MULTI-VARIABLE STATISTICAL ANALYSIS TECHNIQUES for Social Sciences- STATA, SPSS and R Applications

Since the units examined in the studies in the field of social sciences have various characteristics, the data is defined by more than one variable. In the case of a large number of variables, multivariate statistical analysis techniques are needed to analyze them together.
In this book, which is prepared as an undergraduate and graduate level textbook, multivariate statistical analysis techniques are discussed both theoretically and practically. In addition to the main multivariate statistical analysis techniques, advanced multivariate statistical analysis techniques are also included in the book. Analysis techniques in the content of the book were explained within the framework of basic conceptual information and applications were made with Stata, SPSS and R programs.
In order to guide students and researchers who will apply multivariate statistical analysis techniques, the book includes applications on various subjects in the field of social sciences, and attention has been paid to the detailed interpretation of the analysis results. Since these applications seek answers to various research questions by using real data, the book also has the feature of a research book, so it can be used by sectoral researchers, undergraduate and graduate students in all areas where multivariate analysis techniques are applied.
This book, which consists of twenty-one chapters, has been prepared by academics working at various universities in Turkey, whose fields of expertise are statistics, econometrics and operations research, and includes the following topics.

• Basic Assumptions of Multivariate Statistical Analysis Techniques
• Analysis of Data Entry and Multivariate Analysis Assumptions with Stata, SPSS and R Programs
• Multiple Linear Regression Models
• Logistic Regression Analysis
• Multivariate Analysis of Variance
• Factor Analysis
• Cluster Analysis
• Discriminant Analysis
• Canonical Correlation Analysis
• Multidimensional Scaling Analysis
• Reliability Analysis
• Conjoint Analysis
• Compliance Analysis
• Classification Decision Trees
• Artificial neural networks
• Survival Analysis
• System Simulation
• Hidden Class Analysis
• Text Mining