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)

Pvalues
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
moderatormediator 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 (ErcegHurn
& 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.



