How to Build Social Science Theories

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Pamela J. Shoemaker, James William Tankard Jr. & Dominic L. Lasorsa

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  • Dedication

    Dedicated to…

    F. Scott Sherman

    Elaine F. Tankard

    L. Richard Scroggins

    Copyright

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    Preface

    One of us (Tankard) was teaching at the University of Wisconsin in 1970–71 when two graduate students, Robert Krull and James Watt, mentioned an exciting course they were taking from a sociology professor named Jerald Hage. The course dealt with theory building in sociology, and the students said Hage had just written his own text for the course and distributed it to the class in mimeographed form. Tankard showed interest in seeing the document, and Krull and Watt obtained a copy for him. It was 220 mimeographed pages, and the figures were drawn by hand. The title of the manuscript was Techniques and Problems of Theory Construction. The manuscript was not always easy reading, with examples taken primarily from sociology, but it dealt with the important matter of theory building in a more systematic and comprehensive way than anything else that was around.

    In 1972, the manuscript was published by John Wiley & Sons under the title Techniques and Problems of Theory Construction in Sociology. Tankard, who was teaching theory and research methods at the University of Texas, began assigning chapters in graduate seminars. Despite a few student complaints about the difficulty of the material, he continued to assign chapters from the Hage book for years because the ideas were so important and useful.

    Another of us (Shoemaker) remembers going through graduate classes at the University of Wisconsin in which theory building and testing were discussed and expecting to see an example of a theory with all its various parts. She discovered that there weren't any—social science theories tend to be spread in a nonsystematic way across many articles and books. This was the beginning of her interest in theory building. Shoemaker came to the University of Texas to teach in 1982 and began assigning chapters from the Hage book, which she knew about from her graduate courses at Wisconsin. The Hage “system” played a major role in the development of her dissertation at Wisconsin, causing her for the first time to systematically provide theoretical and operational definitions for all concepts, along with theoretical and operational linkages for all hypotheses. At Texas, she required the book in her graduate theory classes until it went out of print. The students complained at the time about how difficult the book was to read and comprehend, but for years later, those same scholars said again and again how important the book ultimately was to their research programs.

    In the spring of 1981, another of us (Lasorsa) was a master's student in a communication theory course taught by Tankard and received his first exposure to the Hage book. His initial reaction was typical of many students faced with Hage for the first time, reading Chapter 1 on theoretical statements and trying to digest it all: “Specific nonvariables? Primitive terms? Discrepancy variables? Do I really need to know this stuff?” The teacher tried his best to convince the student that if he wanted to learn how to construct theory, then, yes, he probably did need to know this stuff. Lasorsa accepted Tankard's word and went on to become a teacher himself. Now he, too, tries to instruct others in the ways of Hage.

    The models chapter of this book includes a model of the cultivation process that Lasorsa first began developing while a student in Tankard's communication theory seminar.

    One of the shortcomings of the Hage book that became noticeable in the 1980s was that it dealt mostly with theoretical statements relating two variables. This limitation prompted Tankard to write a paper titled “Beyond Hage: Building Communication Theory by Exploring Three-Variable Relationships,” which was presented at an international conference in Montreal in 1987. Shoemaker began using this paper in seminars along with the Hage readings and also added some figures that helped in the visualizing of three-variable relationships.

    Lasorsa finished his master's degree and went to study for a doctorate at Stanford. He returned to Texas to teach in 1986. At this point, all three of us were at the University of Texas, teaching courses in social science research methods and communication theory. We began talking about writing our own book on theory building. The book would update and extend some of Hage's thinking but would also explore new areas, such as three- and four-variable relationships and the role of creativity in theory building. Another factor motivating us was that, at a certain point, the Hage volume went out of print. A recent search of http://amazon.com found a used copy of Hage's book for sale for $126.96.

    In 1989, the three of us began meeting for lunch at Les Amis, a sidewalk café (now replaced by a Starbucks) a block away from the university, and discussing theory building. After a meeting or two, we gave ourselves the assignment of each writing every 2 weeks a five-page essay on any aspect of theory building that interested us. We also came up with our first outline for a book dealing with theory building.

    In 1991, Shoemaker left the University of Texas to become an administrator at Ohio State University, and the theory-building project was shelved for a while. When Shoemaker went back to research and teaching at Syracuse University in 1994, we revived the book idea. We circulated our old outline, revised it, and assigned chapters. The chapters were completed in December 2001. The project took more than a decade to complete, but it now reflects our more mature understanding of social science theory structure. It was not an easy book to write, but it is some of the most satisfying work we have done.

    We would like to acknowledge the influence of Jerald Hage, now in the Department of Sociology at the University of Maryland, and thank him for being a pioneer in this new and important area. We hope readers of this book will come to share our excitement about theory building and use the ideas and principles contained in this book to conduct better research and build better theories.

    Pamela Shoemaker
    James W. Tankard, Jr.
    Dominic L. Lasorsa June 2003

    Foreword

    Periodic cycles apply to topics in the social sciences as they do in fashion. In the 1960s, there was considerable interest in the building of social science theory when the accent was on the word science. A proliferation of books on the logic of theory or techniques for theory construction was its direct manifestation. Then, in a very short time, by the 1970s, the focus had shifted to the opposite extreme, namely a distrust of theory and even an ideological opposition to the word science. The word social, as in social construction, has now become operative.

    So when I received an e-mail from Jim Tankard that the book How to Build Social Science Theories (hereafter HBSST) was about to be published, I was excited and for several reasons. First, I had been thinking of returning to the topic of theory construction myself. Now, the publication of this excellent book frees me to move on to some of my other concerns. Here is a book in which the most important ideas in my own effort have been retained, elaborated, and improved upon in a number of interesting ways. Second, and more critically, HBSST is a hopeful sign of a renewed interest in building social science theories and in science as a way to make this world a better world to live, a raison d’être that postmodernists have failed to appreciate.

    After receiving a copy of the manuscript and reading the preface, I must also admit that I was deeply touched. I had had a very strong commitment to teaching theory construction because I had always believed that many more individuals could be creative if they were provided with the right language and the right tools. This was the objective of Techniques and Problems of Theory Construction (hereafter TPTC): to deconstruct the problem of constructing good, solid, and valid theory that would withstand empirical tests into a series of manageable steps such as finding continuous variables and specifying their definitions and linkages. So I rejoiced when I discovered that Shoemaker, Tankard, and Lasorsa had had these same concerns for a number of years.

    As I have suggested above, HBSST has improved upon my effort by

    • Providing a number of examples and in enough detail so that readers can more easily grasp quite difficult and abstract ideas involved in such notions as continuous variables, theoretical definitions, and operational linkages
    • Integrating the discussion of theoretical and operational linkages with statistics and research methodology more generally
    • Adding three- and four-variable sets of hypotheses and linkages
    • Including a whole chapter on models and their use in theory building, which can be a wonderful source of new ideas about variables

    Each of these kinds of elaborations requires a few comments.

    My original effort included too many new terms for the typical graduate student. Shoemaker, Tankard, and Lasorsa have rightly concentrated on the analytical importance of continuous versus categorical concepts, theoretical and operational definitions, and theoretical and operational linkages. These are the core ideas, which they have carefully explained with numerous examples. The richness of the examples, I am sure, will make these abstract ideas concrete enough so that they are comprehensible. Indeed, it is clear from the preface that the authors have spent some time teaching these ideas and learning how best to communicate them to their students.

    A truly important contribution is the integration of the theoretical ideas of operational definitions and linkages with statistics. In particular, Chapter 4 has a rich variety of examples that make apparent the connection between theory and research. I do not know if graduate departments of mass communication resemble those in sociology, but I do know that in the latter there has been a wide separation between those interested in theory and those interested in research. This chapter, like other parts of the book, narrows the distance between these alternative ways of thinking.

    At the time I was writing TPTC (in 1968, while I was living in Birmingham, England, and teaching at the University of Aston), path analysis did not exist, and it was difficult to get students to think in terms of independent and dependent variables, let alone anything more complex than this. But today three- and four-variable analyses are commonplace. HBSST has a separate chapter devoted to each of these issues, which is wise indeed. The three-variable case is a paradigm that is well integrated with the analytical strategies of Columbia University (taught by Paul Lazarsfeld and Herbert Hyman, by whom I was trained as a graduate student) and is another illustration of how theory and research can be usefully combined. The next chapter examines the set of concerns involved in analyses of four and more variables.

    What could also be added to the latter discussion is the importance of combining alternative paradigms as a fruitful theoretical and research strategy. Sociology, like many of the social sciences, has been riddled with controversies over the past few decades. It seems to me that rather than debating opposing viewpoints it is much more fruitful and appropriate to include them within the same research study. Of course, one might question whether it is possible to represent another perspective accurately when one is committed to a specific viewpoint. Each of us has to provide his or her own answer to that question. But without a doubt, the field advances more assuredly when opposing paradigms are combined in the same theoretical framework and data analysis.

    A special chapter on the use of models constitutes a wonderful addition to the armamentarium of theoretical thinking. Indeed, models can be a useful source for generating hypotheses or theoretical linkages. Again, ideally, one would like to combine several of them relative to a single subject.

    Now, who does this book address? All its examples involve the field of mass communications, yet the book is entitled How to Build Social Science Theories. Though it would seem to have a very specific audience in mind, I would argue that it should be read by anyone preparing for a career in social science research, whether in an academic, governmental, or private sector setting. Researchers—and this necessarily includes PhD candidates—are required to frame their subject matter theoretically, indicating what is missing and suggesting what might be helpful in filling the gaps in knowledge. The separate chapters as well as the discussion of evaluation provide a framework to do just that—determine the missing parts—and provide ideas about how to eliminate knowledge gaps. Increasingly, people hired with either MSs or PhDs are asked to demonstrate that they have this capacity to generate new theoretical concepts and hypotheses. Certainly, this is the gist of questions I have been asked to answer when writing references for students who have used my name. This book can help them in this endeavor and ensure a more successful career for them.

    Finally, and because the last chapter touches on the problem of creativity, let me make a few comments about this issue. Without the same labels, many of these ideas were buried in the examples in my own book TPTC in specific discussions of how to think about a new concept, operational definition, or theoretical linkage. Creativity in thinking is also affected by the variety and sources of information that we monitor, as any student of mass communication would probably agree with. Here are some the ways in which the variety can be increased:

    • Cultivating a diverse set of friends in terms of age as well as culture, politics, and religion
    • Knowing multiple languages well enough to be able think in them
    • Living in another country and adapting successfully to its culture rather than remaining in an American ghetto
    • Reading books and newspapers or listening to television or radio programs, especially when foreign, that disagree with our cherished perspectives and attempting to understand their criticisms of our own ways
    • Taking double majors in college or shifting disciplines between college and graduate school, especially if they represent disparate ways of thinking
    • Studying and working in teams, learning how to read the other team members’ feelings and hidden ideas

    The list could be extended, but one can easily see that the general theme is learning how to live with diversity, embracing it and understanding it. This leads to much more complex cognitive structure, which in turn will facilitate creativity in our own minds and lead to our developing new continuous variables, theoretical and operational definitions, and theoretical and operational linkages.

    Let me close by saying once again how delighted I am that so much careful thought has been given to how to communicate with students so that they can grasp the importance of thinking theoretically.

    Jerald Hage, Arrigas, France, June 2002
  • Appendix A

    Guidelines for Preparing Tables and Figures

    Before you collect data for any quantitative research project, you should think carefully about the kind of statistical analyses you'll need to test your hypotheses. Before collecting any data, we recommend preparing preliminary tables and figures to show all analyses necessary to test the hypotheses. Of course, this does not preclude post hoc analysis (correctly identified as such), but it ensures that you will have all of the data you need and that you will understand what you're about to do.

    Attached are some examples of using various statistics. These are not presented as the only way to show statistics, but they are straightforward and easy to read.

    • Univariate (descriptive) statistics—You should provide one or more tables with a univariate statistic for every variable used in your study. Variables that are measured at interval or ratio levels can be adequately described with means and standard deviations, and many of these can be presented in one table. Variables that are measured at nominal or ordinal levels should be presented as percentages (“valid” percentages, that is, with missing data removed). You may be able to group several “percentage” variables on one table, or you may need to present one per table. Be sure to give an N for each variable, whether nominal/ordinal or interval/ratio.
    • Bivariate (inferential) statistics—This is your first run at testing your hypotheses, but if you have some “control” variables (we encourage you to use them), you will probably also want to look at the bivariate relationships between your control variables and the independent and dependent variables. For interval/ratio variables, this will probably mean a correlation analysis, such as Pearson's r. For ordinal variables, use Kendall's tau, unless you are using rank-order data; then use Spearman's rho. (Table formats for Pearson's r, Kendall's tau, and Spearman's rho are all the same. Hence, only Pearson's r is shown in the following examples.) For nominal variables, use chi-square and either phi or Cramer's V (phi for a 2 × 2 table, Cramer's V for all other tables). Chi-square tells you whether the two concepts are related; phi and V tell you how strong the relationship is.
    • Multivariate (inferential) statistics—One of the steps involved in establishing a causal relationship in research is ruling out alternative explanations for the bivariate relationships observed. This is done by statistically controlling for third (and fourth, etc.) variables. For relationships between nominal and ordinal variables, this will probably be cross-tabulation with a control variable (and either the chi-square/phi/Cramer's V combination or Kendall's tau to test the original relationship within each category of the control variable). For interval and ratio variables, this will probably be partial r or hierarchical regression analysis. For a good analysis of three-variable relationships, see Chapter 5 of this book. It is generally more useful to show three-variable relationships in figure form, but both the table and figure formats are shown here.
    Blank Table Guidelines

    Although there are as many ways to present research products as there are researchers, the following have served us (and our students) well in the past. Some journals have different styles, and researchers should always follow their field's protocols.

    • We prefer that you indicate the level of statistical significance as either p > .05, p > .01, p > .001, or ns (not significant). Presenting the exact probability estimates given by a statistical program implies more precision of estimates than we can really have, based on our imprecise operational definitions.
    • Statistics are generally rounded to two decimal places. Do not use leading zeros (e.g., 0.45 should be .45). Use a format that will align numbers in a column along the decimal point.
    • We believe that the operational definitions for all variables in the tables should be presented as footnotes. This aids in interpretation of the statistics: for example, whether a Likert scale uses 1 or 5 for strongly disagree. Interpreting a mean of 4.25 will depend on the information. Any statistics based on correlations will also need operational definition information to interpret. The object is for a table to be interpretable on its own, without reference to the text.
    • We've replaced the real numbers with N's: nn.n. You can omit these in the tables you prepare before you collect your data. Just leave the spaces blank.
    Tables That Present Descriptive Statistics

    Table 1. Means and standard deviations for interval- or ratio-level variables

    Table 2. Percentages for nominal- or ordinal-level variables

    Tables That Present Bivariate Statistics

    Table 3. Cross-tabulation of a nominal variable by a nominal variable*

    Table 4. Independent tests for interval- or ratio-level variables by a dichotomous variable consisting of independent groups of cases

    Table 5. Correlated t tests for interval- or ratio-level variables using the same scale

    Table 6. One-way analysis of variance for interval- or ratio-level variables by one categorical variable, with means and standard deviations

    Table 7. Pearson correlation coefficients for interval- and ratio-level variables**

    Tables That Present Multivariate Statistics

    Table 8. Cross-tabulation with one control variable

    Table 9. Two-way analysis of variance

    Figure 1. Two-way analysis of variance

    Table 10. Analysis of covariance

    Table 11. Partial correlation coefficients

    Table 12. Multiple regression analysis

    Table 13. Factor analysis

    * Or nominal by ordinal (or vice versa) or ordinal by ordinal.

    ** This same format can be used for matrices producing other correlation coefficients, including Kendall's tau (for ordinal variables) and Spearman's rho (for rank-order variables).

    Table 1 Means and standard deviations for media use, likelihood to vote, election knowledge, and demographic variables

    Table 2 Percentages for gender and media reliance variables

    Table 3 Cross-tabulation of main source of news and public affairs information by respondent's gender

    Table 4 Independent t tests for media use variables by respondent's gender

    Table 5 Correlated t tests for media use variables

    Table 6 One-way analyses of variance for election knowledge and likelihood of voting by media reliance

    Table 7 Pearson correlation coefficients for media use and election variables

    Table 8 Cross-tabulation of main source of news and public affairs information by respondent's gender, controlling for whether the person voted in the 2000 election for U.S. president*

    Table 9 Two-way analysis of variance of gender and voting history on knowledge about the election

    Figure 1 Two-way analysis of variance of gender and voting history on knowledge about the election

    Table 10 Analysis of covariance (age and TV news exposure) of gender and voting history on knowledge about the election

    Table 11 Partial correlation coefficients for election knowledge by media exposure, controlling for education and income, N = nnn

    Table 12 Hierarchical regression analysis of demographic variables, likelihood to vote, and media use on election knowledge, N = nnn

    Table 13 Factor analysis (principal components analysis and varimax rotation) of measures of political legitimacy.

    Appendix B

    Acceptable Levels of Measurement for Various Statistics

    “x” indicates that either the “independent” or the “dependent” variable can be of this level.

    “A” indicates that the “grouping” or “factor” variable can be of this level.

    “B” indicates that the variable on which means are calculated can be of this level.

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    About the Authors

    Dominic L. Lasorsa is an Associate Professor of Journalism at the University of Texas at Austin. He received a BA in journalism from St. Bonaventure University, an MA in journalism from the University of Texas at Austin, and a PhD in communication from Stanford University. In college, he served as editor-in-chief of his school newspaper, the Bonaventure, and worked at the Suffolk (N.Y.) Sun as a Dow Jones Newspaper Fund Editing Intern. Upon graduation, he entered the U.S. Air Force, where he served as a Radio Communications Specialist and a Curriculum Development Specialist. He then worked as a reporter and editor at the Wichita (Kansas) Eagle and the Austin (Texas) American-Statesman. Before beginning his academic career, he served as editor-in-chief of the Marble Falls (Texas) Highlander. He studies and teaches communication theory and methods, focusing on political communication and media effects. He has published articles in numerous books, journals, and other publications. His works have appeared in the Encyclopedia of International Media and Communications, the Historical Dictionary of Political Communication in the United States, the International Journal of Public Opinion Research, the Journal of Communication, the Journal of Media Economics, the Journal of Reading, the Journalism and Mass Communication Quarterly, Journalism Studies, and the Newspaper Research Journal. He was a co-author of the three-volume National Television Violence Study.

    Pamela J. Shoemaker (PhD, Wisconsin-Madison) is the John Ben Snow Professor in the S. I. Newhouse School of Public Communications at Syracuse University. Her research chair is devoted to the study of news, and she has recently completed a 10-country study on the definition of news across cultures, building and testing her own theory. She is the coeditor of Communication Research, a top social science communication journal. She is also the coauthor of Mediating the Message: Theories of Influences on Mass Media Content, author of Gatekeeping, and editor of Communication Campaigns About Drugs: Government, Media, Public. She is Past-President of the Association for Education in Journalism and Mass Communication and Chair of the Mass Communication Division of the International Communication Association. She teaches courses in social science research methods and statistics, as well as theory. At this writing, she has lectured in 16 countries.

    James W. Tankard, Jr., is the Jesse H. Jones Professor in Journalism at the University of Texas at Austin. He was born in Newport News, Va. He attended Virginia Tech, where he was coeditor of the student newspaper and received a BS in general science. He received a master's of journalism degree from the University of North Carolina and a PhD in communication from Stanford University. He has worked for the Associated Press in Charlotte, N.C., and for the Raleigh Times as a county government reporter. He has also held summer jobs and other short-term positions with the Newport News (Va.) Daily Press, the U.S. Information Agency, and the Lampasas (Texas) Dispatch and Record. He has taught journalism at the University of Wisconsin, Temple University, and the University of Texas at Austin. He taught one of the first classes in the Senior Fellows Program, the honors program within the College of Communication at the University of Texas. He served for 6 years as the editor of Journalism Monographs. He is the author of The Statistical Pioneers, the coauthor of Basic News Reporting (with Michael Ryan), and the coauthor of Communication Theories (with Werner Severin). The latter is in its fifth edition and has been translated into five languages.


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