to their attention the significance of concepts and theory in the study of history. Moreover, it has illustrated the ontological and epistemological differences between the disciplines of history and social science disciplines and hopefully alerted them to the manifold possibilities that inter-disciplinary research presents. On a more prosaic but equally important level it has improved their IT skills in what has become an increasingly competitive post-graduate job market.

.05 Bibliography

Bale, T. (1999). "The logic of no alternative? Political Scientists, Historians and the Politics of Labour's Past," British Journal of Politics and International Relations 1 (2): 192-204.

Dunleavey, P. (1990). "Mass Political Behaviour: Is There More to Learn?" Political Studies XXXV111: 453-469.

Devine, F. (1994). "Learning More about Mass Political Behaviour; Beyond Dunleavy," in D. Broughton, D. Farrell, D. Denver, and C. Rallings, (eds). British Elections and Parties Yearbook 1994 , London: Frank Cass, pp. 215-228.

Hudson, P. (2000). History by Numbers : An Introduction to Quantitative Approaches . London: Arnold.

Kavanagh, D. (1991). "Why Political Science Needs History." Political Studies , 479-495.

Nossiter, T. (1996). "Survey and Opinion Polls." B. Brivati, J. Buxton, and A. Seldom, (eds). The Contemporary History Handbook , Manchester: Manchester University Press, pp. 326-341.

Rallings,C. and M. Thrasher. (1997). Local Elections in Britain . London: Routledge.

Ramsden, J. (1992). "History Journals for Political Scientists." Political Studies XL: 554-560.

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Quantitative history

Quantitative history involves the use of methods of statistical analysis drawn from the social sciences, but used on historical data. It was posited by its exponents as providing a way for historians to obtain more 'scientific' results – for instance, allowing the analysis of census returns to obtain accurate breakdowns of the population at a particular time, rather than relying on the qualitative but selective reading of a variety of different sources which had characterised the practise of history hitherto. Its emergence in the 1960s coincided both with the increasing popularity of social science methodology and with the dawning of the computer age. Critics have suggested that quantitative history makes assumptions about the nature of historical data ignore the factors influencing its production, and the cultural turn has called into question more broadly the epistomology of the social sciences, but particularly in economic history (cliometrics) the application of quantitative methods has become integrated as part of a broader historical approach.

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Quantitative Research

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Quantitative research methods are concerned with the planning, design, and implementation of strategies to collect and analyze data. Descartes, the seventeenth-century philosopher, suggested that how the results are achieved is often more important than the results themselves, as the journey taken along the research path is a journey of discovery. High-quality quantitative research is characterized by the attention given to the methods and the reliability of the tools used to collect the data. The ability to critique research in a systematic way is an essential component of a health professional’s role in order to deliver high quality, evidence-based healthcare. This chapter is intended to provide a simple overview of the way new researchers and health practitioners can understand and employ quantitative methods. The chapter offers practical, realistic guidance in a learner-friendly way and uses a logical sequence to understand the process of hypothesis development, study design, data collection and handling, and finally data analysis and interpretation.

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Babbie ER. The practice of social research. 14th ed. Belmont: Wadsworth Cengage; 2016.

Google Scholar  

Descartes. Cited in Halverston, W. (1976). In: A concise introduction to philosophy, 3rd ed. New York: Random House; 1637.

Doll R, Hill AB. The mortality of doctors in relation to their smoking habits. BMJ. 1954;328(7455):1529–33. https://doi.org/10.1136/bmj.328.7455.1529 .

Article   Google Scholar  

Liamputtong P. Research methods in health: foundations for evidence-based practice. 3rd ed. Melbourne: Oxford University Press; 2017.

McNabb DE. Research methods in public administration and nonprofit management: quantitative and qualitative approaches. 2nd ed. New York: Armonk; 2007.

Merriam-Webster. Dictionary. http://www.merriam-webster.com . Accessed 20th December 2017.

Olesen Larsen P, von Ins M. The rate of growth in scientific publication and the decline in coverage provided by Science Citation Index. Scientometrics. 2010;84(3):575–603.

Pannucci CJ, Wilkins EG. Identifying and avoiding bias in research. Plast Reconstr Surg. 2010;126(2):619–25. https://doi.org/10.1097/PRS.0b013e3181de24bc .

Petrie A, Sabin C. Medical statistics at a glance. 2nd ed. London: Blackwell Publishing; 2005.

Portney LG, Watkins MP. Foundations of clinical research: applications to practice. 3rd ed. New Jersey: Pearson Publishing; 2009.

Sheehan J. Aspects of research methodology. Nurse Educ Today. 1986;6:193–203.

Wilson LA, Black DA. Health, science research and research methods. Sydney: McGraw Hill; 2013.

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Quantifying Interdisciplinary History - The Record of (nearly) fifty years.

When the Journal of Interdisciplinary History began publication in 1970, it was not present at the founding of quantitative history. That transformation had already been underway for nearly two decades, led by an early generation of quantitative political historians, by the Annales school in France, by historical demographers in France, England, and the U.S., and by generations of economic historians who had studied prices and moneys, among others. Other publications had emerged, beyond the Annales E.S.C. : the Historical Methods Newsletter , for example, began publication in 1967. The great innovation by the editors of JIH lay elsewhere, in a broad conception of what constituted an interdisciplinary approach to historical research, and the energy to find the authors who were engaged in that research and who were ready to publish.

The development of quantitative approaches to history, especially during the critical decades of the 1950s through the 1970s, is well documented elsewhere, and is not the main subject of this article. Anderson’s brief exploration, and more developed discussions by Bogue, Sewell, and de Vries, among others, describe that experience. Historians interested in new sources of information and in social science theory and approaches, and social scientists interested in historical problems and long-term issues converged in the 1950s and the 1960s to argue for theoretical and empirical approaches that made increasing use of quantitative materials and statistical analyses. The increased quantification of social science, and the ever-greater capacity for computation both encouraged and enabled this transformation, something that has continued over the last half-century and longer as computational technology improved. Along the way, researchers have increased the size of their data sets, the sophistication of their statistical analysis, and the breadth of topics they engage, moving from analysis of elections and legislators, to large samples of census data, to integration with information that is spatial and environmental, all with ever-growing computational requirements. 1

The development of quantitative approaches to historical research and analysis was not without its critics, and its experience has not been one of continuous growth. As early as 1962, Bridenbaugh criticized quantitative approaches in his presidential address to the American Historical Association, alongside his argument that urban-born historians (probably a euphemism for immigrants and their children) were unable to understand American life. Lawrence Stone raised a different point in his 1979 article, “The Revival of Narrative,” where he challenged the change of argument in historical publications away from a narrative organization to one that dealt with structures and questions, and the need for historical discourse to return to arguments that were largely temporal. More recently, William Sewell has eloquently described his own journey into and out of quantitative approaches, arguing for the value of what he calls cultural strategies drawn from anthropology. Whatever the nature of the criticism, in the broader field of historical research the hopes for quantitative approaches have not been achieved. While there is still plenty of research published each year that makes use of quantitative data and methods, it never succeeded in becoming a dominant force among North American historians, and it has probably diminished in importance since the 1980s, with the exception of a number of sub-fields, more broadly those that align with social scientific approaches, and especially historical demography and economic history. The two forces I have just described animate the history of quantitative approaches to historical research: it is a movement that began in the 1950s has been sustained in some ways, but has flagged in others, especially in North America. 2

Although not exclusively engaged by the quantitative, the Journal has from its very beginning supported the effort to quantify historical evidence. This article looks at that history, both on its own, and in the context of broader developments in historical methodology. It asks how JIH engaged with and published articles that used quantitative data, how that engagement and publication has changed over nearly fifty years, and how developments in JIH are related to broader changes in the presentation of historical data. Looked at through the lens of the Journal , quantitative methods in interdisciplinary history have both changed dramatically over the past fifty years, and stayed remarkably the same. That will be the story that I tell in this article. In doing so I will examine the ways that quantitative approaches appear in the corpus of articles published in the Journal of Interdisciplinary History , and the ways that they fit into the larger pattern of development of quantitative approaches to historical scholarship over that same long period of time.

The argument I make revolves around three elements. The first two are the rise and fall of quantitative methods in historical research, a fact that the broader history I have summarized earlier already shows. I will show the extent to which quantitative approaches changed over time, both in terms of their proportion of articles in the Journal and in the kinds of methods they employ. While the proportion of quantitative articles published in the Journal has not declined noticeably over time, some attributes of the Journal’s publication record have changed substantially, and that is both interesting and important. I reveal that information by looking beyond the proportion that are quantitative and the methods they use, to the topics covered and some characteristics of authorship, especially the number of authors, their place in the profession, and the country where they work, to relate changes in the nature of quantitative methods to transformations in the professions of historical research, and in the character of the Journal itself.

The JIH Corpus

The Journal of Interdisciplinary History has published 948 items in its first 49 volumes, not including review essays, book reviews, and corrections for errata. In Table 1 , I classify those 948 items into five categories, reflecting the Journal’s mix of content. The single largest group of published items (779 in all, or 82 percent) I call Research Articles, a category that combines both mainline articles and Research Notes, and includes content that is both substantive and methodological, and articles that appeared in special topical issues or in general issues. Research notes have diminished in frequency in recent years, and represent a mix of substantive and methodological research, so I have not attempted to distinguish them.

Type of Article, by Decade, Excluding Reviews and Errata

One of the special characteristics of the Journal is its relatively frequent practice of publishing topical issues, something that began with its first volume, continues through a series based on conferences organized by the editors and culminates with topical numbers organized by specialists in a field. These issues generally included an introduction, and they sometimes included comments on the articles. The editors also published responses to articles and reviews, and replies to those responses, under the category of “Comment and Controversy.” Finally, I classify a small group of publications as “Synthetic” articles, each more a synthesis and evaluation of prior literature than original research 3

The distribution of articles between quantitative and non-quantitative ( Table 2 ) has also changed over the years. I classified an article as quantitative if it included quantitative information, drew conclusions from those data, and that made use of the data beyond one or two context-setting tables or graphs reporting information published elsewhere. Overall, nearly two thirds of all Research and Methods articles had quantitative content, but the proportion varied from decade to decade. The Journal published the smallest proportion of quantitative material in the 1990s and 2000s, but there has been a sharp rebound in the 2010s, with quantitative content in three-fourths of the 128 articles published thus far. This is an interesting and important change, one worth discussing further later in this article.

Quantitative Analysis in JIH Articles by Decade (Research & Methods only)

A number of forces led to the smaller number of quantitative articles in the 1990s and 2000s, and the resurgence in the 2010s. Part of this change is a consequence of the reduction in interest -- especially among North American scholars -- in quantitative approaches after the 1980s. Another part is a function of choices the editors have made, specifically by organizing topical journal issues with a small number of quantitative articles over those years (especially one on social capital in 1999 and one on poverty and charity in 2005). A third, highly correlated part comes from a surge in articles, especially quantitative articles, from authors outside of North America and the UK, itself a function of what appears to be a combination of declining submissions from North America and the UK and increased interest in quantitative approaches outside those areas. 4 I will show the extent of the change in the region where authors work later.

Interdisciplinary Quantitative Methods since 1970

Quantitative methods in historical research have changed in significant ways since the first number of the Journal was published in 1970, something we can see in the articles published in the Journal itself. At the same time -- and probably not surprisingly -- the content of the Journal (an average on the order of ten articles per year in the past decade) cannot represent all of academic publishing on historical topics. It can give us hints, and by looking at the content published in the Journal and in the quantitative approaches that have had more or less exposure there, we can say something both about the evolution of quantitative methods in historical research generally, and about the Journal more specifically. While the change in quantitative methods is not linear, I argue here that there are four distinct elements in the development of quantitative methods for interdisciplinary historical research, and that they are at least partly visible in the Journal . These elements overlap, and one could make a more complex argument, but for my purposes this structure works well.

The first element in the history I narrate is one that continues through the 49-year experience of the Journal : the use of descriptive statistics, expressed in tables, graphs, and maps, or computed as an index, such as the Gini Index of inequality or something derived from core demographic methods, such as a birth or death rate. A second element emerged as technology changed and researchers raised their expectations for themselves and for the articles they read. This second stage saw new strategies for managing data, especially for projects that involved multiple sources of information. The third element in my narrative is the adoption of new statistical approaches, including more sophisticated sampling strategies and an increase in the use of inferential statistics such as correlation and regression, followed by the introduction of spatial statistics to supplement simpler spatial approaches such as mapping. One of the interesting intersections in this history is the contrast between the continued development of quantitative methods, which has shown significant advances, and the fact that overall interest in quantitative methods, especially in the U.S. and Canada, has declined.

The fourth element in my narrative engages us today, and signals opportunities for the future. The academic historical world has begun to confront new sources of data, with new quantities of information. We are beginning to see historical research that makes use of “big data,” as it has come to be called, and new approaches to traditional sources (such as newspapers) under the rubric of “digital history” or “digital humanities.” These new developments are starting to break the mold in which the Journal was forged. The rise of big data has moved quantitative methods beyond inferential statistics, because with a universe of data the researcher doesn’t need to infer the characteristics of the population from those of a sample. And the increasing interest in what is coming to be called digital humanities has increased the context in which quantitative methods flourish, most notably by adopting humanistic interpretation instead of relying on social science theory and methods. The Journal is just begun to see these kinds of sources, but what has been published is a glimpse into the future, with the understanding that the Journal’s conception of what is interdisciplinary will continue to evolve.

Article Subject Matter and Quantitative Content

The content of the Journal of Interdisciplinary History is wonderfully diverse. Topics range from art history to urban history, with many other fields of historical study in between. Table 3 displays the range of topics included in the 779 research and methods articles published in the Journal’s first 49 volumes. The table is vastly simplified from the reality of the Journal’s content. While articles categorized as religion are generally about religion, those categorized as the arts include the fine arts, music (including opera), and architecture. The category that I labeled “social history, status, mobility & capital,” includes all of those elements, and more kinds of social analysis, and is especially diverse, both in content and method. Even with those larger groupings, there are 18 categories, plus a residual. The number of articles in each category ranges from 9 (military and spatial & transportation) to 130 (economic & labor force history), with the largest categories being economic and labor force (16.7% of all articles), demographic (13.4%), social history, status, and mobility (12.1%), political (9.1%), and family (7.3%).

Field of Research, by decade of Publication (Research and Methodology Article only)

The number and percentage of articles in any given category vary considerably from decade to decade, a function of changing historical tastes as well as the organization of conferences organized by the editors or topical issues that focused a decade’s publication in one direction or another. Some of the original topics included in the journal have rapidly or slowly diminished in importance, for example psychohistory (7.6% of articles in the 1970s but never more than 1.6% since), family history (from 12.3% to 1.6%) or political history (from 11.7% to 5.5%), while others have held their ground, including economic and labor history, demographic history, and a combined category I call “health, medicine, & nutrition.” The number of articles in some fields of study have bounced around, possibly a reflection of changes in academic interest, but more likely a result of the choice of special topical issues, such as international relations in the 1980s, the arts in the 1980s and the 2000s, or religion in the 1990s. All the same, a number of the fields that maintained their numbers through the years have also been buoyed by conferences and topical issues, including social mobility and social capital, climate history, demographic topics, and health and nutrition. 5

Articles in different fields of study ( Table 4 ) had different levels of quantitative content, ranging from one or two articles (arts, international relations, psychohistory, and religion), to three-fourths or more of all articles (climate, environment, & agriculture; crime & justice; demographic; economic & labor force; methodology; politics; and spatial and transportation). What is perhaps most interesting, however, is the extent to which the Journal has consistently published a mix of quantitative and qualitative articles even in fields where much of the interest has been quantitative: notably in the varied social history topics (42.6% not quantitative), family history (29.8% not quantitative), health, medicine, & nutrition (45.5% not quantitative), and race & slavery (33.3% not quantitative). This introduction helps us understand the starting point for quantitative methods, and the long stability in descriptive findings based on quantitative data.

Quantitative Articles by Field of Research (Research and Methodology Articles Only)

The Starting Point: Using Quantifiable Data as Historical Evidence.

The starting point for understanding the use of quantitative methods in historical research and publication comes from the fact that the most common forms of quantitative analysis and data display included in articles in the Journal are the simplest: descriptive tables, graphs, and maps, which represent tabulations of sums, distributions (medians, quartiles, etc.), or averages of data collected or transcribed by the authors and their research assistants and collaborators. Even with changes in technology and the availability of more sophisticated statistical tools, virtually every quantitative article in the first 49 volumes of the Journal make use of these descriptive strategies. To the extent that authors go further, as I will show, they then make use of relatively straightforward strategies for creating derivative indexes from the raw data, most commonly those derived from the demographer’s toolkit.

This consistency in the use of descriptively presented data contradicts a counter argument that appeared by the end of the 1970s, suggesting that what were then new historical research strategies had run their course, vividly in Lawrence Stone’s 1979 article, “The Revival of Narrative.” This is true both because researchers found quantitative evidence essential for some studies, and because quantitative evidence was not ever going to be a substitute for other historical sources. Rather, early adopters found it possible to document the conditions of life, the expression of opinion through practices like voting in elections and legislatures, and their change through time (among many other things) by mobilizing quantifiable information that could be tabulated and represented efficiently and convincingly. Stone’s complaint, after all, was more about the change of argument in historical publications away from a narrative organization to one that dealt with structures and questions, and the need for historical discourse to return to arguments that were largely temporal. Nonetheless, at the scale of the academic article, quantitative evidence usually stands on its own, supporting an argument that is thematic, structural and interdisciplinary, and only infrequently mixing quantitative and qualitative sources. What had changed in the 1960s, and has continued to change since, is the availability of new technology that made it easier to manage data for historical research and to tabulate and analyze those data for the purposes of drawing conclusions and testing hypotheses. 6

We see the role of descriptive presentations of data clearly in the corpus of the Journal . Much of the quantitative evidence presented in the JIH is very straightforward, essentially counts or sums of items collected from a traditional source such as wills or city directories, or copied from published data, such as the Census. The first substantive article in the first issue is Thernstrom and Knights’ “Men in Motion: Some Data and Speculations about Urban Population Mobility in Nineteenth-Century America,” with tables of migration data, and the next-to-last article in Volume 49 is Li, Shelach-Lavi, and Ellenblum’s “Short-Term Climatic Catastrophes and the Collapse of the Liao Dynasty (907-1125): Textual Evidence,” with rich graphical representations of climate and societal change designed to support an argument about their relationships. Between those examples there are many others (nearly 500 in all). If we look at one category of analysis, using probate inventories to capture wealth inequality, we find good examples in Nash’s “Urban Wealth and Poverty in Pre-Revolutionary America” (volume 6), which examines the ways that Boston’s poor differed from its rich; Shammas’s “How Self-Sufficient was Early America?” (volume 13), which looks at evidence in probates for the kinds of tools that would have allowed families to be self-sufficient; and Urdank’s “The Consumption of Rental Property: Gloucestershire Plebeians and the Market Economy, 1750-1860,” (volume 21), which examines the likelihood of rural testators owning rental property in England in the eighteenth and nineteenth centuries (and uses both descriptive statistics and regression analysis). 7

In addition to the descriptive presentation of quantitative evidence, authors have often found it necessary to manipulate the data to produce a well-understood index, statistic, or indicator, something that they have done consistently during the history of the Journal . One of the most common indicators that appears in the historical and contemporary literature about social inequality is the Gini coefficient, which allows the researcher to compare the extent of inequality, and fits well with the research based on wills or probates just discussed. These measures are represented in the corpus of JIH articles, for example in Warden’s study of Boston (volume 6) and Main’s study of Massachusetts and Maryland (volume 7), in the context of a wide variety of data about Early America. While use of the Gini Index is valuable, demographic rates and ratios, plus the life table, are the most common set of constructed indicators used in quantitative historical research, and they are well represented in the JIH . As early as Wells’s “Demographic Change and the Life Cycle of American Families” (volume 2), and as recently as Bonneuil and Fursa’s “Learning Hygiene: Mortality Patterns by Religion in the Don Army Territory (Southern Russia), 1867-1916” (volume 47), these methods predominate. As Table 4 shows, research about historic populations and families together constitute a significant part of the JIH corpus (roughly one in five of all 779 articles, and an even larger fraction of all articles that include quantitative evidence), and they make use of core demographic measures as a primary indicator in roughly fifteen percent of all quantitative articles ( Table 5 ), a figure that would be higher if I included articles where inferential statistics based on those demographic measures were the primary method. The distribution of articles that use core demographic methods over time is also included in Table 5 , which shows their relatively high level, with a peak in the 1990s and a falling off in the 2010s as the Journal moved to other topics. 8

Use of Core Demographic Methods, By Decade (Research and Methods Articles)

Source: JIH Content Database. I define core demographic methods as birth, death, marriage, and migration rates, infant mortality and illegitimacy rates (which are actually ratios), and the life table.

My point in this discussion is to say that quantitative methods have been consistently employed in the Journal from its founding to the most recent issues, and that the most common methods have been the most straightforward: descriptive tables, graphs, and maps, and indexes computed and presented in a way that is readily understandable to readers. These patterns should not surprise us, but they should show that there has been significant stability in the quantitative methods presented in the Journal, as they are in the rest of the general-purpose historical literature.

Changing technology: Learning to manage information in new ways, and the move from descriptive to inferential statistics

The emergence and continued importance of quantitative approaches to historical writing -- in the JIH and other publications -- is tightly linked to the availability and improvement of computing technology, both hardware and software. Historical researchers have learned how to use these new technologies, and their work is reflected in the Journal , especially in the early years. JIH published articles, reviews, and notes making use of and dealing with quantitative methods from its origins, with a series of articles and reviews in its early issues that chronicled the development of quantitative methods, the use of computers, and the opportunities for new insights. Those articles and reviews set the tone for the following years. After the early years, basic methods were less frequently represented in the Journal, as those methods became more widely used and other publishing venues opened up, and methods became something explained as part of articles that were primarily substantive in their content. 9

Much of the methods discussion in the Journal and other publications focused on ways to manage data. The classic example of a data management challenge is record linkage, a topic that shows up in the Journal’s first issue, in an article by Winchester, and later in an article about sampling by Phillips (“Achieving a Critical Mass While Avoiding an Explosion: Letter-Cluster Sampling and Nominal Record Linkage”, volume 9) but one that has not been visible in the Journal since, despite an upsurge in interest in record linkage approaches due to new volumes of data and new technologies. Articles on managing data and dealing with new technologies have appeared infrequently in the Journal , perhaps most visibly in a couple of articles about capture-recapture approaches, but as historical research has evolved, this has not been one of its primary focuses. Managing data and dealing with technology have moved to more specialized journals such as Historical Methods (successor to Historical Methods Newsletter , mentioned earlier), and to specialized books about computers and data management for historical research. 10

If specific articles about the use of computers or complex data construction are no longer published in the Journal , the fruits of that work continue to appear. In many cases, linked records lead to longitudinal data collections that allow the researcher to follow the experiences of a person, a family, or even a piece of property over time, capturing its experiences and its responses to internal and external stimuli. Many social and spatial mobility studies have used linked data (as did Thernstrom and Knights in the first volume of the Journal), and one of the most widely discussed record linkage activities has been family reconstitution, which has generated numerous longitudinal data collections, and a number of articles in the Journal over the years. Family reconstitution and its extensions (for example, research using a mix of sources beyond church registers, or continuous registers of population) lend themselves to a variety of analytic approaches, starting with core demographic rates and the life table, but also including statistical regression models with various names, including proportional hazards models and event-history regression.

Beginning in the 1990s, these more advanced methods of analysis of longitudinal data began to appear in the Journal, thus connecting the second element in my conceptualization -- dealing with improved computation -- together with the third, advanced statistical methods, especially correlation and regression. In Van Poppel’s 1998 article, “Nineteenth-Century Remarriage Patterns in the Netherlands” (volume 28), he uses proportional hazards regression models to show the factors that were most important in determining the timing and extent of remarriage for Dutch widows, widowers, and divorcees, to show that most people remarried, but that their chances of remarriage varied by sex (men more than women), age (younger women most likely to remarry), religion, and the causes of marriage dissolution (divorcees were likely to remarry, even more than the widowed). A steady flow of articles using these data and methods followed, even including a complete topical issue (which I co-edited) of six articles about fertility, mortality and child abandonment in Europe and the United States. 11

The appearance of regression-based techniques in demographic analysis in the 1990s is part of a longer-term trend in the Journal and elsewhere in historical research, in which an increasing proportion of quantitative articles made use of advanced and multi-variate statistical techniques, often extending to the area of inferential statistics in which the researchers analyzed a sample of data in order to estimate the characteristics of the universe from which it was drawn. In the Journal we can see this in the data reported in Table 6 , which shows the number of research and methods articles that made use of correlation or regression methods (or both). The number of such articles increased steadily from the 1970s (18.5%) to the 2000s (45.6%), before falling back somewhat in the rather different content of the Journal of the 2010s. The data reported in the table are simplified in a significant way, because they consolidate a number of approaches to regression, depending on the structure of the data, the kind of outcome possible, and the software used. Even with that caveat, the findings are meaningful.

Use of Correlation and Regression, By Decade (Research and Methods Articles)

The data in Table 6 show a trend, which culminates in the reality that multivariate correlation and regression have become an expected part of the toolkit that quantitatively-oriented historical researchers bring to their problems. Having said that, it is important to recall that articles using regression and correlation were included in the earliest issues of the Journal, including work by Tilly (“The Food Riot as a Form of Political Conflict in France” - Volume 2), Kousser (“Ecological Regression and the Analysis of Past Politics” - volume 4), Vinovskis (“Socioeconomic Determinants of Interstate Fertility Differentials in the United States in 1850 and 1860” - volume 6), and Luria (“Wealth, Capital, and Power: The Social Meaning of Home Ownership” - volume 7). These four articles are characteristic of the Journal’s content, both early in its history and late, by the diversity of their subject matter: political conflict, political (voting and legislative) analysis, population, and social status and social mobility. 12

The opportunities offered by new technology and innovative forms of interdisciplinary analysis have generated new strategies for historical research and writing, which the Journal has engaged. Beyond an increase in advanced statistical methods, its interest in interdisciplinary approaches have brought a variety of new angles. If regression was the increasing area of methodological activity in the 1990s and 2000s, spatial analysis, the environment, and climate were the areas of innovation in the 2010s. Some of these new areas of emphasis required new modes of quantitative analysis -- especially spatially-oriented regression and the integration of climate data with other aspects of life -- while others required new strategies for data management and visualization. The Journal has been substantially recast in the process. Before I turn back to the methodological transformation of the Journal in the 2010s (and prospects for the future), I will present more data about the Journal’s corpus, in the process attempting to explain some of the factors that led to those changes. 13

Who wrote quantitative history, and where do they live and work?

Something interesting happens to the Journal in the 2010s, and Table 7 helps us understand what it is. This table distributes the 778 research articles published in the journal by decade and by the country of residence of the first author of each article. I have focused on the first author because it is not easy -- nor necessarily valuable -- to try to determine the weight to give each author in articles with multiple authors, which we will see later are increasingly frequent in the Journal . For this analysis, therefore, I focus on the first author. At the same time, it is difficult to know exactly what it means to be the first author in a journal with content as diverse as the JIH . Some articles follow a model in which the first author is the one who has done the most work, but there are clearly other models, including the economics model of listing all authors in alphabetical order, or the practice in some scientific laboratories of listing the most senior author either first or last. Despite that caveat, there is no reason to think that selecting the first author is more problematic than some other choice, with the understanding that somehow counting every author might be better, but much more difficult.

Articles in the Journal of Interdisciplinary History, by Country of First Author and Decade (Research Articles Only)

The striking evidence in Table7 is the steady decline in contributions from Canada and the United States, falling from 90 percent in the 1970s to about two-thirds in the 2000s, and then dropping precipitously in the 2010s to little more than a third. At the same time, while all other regions increase, European countries, including the United Kingdom, increase to account for more than half of all articles during the most recent decade. Table 8 adds more to the story by allowing us to categorize articles by country and whether they are quantitative or not. Articles with first authors from Israel, the United Kingdom and North America were less likely to be quantitative than those from the rest of the world, and especially from Europe.

Quantitative Articles by Country of First Author

We can look at the role of country of origin by decade in Table 9 , for a simplified list of regions (to allow us to see temporal detail). Two conclusions stand out. First, the US and Canada, together with the countries in all other regions (Africa, Asia, Australia, New Zealand, and the rest of the Americas besides the US and Canada) were more likely than the enlarged European region to publish quantitative material in the 1970s and 1980s, but less likely in the 1990s, 2000s, and 2010s. And second, the upsurge in quantitative publication in the 2010s comes from all regions, with the enlarged European region the most important contributor.

Quantitative Articles by Country of First Author and Decade

Another aspect of article authorship tells us an interesting story, this time about the role of multiple authors in interdisciplinary research, and their contributions to quantitative approaches to that research. Did that change over time? Recent trends towards new varieties of interdisciplinary history and ever-bigger data bases are often undertaken by larger teams of researchers. Projects that require expertise in climate and environment, together with population, for example, benefit from research groups that include ecologists, meteorologists, and demographers, all with a taste for solving historical problems. Similarly, data projects with tens or hundreds of millions of cases can require a team of researchers with a mix of expertise in data management and statistical analysis, together with understanding of the historical context. That appears to be the case in the content of the Journal .

Table 10 reports the distribution of number of authors for JIH research and methods articles by decade, and it confirms the idea that the nature of interdisciplinary historical writing has changed over time. The proportion of articles with a single author declined slowly from the 1970s to the 2000s, and then fell off rapidly in the 2010s, to fewer than half of all articles. At the same time, the number of articles with two authors increased slowly, with the dramatic shift in the number of authors a reflection of a big jump in articles with three or more authors in the 2010s. The relationship between number of authors and quantitative content is also interesting, as Table 11 shows. Articles in the Journal with a single author are slightly more likely to have quantitative content than not, while those with two or more authors have quantitative content are very likely to have quantitative content (nearly five out of six).

Number of Authors, by Decade

Quantitative Articles by Number of Authors

Who writes articles published in the journal is illuminating beyond the question of nationality and number of authors. In the early years of the Journal’s history, quantitative methods appeared to be primarily the work of younger researchers, who were crossing disciplinary boundaries, mastering the technical skills associated with computers, and learning about statistics. Was that the case? And has it continued? In order to get the answer to those questions I had to find a way to distill all the different types of statuses held by the Journal’s authors into six categories. This was problematic because the Journal’s community of authors is diverse in terms of nationality, challenging comparisons of academic status from author to author and country to country. Each published article lists the position held by each author at the time of publication. I converted them to their equivalent in the U.S. academic status hierarchy, using the best information I could find about those status comparisons.

The results in Table 12 give support to this hypothesis: researchers earlier in their career appear to be more likely to publish quantitative work, with a significant difference between the most senior (U.S. full professors) and students, with others roughly in the middle. That’s an interesting finding on its own, but somewhat difficult to interpret without taking time into account. Many of the authors who were students or junior faculty at the beginning of the Journal’s history have passed through the academic ranks by now (many are retired, and some have died). Did the later senior faculty give up their quantitative ways? Some of the answer is in Table 13 , which reports the distribution by decade (and which combines all the non-teaching ranks because of small numbers), which generally confirms that full Professors were less likely to publish quantitative articles than all other ranks, even in the 2010s, when full Professors were more likely to do so than previously, but so was everyone else.

Quantitative Articles by First Author Status (Based on U.S. Academic Status System)

Quantitative Articles by First Author Status and Decade (Based on U.S. Academic Status System)

Into the Future: Big data and the new digital history

The world of interdisciplinary history and its quantitative elements have changed continuously over the nearly fifty years of the Journal’s publication. New approaches continue to emerge, and the Journal continues to both welcome and encourage them. Two fairly recent developments are worth notice here. The first of them is the emergence of what has come to be called “big data,” reflecting the availability of very large data sets, numbering in the tens or hundreds of millions of data items, often based on information drawn from full-count censuses, financial or other business transactions, or environmental data with a high spatial or temporal resolution. Recent articles about the role of big data in demographic analysis and in economic history set the stage for what is to come. In the case of the Journal’s content, we see it beginning to appear, especially for modern climate data, which by definition is built up from a multitude of observations, with many observation points recording data nearly continuously, or with other environmental data, such as those about soils or terrestrial elevations, or remotely sensed satellite imagery, which are often recorded in a highly-resolved grid. Recently released full-count historical U.S. Census data constitute another source of high-resolution data, with the publicly available digital version of the 1940 census containing information about 134 million individuals. One of the interesting elements that arises in the discussion of these large data collections is the knowledge that with very large data sets that come close to representing the universe of potential observations, inferential statistics are not necessary, because there is no need to infer from a sample to a complete population. Everything is a descriptive statistical problem, and indicators like statistical significance have questionable value. The implicatons for future research are great, and are still being digested by the research community. 14

Another area where innovation is occurring in historical research builds on the new capacity to analyze large data collections, and new strategies for thinking about historical problems, under the broad title of “digital humanities” or “digital history.” These new approaches involve a number of potential new sources, many of them analyzed in ways that get at the composition of large corpora of texts, drawn from books, journals, magazine, and especially newspapers, or from new ways of looking at historical problems, such as the idea of a qualitative approach to geographical information systems. Here again, we see the beginning of content appearing recently in the Journal, most notably in Atkinson and Gregory’s “Child Welfare in Victorian Newspapers: Corpus-Based Discourse Analysis.” 15

Much of the data analysis in the history of the JIH has had its intellectual origins in the social sciences, but the success of the Journal derives in good measure from its success at showing what interdisciplinary approaches that included music, religion, or the visual arts alongside important historical questions. Relatively few of those older humanistic interdisciplinary articles made use of large scale data or quantitative strategies. What makes these newly emergent areas of interdisciplinary inquiry so exciting is the potential to broaden quantitative methods to include the humanities and more of the natural sciences, and to find ways to create interdisciplinary connections that span all at once.

From its very beginnings, the Journal of Interdisciplinary History published articles that made use of quantitative methods, but in its effort to engage across as many disciplines as possible, it did much more. Over the nearly fifty years of its publishing history, it has continued to publish in this way, and it has continued to present leading-edge research. All the while, quantitative methods have both changed and stayed the same, just as interest in quantitative approaches to historical research has diminished, especially in the United States and Canada.

Much of the dynamic in quantitative historical research has been a function of changing technology, with computers increasing in their capacity and speed, and software for managing and analyzing data becoming ever more sophisticated. Despite those improvements, among the most striking conclusions of my analysis of 49 years of JIH articles is the fact that most of the quantitative methods used are uncomplicated ways of presenting data that describe what the researcher has learned from the core elements of the sources they used, as well as the understanding that quantitative approaches have continued to be the domain of researchers who are still building their careers, and not those who are the most established. We should not make too much of the steady pace of articles with descriptive tables, graphs, and maps. While that has been the case, the proportion of articles that make use of more sophisticated statistics, especially regression-type analysis, increased steadily from the 1970s through the 2000s, before falling off in the 2010s.

The 2010s have been a time of significant change for the Journal , beyond the end of trends just described. Authorship changed very significantly, as authors from the U.S. and Canada became far less frequent, and authors from Europe much more so. At the same time, the frequency of articles with multiple authors jumped ahead, both for articles with two and for those with three or more authors. All this accompanied a transformation in the content of articles, away from many of the traditional areas of interest for the Journal , and towards topics like long-term climate trends and their historical context. This is all a signal of a new way of thinking about interdisciplinary history, embedded in a global intellectual network that has not given up on quantitative approaches. The suggests that the Journal is playing a new role in a much more international intellectual context.

The most recent years have begun to bring significant changes to quantitative methods in history, giving the Journal the opportunities to forge a new kind of interdisciplinarity. Immense data sets, some with modes of interpretation drawn from the social sciences, and others with modes of interpretation in the humanities, natural sciences, and medicine, will transform future research. The opportunities are enormous, and beyond quantification themselves.

Acknowledgements

This work was supported by the Institute of Behavioral Science at the University of Colorado Boulder. The author thanks Lindy Schultz for help with assembling the database of JIH content, and George Alter, Vilja Hulden, and Emily Merchant for their review of a draft manuscript, and the Journal’s editors for their suggestions, and for years of friendship, mentoring, and collaboration.

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The Dimensions of Quantitative Research in History

• William O. Aydelotte , Robert William Fogel and Allan G. Bogue
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• Language: English
• Publisher: Princeton University Press
• Copyright year: 1972
• Audience: Professional and scholarly;College/higher education;
• Main content: 448
• Keywords: Result ; Corn Laws ; Voting ; Income ; Legislator ; Social mobility ; Ennoblement ; Economics ; Radicalism (historical) ; Legislation ; Tax ; Industrialisation ; Wealth ; Political history ; Career ; Nobility ; Proportion (architecture) ; Historical thinking ; Quantitative research ; Social science ; Political science ; Political radicalism ; Historical method ; National Bureau of Economic Research ; Literature ; Politics ; Incumbent ; Politique ; Measures of national income and output ; Calculation ; Comparative politics ; Agriculture ; Intellectual history ; Economic history ; Seniority ; Urbanization ; Social history ; Statistician ; Capital gain ; Archives nationales (France) ; The Origin of Capitalism ; Institution ; Stephan Thernstrom ; Right-wing politics ; The Protestant Ethic and the Spirit of Capitalism ; Economic history of the United States ; Whigs (British political party) ; Sociology ; Policy ; Nationalization ; Harvard University ; Reformism ; An Economic Theory of Democracy ; Total factor productivity ; Statistical Abstract of the United States ; Urban renewal ; Political philosophy ; Factor analysis ; Urban history ; Radical right (United States) ; Charles Tilly ; Ownership (psychology) ; National Policy ; Immigration Restriction League ; Hearth tax ; Statistics ; Fiscal policy ; Contemporary society ; Of Education ; Chartism ; Consideration ; Statistical significance ; New Historians ; The Other Hand ; Comparative literature ; The Journal of American History ; Lawrence Stone ; Charles Sumner ; National Affairs ; Marxism ; Middle class ; Rate of return ; Social Science Research Council ; Jacksonian democracy ; Bourgeoisie ; Gross national product ; Percentage ; Chairman ; Modern history ; Historical demography ; American Council of Learned Societies ; Frontier Thesis ; American Economic Association ; American Journal of Sociology ; Political Warfare Executive ; Tax incidence ; Social theory ; Radical Republican ; Political party ; Republicanism
• Published: March 8, 2015
• ISBN: 9781400867127

The Dimensions of Quantitative Research in History

William O. Aydelotte

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• United States
• United Kingdom
• William O. Aydelotte , Robert William Fogel , and Allan G. Bogue
• Princeton Legacy Library
• Quantitative Studies in History

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Nine papers consider problems in American, French, and British history that range from economic history to political behavior and social structure. Originally published in 1972. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.

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Methodology

• What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

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Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalized to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

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Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardize data collection and generalize findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardized procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

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

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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