This site contains article references, online tutorials/calculators, and other sources that people have sent to me or that I've looked up to help students and colleagues. Rather than letting these resources sit in my email folders and web favorites list, I thought it would be good to share. Some basic knowledge of statistics is assumed. My aim, therefore, is to help people branch out from their current statistical repertoire and/or troubleshoot common roadblocks with various techniques. If you'd like to suggest other links and sources, please email me via my faculty webpage.
LAST UPDATED: July 12, 2018
General Tools, Multiple Techniques 
Stat Pages (compilation
of hundreds of online statistical calculators, from John C. Pezzullo) 
Selecting statistical techniques based on number and types of variables 
Interactive Decision Tree Article "Choosing Statistical Tests" UCLA  Tabular form, with advice specific to different software programs Links to additional tables 
Data Management 
Data Screening/Cleaning Checklist
Skewness and kurtosis (note that skewness and kurtosis should
each be divided by its respective standard error to see if the traditional cutoff for
twotailed .05 significance on the normal curve, +/ 1.96, is attained
[for simplicity, a ratio of 2 is often cited]. Some authors
recommend larger [absolutevalue] cutoffs). Also, the traditional
definition of kurtosis in terms of "peakedness" is
wrong.
Guidelines for transforming variables to improve distributional
qualities
Index (composite variable) creation (SPSS; discusses situation of participants missing responses to some of the variables comprising the index) Missing Data Acock, A. (2005). Working with missing values. Journal of Marriage and Family, 67, Dong, Y., & Peng, CY. J. (2013). Principled missing data methods for researchers. Springer Open/Springer Plus, 2, 222. Graham, J. W. (2009). Missing data analysis: Making it work in the real world. Annual Revuew of Psychology, 60, 549–576. Little, T. D., Jorgensen, T. D., Lang, K. M., & Moore, E. W. G. (2013). On the joys of missing data. Journal of Pediatric Psychology, 39, 151–162 Longitudinal attrition: Coertjens L, Donche V, De Maeyer S, Vanthournout G, Van Petegem P (2017) To what degree does the missing data technique influence the estimated growth in learning strategies over time? A tutorial example of sensitivity analysis for longitudinal data. PLoS ONE 12(9): e0182615. Nicholson, J. S., Deboeck, P. R., Howard, W. (2017). Attrition in developmental psychology: A review of modern missing data reporting and practices. International Journal of BehavioralDevelopment, 41, 143153. Young, R., & Johnson, D. R. (2015). Handling missing values in longitudinal panel data with multiple imputation. Journal of Marriage and Family, 77, 277–294. Regression diagnostics (see also Outliers under TroubleShooting, below) Reverse scoring of variables  On measures with a strongly disagreestrongly agree (Likert) format, where one or more items have an oppositely toned wording to the majority of items (e.g., "I dislike Restaurant A," where the other items are "I like Restaurant A," "I plan to keep going back to Restaurant A," "The food is great at Restaurant A," etc.), the scoring of the oppositely toned item(s) should be reversed before the items are combined into an overall scale. This first document explains how to use the compute command in SPSS, so that for example, on a 17 scale, taking 8 minus the original value converts a 1 into a 7, a 2 into a 6, etc. This second document explains using the recode command. I always recommend giving the new variable a new name (e.g., "item4r" where "r" stands for "reverse"), so that the original variable "item4" remains unperturbed in case you need to go back to it for any reason. 
Overview Intros 
ANOVA:
oneway Canonical Correlation (here and here) Cluster Analysis ......Rapkin, B. D., & Luke, D. A. (1993). Cluster analysis in community research... American J. of Community Psychol., 21, 247277. Correlation
analogues (e.g., biserial, pointbiserial, phi)
40,
532538.
Latent Class Analysis (here)
Mediation/Moderation (Kristopher
Preacher's
resources; Andrew Hayes's
resources and article "Beyond
Baron and Kenny")
Poission Regression (here
and
here; for count data of rare events and where mean = variance)
Regression:
10 Types of Regression
(based on data distributions and other considerations) 
Freeware 
Penn State Methodology Center (free
software page, includes many applications) 
"R":
The R Project;
R Links (Psychology Focus);
Intro to R from the Personality Project;
J. Baron's R Help Page
R Commander*;
R Studio*;
Quick R* ;
R for SAS and SPSS Users; Videos instructing on R: Decision Science News
(beginning
video,
web overview); Google/Flowing Data (here)
RBased Structural Equaltion Modeling Programs: Lavaan; Onyx*; WebSEM* ....................[*Offers or discusses GUI (Graphical User Interface) packages] 

Trouble Shooting, Difficulties 
General: Cortina, J. M. (2002). Big things have small beginnings: An assortment of “minor” methodological misunderstandings. Journal of Management, 28, 339–362 (abstract; thanks to Austin Houghtaling). Reviews scenarios such as outliers; standardized regression Betas greater than 1; negative variances (Heywood Cases) and correlated errors in SEM; and more! Chisquare: Which cell(s) are the major contributors to an overall significant chisquare? (standardized residuals) Comparing correlations: There are different types of correlation comparisons. Do you want to compare correlations of the same variables (A & B) in two independent samples? Or, within the same sample, is the correlation of A & B different from that between A & C (dependent correlations)? This document explains the different types of comparisons and links to an online calculator (where it says "Go to procedure"). For more specialized issues, such as comparing partial correlations, see:
Effect sizes with repeated
measures:
Dunlap,
W. P., Cortina, J. M., Vaslow, J. B., & Burke, M. J. (1996).
Metaanalysis of experiments with matched groups or repeated measures designs.
Psychological Methods, 1,
170177. Interactions in multiple regression and related techniques: Resources from Preacher, Curran, and Bauer Moderation and Mediation: Resources from Kristopher Preacher Multicollinarity (here, here, here, and here) Outliers (suggestions from Peter Westfall) Presenting Data: "Just Plain Data Analysis" Spuriousness and Suppressor Effects/Suppressor Variables (here, here, and here; suppression refers, for example, to when a relationship between variables switches from positive to negative, with the introduction of control variables) Syntax
(SPSS) 
Terminology 
Compendium of terms (each place it says "Category" opens to a set of
more specific links) Odds vs. probability (pertinent to logistic regression) 
Tips and Tools for Constructing Tables and Figures 
Tables for multiple regression (Alan Acock) Plotting regression interactions (Jeremy Dawson) Lane, D. M. & Sandor, A. (2009) Designing better graphs by including distributional information and integrating words, numbers, and images. Psychological Methods, 14, 239257. 
Power Analysis for Advanced Techniques 
Power for multiple
techniques (here) LIFESPAN: Longitudinal Study Planner (A. M. Brandmaier) Chris Aberson's poweranalysis resources ("downloadable resources include SPSS syntax for completing power analysis for a ton of designs. These do come from my text on power analysis but the files can be used without purchase of the book") Hertzog, C., Lindenberger, U., Ghisletta, P., & von Oertzen, T. (2006). On the power of multivariate latent growth curve models to detect correlated change. Psychological Methods, 11, 244252. Preacher, K. J., & Coffman, D. L. (2006, May). Computing power and minimum sample size for RMSEA [Computer software]. 
Other Resource Pages  Online journals
offering free tutorial articles:
Practical
Assessment, Research, and Evaluation and
Frontiers in Quantitative Psychology and Measurement (more
advanced) Carolyn Anderson's syllabus/lecture notes for Applied Categorical Data Analysis and Multilevel Analysis/HLM" (lots of examples!) James Grice's Personality Research Lab resource page (discussion of advanced topics in methods and statistics) Kevin Grimm's computer scripts for advanced longitudinal analyses
Winfred Arthur's
vita (on conducting metaanalysis with different software
programs; see book [2001] and articles from 1990s)
Statistics Resources for Businesses and Educators (Thanks to
Stacy Kozak)
Dr. Reifman's Introductory Graduate Statistics Dr. Reifman's Multivariate Statistics Dr. Reifman's Structural Equation Modeling 
Lines of Argument
for Why Small Effect Sizes Can Still Be Meaningful 
1. Importance (i.e., life and death) of the outcome variable. 
Secondary Data Analysis  U. of Michigan (ICPSR)
archive of datasets Andersen, J., Prause, J., & Silver, R. C. (2011). A stepbystep guide to using secondary data for psychological research. Social and Personality Psychology Compass, 5, 5675 (abstract). Trzesniewsk, K. , Donnellan, M. B., & Lucas, R. E. (Eds.) (2010). Secondary data analysis: An introduction for psychologists. Washington, DC: American Psychological Association (publisher page). 
Dr. Reifman's Other Website Compilations 
Summer statistics/methodology workshops around the world Questionnaire instruments (personality traits, social behavior) in the public domain 