Most of my quantitative methods programs are now available as R packages or shiny apps, with some additional programs provided on this page. References appear below.
Taxometric Analysis
As of May 30, 2017, the program code to perform taxometric analyses that was previously available via this web site has been replaced with the RTaxometrics package (Ruscio & Wang, 2021). More information is available here.
Generating Multivariate Nonnormal Data
Program code to implement the methods described in Ruscio and Kaczetow (2008) is provided in the RGenData package.
Dipl. Stat. (FH) Thomas Franke wrote SAS code and graciously allowed me to provide it here.
EFA with Comparison Data
Program code to implement the methods described in Ruscio and Roche (2012a) is provided in the RGenData package. The code has been extended to allow the use of Spearman rank-order correlations, which can accommodate data that are ordinal and/or non-normally distributed.
Bootstrap CI for A
Program code to implement the methods described in Ruscio and Mullen (2012) is provided in the RProbSup package. Daniel Mattei created a version of this code to perform the analyses in SAS and has kindly permitted me to share his SAS code here.
Generalizations of A
Program code to implement the methods described in Ruscio & Gera (2013) is provided in the RProbSup package. Daniel Mattei created a version of this code to perform the analyses in SAS and has kindly permitted me to share his SAS code here.
Citation-Based Indices
Program code to implement the methods described in Ruscio, Seaman, D’Oriano, Stremlo, and Mahalchik (2012) is provided in the RImpact package.
Standardized Variance Heterogeneity
Program code to implement the methods described in Ruscio and Roche (2012b) is available here.
Adjusting Scores
Program code to implement the methods described in Kuhlthau, Ruscio, Bastedo, & Furey (2017) is available here.
NFL Win Probability Calculator
At this web site, you can enter the details of a game situation (e.g., score, down and distance, field position, time remaining) to obtain an estimate of the probability that the team on offense will win the game:
https://ruscio.shinyapps.io/winprob_app/
The statistical model, and supporting research, are described in this paper.
References
Kuhlthau, K., Ruscio, J., Bastedo, C., & Furey, M. (2017). What’s in a grade? A professor’s guide to adjusting scores. Journal on Excellence in College Teaching, 28, 81-110.
Ruscio, J., & Gera, B. L. (2013). Generalizations and extensions of the probability of superiority effect size estimator. Multivariate Behavioral Research, 48, 208-219.
Ruscio, J. Haslam, N., & Ruscio, A. M. (2006). Introduction to the taxometric method: A practical guide. Mahwah, NJ: Lawrence Erlbaum Associates.
Ruscio, J., & Kaczetow, W. (2008). Simulating multivariate nonnormal data using an iterative technique. Multivariate Behavioral Research, 43, 355-381.
Ruscio, J., & Mullen, T. (2012). Confidence intervals for the probability of superiority effect size measure and the area under a receiver operating characteristic curve. Multivariate Behavioral Research, 47, 201-223.
Ruscio, J., & Roche, B. (2012a). Determining the number of factors to retain in an exploratory factor analysis using comparison data of known factorial structure. Psychological Assessment, 24, 282-292.
Ruscio, J., & Roche, B. (2012b). Variance heterogeneity in published psychological research: A review and a new index. Methodology, 8, 1-11.
Ruscio, J., Seaman, F., D’Oriano, C., Stremlo, E., & Mahalchik, K. (2012). Measuring scholarly impact using modern citation-based indices. Measurement: Interdisciplinary Research and Perspectives, 10, 123-146.
Ruscio, J., & Wang, S. B. (2021). RTaxometrics: Taxometric analysis. R package version 3.1. Available at https://CRAN.R-project.org/package=RTaxometrics.