Amusing Graphs

ANOVA, Analysis of Value -- Is your research worth anything? (humor)

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

Bayesian Stats, Brief Intro

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 k > 2 Independent Correlation Coefficients

Comparing Reliability Coefficients -- procedures for comparing one reliability coefficient to another

Confidence Interval for R² -- putting a confidence interval on R²

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.

Errors Often Made When Testing Hypotheses

Excel:

• Create a Column With a Constant in Every Cell

• 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)

• Removing Blank Rows in Dowloaded Course Rosters

• 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

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

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

• Student's memoria technica

• Normalcurvisaurus

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?

Mediation and Moderation

• Interpreting Interaction Effects -- Jeremy Dawson

• Italassi Interaction Viewer

• 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

Meta-Analysis -- instructional material, effect size calculator spreadsheet, and computational macros for SPSS, SAS, and Stata

Monty Hall Dilemma-- would you switch doors?

Missing Data -- suggested readings on the topic of how to deal with missing data

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

OPSCAN -- using OPSCAN data sheets in your research

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

Pearson r, Critical Values

Pitman's T -- testing variances with correlated samples

Poems -- Poems about statistics

POWER -- An overview

• In Power Point format

• G*Power3:  Tests available

• Internet Resources

• Correlated t versus Independent t

• Power, Retrospective -- Foolishness

Probability of Replication -- is this new statistic of any value?

Quotes about Statistics -- The average human has one breast and one testicle.

Regression Towards the Mean

• Estimating Expected 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

STATISTICAL METHODS IN PSYCHOLOGY -- Support page for David Howell's Outstanding Text

Statistician as Detective

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?