Social Science Data Research Guide

The book is a comprehensive, straightforward introduction to the field of research as practiced by social scientists. It emphasizes the research process by demonstrating how to design research studies, introducing the various observation modes in use today, and answering questions about research methods--such as how to conduct online surveys, and analyze both qualitative and quantitative data.

The book provides clear identification of key issues in research design (i.e. measurement, sampling and causation) as well as major data collection techniques. Excellent balance between qualitative and quantitative methods. Integration of research ethics with the pertinent methodological topics.

Bruce Thompson shows readers how to use the latest techniques for interpreting research outcomes as well as how to make statistical decisions that result in better research. Thompson integrates a broad array of methods involving only a single dependent variable, ranging from classical and robust location descriptive statistics, through effect sizes, and on through ANOVA, multiple regression, loglinear analysis and logistic regression.

This easy-to-use resource provides a clear explanation of mixed modeling techniques and theories and demonstrates the use of five popular statistical software procedures (SAS, SPSS, Stata, R/S-plus, and HLM) for fitting linear mixed models (LMMs) using real-world data.
The authors fit LMMs based on both general and hierarchical model specifications, develop the model-building process step-by-step, and demonstrate the estimation, testing, and interpretation of fixed-effect parameters and covariance parameters associated with random effects.

Focusing on an analysis of models and data that arise from repeated observations of a cross-section of individuals, households or firms, this book also covers important applications within business, economics, education, political science and other social science disciplines. The author introduces the foundations of longitudinal and panel data analysis at a level suitable for quantitatively oriented graduate social science students as well as individual researchers. He emphasizes mathematical and statistical fundamentals but also demonstrates substantive applications from across the social sciences. These applications are enhanced by real-world data sets and software programs in SAS and Stata.

The book carefully designs its approach, starting from single-level then to multi-level models, also provides the methodologies with broad practical examples (using R) and graphical displays. It gives useful guidance for social scientist and other applied statisticians into linear regression, logistic regression, GLM, and hierarchical models.

An introductory book provides theoretical concepts on time series analysis with plenty of intuitive insight of how exactly these models work. It covers a comprehensive and systematic account of financial econometric models: classic one-factor linear models (AR, MA, ARMA, Unit-root, ARCH, VAR, etc), and also multi-factor models and non-linear models. New edition adds chapters for PCA and MCMC too.

Dyadic data is a commonly found data structure in social psychology and social relations research. The authors describe and demonstrate several statistical methods, including multilevel and structural equation modeling approaches.

Guides business analysts, scientists, engineers and researchers through all stages of data analysis, model building and implementation. This book helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for application.

All of the most commonly used tests are clarified for researchers. Information on how to calculate and interpret results is given with sample datasets and expanded information on the limitations of tests.