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This page will have articles specifically for class and others that will be beneficial to your research efforts in general and will also help you learn the material, but which are not required reading.  All assigned readings will also be found in the Related Readings section. At the bottom is a selected bibliography of texts that I have used in developing my courses in both alphabetical form and by topic..

Readings for class Related Readings
5700

5710

6810

Science General
Overcoming Veriphobia (Bailey)
Popper, Fisher, Bayes
Relativism (SEP)
Science: Conjectures and Refutations (Popper)
Investigating fraud in scientific research
Stats and Psychological Science (Rosnow)
Our Faith in Science (Tenzin Gyatso)
Haack 1996: Concern for truth
Rosnow & Rosenthal Chapter 1, 2, 3
Myths and Legends in Quantitative Psychology (Grayson)
 
NHST
APA task force guidelines
The fallacy of the null hypothesis significance test (Rozeboom)
P-values are not error probabilities (Hubard & Bayarri)
Even stats guys misinterpret NHST (Lecoutre et al.)
Things I have learned so far... (Cohen)
The earth is round, p < .05 (Cohen)
The problem is epistemology not statistics (Meehl)
Down with statistical rituals! (Gigerenzer)
Krantz, NHST controversy
Fisher <3 Bayes?? (Aldrich)
Summary (me)
The difference between “significant” and “not significant” is not itself statistically significant (Gelman & Stern)
 
Data Exploration
Initial Data Analysis (Chatfield)
Biases in research interpretation (MacCoun)
The normal unicorn (Micerri)
The median is not the message (Gould)
Avoiding statistical pitfalls (Chatfield)
Missing Data (Enders)  (Roth)
Measures of Central Tendency (Streiner)
 
CIs, ICIs, Equivalence
Inferential Confidence Intervals, 2 (Tryon)
Equivalence Testing (Rogers,Howard, Vessey)
Equivalence testing (Yours truly)
Graphical CIs (Masson & Loftus)
Unicorns do exist (Streiner)
 
Effect size
Effect sizes compared (Rosnow & Rosenthal)
More effect size (Olejnik and Algina)
Power and Effect Size (Cohen)
Small effect sizes, practical importance (Prentice & Miller)
When a little is a lot (Abelson)
CIs for effect size (Thompson)
One more about small effects and practical importance (Rutledge and Loh)
Beyond the F test (Steiger)
 
ANOVA
False Discovery Rate (Benjamini & Hochberg)
Multiple comparisons compared 1,2
Bluffer's guide to sphericity (Field)
Reader Questions involving Anova (J of Consumer Psyc)
Dealing with Heterogeneity of Variance (Bryk & Raudenbush)
 
Regression
Why you should not categorize continuous variables (Harrell)
The moderator-mediator variable distinction. (Baron & Kenny).
Comparison of Moderation, Confounding, Mediation (MacKinnon et al.)
Classic on the General Linear Model (Cohen)
 
Multivariate specific
Handout regarding IDA for the multivariate situation
Canonical Correlation Primer (Thompson)
My Canonical Correlation article
Multivariate Effect Size Estimation (Kline supplemental)
Exploratory Factor Analysis book (Tucker & MacCallum)
History of Exploratory Factor Analysis (Mulaik)
Cluster Analysis (me; very intro.  I was still a student and had not taught multivariate)
Path Analysis Intro (Streiner)
 
Modern approaches
How many discoveries lost? (Wilcox)
Wilcox 2: Resistant Boogaloo (Erceg-Hurn & Mirosevich)
Psychology's Weak Link (Wilcox)
Modern Insights regarding correlation (Wilcox)
Herrington: Robust Measures of Location, Robust effect size, Estimating Power with the Bootstrap
Bootstapping Cohen's d (Kelley)
Resampling methods
Several dozen applications of modern approaches
 
Applied use of modern methods
These are big files (even zipped) containing many articles (about 70 not counting the methods) so if you want to download in parts or whole it's up to you.  Everything. Robust examples. Equivalence Testing examples. Methods articles. Description.
 

 

Selected Bibliography
Ableson, R.P. (1995). Statistics as principled argument.
Aiken & West (1991). Multiple regression: testing and interpreting interactions.
Bartholomew et al. (2002). The analysis and interpretation of multivariate data for social scientists.
Berry, D. (1995). Statistics: A Bayesian Perspective.
Bernstein (1988). Applied multivariate analysis.
Bray & Maxwell (1985). Multivariate analysis of variance.
Bryk, A. S. & Raudenbush, S. W. (1992): Hierarchical Linear Models.
Carroll & Green (1997). Mathematical tools for applied multivariate analysis.
Chalmers, A.F. (1976). What is this thing called science?
Cohen, J. (1988). Statistical Power Analyses for the Behavioral Sciences.
Cohen & Cohen (1983). Applied regression analysis for the Behavioral Sciences
Dunteman, G. (1989). Principal components analysis.
Fox, J. (1997). Applied Regression Analysis, Linear Models, and Related Methods.
Gelman & Hill (2007). Data Analysis Using Regression and Multilevel/ Hierarchical Models
Gill, J. (2006). Essential Mathematics for Political and Social Research.
Hastie, Tibshirani, & Friedman (2001). Elements of Statistical Learning.
Harrell, F. (2001). Regression modeling strategies.
Harlow, L. (1999). What if there were no significance tests? (Ed).
Harlow (2005). The essence of multivariate thinking.
Howell, D. (2007). Statistical Methods for Psychology.
Keith, T. Z. (2005). Multiple regression and beyond.
Keppel, G. (2004). Design and Analysis.
Keren & Lewis (Eds.) (1993), A Handbook for Data Analysis in the Behavioral Sciences: Methodological Issues.
Kim & Mueller (1978). Introduction to factor analysis: What it is and how to do it.
Kim & Mueller (1978). Factor analysis: Statistical methods and practical issues.
Kirk (1995). Experimental design.
Klecka (1980). Discriminant analysis.
Kline, R. (2004). Beyond significance testing.
Kline (2005). Principles and practice of structural equation modeling.
Kourany, J.A. (1987). Scientific knowledge.
Kreft & de Leuuw (1998). Introducing Multilevel Modeling
Lattin, Carroll, and Green (2003). Analyzing Multivariate Data.
Laudan, L (1990). Science and relativism.
Maxwell & Delaney. (1990).  Designing experiments and analyzing data.
Menard, S.(1995). Applied Logistic Regression
McDonald (1999). Test theory: A unified treatment.
McDonald (1985). Factor analysis and related methods.
Pampel, F. (2000). Logistic Regression: A Primer.
Pedhazur, E. (1997).Multiple Regression in Behavioral Research
Rutherford, A. (2001). Introducing ANOVA and ANCOVA, a GLM approach.
Stevens (multiple editions). Applied multivariate statistics for the social sciences.
Rosnow, R, & Rosenthal (1991). Essentials of Behavioral Research.
Rosnow, R, & Rosenthal, R (2003). Effect Sizes for Experimenting Psychologists.
Tabachnick & Fidell (2006). Using multivariate statistics.
Tastuoka (1971). Multivariate analysis
Thompson (1984). Canonical correlation analysis: Uses and interpretation.
Thompson (1991). A primer on the logic and use of canonical correlation analysis.
Thompson, B. (2004). Exploratory and Confirmatory Factor Analysis
Wilcox, R. (2003). Applying Contemporary Statistical Techniques
Wilcox, R. (2002). Fundamentals of Modern Statistical Methods: Substantially Improving Power and Accuracy
Wilcox, R. (1997). Introduction to Robust Estimation and Hypothesis Testing
Winer (1991). Statistical principles in experimental design.

By category

Intro
Ableson, R.P. (1995). Statistics as principled argument.
Chalmers, A.F. (1976). What is this thing called science?
Howell, D. (2007). Statistical Methods for Psychology.
Kourany, J.A. (1987). Scientific knowledge.
Laudan, L (1990). Science and relativism.
 
Research Design
Cohen, J. (1988). Statistical Power Analyses for the Behavioral Sciences.
Keppel, G. (2004). Design and Analysis.
Kirk (1995). Experimental design.
Maxwell & Delaney. (1990).  Designing experiments and analyzing data.
Rutherford, A. (2001). Introducing ANOVA and ANCOVA, a GLM approach.
Rosnow, R, & Rosenthal (1991). Essentials of Behavioral Research.
Winer (1991). Statistical principles in experimental design.
 
Null Hypothesis Testing
Harlow, L. (1999). What if there were no significance tests? (Ed).
Keren & Lewis (1993), A Handbook for Data Analysis in the Behavioral Sciences: Methodological Issues. (Ed.)
Kline, R. (2004). Beyond significance testing.
 
Mutliple Regression
Aiken & West (1991). Multiple regression: testing and interpreting interactions.
Fox, J. (1997). Applied Regression Analysis, Linear Models, and Related Methods.
Cohen & Cohen (1983). Applied regression analysis for the Behavioral Sciences
Harrell, F. (2001). Regression modeling strategies.
Keith, T. Z. (2005). Multiple regression and beyond.
Menard, S.(1995). Applied Logistic Regression
Pampel, F. (2000). Logistic Regression: A Primer.
Pedhazur, E. (1997).Multiple Regression in Behavioral Research.
 
Multivariate (general)
Bartholomew et al (2002). The analysis and interpretation of multivariate data for social scientists.
Bernstein (1988). Applied multivariate analysis.
Bray & Maxwell (1985). Multivariate analysis of variance.
Carroll & Green (1997). Mathematical tools for applied multivariate analysis.
Gill, J. (2006). Essential Mathematics for Political and Social Research.
Harlow (2005). The essence of multivariate thinking.
Lattin, Carroll, and Green (2003). Analyzing Multivariate Data.
Stevens (multiple editions). Applied multivariate statistics for the social sciences.
Tabachnick & Fidell (2006). Using multivariate statistics.
Tastuoka (1971). Multivariate analysis
 
Multivariate (specific)
Bryk, A. S. & Raudenbush, S. W. (1992): Hierarchical Linear Models.
Dunteman, G. (1989). Principal components analysis.
Gelman & Hill (2007). Data Analysis Using Regression and Multilevel/ Hierarchical Models
Kim & Mueller (1978). Introduction to factor analysis: What it is and how to do it.
Kim & Mueller (1978). Factor analysis: Statistical methods and practical issues.
Klecka (1980). Discriminant analysis.
Kreft & de Leeuw (1998). Introducing Multilevel Modeling
Kline, R. (2005). Principles and practice of structural equation modeling.
McDonald, R. (1999). Test theory: A unified treatment.
McDonald, R. (1985). Factor analysis and related methods.
Thompson, B. (2004). Exploratory and Confirmatory Factor Analysis
Thompson, B. (1984). Canonical correlation analysis: Uses and interpretation.
 
Modern approaches
Berry, D. (1995). Statistics: A Bayesian Perspective.
Hastie, Tibshirani, & Friedman (2001). Elements of Statistical Learning.
Wilcox, R. (2003). Applying Contemporary Statistical Techniques.
Wilcox, R. (2002). Fundamentals of Modern Statistical Methods: Substantially Improving Power and Accuracy.
Wilcox, R. (1997). Introduction to Robust Estimation and Hypothesis Testing.