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Highly recommended by the Journal of Official Statistics, The American Statistician, and other journals, Applied Survey Data Analysis, Second Edition provides an up-to-date overview of state-of-the-art approaches to the analysis of complex sample survey data. Building on the wealth of material on practical approaches to descriptive analysis and regression modeling from the first edition, this second edition expands the topics covered and presents more step-by-step examples of modern approaches to the analysis of survey data using the newest statistical software. Designed for readers working in a wide array of disciplines who use survey data in their work, this book continues to provide a useful framework for integrating more in-depth studies of the theory and methods of survey data analysis. An example-driven guide to the applied statistical analysis and interpretation of survey data, the second edition contains many new examples and practical exercises based on recent versions of real-world survey data sets. Although the authors continue to use Stata for most examples in the text, they also continue to offer SAS, SPSS, SUDAAN, R, WesVar, IVEware, and Mplus software code for replicating the examples on the book's updated website.--

Social sciences --- Social surveys --- Sciences sociales --- Enquêtes sociales --- Statistics. --- Statistical methods. --- Statistiques --- Méthodes statistiques --- Statistics --- Statistical methods --- Datenanalyse --- Empirische Sozialforschung --- Ricerca --- Social sciences. --- Analisi dei dati --- Metodi statistici. --- Datenanalyse. --- Social sciences - Statistics --- Social surveys - Statistical methods --- #SBIB:303H520 --- Behavioral sciences --- Human sciences --- Sciences, Social --- Social science --- Social studies --- Civilization --- Community surveys --- Surveys, Social --- Surveys --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Data processing --- Research

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This book provides a clear explanation of mixed modeling techniques, introducing their application through the analyses of real datasets and presenting each example using the most commonly used software packages - R, SAS, SPSS, HLM, and STATA. The authors describe common research designs and corresponding data structures for which mixed models analysis is an appropriate statistical tool, and they include detailed descriptions on how to set up datasets for these types of analyses. The treatment also includes real-world datasets that contain common problems, such as missing data, that must be addressed using mixed models. A supporting Web site provides software code and the datasets discussed in the book.

Linear models (Statistics) --- Data processing. --- Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- 681.3*G3 Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- 519.2 --- 681.3*G3 --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- Models, Linear (Statistics) --- Mathematical models --- Mathematical statistics --- Statistics --- Data processing --- Methoden en technieken --- statistiek. --- Linear models (Statistics) - Data processing --- Statistiek.

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This entry provides readers with an overview of essential concepts related to survey weighting, including the development of survey weights and how weights should be used in the analysis of survey data. The entry reviews state-of-the-art approaches to the construction of survey weights and provides practical guidance on various techniques for incorporating survey weights in data analysis. References to current statistical software procedures that can be used for either the development of survey weights or the analysis of weighted survey data are provided whenever possible, and open areas for future research in survey weighting are provided.

Sociology. --- Science. --- Mathematics.

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Quantitative methods in social research --- Error analysis (Mathematics). --- Surveys.