Karl Wuensch's Statistics Lessons

    Years ago I converted all of my hand-written lecture notes into Word documents. Then I started putting them online so that students could access them if they missed class. Here is a collection of them from my statistics classes.  I do not know how many people outside of East Carolina University are using them to educate themselves, but I get a good number of emails that indicate that there are quite a few who do.

Introduction and Descriptive Statistics

• Choosing an Appropriate Bivariate Inferential Statistic -- This document will help you learn when to use the various inferential statistics that are typically covered in an introductory statistics course.

• PSYC 2101: Howell Chapters 1 & 2 -- Elementary material covered in the first two chapters of Howell's Fundamentals text.

• Scales of Measurement -- This document covers the topic of Scales of Measurement.

• Ratio versus Interval Scales of Measurement -- a graphical explanation.

• Descriptive Statistics -- This document covers Basic Descriptive Statistics.

  • Exercises involving descriptive statistics

• The Three Quarter Rule -- This document illustrates how the graph can be used to either minimize or exaggerate your perception of differences.

• Exploratory Data Analysis (EDA) -- This document covers Exploratory Data Analysis.

• Skewness, Kurtosis, and the Normal Curve -- Platykurtic curves are short in the tails like platypuses; leptokurtic curves are heavy in the tails like kangaroos, noted for 'lepping.'

• The Normal Distribution -- This document introduces the normal probability density function.

• Describing the Shapes of Frequency Distributions -- advice for the novice

• An Introduction to Research Design

• Making Inferences About Parameters -- An introduction to parametric inferential statistics.

• An Introduction to Power Analysis, N = 1 -- See how to calculate power, using the normal curve, and how various factors affect power.

• Basic Probability -- An introduction to the most basic concepts in probability theory and working with contingency tables.

• Monte Carlo -- Use an applet to simulate various sampling distributions.

• Testing Hypotheses with the Binomial Probability Distribution -- An introduction to the binomial distribution, including using it to test hypotheses about the binomial parameter p.

• Confidence Intervals for Proportions

• Comparing Correlated Proportions -- McNemar's Test

• The Statistics of Democracy -- an interesting application of the binomial distribution

• Common Univariate and Bivariate Applications of the Chi-square Distribution -- one sample variance, one-way chi-square, two-way chi-square.

• Odds Ratios and the Wald Chi-square -- you can get a CI that includes one when the Pearson or LR Chi-square is significant

• Reporting the Strength of Effect Estimates for Simple Statistical Analyses -- Independent t, one-way independent ANOVA, correlation/regression, contingency table analysis.

• Power Analysis for Contingency Tables -- Using G*Power 3.

• Reversal Paradox -- also known as Simpson's Paradox.

• One Mean Inference -- Testing hypotheses about a single population mean, constructing confidence intervals, effect size estimation, and writing APA-style summary statements.

• Two Mean Inference -- Testing hypotheses about the difference between two population means (independent or correlated) or variances, constructing confidence intervals, effect size estimation, and writing APA-style summary statements.

  • Standardized Difference Between Means, Independent Samples

  • Tests of Equivalence and Confidence Intervals for Effect Sizes

  • Standardized Difference Between Means, Related Samples

  • Two Groups and One Continuous Variable -- An overview of methods that may be used when one variable in continuous and the other is dichotomous

  • Confidence Intervals, Pooled and Separate Variances T - unstandardized and standardized confidence intervals for difference in means.

• Measurement Scales and Psychological Statistics: Empirical Science or Metaphysics?

• CL: The Common Language Effect Size Statistic -- this may help you better understand effect size estimates such as the d statistic.

• Reporting the Strength of Effect Estimates for Simple Statistical Analyses -- Independent t, one-way independent ANOVA, correlation/regression, contingency table analysis.

• Cohen's Conventions for Small, Medium, and Large Effects

• Power Analysis -- Learn how to do power analysis for one and two sample designs.

• Examples of the Use of Power Analysis in Actual Research Projects

• Estimating the Sample Size Necessary to Have Enough Power -- for common designs.

• Power Analysis for Contingency Tables -- Using G*Power 3.

• Power Analysis for t Tests -- Using G*Power -- one sample, two samples, Pearson r.

  • Version 2 of G*Power

  • Version 3 of G*Power

• Power Analysis for One-Way Independent Samples ANOVA -- Using G*Power

  • Version 2 of G*Power

  • Version 3 of G*Power

• Power Analysis for Two-Way Independent Samples ANOVA, G*Power 3

• Power Analysis for Three-Way Independent Samples ANOVA, G*Power 3

• Power Analysis for One-Way Repeated Measures ANOVA

• Power Analysis for an ANCOV

• Power Analysis for Correlation and Regression Models

• Power Analysis for Change in R², Multiple Linear Regression -- G*Power3

• Workshop  - overview of power analysis

  • In Power Point format

  • G*Power3:  Tests available

  • Internet Resources

  • Power, Retrospective -- Foolishness

• Biserial and Polychoric Correlation Coefficients

• Bivariate Linear Correlation

• Bivariate Linear Regression

• What is R²  When N = p + 1 (and df = 0)? -- why you need to adjust (shrink) the correlation coefficient when sample size is small.

• Confidence Intervals for R and R²

• Contingency Tables with Ordinal Variables -- partition the overall effect into linear and nonlinear components

• Comparing Correlation Coefficients, Slopes, and Intercepts

• Inter-Rater Agreement

• Intraclass Correlation Coefficient

• Phi and Pearson r

• Plots of Residuals, SAS        Plots of Residuals, SPSS

• Power Analysis for Correlation and Regression Models

• Tetrachoric Correlation

• Reporting the Strength of Effect Estimates for Simple Statistical Analyses -- Independent t, one-way independent ANOVA, correlation/regression, contingency table analysis.

• One-Way Independent Samples Analysis of Variance

  • Easy Intro (PP Slides)

• Confidence Intervals for Eta-Squared and RMSSE -- what should the confidence coefficient be?

• Familywise Alpha and the Boogie Men

• Homogeneity of Variance Tests

• Omega-Squared Discussion -- EDSTAT-L posting on Omega-Squared.

• One-Way Multiple Comparisons Tests

• Power Analysis for One-Way ANOVA -- Using G*Power

• PSY:  A Program for Contrast Analysis

• Reporting the Strength of Effect Estimates for Simple Statistical Analyses -- Independent t, one-way independent ANOVA, correlation/regression, contingency table analysis.

• T Tests, ANOVA, and Regression Analysis -- mathematical equivalence of these

• Factorial-Basics -- Basic concepts in factorial ANOVA.

• An Example of Factorial Design in Cross Cultural Research.

• Factorial-Computations -- Computations in factorial ANOVA.

• Triv-Int.doc: Trivial interactions in factorial ANOVA.

• Example Presentation of Results from a Two-Way Factorial ANOVA.

• Weighted and Unweighted Means ANOVA

• Two-Way Independent Samples Trend Analysis.

• Mixed-Effects ANOVA -- one fixed factor, one random

• Random Effects, Omega-Squared, and the Intraclass Correlation Coefficient -- random effects factors and nested effects are included in the analysis presented here.

• The Factorial ANOVA Is Done, Now What Do I Do?

• Nonparametric Statistics.    Power Point

• Effect Sizes

• Resampling Statistics.

• Data Screening

• An Introduction to Multivariate Statistics

• Canonical Correlation

• Cluster Analysis        Slideshow

• Curvilinear Bivariate Regression.

• Bumblebee Regression -- guaranteed to fit any data.

• A Brief Introduction to Multiple Correlation/Regression Analysis

• Presenting the Results of a Multiple Correlation/Regression Analysis.

• Trivariate Correlation/Regression By Hand.

• Multiple R² and Partial Correlation/Regression Coefficients.

• Redundancy and Suppression in Trivariate Correlation/Regression Analysis.  Also available in PowerPoint format.

• Example of Multiple Correlation/Regression With Three Predictor Variables -- checking assumptions, transformation, suppression.

• Inverting Matrices: Determinants and Matrix Multiplication.  Also available in PowerPoint format.

• Using Matrix Algebra to do Multiple Correlation/Regression.  Also available in PowerPoint format.

• Matching and ANCOV With Confounded Variables.

• Regression Diagnostics.

• Stepwise Multiple Correlation/Regression.

• Comparing Regression Lines From Independent Samples.    Also available in PowerPoint format.

• Continuous Moderator Variables.  Also available in PowerPoint format.

• Binary Logistic Regression with SPSS.  Also available in PowerPoint format.

• Logistic Regression with SAS.

• Statistical Tests of Models That Include Mediating Variables         Also available in PowerPoint format.

• Power Analysis for Change in R², Multiple Linear Regression -- G*Power3

• Least Squares Analyses of Variance and Covariance.

• The Pretest-Posttest x Groups Design:  How to Analyze the Data

• An Introduction to Within-Subjects Analysis of Variance

• The Multivariate Approach to the One-Way Repeated Measures ANOVA

• Power Analysis for One-Way Repeated Measures ANOVA

• Two x Two Within-Subject ANOVA Interaction = Correlated t on Difference Scores

• The A X (B X S) ANOVA: A Multivariate Approach

• Three-Way Analyses of Variance Containing One or More Repeated Factors

• Doubly Multivariate Analysis of Repeated Measures Designs: Multiple Dependent Variables

• Mixed ANOVA With a Continuous Predictor and All Interactions

• A Brief Introduction to Bayesian Statistics

• Two Group Discriminant Function Analysis

• Discriminant Function Analysis With Three or More Groups

• Principal Components Discriminant Function Analysis

• Stepwise Discriminant Function Analysis

• One-Way Multiple Analysis of Variance.

• Factorial MANOVA.

• Log-Linear Contingency Table Analysis, Two-Way

• Three-Way Hierarchical Log-Linear Analysis: Positive Assortative Mating

• Three-Way Nonhierarchical Log-Linear Analysis: Escalators and Obesity

• Four Variable LOGIT Analysis: The 1989 Sexual Harassment Study

• A Brief Introduction to Reliability, Validity, and Scaling

  • Power Point Show

• Cronbach's Alpha and Maximized Lambda4

• Principal Components Analysis

  • SAS:     MS Word Document    Power Point Show

  • SPSS:  MS Word Document    Power Point Show

• Factor Analysis

  • SAS:     MS Word Document    Power Point Show

  • SPSS:  MS Word Document    Power Point Show

• SPSS Discriminant Analysis on Factor Scores Produced By SAS.

• An Introduction to Path Analysis

• Conducting a Path Analysis With SPSS/AMOS

• Conducting a Path Analysis With SAS Proc Calis

• An Introduction to Structural Equation Modeling (SEM)

• SEM with AMOS

• SEM with SAS Proc Calis

• SEM Assignment

  • Tender-Heartedness and Attitude About Animal Research

  • The Power Point slide for creating the diagram -- save this to your computer, do not open it in your browser.

• CFA with AMOS

• CFA with SAS Proc Calis

• Draw an SEM Diagram in Power Point

• An Introduction to Hierarchical Linear Modeling
• Resources for Hierarchical Linear Modeling

Contact Information for the Webmaster,
Dr. Karl L. Wuensch

This page most recently revised on the 15^th of May, 2012.