East Carolina University
Department of Psychology
ANOVA, Half-Tailed Tests -- ANOVA, F, t, directional tests, and the foolishness of "post hoc" power analysis.
ANOVA2-Followup -- heterogeneity of variance in the factorial ANOVA in Howell (grad) page 404
Beyond Significance Testing -- my notes from the text by Rex B. Kline (2004).
Canonical -- why is it called "canonical" correlation?
Comparing Correlated, Overlapping Correlation Coefficients -- Steiger's z.
Comparing Correlated but Nonoverlapping Correlation Coefficients -- the Pearson-Filon statistic and the ZPF statistic
Comparing Reliability Coefficients -- procedures for comparing one reliability coefficient to another
Confidence Interval for R^{2} -- putting a confidence interval on R^{2}
Contingency Tables, Low Expected Frequencies -- Is this a problem or not?
Correlation and Causation -- When does correlation imply causation?
Correlation and Regression, Assumptions -- fixed versus random X
Data-Help -- suggestions on setting up a data file
Dichotomizing Continuous Variables -- a bad idea: Discussion from Edstat-L.
Effect Size Estimation -- An overview of this topic
Odds Ratios -- why I prefer them over probability ratios
Odds Ratios, Phi, and Base Rates -- phi can be greatly affected by base rates, odds ratios are not.
Eigenvalues -- origin of the term
Excel:
Cohen's d for Correlated Samples -- compute it with this spreadsheet
Column Headers -- Print column headers atop each page of Excel output
Error Bars -- Using Excel to Create a Plot of Group Means With Error Bars
Freeze Panes -- Keep specified rows/columns in view
Interaction Plots -- much nicer than the clunky ones produced with SAS' Proc Plot (but not as fancy as SAS' Gplot)
Sum Cells -- Summing Cells in Excel. Shows how little I know about Excel.
Exploratory Factor Analysis -- review of 1999 article by Fabrigar et al.
e-history -- base of the natural log
EX16-11 -- from Howell's fundamentals text, page 333
Exact-P -- quotes and discussions on the use of exact p values
Factor Analysis, Number of Factors, code for Parallel Analysis and Velicer's MAP Test
Greek Alphabet -- many of these letters are used as statistical symbols.
History of Statistics -- useful links to Internet Resources, University of York
HOWL13-6 -- exercise 13-6 from the Fundamentals text
Humor -- jokes involving statistics
Interaction Significant, Simple Main Effects Not -- suggestion on how to deal with this outcome.
IV-DV -- Independent and Dependent Variables
KR20 -- Kuder-Richardson 20
Kurtosis -- discussion of kurtosis
Likert -- what is a Likert scale and how do you pronounce "Likert?"
Linear Models -- Games to help the student learn about linear model, correlation, and regression.
Logit -- pronunciations of the word
LESSONS -- these are my online lecture notes for my stats classes
Low Power, Low Reliability -- Is it a big problem when results are significant?
Interpreting Interaction Effects -- Jeremy Dawson
Moderation/Mediation Help Centre -- Paul Jose
Quantpsy.org -- Kristopher Preacher
Research In Prevention Laboratory -- David MacKinnon
Statistical Mediation and Moderation Analysis -- Facebook Group
SAS Macros -- Yung-Jui Yang
SPSS and SAS Macros, Andrew Hayes -- check out Process for SPSS and SAS
Meta-Analysis -- links to useful resources
Monty Hall Dilemma-- would you switch doors?
Missing Data -- suggested readings on the topic of how to deal with missing data
Multiple Regression, Assumptions -- Osborne, J., & Waters, E. (2002). Four assumptions of multiple regression that researchers should always test. Practical Assessment, Research & Evaluation, 8(2).
Multivariate Normality -- a few links to get one started on this topic
NHST-SHIT -- Jack Cohen's wit and a list of references on significance testing.
NHST-Quiz-- A little True-False quiz to test your understanding of Statistical Hypothesis Inference Testing
Nonparametrics -- common misconception that nonparametrics have no assumptions
Norm-Sample Comparison -- comparing your sample mean and variance to that of a normative group
Normality Assumption -- EDSTAT-L discussion on checking data for compliance with an assumption of normality
Omega-Squared -- advice on interpreting the omega-squared statistic
One-Tailed NHST -- does it ever make sense to test directional hypothesis?
OPSCAN -- using OPSCAN data sheets in your research
p values suspiciously distributed -- something here smells like rotten p
Pairwise Comparisons -- discussion of pairwise/multiple comparisons procedures, including common misconceptions about them
Path-Matrix -- using matrix algebra to get effect coefficients in path analysis
Pitman's T -- testing variances with correlated samples
Poems -- Poems about statistics
POWER -- An overview
Probability of Replication -- is this new statistic of any value?
Quotes about Statistics -- The average human has one breast and one testicle.
Randomness -- what does it really mean when we say an event is "random?"
Regression Towards the Mean
History -- origin of the term "regression"
Reliability -- a few notes on reliability from Nunnally's text
Resampling Statistics -- a brief introduction to bootstrapping and permutation/randomization tests
Reversal-Paradox -- discussion of the reversal paradox, also known as Simpson's paradox.
SAS -- help using the SAS stat pack
Scales-Transform -- nonlinear data transformations
Signif-Testing -- why Frank Schmidt thinks significance testing the devil's work
Simpson -- Simpson's paradox, with bibliography
Skew -- location of mean, median, mode in skewed distributions
Split in Half --Splitting a data file into two random halves
SPSS -- Help using the SPSS stats pack
SPSS2Excel2SAS -- write SPSS data to Excel and then bring into SAS
SS-Type -- different types of sums of squares
Standardized Confidence Intervals -- why not standardize confidence intervals
Stepwise -- stepwise procedures are the devil's work
t-CLT -- nonrobustness of the t statistic
t-Crit -- t approximated by z and some criticisms of significance testing
Type I Errors -- How frequent are Type I errors in the published literature?
Type-I-II-Errors -- Evaluating the Relative Seriousness of Type I versus Type II Errors in Classical Hypothesis Testing
Type III Errors -- Correctly rejecting the null but incorrectly inferring the direction of effect
Why Standardize? -- when is it helpful to standardize effect size estimates rather than presenting them in unstandardized form?
Contact Information for the Webmaster,
Dr. Karl L. Wuensch
This page most recently revised on the 30^{th} of January, 2014.