Clear, intuitive and written with the social science student in mind, this book represents the ideal combination of statistical theory and practice. It focuses on questions that can be answered using statistics and addresses common themes and problems in a straightforward, easy-to-follow manner. The book carefully combines the conceptual aspects of statistics with detailed technical advice providing both the why of statistics and the how . Built upon a variety of engaging examples from across the social sciences it provides a rich collection of statistical methods and models. Students are…mehr
Clear, intuitive and written with the social science student in mind, this book represents the ideal combination of statistical theory and practice. It focuses on questions that can be answered using statistics and addresses common themes and problems in a straightforward, easy-to-follow manner. The book carefully combines the conceptual aspects of statistics with detailed technical advice providing both the why of statistics and the how .
Built upon a variety of engaging examples from across the social sciences it provides a rich collection of statistical methods and models. Students are encouraged to see the impact of theory whilst simultaneously learning how to manipulate software to meet their needs.
The book also provides: Original case studies and data sets Practical guidance on how to run and test models in Stata Downloadable Stata programmes created to work alongside chapters A wide range of detailed applications using Stata Step-by-step notes on writing the relevant code. This excellent text will give anyone doing statistical research in the social sciences the theoretical, technical and applied knowledge needed to succeed.
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Autorenporträt
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 35 different refereed international journals, among which include Personality and Individual Differences, Evolutionary Psychology and the Journal of Statistical Software.
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.
Inhaltsangabe
Research and statistics 1.1The methodology of statistical research 1.2The statistical method 1.3The logic behind statistical inference 1.4General laws and theories 1.5Quantitative research papers 2.Introduction to Stata 2.1What is Stata? 2.2Entering and importing data into Stata 2.3Data management 2.4Descriptive statistics and graphs 2.5Bivariate inferential statistics 3.Simple (bivariate) regression 3.1What is regression analysis? 3.2Simple linear regression analysis 3.3Example in Stata 4.Multiple regression 4.1 Multiple linear regression analysis 4.2 Example in Stata 5.Dummy-Variable Regression 5.1Why dummy-variable regression? 5.2Regression with one dummy variable 5.3Regression with one dummy variable and a covariate 5.4Regression with more than one dummy variable 5.5Regression with more than one dummy variable and a covariate 5.6Regression with two separate sets of dummy variables 6.Interaction/moderation effects using regression 6.1Interaction/moderation effect 6.2Product-term approach 7.Linear regression assumptions and diagnostics 7.1Correct specification of the model 7.2Assumptions about residuals 7.3Influential observations 8.Logistic regression 8.1What is logistic regression? 8.2Assumptions of logistic regression 8.3Conditional effects 8.4Diagnostics 8.5Multinomial logistic regression 8.6Ordered logistic regression 9.Multilevel analysis 9.1Multilevel data 9.2Empty or intercept-only model 9.3Variance partition / intraclass correlation 9.4Random intercept model 9.5Level-2 explanatory variables 9.6Logistic multilevel model 9.7Random coefficient (slope) model 9.8Interaction effects 9.9Three-level models 10.Panel data analysis 10.1Panel data 10.2Pooled OLS 10.3Between effects 10.4Fixed effects (within estimator) 10.5Random effects 10.6Time-series cross-section methods 10.7Binary dependent variables 11.Exploratory factor analysis 11.1What is factor analysis? 11.2Factor analysis process 11.3Composite scores and reliability test 11.4 Example in Stata 12.Structural equation modelling and confirmatory factor analysis 12.1What is structural equation modelling? 12.2Confirmatory factor analysis 12.3Latent path analysis 13.Critical issues 13.1Transformation of variables 13.2Weighting cases 13.3Robust regression 13.4 Missing data
Research and statistics 1.1The methodology of statistical research 1.2The statistical method 1.3The logic behind statistical inference 1.4General laws and theories 1.5Quantitative research papers 2.Introduction to Stata 2.1What is Stata? 2.2Entering and importing data into Stata 2.3Data management 2.4Descriptive statistics and graphs 2.5Bivariate inferential statistics 3.Simple (bivariate) regression 3.1What is regression analysis? 3.2Simple linear regression analysis 3.3Example in Stata 4.Multiple regression 4.1 Multiple linear regression analysis 4.2 Example in Stata 5.Dummy-Variable Regression 5.1Why dummy-variable regression? 5.2Regression with one dummy variable 5.3Regression with one dummy variable and a covariate 5.4Regression with more than one dummy variable 5.5Regression with more than one dummy variable and a covariate 5.6Regression with two separate sets of dummy variables 6.Interaction/moderation effects using regression 6.1Interaction/moderation effect 6.2Product-term approach 7.Linear regression assumptions and diagnostics 7.1Correct specification of the model 7.2Assumptions about residuals 7.3Influential observations 8.Logistic regression 8.1What is logistic regression? 8.2Assumptions of logistic regression 8.3Conditional effects 8.4Diagnostics 8.5Multinomial logistic regression 8.6Ordered logistic regression 9.Multilevel analysis 9.1Multilevel data 9.2Empty or intercept-only model 9.3Variance partition / intraclass correlation 9.4Random intercept model 9.5Level-2 explanatory variables 9.6Logistic multilevel model 9.7Random coefficient (slope) model 9.8Interaction effects 9.9Three-level models 10.Panel data analysis 10.1Panel data 10.2Pooled OLS 10.3Between effects 10.4Fixed effects (within estimator) 10.5Random effects 10.6Time-series cross-section methods 10.7Binary dependent variables 11.Exploratory factor analysis 11.1What is factor analysis? 11.2Factor analysis process 11.3Composite scores and reliability test 11.4 Example in Stata 12.Structural equation modelling and confirmatory factor analysis 12.1What is structural equation modelling? 12.2Confirmatory factor analysis 12.3Latent path analysis 13.Critical issues 13.1Transformation of variables 13.2Weighting cases 13.3Robust regression 13.4 Missing data
Rezensionen
Newly updated, now with more advanced content, this book remains a must have for those studying applied statistics. The book is practically orientated with intuitive theoretical explanations, a wide array how-to-do-it examples and an engaging narrative. You won t be sorry! Franz Buscha 20211119
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