Analysis Techniques for non-Experimental Data: An Introduction discusses five different quasi-experimental study designs: propensity score matching; instrumental variables; the regression discontinuity design; fixed effects models; and trajectory analysis. The book starts with a short refresher of regression analysis. Next, the five analysis techniques are discussed relatively briefly and conceptually, with a minimum of formulas. Each technique is also illustrated with a worked example on a fictitious dataset. For all analyses, the complete syntax for both SPSS and Stata is given in order that readers can replicate the examples and use the syntax as a starting point for their own analyses. Although most of the book's examples are criminological, the techniques can be applied in many other disciplines, such as sociology, psychology, demography, and political science. [Subject: Criminology]
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This book starts with a short refresher course of regression analysis, the workhorse of quantitative analysis. The book next discusses five analysis techniques that make it possible to make causal inference - with the necessary caution: - namely propensity score matching, instrumental variables, the regression discontinuity design, fixed effects models and trajectory analysis. The five analysis techniques are discussed relatively briefly and conceptually, with a minimum of formulas - reason this book cannot be regarded as more than an introduction to each of these techniques. At the end of each chapter there is a list with key publications for each technique that the reader can refer to for more information on statistical issues, and for variations on and special cases of the techniques.
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