What is a model? How do you construct one? What are some common models in the social sciences? How can models be applied in new situations? What makes a model good? Focusing on answers to these and related questions, this multidisciplinary introduction to model building in the social sciences formulates interesting problems that involve students in creative model building and the process of invention. The book describes models of individual choice, exchange, adaptation, and diffusion. Throughout, student participation in analytical thinking is encouraged. Originally published in 1975 by HarperCollins Publishers.

A formal model in the social sciences builds explanations when it structures the reasoning underlying a theoretical argument, opens venues for controlled experimentation, and can lead to hypotheses. Yet more importantly, models evaluate theory, build theory, and enhance conjectures. Formal Modeling in Social Science addresses the varied helpful roles of formal models and goes further to take up more fundamental considerations of epistemology and methodology. The authors integrate the exposition of the epistemology and the methodology of modeling and argue that these two reinforce each other. They illustrate the process of designing an original model suited to the puzzle at hand, using multiple methods in diverse substantive areas of inquiry. The authors also emphasize the crucial, though underappreciated, role of a narrative in the progression from theory to model. Transparency of assumptions and steps in a model means that any analyst will reach equivalent predictions whenever she replicates the argument. Hence, models enable theoretical replication, essential in the accumulation of knowledge. Formal Modeling in Social Science speaks to scholars in different career stages and disciplines and with varying expertise in modeling.

"Princeton University Press published Imai's textbook, Quantitative Social Science: An Introduction, an introduction to quantitative methods and data science for upper level undergrads and graduates in professional programs, in February 2017. What is distinct about the book is how it leads students through a series of applied examples of statistical methods, drawing on real examples from social science research. The original book was prepared with the statistical software R, which is freely available online and has gained in popularity in recent years. But many existing courses in statistics and data sciences, particularly in some subject areas like sociology and law, use STATA, another general purpose package that has been the market leader since the 1980s. We've had several requests for STATA versions of the text as many programs use it by default. This is a "translation" of the original text, keeping all the current pedagogical text but inserting the necessary code and outputs from STATA in their place"--

Beloved for its engaging, conversational style, this valuable book is now in a fully updated second edition that presents the latest developments in longitudinal structural equation modeling (SEM) and new chapters on missing data, the random intercepts cross-lagged panel model (RI-CLPM), longitudinal mixture modeling, and Bayesian SEM. Emphasizing a decision-making approach, leading methodologist Todd D. Little describes the steps of modeling a longitudinal change process. He explains the big picture and technical how-tos of using longitudinal confirmatory factor analysis, longitudinal panel models, and hybrid models for analyzing within-person change. User-friendly features include equation boxes that translate all the elements in every equation, tips on what does and doesn't work, end-of-chapter glossaries, and annotated suggestions for further reading. The companion website provides data sets for the examples--including studies of bullying and victimization, adolescents' emotions, and healthy aging--along with syntax and output, chapter quizzes, and the book’s figures. New to This Edition: *Chapter on missing data, with a spotlight on planned missing data designs and the R-based package PcAux. *Chapter on longitudinal mixture modeling, with Whitney Moore. *Chapter on the random intercept cross-lagged panel model (RI-CLPM), with Danny Osborne. *Chapter on Bayesian SEM, with Mauricio Garnier. *Revised throughout with new developments and discussions, such as how to test models of experimental effects.

Click ′Additional Materials′ to read the foreword by Jerald Hage As straightforward as its title, How to Build Social Science Theories sidesteps the well-traveled road of theoretical examination by demonstrating how new theories originate and how they are elaborated. Essential reading for students of social science research, this book traces theories from their most rudimentary building blocks (terminology and definitions) through multivariable theoretical statements, models, the role of creativity in theory building, and how theories are used and evaluated. Authors Pamela J. Shoemaker, James William Tankard, Jr., and Dominic L. Lasorsa intend to improve research in many areas of the social sciences by making research more theory-based and theory-oriented. The book begins with a discussion of concepts and their theoretical and operational definitions. It then proceeds to theoretical statements, including hypotheses, assumptions, and propositions. Theoretical statements need theoretical linkages and operational linkages; this discussion begins with bivariate relationships, as well as three-variable, four-variable, and further multivariate relationships. The authors also devote chapters to the creative component of theory-building and how to evaluate theories. How to Build Social Science Theories is a sophisticated yet readable analysis presented by internationally known experts in social science methodology. It is designed primarily as a core text for graduate and advanced undergraduate courses in communication theory. It will also be a perfect addition to any course dealing with theory and research methodology across the social sciences. Additionally, professional researchers will find it an indispensable guide to the genesis, dissemination, and evaluation of social science theories.

Written specifically for graduate students and practitioners beginning social science research, Statistical Modeling and Inference for Social Science covers the essential statistical tools, models and theories that make up the social scientist's toolkit. Assuming no prior knowledge of statistics, this textbook introduces students to probability theory, statistical inference and statistical modeling, and emphasizes the connection between statistical procedures and social science theory. Sean Gailmard develops core statistical theory as a set of tools to model and assess relationships between variables - the primary aim of social scientists - and demonstrates the ways in which social scientists express and test substantive theoretical arguments in various models. Chapter exercises guide students in applying concepts to data, extending their grasp of core theoretical concepts. Students will also gain the ability to create, read and critique statistical applications in their fields of interest.

Biologists, climate scientists, and economists all rely on models to move their work forward. In this book, Stephen M. Downes explores the use of models in these and other fields to introduce readers to the various philosophical issues that arise in scientific modeling. Readers learn that paying attention to models plays a crucial role in appraising scientific work. This book first presents a wide range of models from a number of different scientific disciplines. After assembling some illustrative examples, Downes demonstrates how models shed light on many perennial issues in philosophy of science and in philosophy in general. Reviewing the range of views on how models represent their targets introduces readers to the key issues in debates on representation, not only in science but in the arts as well. Also, standard epistemological questions are cast in new and interesting ways when readers confront the question, "What makes for a good (or bad) model?" All examples from the sciences and positions in the philosophy of science are presented in an accessible manner. The book is suitable for undergraduates with minimal experience in philosophy and an introductory undergraduate experience in science. Key features: The book serves as a highly accessible philosophical introduction to models and modeling in the sciences, presenting all philosophical and scientific issues in a nontechnical manner. Students and other readers learn to practice philosophy of science by starting with clear examples taken directly from the sciences. While not comprehensive, this book introduces the reader to a wide range of views on key issues in the philosophy of science.