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This pocket guide provides an in-depth introduction to 29 of the most widely used data sets in social work and the social sciences. Readers will find information about each data set, how to locate and use the data, what types of questions the data may answer, and the key variables in the data.

A practical introduction to using Mplus for the analysis of multivariate data, this volume provides step-by-step guidance, complete with real data examples, numerous screen shots, and output excerpts. The author shows how to prepare a data set for import in Mplus using SPSS. He explains how to specify different types of models in Mplus syntax and address typical caveats--for example, assessing measurement invariance in longitudinal SEMs. Coverage includes path and factor analytic models as well as mediational, longitudinal, multilevel, and latent class models. Specific programming tips an

Applied Cross-Cultural Data Analysis for Social Work offers practical guides for data analysis in cross-cultural research. It shows how social workers can employ commonly-used statistical approaches to make meaningful comparisons in health, mental health, social and psychological phenomena across cultural groups. The book can also be used as a textbook for advanced research course in social work and allied fields and would benefit graduate students, faculty, and researchers.

This volume offers practical guides for data analysis in cross-cultural research. It shows how social workers can employ commonly-used statistical approaches to make meaningful comparisons in health, mental health, social and psychological phenomena across cultural groups. It can also be used as a textbook for advanced research course in social work and allied fields and would benefit graduate students, faculty, and researchers.

The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis. Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. This edition also features: An emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models. Two new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis. New sections introducing the Bayesian approach for methods in that chapter. More than 100 analyses of data sets and over 600 exercises. Notes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources. A supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions. Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.

"Two developments in computational text analysis may change the way qualitative data analysis in social sciences is performed: 1. the availability of digital text worth to investigate is growing rapidly, and 2. the improvement of algorithmic information extraction approaches, also called text mining, allows for further bridging the gap between qualitative and quantitative text analysis. The key factor hereby is the inclusion of context into computational linguistic models which extends conventional computational content analysis towards the extraction of meaning. To clarify methodological differences of various computer-assisted text analysis approaches the article suggests a typology from the perspective of a qualitative researcher. This typology shows compatibilities between manual qualitative data analysis methods and computational, rather quantitative approaches for large scale mixed method text analysis designs." (author's abstract)