Applied Statistics Using Stata:A Guide for the Social Sciences

Applied Statistics Using Stata:A Guide for the Social Sciences
新闻理论

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￥ 506.25

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优惠

平台大促低至8折优惠

发货周期：预计5-7周发货

作者

Mehmet Mehmetoglu~Tor Georg Jakobsen

出版社

Sage

出版时间

2016年11月22日

装帧

ＩＳＢＮ

9781473913233

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页码

376

语种

英文

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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 What is multiple linear regression analysis? \\ 4.2 An 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 What is the nteraction/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 model / 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 What is 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 Factor analysis using 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

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