importance of quantitative research in 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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A. E. C. M.; Quantitative Methods in the Humanities: An Introduction. The Journal of Interdisciplinary History 2020; 51 (1): 137–139. doi: https://doi.org/10.1162/jinh_r_01527

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History is notoriously a “big tent” discipline. Because everything has a past, every subject has a history. The tools appropriate to ferret out those histories multiply just as easily as the topics, depending on the questions being asked and the nature of the evidence preserved (accidentally or otherwise) that might answer them. In what sense is History a coherent “discipline” at all? Is there more to hold it together than just a ferocious commitment to the past tense? Must historians adhere to a recognized and common methodology of practice, but of what might it consist, in the face of so much variety? These questions bedevil historians everywhere, especially when they are trying to figure out what their students should know and/or know how to do. Whatever the answers might be, these questions frame both the motivation for the book under review and its value for readers.

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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.

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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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The History of Quantification in History: The JIH as a Case Study

Steven ruggles.

Regents Professor of History and Population Studies and Director of the Integrated Public Use Microdata Series ( ipums ), University of Minnesota.

Diana L. Magnuson

Professor of History, Bethel University, and Director of Archives, History Center of Bethel University and Converge.

The use of quantitative methods in leading historical journals increased dramatically in the 1960s and declined sharply after the mid-1980s. The JIH is an invaluable source for analysis of the boom and bust in the use of quantitative methods in history; the journal remained under the same editors for almost fifty years and made no attempt to change editorial policies during that period. Shifting patterns of content and authorship in the JIH from the 1980s to the early 2000s reveal how the journal responded to a dramatic decline in quantitative submissions by U.S.-based historians. Recent years have seen a revival of quantification both in the JIH and in mainstream historical journals, especially among historians located at institutions outside the United States.

Quantification was highly fashionable when the first issue of the JIH appeared in autumn 1970. In the opening paragraph of the opening essay, the JIH editors enthused that “Whole new fields, such as historical demography, and entirely new techniques, such as computer data processing, have appeared and have made a broad impact on many areas of research.” The fiftieth anniversary of the JIH presents a timely opportunity to reflect on the changing applications of data and statistics in interdisciplinary historical research. This half-century spans a boom and bust of historical quantification, as powerful new intellectual currents sweeping over the humanities and social sciences buffeted the field. This special article uses the JIH as a case study to trace the shifting contours of quantification in history. 1

Quantification was the central defining element of the “new” histories—the new social history, new economic history, and new political history—that transformed the landscape of historical research in the 1960s and 1970s. The new social history focused on the lives of ordinary people. Literary evidence produced by and for a small elite was seen as a problematical source for understanding non-elite populations. Accordingly, most new social historians viewed quantitative evidence as indispensable for history from the bottom up. They especially prized sources that covered the bulk of the population, such as parish registers, censuses, and city directories.

Historical demography took off in 1956 with the publication of a manual on the use of parish registers for demographic research by Fleury and Henry, spawning hundreds of “family reconstitution” studies of fertility, mortality, and marriage over the following decades. In 1963, Laslett and Harrison used population listings to show that the seventeenth-century village of Clayworth, Nottinghamshire, had extremely high geographic mobility, and that few families included extended kin. U.S. historians reported similar findings of high geographic mobility and simple family structure. In 1964, Thernstrom used linked nineteenth-century census records from Newburyport, Massachusetts, to argue that opportunities for upward social mobility were highly constrained for those at the bottom of the social hierarchy. 2

Economic historians had always used numbers, but before the late 1950s, most of them had focused on descriptive analyses using aggregated economic data series. The new economic history combined quantitative measurement and economic theory, creating a “cliometric revolution.” Many of the new studies combined firm-level or individual-level data with increasingly sophisticated statistical methods to test economic hypotheses about the past; as North expressed it, “The new economic history employs quantitative methods to test the hypotheses, the old economic history employs statistics as supporting evidence.” Among prominent early examples of the new economic history, Conrad and Meyer renewed the debate over the profitability of slavery; Fogel examined the impact of railroads on the economy; and North estimated the impact of cotton on interregional trade and economic growth. 3

The new political history focused on the quantitative analysis of voting statistics and legislative roll calls, looking mainly at the United States. In the late 1950s and early 1960s, Benson, Hays, and others argued that ethnic and religious divisions, rather than economic ones, were the main determinants of U.S. voting behavior in the nineteenth century. To facilitate quantitative political research, in 1963 the American Historical Association formed an ad hoc committee for the collection of data on American political history, chaired by Benson. The committee oversaw the first large-scale efforts to digitize historical data for shared use. This project, funded by the National Science Foundation and executed by the Inter-university Consortium for Political Research in Ann Arbor, Michigan (now ICPSR), created massive files of historical election returns and county-level demographic and economic characteristics, and made the data broadly accessible to the research community. 4

The quantitative turn of the new histories had a profound impact on mainstream historical scholarship. Figure 1 shows the number of statistical tables and graphs per 100 articles in four major historical journals—the American Historical Review, Journal of American History, Journal of Modern History , and Past & Present . By this measure, statistical presentations increased eightfold between the 1960s and the first half of the 1980s. At the peak in the 1980s, every 100 articles featured 91 tables or graphs, and about one-fourth of articles included at least one table or statistical graph. The heyday of quantification in elite historical journals lasted just two decades, from 1970 to 1990. After 1990, the decline of quantification was almost as precipitous as its ascent. Quantification reached a nadir from 2004 to 2009, recovering slightly since then. This recovery is evident in all four journals. 5

Statistical Graphs and Tables per 100 Articles in Four Prominent Historical Journals, 1945–2019

Historians have offered a variety of explanations for the rejection of quantification by the discipline after 1990. Some point to the heated controversies about Fogel and Engerman’s Time on the Cross: The Economics of American Slavery , published in 1974, which, as Klein has argued, “encouraged an outright rejection of quantitative studies as a tool of historical research.” In the late 1970s, historians seeking a “revival of narrative” began to unleash a backlash against quantification. Stone’s article about that topic concluded that the movement to narrative history “marks the end of an era: the end of the attempt to produce a coherent and scientific explanation of change in the past.” The traditionalist critique of quantification in history was soon joined by the cultural turn that blossomed across the humanities and many social sciences during the 1980s and 1990s. Cultural theorists rejected narrative history, but they also rejected positivist social science in general and quantification in particular; instead, they focused on semiotics, language, and meaning. 6

As historians abandoned quantification after 1990, other disciplines took up the slack. In the top three economics journals, the percentage of articles classified as “economic history” has roughly tripled since 1990. Our preliminary investigation suggests that the top journals of sociology, demography, and political science have also seen significant increases in quantitative historical analyses. 7

QUANTIFICATION IN THE JIH

The JIH offers a useful case study for analysis of the boom and bust of historical quantification. The journal had the same editors for almost five decades, and it made no intentional shift of disciplinary or topical focus across those years. The journal’s core principle is the application of interdisciplinary approaches. For the most part, this approach has usually meant the application of methods drawn from the social sciences to investigate historical problems, but the journal has also featured work using methods from the natural sciences or the humanities. According to managing editor Edward Freedman, the main criterion is that “we donʼt accept narrative history of any kind (be it literary, intellectual, religious, cultural, etc.) or history that strikes as too arcane or specialized to have any appeal to our readership.” From the outset, most JIH authors were historians, but a substantial minority was spread across a wide variety of other disciplines. A close look at the shifting content, methodology, and authorship of JIH articles can lend insight into broader intellectual currents of the past half-century. 8

Figure 2 shows the number of graphs and tables per 100 articles for the JIH . Unsurprisingly, the levels are much higher for the JIH than for the mainstream journals shown in Figure 1 , ranging from 200 to 400 graphs and tables for every 100 articles. The JIH shows a modest increase in quantification between the 1970s and the 1980s, but after the 1980s, it diverged dramatically from the trend shown in Figure 1 . Instead of the sharp decline in quantification seen in the mainstream journals, the frequency of graphs and tables in the JIH held steady through the 1990s and 2000s. In the 2010s, the JIH saw a substantial increase in quantitative displays; they now stand at the highest levels in the history of the journal.

Statistical Graphs and Tables per 100 articles in the JIH from 1970–2018

Despite the stability in the ratio of graphs and tables to articles between the 1980s and the 2000s, the absolute number of articles making use of quantitative analysis declined steeply. We define quantitative articles as those with at least one statistical graph or table. In the 1980s, the JIH published an average of 15.2 quantitative articles per year; the number fell to just 7.1 in the period from 2000 to 2009. The reason is simple: The journal dramatically reduced the number of research papers published in this period, from an average of 23.0 per year in the 1980s to just 12.8 per year in the 2000s. These changes are shown in Figure 3 .

Average Number of Research Papers per Year in the JIH from 1970–2018

The overall number of pages in the journal did not change appreciably, but the content moved away from original research toward review essays and book reviews. At the low point in the early 2000s, most issues had just one or two research papers. This apparently was not an intentional policy. Editor Robert Rotberg and Managing Editor Freedman concur that the journal made no deliberate attempt to reduce the pages available to publish research. Rotberg and Freedman agree that the most plausible explanation for the decline was “a lack of good or appropriate submissions.” 9

Why did the number of submissions decline? Figure 4 breaks down the published quantitative articles by discipline and institutional location (nationality) of the first-named author. U.S.-based historians, shown in blue, accounted for almost two-thirds of the quantitative articles in the 1970s, averaging 8.2 quantitative articles per year. The U.S. representation dropped precipitously in the following decades, to a low point of just 1.5 articles per year in the 2000s. The decrease in quantitative publications by U.S. historians can account for the entire decline in quantitative publications in the JIH between the 1970s and the 2000s, and 88 percent of the decline in the total number of research articles published.

Average Number of Quantitative Articles per Year in the JIH from 1970–2019, by Leading Author’s Discipline and Nationality

Since the 2000s, the number of quantitative articles in the JIH has jumped sharply, nearly attaining the level of the 1970s. Little of this increase, however, can be ascribed to U.S.-based historians. Instead, it reflects a major expansion in articles from historians based in other countries, primarily Europe, in combination with a steady growth in the number of articles from other disciplines.

The most plausible explanation for these developments is that the number of high-quality quantitative submissions for U.S.-based historians dried up after the 1980s. The decline of research by U.S.-based historians was not, however, confined to quantitative research. Figure 5 shows the disciplinary distribution for both non-quantitative (Panel A) and quantitative (Panel B) articles published in the JIH . In the 1970s, 61 percent of non-quantitative articles were written by U.S.-based historians; this number declined to just 19 percent in the 2010s. The shift was only slightly greater for the quantitative articles; about 64 percent were from U.S.-based historians in the 1970s, compared with 19 percent in the 2010s. The striking decline in articles by U.S.-based historians may be indicative of a broader rejection of interdisciplinary approaches, not simply a rejection of quantification. 10

Disciplinary Distribution of Articles and Research Notes in the JIH (Percentages)

TRENDS IN THE CHARACTERISTICS AND IMPACT OF ARTICLES IN THE JIH

Despite the dramatic change in the disciplinary affiliations of JIH authors, the broad distribution of JIH research topics has remained relatively stable. Figure 6 shows a classification of JIH articles according to the three new histories (social, political, and economic). We also identify demographic/family history and psychohistory. The rest of the social category includes urban history, social-mobility studies, education, and labor history, as well as investigations of race, gender, and ethnicity. The “other” category is a catch-all, including environmental history, cultural history (including studies of art, architecture, literature, and music), and religion.

Topical Distribution of Articles and Research Notes in the JIH

The top panel of Figure 6 shows the trends in the topical distribution of all JIH articles and research notes. In the 1970s, psychohistory was a significant category, but it disappeared in the 1980s as psychoanalytic theory fell from favor. Social history expanded in the 1990s and 2000s; a diverse array of both qualitative and quantitative articles focused on poverty, labor, voluntary associations, immigration, education, gender, and race. Studies in demography and family history have remained stable as a percentage of JIH articles. Politics has declined significantly, and economics has expanded slightly in the last decade.

The overall distribution of topics may have changed only modestly, but the topics of non-quantitative articles changed dramatically. Panels B and C of Figure 6 compare the topical distributions of non-quantitative and quantitative articles, respectively. The three new histories comprised a small minority of qualitative articles, whereas the “other” category started to dominate, accounting for more than half of the material published during the twenty-first century. Many of these articles were recruited for publication in special issues on religion, opera, and biography. By contrast, the distribution of quantitative articles, shown in Panel C, continues to be dominated by articles in the traditional new history domains, especially demography, family, and social history. The upward bumps in the “other” category during the 1980s and the 2010s reflect two spurts of articles on environmental history, especially climate change.

Some of the fluctuations in the characteristics of JIH articles reflect variations in special issues. The JIH has relied heavily on special issues, which account for 28 percent of all the articles and research notes that it has published to date. In all periods, articles in special issues were significantly less likely to include graphs or tables than were regular articles and research notes, especially in the 1990s and 2000s, when just 18 percent of articles in special issues included quantitative analysis, compared with 72 percent of regular research articles.

The quantitative methods featured in the JIH have become much more sophisticated over the past five decades. In the 1970s, almost three-fourths of the quantitative articles used simple descriptive statistics. As shown in Figure 7 , the percentage of quantitative articles driven by more advanced analytic methods, such as multiple regression, increased sharply, reaching a peak from 2000 to 2009, when 57 percent of quantitative articles contained analytic statistics. The slight increase in descriptive analyses in the past decade partly reflects a spike in articles about the environment that rely mainly on descriptive statistics.

Methods Used in Quantitative JIH Articles

We can estimate the impact of different kinds of JIH articles through an index of citations for quantitative and non-quantitative articles. Using citation counts in the Web of Science index, we can calculate the number of times each article has been cited as a percentage of the mean citations for all articles published in the JIH within the same decade. Thus, a citation index of 200 means that an article had twice as many citations as the average article of its decade, and a citation index of 50 means that the article had half as many citations as the average article of that decade. Figure 8 compares the citation indexes for quantitative and non-quantitative JIH articles and research notes. In the first decade, the quantitative articles were more often cited than the qualitative ones, but this pattern reversed in the 1980s. The higher citation rate of qualitative articles relative to quantitative ones increased further in the 2000s. As quantitative approaches lost favor among historians, relative citations of JIH quantitative research diminished. 11

Index of Citations for Quantitative and Non-Quantitative Articles in the JIH

Closer examination of the most-cited articles from each decade of the JIH reveals how the quantitative changes described herein played out. Among the ten most-cited articles in the 1970s, U.S.-based historians wrote six and U.S.-educated scholars teaching in Canada wrote two. The non-historians among the most-cited articles of the 1970s were Gourevitch, a political scientist, who performed a comparative analysis of the depression of 1873–1896, and Wood, an English professor, who wrote about women’s “fashionable diseases” of the nineteenth century. Seven of the ten most-cited articles included quantitative analysis; the exceptions were the Gourevitch and Wood articles, as well as an article by Kuhn about the history of science. 12

Several of the most highly cited JIH articles of the 1970s focused on illegitimacy and premarital sexual activity. In his controversial article about the sexual revolution, Shorter argued that the rise of illegitimacy in nineteenth-century Europe reflected the sexual emancipation of working-class women. Responding to Shorter in another highly cited article, Tilly, Scott, and Cohen argued that the rising numbers of women who had illegitimate children did not reflect the pursuit of sexual pleasure so much as the growing exposure of women to vulnerable circumstances and the erosion of community and familial constraints. The single most-cited article from the 1970s was a paper by Smith and Hindus about trends in premarital pregnancy over the long run (1640 to 1971), primarily in the United States but with comparisons to European countries. Smith and Hindus argued for a cycle of premarital sexual activity driven by both cultural and structural changes. 13

Among the other highly cited JIH articles of the 1970s, Tilly wrote about food riots; Katz proposed a system of occupational classification; Thernstrom and Knights investigated urban mobility; and Kousser discussed ecological regression in political history. Other than Kousser’s, none of the highly cited articles of the 1970s employed methods more sophisticated than percentages, and most of them included just a few descriptive tables. 14

Ten years later, the character of the most prominent articles had shifted. In the 1980s, most of the highly cited articles came from special issues; political scientists and sociologists were more frequent contributors than were historians. Among the ten most-cited articles in the 1980s, just four used quantitative approaches, including just one quantitative article by a U.S. historian—Shammas’ analysis of self-sufficiency in early America. Among the other quantitative articles were one by Abbott, a sociologist, and Forrest, an anthropologist, that used sequence analysis to investigate Morris dances; one by Fogel, an economist, and nine collaborators that described the decline in stature in America and Britain in the nineteenth century; and one by Wrigley of the Cambridge Group for the History of Population and Social Structure that presented an interpretation of urbanization and agricultural change in early modern Europe. 15

These patterns persisted for the next two decades. Of the ten most-cited articles in the 1990s, five used quantitative analysis, and in the 2000s, four did. In these decades, economists accounted for more than half of the most highly cited quantitative articles. For example, economists Goldin and Katz investigated the rise of secondary schooling in the United States; Clark assessed the returns to capital in England in the sixteenth and seventeenth centuries; and Bodenhorn documented the association of skin complexion with stature among free blacks in antebellum Virginia. U.S.-based historians contributed two highly cited quantitative research notes in the 1990s, but none in the 2000s. 16

The decade of the 2010s saw something of a throwback to the patterns of the 1970s. Seven of the ten most-cited articles from the past decade include quantitative analysis; six were written by historians, including three U.S.-based historians and three international historians. A marked revival of interest in climate, a theme that had first emerged in the 1970s, occurred, but in this period, the research was often undertaken by large interdisciplinary teams. For example, McCormick and eleven collaborators documented climate change during and after the Roman Empire, and Halden and fourteen collaborators integrated documentary, archeological, pollen, and stalagmite evidence to understand climate change in Anatolia from 300 to 1400 A.D. Most of the other top-cited quantitative articles of the 2010s focused broadly on demographic topics, but the themes were creative and diverse, using novel sources and methods. Thus, Silveira and colleagues conducted a spatial analysis of the demographic impact of railroads in Portugal, and Dewitt and Slavin combined skeletal and documentary evidence to assess the health impacts of the great famine in fourteenth-century England. 17

THE REVIVAL OF QUANTIFICATION IN HISTORICAL RESEARCH

Quantitative research by scholars affiliated with U.S. history departments expanded rapidly in the late 1960s and 1970s, remained strong through the 1980s, and collapsed precipitously in the 1990s. The rejection of quantitative methods by historians coincided with the cultural turn: Beginning in the late 1970s, U.S. historians began questioning the epistemological foundations of historical social science. Relativist interpretations gained favor over empiricist and positivist approaches. In place of systematic empirical research, many saw the role of the historian as an interpreter of language, agency, and discourse.

The decline in the number of articles using quantitative methods—both in the JIH and in the mainstream historical journals—was not, in all likelihood, driven by changes in editorial policies. It is much more plausible that the change emanated from the bottom up, as the flow of submissions from historians diminished. The mainstream historical journals responded by publishing more non-quantitative articles, especially research in cultural history. The JIH followed a different path, greatly reducing the total number of research articles published and increasing the number of book reviews and review essays to compensate.

Recent years have seen a revival of quantification in historical research, with modest increases in articles containing statistical tables or graphs in the American Historical Review, Journal of American History, Journal of Modern History , and Past & Present . In the JIH , the mean number of articles per year by historians using quantitative methods fell 72 percent from the 1970s to the 2000s, but it grew 126 percent between the 2000s and the 2010s (through the first issue of 2019). The revival of historical quantification has been most pronounced among historians based in Europe. Over the past decade, the mean number of quantitative JIH articles per year by historians located outside the United States grew 230 percent, compared with just 49 percent growth among U.S.-based historians.

The revival of quantification among U.S. historians, which is still new and small-scale, can be detected only through quantitative analysis of publications. Klein, one of the most perceptive analysts of quantification in history, recently wrote, “North American historians still show hostility for any kind of quantitative and comparative work that does not fit into these new styles and ideologies, especially in the field of social history. Although European pioneers in these new historical trends do not unilaterally see any inherent conflict between macro- or micro-history (in Ginzburg’s terms, “serial history and individual biography”), North American cultural historians are reluctant to relate individual experience to the larger world that they inhabit; such a strategy would require an explanation of the universality or the uniqueness of the individuals in question. This rejection of quantitative evaluation marks a good deal of the current cultural history.” 18

There is still broad hostility to quantification among U.S. historians, and it is likely to continue for some time. Nevertheless, the evidence for a broad-based revival of quantification among U.S. historians is strong. What has caused that revival? The cultural turn in history is gradually fading. When postmodern history became entrenched in the establishment, it lost the excitement of its insurgency three decades ago. As Spiegel noted in her presidential address to the American Historical Association, we have seen “accumulating discontent with poststructuralism.” The “science wars” of the 1990s—which pitted postmodernist critics of science against its rationalist defenders—are over. In the face of climate-change denial and general hostility towards science on the political right in the United States, humanistic skepticism about the scientific enterprise seems to be diminishing. 19

The waning influence of the cultural turn has encouraged the revival of quantification in history, but it is not the only factor. New data resources and new methods are creating exciting opportunities for quantitative analysis, and new technology makes data analysis far simpler and less expensive than it ever was in the heyday of historical quantification. We now have free access to billions of individual-level census records describing the entire enumerated populations of multiple countries and hundreds of localities within them between 1703 and 1940. These data include the characteristics of all enumerated individuals nested into households. New machine-learning, record-linkage techniques are allowing investigators to link censuses together to form longitudinal series, allowing scholars to examine the life histories of tens of millions of people, to trace families across multiple generations, and to link individuals to administrative documents (military, tax, and vital records). Census and survey samples for ninety-four counties in the period 1960 through the present are also available. 20

Furthermore, new spatial tools allow us to measure change for consistent geographical footprints in more than ninety counties, and to merge demographic data with historical climate data and land-cover information. Every year, new historical data sources become available for research on myriad topics, such as climate change, agriculture, religion, health, stature, immigration, the slave trade, Spanish colonial expenditures, and Chinese lineages. Perhaps most important, textual sources are being digitized. Millions of digitized books are already available, and increasingly archival manuscript sources are being made available in digital form.

Klein argued that the explosive growth of digital historical data has stimulated a “historical turn” in the social sciences. It would be a big mistake for historians to abandon the quantitative analysis of the past to economists, sociologists, and demographers. Too often, social scientists analyze historical data without understanding the context of the time and place in which the data were generated, and without interrogating the motives and biases of their creators. The revival of quantification allows historians to participate and engage with the rapid growth of historical social science, which will benefit both history and social science. 21

Biographies

Steven Ruggles is Regents Professor of History and Population Studies and Director of the Integrated Public Use Microdata Series ( ipums ), University of Minnesota. He is the author of “Patriarchy, Power, and Pay: The Transformation of American Families 1800–2015,” Demography , LII (2015), 1797–1823; with Catherine Fitch and Evan Roberts, “Historical Census Record Linkage,” Annual Review of Sociology , XLIV (2018), 19–37; with Miriam King, “American Immigration, Fertility Differentials, and the Ideology of Race Suicide at the Turn of the Century,” Journal of Interdisciplinary History , XX (1990), 347–369.

Diana L. Magnuson is Professor of History, Bethel University, and Director of Archives, History Center of Bethel University and Converge. She is co-author of, with Kent Gerber and Charles Goldberg, “Creating Dynamic Undergraduate Learning Laboratories through Collaboration between Archives, Libraries, and Digital Humanities,” Journal of Interactive Technology & Pedagogy (May 16, 2019), available at https://jitp.commons.gc.cuny.edu/creating-dynamic-undergraduate-learning-laboratories-through-collaboration-between-archives-libraries-and-digital-humanities/ ; with Steven Ruggles, Catherine Fitch, and Jonathan Schroeder, “Differential Privacy and Census Data: Implications for Social and Economic Research,” AEA Papers and Proceedings , CIX (2019), 403–408, available at https://doi.org/10.1257/pandp.20191107 .

Contributor Information

Steven Ruggles, Regents Professor of History and Population Studies and Director of the Integrated Public Use Microdata Series ( ipums ), University of Minnesota.

Diana L. Magnuson, Professor of History, Bethel University, and Director of Archives, History Center of Bethel University and Converge.

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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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Home » Historical Research – Types, Methods and Examples

Historical Research – Types, Methods and Examples

Table of Contents

Historical Research

Definition:

Historical research is the process of investigating and studying past events, people, and societies using a variety of sources and methods. This type of research aims to reconstruct and interpret the past based on the available evidence.

Types of Historical Research

There are several types of historical research, including:

Descriptive Research

This type of historical research focuses on describing events, people, or cultures in detail. It can involve examining artifacts, documents, or other sources of information to create a detailed account of what happened or existed.

Analytical Research

This type of historical research aims to explain why events, people, or cultures occurred in a certain way. It involves analyzing data to identify patterns, causes, and effects, and making interpretations based on this analysis.

Comparative Research

This type of historical research involves comparing two or more events, people, or cultures to identify similarities and differences. This can help researchers understand the unique characteristics of each and how they interacted with each other.

Interpretive Research

This type of historical research focuses on interpreting the meaning of past events, people, or cultures. It can involve analyzing cultural symbols, beliefs, and practices to understand their significance in a particular historical context.

Quantitative Research

This type of historical research involves using statistical methods to analyze historical data. It can involve examining demographic information, economic indicators, or other quantitative data to identify patterns and trends.

Qualitative Research

This type of historical research involves examining non-numerical data such as personal accounts, letters, or diaries. It can provide insights into the experiences and perspectives of individuals during a particular historical period.

Data Collection Methods

Data Collection Methods are as follows:

• Archival research : This involves analyzing documents and records that have been preserved over time, such as government records, diaries, letters, newspapers, and photographs. Archival research is often conducted in libraries, archives, and museums.
• Oral history : This involves conducting interviews with individuals who have lived through a particular historical period or event. Oral history can provide a unique perspective on past events and can help to fill gaps in the historical record.
• Artifact analysis: This involves examining physical objects from the past, such as tools, clothing, and artwork, to gain insights into past cultures and practices.
• Secondary sources: This involves analyzing published works, such as books, articles, and academic papers, that discuss past events and cultures. Secondary sources can provide context and insights into the historical period being studied.
• Statistical analysis : This involves analyzing numerical data from the past, such as census records or economic data, to identify patterns and trends.
• Fieldwork : This involves conducting on-site research in a particular location, such as visiting a historical site or conducting ethnographic research in a particular community. Fieldwork can provide a firsthand understanding of the culture and environment being studied.
• Content analysis: This involves analyzing the content of media from the past, such as films, television programs, and advertisements, to gain insights into cultural attitudes and beliefs.

Data Analysis Methods

• Content analysis : This involves analyzing the content of written or visual material, such as books, newspapers, or photographs, to identify patterns and themes. Content analysis can be used to identify changes in cultural values and beliefs over time.
• Textual analysis : This involves analyzing written texts, such as letters or diaries, to understand the experiences and perspectives of individuals during a particular historical period. Textual analysis can provide insights into how people lived and thought in the past.
• Discourse analysis : This involves analyzing how language is used to construct meaning and power relations in a particular historical period. Discourse analysis can help to identify how social and political ideologies were constructed and maintained over time.
• Statistical analysis: This involves using statistical methods to analyze numerical data, such as census records or economic data, to identify patterns and trends. Statistical analysis can help to identify changes in population demographics, economic conditions, and other factors over time.
• Comparative analysis : This involves comparing data from two or more historical periods or events to identify similarities and differences. Comparative analysis can help to identify patterns and trends that may not be apparent from analyzing data from a single historical period.
• Qualitative analysis: This involves analyzing non-numerical data, such as oral history interviews or ethnographic field notes, to identify themes and patterns. Qualitative analysis can provide a rich understanding of the experiences and perspectives of individuals in the past.

Historical Research Methodology

Here are the general steps involved in historical research methodology:

• Define the research question: Start by identifying a research question that you want to answer through your historical research. This question should be focused, specific, and relevant to your research goals.
• Review the literature: Conduct a review of the existing literature on the topic of your research question. This can involve reading books, articles, and academic papers to gain a thorough understanding of the existing research.
• Develop a research design : Develop a research design that outlines the methods you will use to collect and analyze data. This design should be based on the research question and should be feasible given the resources and time available.
• Collect data: Use the methods outlined in your research design to collect data on past events, people, and cultures. This can involve archival research, oral history interviews, artifact analysis, and other data collection methods.
• Analyze data : Analyze the data you have collected using the methods outlined in your research design. This can involve content analysis, textual analysis, statistical analysis, and other data analysis methods.
• Interpret findings : Use the results of your data analysis to draw meaningful insights and conclusions related to your research question. These insights should be grounded in the data and should be relevant to the research goals.
• Communicate results: Communicate your findings through a research report, academic paper, or other means. This should be done in a clear, concise, and well-organized manner, with appropriate citations and references to the literature.

Applications of Historical Research

Historical research has a wide range of applications in various fields, including:

• Education : Historical research can be used to develop curriculum materials that reflect a more accurate and inclusive representation of history. It can also be used to provide students with a deeper understanding of past events and cultures.
• Museums : Historical research is used to develop exhibits, programs, and other materials for museums. It can provide a more accurate and engaging presentation of historical events and artifacts.
• Public policy : Historical research is used to inform public policy decisions by providing insights into the historical context of current issues. It can also be used to evaluate the effectiveness of past policies and programs.
• Business : Historical research can be used by businesses to understand the evolution of their industry and to identify trends that may affect their future success. It can also be used to develop marketing strategies that resonate with customers’ historical interests and values.
• Law : Historical research is used in legal proceedings to provide evidence and context for cases involving historical events or practices. It can also be used to inform the development of new laws and policies.
• Genealogy : Historical research can be used by individuals to trace their family history and to understand their ancestral roots.
• Cultural preservation : Historical research is used to preserve cultural heritage by documenting and interpreting past events, practices, and traditions. It can also be used to identify and preserve historical landmarks and artifacts.

Examples of Historical Research

Examples of Historical Research are as follows:

• Examining the history of race relations in the United States: Historical research could be used to explore the historical roots of racial inequality and injustice in the United States. This could help inform current efforts to address systemic racism and promote social justice.
• Tracing the evolution of political ideologies: Historical research could be used to study the development of political ideologies over time. This could help to contextualize current political debates and provide insights into the origins and evolution of political beliefs and values.
• Analyzing the impact of technology on society : Historical research could be used to explore the impact of technology on society over time. This could include examining the impact of previous technological revolutions (such as the industrial revolution) on society, as well as studying the current impact of emerging technologies on society and the environment.
• Documenting the history of marginalized communities : Historical research could be used to document the history of marginalized communities (such as LGBTQ+ communities or indigenous communities). This could help to preserve cultural heritage, promote social justice, and promote a more inclusive understanding of history.

Purpose of Historical Research

The purpose of historical research is to study the past in order to gain a better understanding of the present and to inform future decision-making. Some specific purposes of historical research include:

• To understand the origins of current events, practices, and institutions : Historical research can be used to explore the historical roots of current events, practices, and institutions. By understanding how things developed over time, we can gain a better understanding of the present.
• To develop a more accurate and inclusive understanding of history : Historical research can be used to correct inaccuracies and biases in historical narratives. By exploring different perspectives and sources of information, we can develop a more complete and nuanced understanding of history.
• To inform decision-making: Historical research can be used to inform decision-making in various fields, including education, public policy, business, and law. By understanding the historical context of current issues, we can make more informed decisions about how to address them.
• To preserve cultural heritage : Historical research can be used to document and preserve cultural heritage, including traditions, practices, and artifacts. By understanding the historical significance of these cultural elements, we can work to preserve them for future generations.
• To stimulate curiosity and critical thinking: Historical research can be used to stimulate curiosity and critical thinking about the past. By exploring different historical perspectives and interpretations, we can develop a more critical and reflective approach to understanding history and its relevance to the present.

When to use Historical Research

Historical research can be useful in a variety of contexts. Here are some examples of when historical research might be particularly appropriate:

• When examining the historical roots of current events: Historical research can be used to explore the historical roots of current events, practices, and institutions. By understanding how things developed over time, we can gain a better understanding of the present.
• When examining the historical context of a particular topic : Historical research can be used to explore the historical context of a particular topic, such as a social issue, political debate, or scientific development. By understanding the historical context, we can gain a more nuanced understanding of the topic and its significance.
• When exploring the evolution of a particular field or discipline : Historical research can be used to explore the evolution of a particular field or discipline, such as medicine, law, or art. By understanding the historical development of the field, we can gain a better understanding of its current state and future directions.
• When examining the impact of past events on current society : Historical research can be used to examine the impact of past events (such as wars, revolutions, or social movements) on current society. By understanding the historical context and impact of these events, we can gain insights into current social and political issues.
• When studying the cultural heritage of a particular community or group : Historical research can be used to document and preserve the cultural heritage of a particular community or group. By understanding the historical significance of cultural practices, traditions, and artifacts, we can work to preserve them for future generations.

Characteristics of Historical Research

The following are some characteristics of historical research:

• Focus on the past : Historical research focuses on events, people, and phenomena of the past. It seeks to understand how things developed over time and how they relate to current events.
• Reliance on primary sources: Historical research relies on primary sources such as letters, diaries, newspapers, government documents, and other artifacts from the period being studied. These sources provide firsthand accounts of events and can help researchers gain a more accurate understanding of the past.
• Interpretation of data : Historical research involves interpretation of data from primary sources. Researchers analyze and interpret data to draw conclusions about the past.
• Use of multiple sources: Historical research often involves using multiple sources of data to gain a more complete understanding of the past. By examining a range of sources, researchers can cross-reference information and validate their findings.
• Importance of context: Historical research emphasizes the importance of context. Researchers analyze the historical context in which events occurred and consider how that context influenced people’s actions and decisions.
• Subjectivity : Historical research is inherently subjective, as researchers interpret data and draw conclusions based on their own perspectives and biases. Researchers must be aware of their own biases and strive for objectivity in their analysis.
• Importance of historical significance: Historical research emphasizes the importance of historical significance. Researchers consider the historical significance of events, people, and phenomena and their impact on the present and future.
• Use of qualitative methods : Historical research often uses qualitative methods such as content analysis, discourse analysis, and narrative analysis to analyze data and draw conclusions about the past.

Advantages of Historical Research

There are several advantages to historical research:

• Provides a deeper understanding of the past : Historical research can provide a more comprehensive understanding of past events and how they have shaped current social, political, and economic conditions. This can help individuals and organizations make informed decisions about the future.
• Helps preserve cultural heritage: Historical research can be used to document and preserve cultural heritage. By studying the history of a particular culture, researchers can gain insights into the cultural practices and beliefs that have shaped that culture over time.
• Provides insights into long-term trends : Historical research can provide insights into long-term trends and patterns. By studying historical data over time, researchers can identify patterns and trends that may be difficult to discern from short-term data.
• Facilitates the development of hypotheses: Historical research can facilitate the development of hypotheses about how past events have influenced current conditions. These hypotheses can be tested using other research methods, such as experiments or surveys.
• Helps identify root causes of social problems : Historical research can help identify the root causes of social problems. By studying the historical context in which these problems developed, researchers can gain a better understanding of how they emerged and what factors may have contributed to their development.
• Provides a source of inspiration: Historical research can provide a source of inspiration for individuals and organizations seeking to address current social, political, and economic challenges. By studying the accomplishments and struggles of past generations, researchers can gain insights into how to address current challenges.

Limitations of Historical Research

Some Limitations of Historical Research are as follows:

• Reliance on incomplete or biased data: Historical research is often limited by the availability and quality of data. Many primary sources have been lost, destroyed, or are inaccessible, making it difficult to get a complete picture of historical events. Additionally, some primary sources may be biased or represent only one perspective on an event.
• Difficulty in generalizing findings: Historical research is often specific to a particular time and place and may not be easily generalized to other contexts. This makes it difficult to draw broad conclusions about human behavior or social phenomena.
• Lack of control over variables : Historical research often lacks control over variables. Researchers cannot manipulate or control historical events, making it difficult to establish cause-and-effect relationships.
• Subjectivity of interpretation : Historical research is often subjective because researchers must interpret data and draw conclusions based on their own biases and perspectives. Different researchers may interpret the same data differently, leading to different conclusions.
• Limited ability to test hypotheses: Historical research is often limited in its ability to test hypotheses. Because the events being studied have already occurred, researchers cannot manipulate variables or conduct experiments to test their hypotheses.
• Lack of objectivity: Historical research is often subjective, and researchers must be aware of their own biases and strive for objectivity in their analysis. However, it can be difficult to maintain objectivity when studying events that are emotionally charged or controversial.
• Limited generalizability: Historical research is often limited in its generalizability, as the events and conditions being studied may be specific to a particular time and place. This makes it difficult to draw broad conclusions that apply to other contexts or time periods.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Importance of Quantitative Research Across Fields

First of all, research is necessary and valuable in society because, among other things, 1) it is an important tool for building knowledge and facilitating learning; 2) it serves as a means in understanding social and political issues and in increasing public awareness; 3) it helps people succeed in business; 4) it enables us to disprove lies and support truths; and 5) it serves as a means to find, gauge, and seize opportunities, as well as helps in finding solutions to social and health problems (in fact, the discovery of COVID-19 vaccines is a product of research).

Now, quantitative research, as a type of research that explains phenomena according to numerical data which are analyzed by means of mathematically based methods, especially statistics, is very important because it relies on hard facts and numerical data to gain as objective a picture of people’s opinion as possible or an objective understanding of reality. Hence, quantitative research enables us to map out and understand the world in which we live.

In addition, quantitative research is important because it enables us to conduct research on a large scale; it can reveal insights about broader groups of people or the population as a whole; it enables researchers to compare different groups to understand similarities and differences; and it helps businesses understand the size of a new opportunity. As we can see, quantitative research is important across fields and disciplines.

Let me now briefly discuss the importance of quantitative research across fields and disciplines. But for brevity’ sake, the discussion that follows will only focus on the importance of quantitative research in psychology, economics, education, environmental science and sustainability, and business.

First, on the importance of quantitative research in psychology .

We know for a fact that one of the major goals of psychology is to understand all the elements that propel human (as well as animal) behavior. Here, one of the most frequent tasks of psychologists is to represent a series of observations or measurements by a concise and suitable formula. Such a formula may either express a physical hypothesis, or on the other hand be merely empirical, that is, it may enable researchers in the field of psychology to represent by a few well selected constants a wide range of experimental or observational data. In the latter case it serves not only for purposes of interpolation, but frequently suggests new physical concepts or statistical constants. Indeed, quantitative research is very important for this purpose.

It is also important to note that in psychology research, researchers would normally discern cause-effect relationships, such as the study that determines the effect of drugs on teenagers. But cause-effect relationships cannot be elucidated without hard statistical data gathered through observations and empirical research. Hence, again, quantitative research is very important in the field of psychology because it allows researchers to accumulate facts and eventually create theories that allow researchers in psychology to understand human condition and perhaps diminish suffering and allow human race to flourish.

Second, on the importance of quantitative research in economics .

In general perspective, the economists have long used quantitative methods to provide us with theories and explanations on why certain things happen in the market. Through quantitative research too, economists were able to explain why a given economic system behaves the way it does. It is also important to note that the application of quantitative methods, models and the corresponding algorithms helps to make more accurate and efficient research of complex economic phenomena and issues, as well as their interdependence with the aim of making decisions and forecasting future trends of economic aspects and processes.

Third, on the importance of quantitative research in education .

Again, quantitative research deals with the collection of numerical data for some type of analysis. Whether a teacher is trying to assess the average scores on a classroom test, determine a teaching standard that was most commonly missed on the classroom assessment, or if a principal wants to assess the ways the attendance rates correlate with students’ performance on government assessments, quantitative research is more useful and appropriate.

In many cases too, school districts use quantitative data to evaluate teacher effectiveness from a number of measures, including stakeholder perception surveys, students’ performance and growth on standardized government assessments, and percentages on their levels of professionalism. Quantitative research is also good for informing instructional decisions, measuring the effectiveness of the school climate based on survey data issued to teachers and school personnel, and discovering students’ learning preferences.

Fourth, on the importance of quantitative research in Environmental Science and Sustainability.

Addressing environmental problems requires solid evidence to persuade decision makers of the necessity of change. This makes quantitative literacy essential for sustainability professionals to interpret scientific data and implement management procedures. Indeed, with our world facing increasingly complex environmental issues, quantitative techniques reduce the numerous uncertainties by providing a reliable representation of reality, enabling policy makers to proceed toward potential solutions with greater confidence. For this purpose, a wide range of statistical tools and approaches are now available for sustainability scientists to measure environmental indicators and inform responsible policymaking. As we can see, quantitative research is very important in environmental science and sustainability.

But how does quantitative research provide the context for environmental science and sustainability?

Environmental science brings a transdisciplinary systems approach to analyzing sustainability concerns. As the intrinsic concept of sustainability can be interpreted according to diverse values and definitions, quantitative methods based on rigorous scientific research are crucial for establishing an evidence-based consensus on pertinent issues that provide a foundation for meaningful policy implementation.

And fifth, on the importance of quantitative research in business .

As is well known, market research plays a key role in determining the factors that lead to business success. Whether one wants to estimate the size of a potential market or understand the competition for a particular product, it is very important to apply methods that will yield measurable results in conducting a  market research  assignment. Quantitative research can make this happen by employing data capture methods and statistical analysis. Quantitative market research is used for estimating consumer attitudes and behaviors, market sizing, segmentation and identifying drivers for brand recall and product purchase decisions.

Indeed, quantitative data open a lot of doors for businesses. Regression analysis, simulations, and hypothesis testing are examples of tools that might reveal trends that business leaders might not have noticed otherwise. Business leaders can use this data to identify areas where their company could improve its performance.