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About the Authors
Preface
1 RESEARCH AND STATISTICS
1.1 The methodology of statistical research
1.2 The statistical method
1.3 The logic behind statistical inference
1.3.1 Central limit theorem
1.3.2 t-distribution
1.3.3 Why do I need significance levels if I am investigating the whole
population?
1.4 General laws and theories
1.4.1 Objectivity and critical realism
1.5 Survey data
1.6 Quantitative research papers
1.6.1 p-hacking
1.7 Concluding remarks
Key terms
Questions
Practical exercises
List of commands
Further reading
References
2 INTRODUCTION TO STATA
2.1 What is Stata?
2.1.1 The Stata interface
2.1.2 How to use Stata
2.2 Entering and importing data into Stata
2.2.1 Entering data
2.2.2 Importing data
2.3 Data management
2.3.1 Opening data
2.3.2 Examining data
2.3.3 Making changes to variables
2.3.4 Generating variables
2.3.5 Subsetting data
2.3.6 Labelling variables
2.4 Descriptive statistics and graphs
2.4.1 Frequency distributions
2.4.2 Summary statistics
2.4.3 Appending data
2.4.4 Merging data
2.4.5 Reshaping data
2.5 Bivariate inferential statistics
2.5.1 Correlation
2.5.2 Independent t-test
2.5.3 Analysis of variance (ANOVA)
2.5.4 Chi-squared test
2.6 Conclusion
Key terms
Questions
Practical exercies
List of commands
Further reading
3 SIMPLE (BIVARIATE) REGRESSION
3.1 What is regression analysis?
3.2 Simple linear regression analysis
3.2.1 Ordinary least squares
3.2.2 Goodness of fit
3.2.3 Hypothesis test for slope coefficient
3.2.4 Prediction in linear regression
3.3 Example in Stata
3.4 Conclusion
Questions
Practical exercise
List of commands
Further reading
References
Supplemental Appendix
A3.1 Calculating a bivariate regression
A3.2 Calculating standard errors
4 MULTIPLE REGRESSION
4.1 Multiple linear regression analysis
4.1.1 Estimation
4.1.2 Goodness of fit and the F-test
4.1.3 Adjusted R²
4.1.4 Partial slope coefficients
4.1.5 Prediction in multiple regression
4.1.6 Standardization and relative importance
4.2 Example in Stata
4.3 Conclusion
Key terms
Questions
Practical exercises
List of commands
Further reading
References
5 DUMMY-VARIABLE REGRESSION
5.1 Why dummy-variable regression?
5.1.1 Creating dummy variables
5.1.2 The logic behind dummy-variable regression
5.2 Regression with one dummy variable
5.2.1 Example in Stata
5.3 Regression with one dummy variable and a covariate
5.3.1 Example in Stata
5.4 Regression with more than one dummy variable
5.4.1 Example in Stata
5.4.2 Comparing the included groups
5.5 Regression with more than one dummy variable and a covariate
5.5.1 Example in Stata
5.6 Regression with two separate sets of dummy variables
5.6.1 Example in Stata
5.7 Conclusion
Key terms
Questions
Practical exercise
List of commands
Further reading
References
6 INTERACTION/MODERATION EFFECTS USING REGRESSION
6.1 Interaction/moderation effect
6.2 Product-term approach
6.2.1 Interaction between a continuous predictor and a continuous
moderator
6.2.2 Interaction between a continuous predictor and a dummy moderator
6.2.3 Interaction between a dummy predictor and a dummy moderator
6.2.4 Interaction between a continuous predictor and a polytomous
moderator
6.3 Conclusion
Key terms
Questions
Practical exercise
List of commands
Further reading
References
7 LINEAR REGRESSION ASSUMPTIONS AND DIAGNOSTICS
7.1 Correct specification of the model
7.1.1 All relevant and no irrelevant X-variables
7.1.2 Linearity and polynomial regression
7.1.3 Additivity
7.1.4 Absence of multicollinearity
7.2 Assumptions about residuals
7.2.1 The error term has a conditional mean of zero
7.2.2 Homoskedasticity
7.2.3 Uncorrelated errors
7.2.4 Normally distributed errors
7.3 Influential observations
7.3.1 Leverage
7.3.2 DFBETA
7.3.3 Cook's distance
7.4 Conclusion
Key terms
Questions
Practical exercise
Further reading
References
8 LOGISTIC REGRESSION
8.1 What is logistic regression?
8.1.1 Tests of significance
8.2 Assumptions of logistic regression
8.2.1 Example in Stata
8.3 Conditional effects
8.4 Diagnostics
8.5 Multinomial logistic regression
8.6 Ordered logistic regression
8.7 Conclusion
Key terms
Questions
Practical exercise
Further reading
References
9 SURVIVAL ANALYSIS
9.1 Dara structure
9.2 Censoring
9.3 Life table
9.4 Hazard function
9.5 Survival function
9.6 Example in Stata: Life tables with Kaplan-Meier
9.6.1 Kaplan-Meier estimator
9.6.2 Hazard function
9.7 Proportional hazard models (Cox regression)
9.7.1 Assumption of proportional hazard model
9.7.2 Extending the Cox regression model (time-varying covariates)
9.7.3 Multiple events
9.7.4 Competing risks
9.8 Conclusion
Key terms
Questions
Practical exercise
List of commands
Further reading
References
10 MULTILEVEL ANALYSIS
10.1 Multilevel data
10.1.1 Statistical reasons for using multilevel analysis
10.2 Empty or intercept-only model
10.2.1 Example in Stata
10.3 Variance partition (inraclass correlation)
10.4 Random intercept model
10.5 Level-2 explanatory variables
10.5.1 How much of the dependent variable is explained?
10.6 Logistic multilevel model
10.7 Random coefficient (slope) model
10.8 Interaction efforts
10.9 Three-level models
10.9.1 Cross-classified multilevel model
10.10 Weighting
10.11 Post-estimation
10.11.1 Deviation from intercept and random slope regression line
10.12 Conclusion
Key terms
Questions
Practical exercises
List of commands
Further reading
References
11 PANEL DATA ANALYSIS
11.1 Panel data
11.2 Pooled OLS
11.3 Between effects
11.4 Fixed effects (within estimation)
11.4.1 Explaining fixed effects
11.4.2 Summary of fixed effects
11.4.3 Time-fixed effects
11.5 Conclusion
Questions
Further reading
References
12 TIME SERIES ANALYSIS
12.1 Time series
12.1.1 Trends and smoothing
12.1.2 How to cope with first-rder autocorrelation
12.2 Autocorrelation
12.2.1 Testing for autocorrelation
12.2.2 How to cope with first-order autocorrelation
12.3 Stationarity
12.3.1 Unit roots: testing for non-stationarity
12.3.2 First difference
12.4 Time-series models
12.4.1 Autoregressive models
12.4.2 ARIMA model (single time series)
12.4.3 Vector autoregression (multiple time series)
12.5 Conclusion
Key terms
Questions
Practical exercise
List of commands
Further reading
References
13 EXPLORATORY FACTOR ANALYSIS
13.1 What is factor analysis?
13.1.1 What is factor analysis used for?
13.2 The factor analysis process
13.2.1 Extracting the factors
13.2.2 Determining the number of factors
13.2.3 Rotating the factors
13.2.4 Refining and interpreting the factors
13.3 Composite scores and reliability testing
13.4 Example in Stata
13.5 Conclusion
Key terms
Questions
Practical exercise
List of commands
Further reading
References
14 STRUCTURAL EQUATION MODELLING AND CONFIRMATORY FACTOR ANALYSIS
14.1 What is structural equation modelling?
14.1.1 Types of structural modelling
14.2 Confirmatory factor analysis
14.2.1 Model specification
14.2.2 Model identitifcation
14.2.3 Parameter estimation
14.2.4 Model assessment
14.2.5 Model modification
14.3 Latent path analysis
14.3.1 Specification of the LPA model
14.3.2 Measurement part
14.3.3. Structural part
14.4 Conclusion
Key terms
Questions
Practical exercise
List of commands
Further reading
References
15 COUNT DATA
15.1 Count data
15.1.1 Poisson regression
15.1.2 Negative binomial regression
15.2 Instrumental regression
15.2.1 Two-stage estimation
15.2.2 Example in Stata
15.3 Transformation of variables
15.3.1 Skewness and kurtosis
15.3.2 Transformations
15.4 Weighting cases
15.5 Missing data
15.5.1 Traditional methods for handling missing data
15.5.2 Multiple imputation
15.6 Conclusion
Key terms
Questions
Practical exercise
List of commands
Further reading
References
16 PROGRAMMING AND DYNAMIC REPORTING USING STATA
16.1 Programming and dynamic reporting using Stata
16.1.1 Macros
16.1.2 Loops
16.1.3 If statements
16.1.4 Stored r- and e-class objects
16.1.5 Creating your own Stata command
16.2 Reproducible and dynamic reporting
16.2.1 Dynamic reporting using dyndoc
16.2.2 Dynamic reporting usinig potdocx
16.2.3 dyndoc versus potdocx
16.3 Conclusion
Key terms
Questions
Practical exercise
List of commands
Further reading
References
Index
