Journal of Social Research & Policy, Vol. 3, Issue 1, July 2012

James Jaccard & Jacob Jacoby (Eds.), Theory Construction and ModelBuilding Skilss. A Practical Guide for Social Scientist. New York: The Guilford Press, 2010, 391 p.1 Book review

Using multivariate statistics is a must if we want to adequately grasp the complexity of social mechanisms. For several years already, it is quite difficult to publish, in a respected journal, papers that aren’t using at least a type of regression analysis (selected according to the type of data that you are using). Even this seems to become insufficient. We now use confirmatory factor analysis, structural equation modelling, multilevel regression or even multilevel structural equation modelling. And this is understandable if we look at theories that we use to explain the social life. For example, one can say that, without high levels of understanding of the link between theory and data, we cannot measure and analyse in a comprehensive way the individualization theory of Beck and Beck-Gernsheim (2011)? A frequent mistake that some researchers do is that they are trying to learn how to use the advanced statistical techniques or even how to work in a specific software, forgetting the utility of the data for theory construction. This way they get lost in technicalities. I often found myself in front of papers that glitter with high-tech analysis but forget to make the connection with the laws of social life (if there are such laws). It is often forgotten the importance of theoretical logic that is so important in carefully selecting and using multivariate statistics. I found these papers no more useful than those that use descriptive statistics to sustain a theoretical point. The book written by James Jaccard and Jacob Jacoby successfully fills this gap. Reading it, you become enlighten about the importance of developing your theory construction and modelbuilding skills. This is an „out-of-the-box” approach to such a delicate subject. And this reflects only the way of thinking of its authors. For example, as it is stated in NYU Silver School of Social Work own faculty profile, James Jaccard is currently developing a general framework for statistical analysis that eschews p values and focuses on magnitude estimation and margins of error. Let me refer, with the purpose of exemplification, to just one of the book sections: chapter 8 “Mathematical Modeling” from the 3rd part “Frameworks for theory construction”. Types of variable is a subject quite beautiful discussed elsewhere too (Agresti & Finlay 1997), but here it emerges more clearly the importance of understanding the nature of the underlying theoretical dimension and not of the scale used to measure that dimension. We are often preoccupied with problems such as the number of scale points to use, if the label should be convergent with the code etc., but we seem to forget that we are using the latent variable in the interpretation process and not a specific item. Here, Jaccard and Jacoby, devote most of their attention to the case in which the theorists are working with continuous variables. Using an easy to read language, they explain graphically and through examples, what is a linear function. In several pages they make clear what is a slope and an intercept, this being sometimes a difficult task when you try 1

Marian Vasile’s work on this paper was supported by the grant UEFISCDI CNCS PN-II-RU-PD-2011-3-0117 and the grant UEFISCDI CNCS PN-II-ID-PCE-2011-3-0210.

140 | JSRP

Marian Vasile

to present it to a non-technical professional (like a student, policy maker, journalist etc.). Then, they seamlessly refer to the error term in a 1st degree function, introducing you in this way to the linear regression equation. Abstract mathematical language, like derivatives and differentiation, is linked to its utility for a data analyst, rates and change. We understand the difference between just-identified, overidentified and underidentified models. In the same chapter they discuss, shortly but comprehensively, about nonlinearity and ways to deal with it. We learn about logarithmic, exponential, power, polynomial and trigonometric functions. They are presented in comparative terms, and linked with their use in real life statistical analysis. The reader understands, in this way, when to choose a specific function for her/his analysis. Most of all, after reading this pages, s/he can start thinking first to the theoretical distribution of the variable and/or it’s relationship with another one before opening the statistical program. And this is quite useful in an earlier stage of the research process: the phase of designing the questionnaire. If the researcher is aware of the association between theory and the questions asked in the survey then the published results can become more comprehensive and not just a statistical techniques museum. The chapter continues with examples about phases in building a mathematical model. Using acknowledged theories, like the model of attitude change developed by Martin Fishbein & Icek Ajzen (1975), he puts the researcher in a simulation room and makes him think at his own research. Besides the model for probability of acceptance, Jaccard and Jacoby work with the relationship between performance, ability and motivation, cognitive algebra, chaos theory or catastrophe theory. This chapter like all the others, ends with three sections: suggested readings, key terms (with pages in the book for quick reference) and exercises to reinforce the concepts. In this way, the book becomes a very useful manual for students and autodidact researchers. This book can be the base for a stand-alone course or a great support for several courses: social statistics, research methods in social research or social theory. It integrates all this discourses, making more clear for the reader the relationship between this domains, all to often and unfortunate considered as different entities. After all, in how many books did we meet information about grand theories (materialism, structuralism, functionalism, symbolic interactionism, evolutionary perspectives, postmodernism) and statistical modeling? The book written by Jaccard & Jacobby seems to follow the 7th rule for social research stated by Glenn Firebaugh in 2008: “Let Method Be the Servant, Not the Master”. For statistical methodology I recommend two very well written and to-day informed books: the 5th edition published in 2007 of Using Multivariate Statistics by Barbara G. Tabachnick and Linda S. Fidell and the 7th edition published in 2010 of Multivariate Data Analysis. A Global Perspective by Joseph F. Hair, William C. Black, Barry J. Babin, & Rolph E. Anderson. The book has four parts. In the one already noted, we can learn, also, about causal models, simulation as a theory development method and ground and emergent theory. In the first part, named “Basic Concepts”, authors talk about the nature of understanding and science as an approach to understanding. In the second part, named “Core Processes” the subjects are creativity and the generation of ideas, focusing concepts and clarifying relationships using thought experiments. In the last part, we are advised about how to read and write scientifically. We have several rules that can increase the probability of producing and communicate significant knowledge: briefer is better, but don’t be too brief; prepare an outline; provide a road map; provide a succinct review of the current knowledge; discuss the implications and importance of your theory; keep your target audience in mind; use figures etc. I believe this is a must-read book for all undergraduate students but, also, for the researcher who looks for meaningful relationships in social world. Marian Vasile

Book Review

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References 1.

Agresti, A., & Finlay, B. (1997). Statistical Methods for the Social Sciences, 3rd edition. New Jersey: Prentice-Hall, Inc.

2.

Beck, U., & Beck-Gernsheim, E. (2011). Individualization. Institutionalized Individualism and its Social and Political Consequences. Los Angeles: Sage.

3.

Firebaugh, G. (2008). Seven Rules for Social Research. Princeton and Oxford: Princeton University Press.

4.

Fishbein, M., & Ajzen, I. (1975), Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research, Reading, Mass.: Addison-Wesley Pub. Co.

5.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis. A Global Perspective, 7th edition. Boston: Pearson.

6.

Tabachnick, B. G., & Fidell, L. S. (2007). Using Multivariate Statistics, 5th edition. Boston: Pearson.

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