Applying Regression and Correlation

Höfundar: Jeremy Miles; Mark Shevlin (Útgáfa: 1)
Applying Regression and Correlation

Kaup valmöguleikar

This book takes a fresh look at applying regression analysis in the behavioural sciences by introducing the reader to regression analysis through a simple model-building approach. The authors start with the basics and begin by re-visiting the mean, and the standard deviation, with which most readers will already be familiar, and show that they can be thought of a least squares model. The book then shows that this least squares model is actually a special case of a regression analysis and can be extended to deal with first one, and then more than one independent variable.

Extending the model from the mean to a regression analysis provides a powerful, but simple, way of thinking about what students believe are the more complex aspects of regression analysis. The authors gradually extend the model to include aspects of regression analysis such as non-linear regression, logistic regression, and moderator and mediator analysis. These approaches are often presented in terms that are too mathematical for non-statistically inclined students to deal with.

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Útgefandi
SAGE Publications, Ltd. (UK)
ISBN
9781446232897
Print ISBN
9780761962304
Format
ePub
Útgáfa
1
Höfundar
Jeremy Miles; Mark Shevlin
Tungumál
English
Útgefið
2000-11-24
Prent takmörkun á líftíma
100
Prent takmörkun
30
Afritunar takmörkun
30

Kaflar

  • 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