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Das Handbuch der sozialwissenschaftlichen Datenanalyse bietet in
über 40 Kapiteln eine umfassende Darstellung multivariater
Analyseverfahren. Schwerpunkte des Handbuchs bilden Grundlagen der
Datenanalyse, regressionsanalytische Verfahren für Quer- und
Längsschnittsdaten sowie Skalierungsverfahren. Behandelt werden u.
a. OLS-, logistische und robuste Regression,
Strukturgleichungsmodelle, Mehrebenen-, Panel-, Ereignisdaten- und
Zeitreihenanalyse, MDS und Rasch-Modelle. Darüber hinaus werden
viele neuere Verfahren dargestellt, etwa multiple Imputation,
Bootstrappen, Analyse latenter Klassen und propensity score
matching. Jedes Kapitel beginnt mit einer allgemein verständlichen
Einführung. Es folgt eine Darstellung der
mathematisch-statistischen Grundlagen. Anschließend wird jedes
Verfahren anhand eines sozialwissenschaftlichen Beispiels
vorgestellt. Die Beiträge enden mit Hinweisen auf typische
Anwendungsfehler und einer kommentierten Literaturempfehlung.
'The editors of the new SAGE Handbook of Regression Analysis and
Causal Inference have assembled a wide-ranging, high-quality, and
timely collection of articles on topics of central importance to
quantitative social research, many written by leaders in the field.
Everyone engaged in statistical analysis of social-science data
will find something of interest in this book.' - John Fox,
Professor, Department of Sociology, McMaster University 'The
authors do a great job in explaining the various statistical
methods in a clear and simple way - focussing on fundamental
understanding, interpretation of results, and practical application
- yet being precise in their exposition.' - Ben Jann, Executive
Director, Institute of Sociology, University of Bern 'Best and Wolf
have put together a powerful collection, especially valuable in its
separate discussions of uses for both cross-sectional and panel
data analysis.' -Tom Smith, Senior Fellow, NORC, University of
Chicago Edited and written by a team of leading international
social scientists, this Handbook provides a comprehensive
introduction to multivariate methods. The Handbook focuses on
regression analysis of cross-sectional and longitudinal data with
an emphasis on causal analysis, thereby covering a large number of
different techniques including selection models, complex samples,
and regression discontinuities. Each Part starts with a
non-mathematical introduction to the method covered in that
section, giving readers a basic knowledge of the method's logic,
scope and unique features. Next, the mathematical and statistical
basis of each method is presented along with advanced aspects.
Using real-world data from the European Social Survey (ESS) and the
Socio-Economic Panel (GSOEP), the book provides a comprehensive
discussion of each method's application, making this an ideal text
for PhD students and researchers embarking on their own data
analysis.
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