Presenting Data: Tabular and graphic display of social indicators
 

Gary Klass
Illinois State University
© 2002

Note: The website will be discontinued shortly, to be replaced by the Just Plain Data Analysis site

Introduction

JPDA: Just plain data analysis

Constructing good tables for the display of social indicators 

 

Constructing good charts and graphs  (revised 7/21/06)

General principles of graphic display

Time series charts.

Bar charts

Tips on using MS\Excel to prepare charts and graphs.

How to construct bad charts and graphs

        Specific Data Topics:

• Budgets
• Poverty  (revised 7/21/06)
• Elections
• Education

The Chart of the Week

References

Introduction: JPDA:  Just Plain Data Analysis (this section is under construction)

The compilation and presentation of numerical information
to support and illustrate arguments about
politics and public issues.

In recent years the discipline of political science has a seen a resumption of the battles waged during the behavioral revolution of the 1950’s.  In the spring of 2000, an email message written under the nom de guerre “Mr. Perestroika” initiated an attack on what was seen as the "hegemony" of hard science methodology and the suppression of case study and qualitative analyses in the discipline's journals and many of its graduate programs. The most serious charges were that the emphasis on hard quantitative science in the discipline's most prestigious journals and many of its graduate programs did not reflect the diversity or pluralism of the discipline and that the research often addressed only apolitical and trivial subject matters, those most amenable to the hard methodology. In political science “hard science” stood to mean two somewhat disparate research enterprises: quantitative research in the form of increasingly econometric research and statistics approach, and formal modeling (a.k.a public choice theory, rational choice, or disparagingly, “Rat choice”). 

While the Perestroikans have pursued the cause of methodological pluralism (a concept laden with many meanings in the writings of political scientists), those on the hard science side have struggled to find a unifying methodology for the discipline that would make the discipline more like, say, economics or psychology.   The two most recent attempts have been Gary King’s effort to articulate the common principles of quantitative and qualititative research (King, 1989 and King and Keohane, 1994) and the development of  the “Empirical Implications of Theoretical Models” (EITM) approach designed to encompass both quantitative and rational choice methods (National Science Foundation, 2002).  

The stakes in this conflict are mostly academic, which is to say trivial, but they do involve issues that are crucially important to many political scientists: which research gets published, which scholars get hired for tenure track positions and what types of courses are to be required of students engaged in graduate and undergraduate political science study. 

What most of this debate misses, however, is that there is a quantitative political science methodology, at least a century old, (cf. Allen, 1906) common to almost every field of political science, practiced by political scientists in academia, government and the private sector, and found in most of the political science textbooks and in much of the most read literature in the discipline.  I call it Just Plain Data Analysis (JPDA).  

What is JPDA?

JPDA is, simply, the compilation and presentation of numerical information to support and illustrate arguments about politics and public issues. 

JPDA differs from what is commonly regarded as quantitative political science methodology in that it usually does not involve formal tests of theories, hypotheses or null hypotheses.  It abjures measures of association and statistical significance.  Rather than relying on statistical analysis of a single dataset, JPDA -- at its best -- involves compiling all the relevant evidence from multiple data sources.   Although the utility of any methodology or statistical procedure is best judged in the context of the research questions and issues being addressed, JPDA has many advantages over the standard hard quantitative analysis and there are many instances where political scientists would be better off using just plain data analysis and foregoing elaborate regression based analyses of single dataset.  The foremost advantage of JPDA is that it is accessible to a broad audience.  While most regression-based analyses of empirical models rest on often elaborate, unexamined assumptions, plain data analysis is accessible and transparent.

This is not to say that plain data analysis does not involve empirical analysis of political science theories.  

Examples of JPDA in political science:

Just plain data analysis has a long tradition in political science and it was essential to what political scientists meant when they first use the term "science" to define what their discipline was all about. 

Figure I.1: Allen (1906) (click on thumbnails)

Perhaps the first analysis of the impacts of technology on elections was Philip Loring Allen’s 1906 study of the effects of variations in state balloting procedures. Allen developed measures, still in use today, of split-ticket voting and what would later be called “roll off” to evaluate the fairness and reliability of a variety voting procedures. Writing in the tradition of the progressive reform movement that was a force in both American politics and American political science at the time, Allen recognized that voting technology was not necessarily politically neutral and was especially critical of the effect of voting mechanisms that favored the “spoilsmen” over the independent voter, while acknowledging these technologies had disparate impacts on the educated and uneducated classes.

Figure I.2 Nie. et. al (1976)

Much of the earliest "hard" quantitative research in political science involved analyses of American voting behavior, made possible by the availability of the American National Election Surveys and the development of the computer equipment and software used to analyze them in the 1960s and 1970s.  One of the classic studies of American voting behavior The Changing American Voter (1976), an excellent example of just plain data analysis, summarized previous quantitative analyses and defined the context for much of quantitative research that was to come later.  The high quality of the tabular and graphical representations of data in the book -- at a time when charts and graphs were done by hand -- 

 

 

 

 

 

Figure I.3 Putnam (2003)

Robert Putnam's Bowling Alone (2000), a work that has probably inspired more conference papers and journal articles across more of the discipline's subfields than any other piece of political science, is another good example of plain data analysis. Almost all of Putnam’s analysis is grounded in some kind of presentation of quantitative data, from a wide variety of sources, and presented in charts and graphs. Putnam describes his strategy as attempting to “triangulate among as many independent sources of information as possible” based on the “core principle” that “no single source of data is flawless, but the more numerous and diverse the sources, the less likely that the could all be influenced by the same flaw” (415). Although almost all of the data are based on public opinion surveys, the data presentations rarely require the use of measures of statistical significance and are presented as illustration of the general theory rather than statistical tests of hypotheses.  Unfortunately, Putnam's graphics leave much to be desired; many, if not most, of his figures violate fundamental principles of graphic design.

 

 

 

 

Figure I.4 Perestroikan JPDA

Just plain data analysis permeates almost every field of political science.  Consider the Perestroikan's own attack on "hard" political science: a Perestroikan sponsored symposium in the American Political Science Association’s newsletter-journal, PS, Political Science and Politics contained 5 essays decrying the hegemony of quantitative research (and its companion non-quantitative formal modeling). And every one supports its argument with reference to quantitative data derived from systematic empirical surveys of the discipline's journals and graduate program curricular requirements. The results are presented bar charts and time series charts (Bennett, Barth, and Rutherford 2003, 375-6) and tabulations (Shwartz-Shea 2003, 380-5) and in textual discussions, such as this from Yanow (2003, 397):

"From 1991–2000, research based on statistics and modeling accounted for 74% of all published articles (53% and 21% respectively), political theory garnered 25% of journal space, and qualitative research captured 1% (one article each in 1992, 1993, 1995, 1996, 1997)."

 Clearly, Perestroikans are not opposed to the use of quantitative data.

Figure I.5 Sniderman (1997)

Doing JPDA well involves an appreciable set of skills and, although JPDA is the most pervasive form of quantitative political science analysis, it is generally not taught to students in research methods courses.  Six basic skills are involved:

• Understanding key political science indicators. 
• Finding meaningful data.
• Constructing appropriate statistical measures.
• Assessing data reliability and validity.
• Data presentation skills
• Use of spreadsheet charting software

Allen, Philip Loring. (1906).  Ballot laws and their workings. Political Science Quarterly, 21(1), 38-58.

Bennett, Andrew, Aharon Barth, Kenneth R. Rutherford. 2003. “Do We Preach What We Practice? A Survey of Methods in Political Science Journals and Curricula.” PS: Political Science and Politics 36 (July): 381–386.

King, Gary 1989. Unifying Political Methodology: The Likelihood Theory of Statistical Inference (University of Michigan Press).

King, Gary, Robert O. Keohane, and Sidney Verba 1994. Designing Social Inquiry: Scientific Inference in Qualitative Research (Princeton University Press).

National Science Foundation (2002) Political Science Program, Directorate For Social, Behavioral and Economic Sciences.   EITM: Empirical Implications of Theoretical Models (Workshop Report) http://www.nsf.gov/sbe/ses/polisci/reports/pdf/eitmreport.pdf