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• Cover Page
• Title
• Copyright
• Contents
• Preface
• Part I: I need to do Regression Analysis Tomorrow
• 1. Building Models with Regression and Correlation
• What are models?
• Least squares models
• A very simple model
• The standard error of the mean
• Modelling relationships
• The standard error and significance of parameter estimates
• Standardised estimates
• Looking more at correlations
• Correlations and scattergraphs
• Correlations and variance
• Correlations and size
• Notes
• Further reading
• 2. More than one Independent Variable Multiple Regression
• Introduction: multiple regression in theory
• What’s multiple regression all about?
• Multiple regression in practice
• R and R square
• Adjusted R square
• Analysis of variance (ANOVA) table
• Coefficients
• Variable entry
• Hierarchical variable entry
• Methods of variable entry
• Note
• Further reading
• 3. Categorical Independent Variables
• Introduction
• Categorical data: a special case
• The t-test as regression
• ANOVA as regression
• Coding schemes for categorical data
• Notes
• Further reading
• Part II: I need to do Regression Analysis Next Week
• 4. Assumptions in Regression Analysis
• Introduction
• Assumptions about measures
• Levels of measurement
• Conservative interpretation of assumptions
• A more liberal approach
• Assumptions about data
• A bit about normal distributions
• Univariate distribution checks
• Outliers and the mean
• Normal distribution
• Detecting and dealing with non-normality
• Calculation-based methods
• Skew and kurtosis
• Outliers
• Dealing with outliers, skew and kurtosis
• Dealing with outliers
• Effects of univariate skew and kurtosis
• Multivariate distributions
• Assumption 1
• Assumption 2
• Assumption 3
• Assumption 4
• Time-series designs
• Clustered sampling designs
• Notes
• Further reading
• 5. Issues in Regression Analysis
• Causality
• Association
• Direction of causality
• Isolation
• The role of theory in determining causation
• Sample size
• Why should we worry about sample sizes?
• Rules of thumb
• Power analysis
• Collinearity
• What is collinearity?
• Detecting collinearity
• Dealing with collinearity
• Measurement error
• Notes
• Further reading
• Part III: I need to know more of The Things that Regression Can do
• 6 Non-Linear and Logistic Regression
• Non-linear regression
• Linear and curvilinear relationhips
• Generating a curve
• Carrying out non-linear regression
• An example of non-linear regression
• Logistic regression
• The case of the dichotomous dependent variable
• The logit transformation
• Using the logit: logistic regression
• An annotated example of logistic regression
• Hierarchical logistic regression
• Polynomial logistic regression
• Further reading
• 7. Moderator and Mediator Analysis
• Introduction
• Moderator analysis
• Two categorical variables
• Categorical and continuous variables
• Two continuous predictors
• Mediator analysis
• Example of mediation
• Some concluding points on moderation and mediation
• Note
• Further reading
• 8. Introducing Some Advanced Techniques: Multilevel Modelling and Structural Equation Modelling
• Multilevel modelling (MLM)
• Algebraic formulation
• Hierarchies everywhere
• Even more hierarchies
• Structural equation modelling
• Why use SEM?
• Identification
• Latent variables
• Estimation in SEM
• Model testing
• Structural models
• Programs for MLM and SEM
• MLM software
• SEM software
• Notes
• Further reading
• Appendix 1 Equations
• Appendix 2 Doing regression with SPSS
• Appendix 3 Statistical tables
• References
• Name index
• Subject index