Research and statistics 1.1 The methodology of statistical research 1.2 The statistical method 1.3 The logic behind statistical inference 1.4 General laws and theories 1.5 Quantitative research papers 2. Introduction to Stata 2.1 What is Stata? 2.2 Entering and importing data into Stata 2.3 Data management 2.4 Descriptive statistics and graphs 2.5 Bivariate inferential statistics 3. Simple (bivariate) regression 3.1 What is regression analysis? 3.2 Simple linear regression analysis 3.3 Example in Stata 4. Multiple regression 4.1 Multiple linear regression analysis 4.2 Example in Stata 5. Dummy-Variable Regression 5.1 Why dummy-variable regression? 5.2 Regression with one dummy variable 5.3 Regression with one dummy variable and a covariate 5.4 Regression with more than one dummy variable 5.5 Regression with more than one dummy variable and a covariate 5.6 Regression with two separate sets of dummy variables 6. Interaction/moderation effects using regression 6.1 Interaction/moderation effect 6.2 Product-term approach 7. Linear regression assumptions and diagnostics 7.1 Correct specification of the model 7.2 Assumptions about residuals 7.3 Influential observations 8. Logistic regression 8.1 What is logistic regression? 8.2 Assumptions of logistic regression 8.3 Conditional effects 8.4 Diagnostics 8.5 Multinomial logistic regression 8.6 Ordered logistic regression 9. Multilevel analysis 9.1 Multilevel data 9.2 Empty or intercept-only model 9.3 Variance partition / intraclass correlation 9.4 Random intercept model 9.5 Level-2 explanatory variables 9.6 Logistic multilevel model 9.7 Random coefficient (slope) model 9.8 Interaction effects 9.9 Three-level models 10. Panel data analysis 10.1 Panel data 10.2 Pooled OLS 10.3 Between effects 10.4 Fixed effects (within estimator) 10.5 Random effects 10.6 Time-series cross-section methods 10.7 Binary dependent variables 11. Exploratory factor analysis 11.1 What is factor analysis? 11.2 Factor analysis process 11.3 Composite scores and reliability test 11.4 Example in Stata 12. Structural equation modelling and confirmatory factor analysis 12.1 What is structural equation modelling? 12.2 Confirmatory factor analysis 12.3 Latent path analysis 13. Critical issues 13.1 Transformation of variables 13.2 Weighting cases 13.3 Robust regression 13.4 Missing data
Mehmet Mehmetoglu is a professor of research methods in the Department of Psychology at the Norwegian University of Science and Technology (NTNU). His research interests include consumer psychology, evolutionary psychology and statistical methods. Mehmetoglu has co/publications in about 30 different refereed international journals, among which, Scandinavian Journal Psychology, Personality and Individual Differences, and Evolutionary Psychological Science. Tor Georg Jakobsen is professor of political science at NTNU Business School at the Norwegian University of Science and Technology. His research interests include political behavior, peace research and statistical methods. Jakobsen has authored and co-authored articles in, among others, European Sociological Review, Work, Employment and Society and Conflict Management and Peace Science.
Practically orientated with a plethora of examples and an engaging narrative, this book is a must have for all those studying applied social statistics. -- Franz Buscha This book provides an extraordinary and very readable account of the applied statistics methods needed in the social sciences. With its captivating didactical exposition, the book will be an invaluable resource for the novice as well as the advanced researcher. -- Sergio Venturini Stata users, especially social scientists, will find helpful advice in fitting statistical models to a diverse set of examples encountered when investigating the complexity and subtlety of real data. The authors emphasize the importance of assumptions behind the models and present clear exposition of the tools embedded in Stata to test these assumptions. -- James L Rosenberger