This file provides the Five-by-Five Resilience Scale, a questionnaire with items grouped into five factors (adaptability, emotion regulation, optimism, self-efficacy, and social support) that can be scored to assess individual resilience and/or its subfactors.
Relevant publication: DeSimone, J. A., Harms, P. D., Vanhove, A. J., & Herian, M. N. (2017). Development and validation of the Five-by-five Resilience Scale. Assessment, 24, 778-797.
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This Python script generates simulated datasets for students, creating a separate dataset (and corresponding grading key) for each student. The script uses a realistic correlations matrix to generate data for students to analyze (output in Microsoft Excel). Students are asked to reverse-score items, average items to calculate scale scores for each construct, and conduct four types of data screening. This script is intended to help instructors provide hands-on assignments that help students practice data handling, including both scale scoring and the application of data quality checks.
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These tools (available for either R of MATLAB) compute the CLtc, SRMRtc, and Dptc indices, estimating the temporal consistency of component loadings, inter-item corrns, and individual response vectors (respectively).
Relevant publication: DeSimone, J. A. (2015). New techniques for evaluating temporal consistency. Organizational Research Methods, 18, 133-152.
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This MATLAB tool computes dominance analysis, relative weights, and joint variance estimation from a correlation matrix, providing effect size estimates, confidence intervals, and dominance relationships among predictors.
Relevant publication: Schoen, J. L., DeSimone, J. A., Meyer, R. D., Schnure, K. A., & LeBreton, J. M. (2021). Identifying, defining, and measuring justification mechanisms: The implicit biases underlying individual differences. Journal of Management, 47, 716-744.
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These MATLAB tools compute various careless responding indices, assisting efforts to enhance data quality through the identification of respondents who may not be providing sufficient attention or effort when completing a survey.
Relevant publication: DeSimone, J. A., Harms, P. D., & DeSimone, A. J. (2015). Best practice recommendations for data screening. Journal of Organizational Behavior, 36, 171-181.
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This tool is designed to make meta-analysis more accessible without specialized statistical software. This tool conducts a meta-analysis from a list of coded information from primary studies (e.g., effect size, sample size, reliability estimates, moderator levels). This tool will also calculate adjustments for attenuation (for either predictor unreliability or both predictor and criterion unreliability), heterogeneity estimates (including Q, H-squared, I-squared, R-squared, and tau-squared), and subgroup analysis. Calculation of the meta-analytic effect size uses correlations, though this tool also includes a worksheet that will convert log odds ratios or Cohen's d estimates to correlations.
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This Excel file explores nonessential multicollinearity in polynomial and moderated multiple regression models, helping researchers understand how variable transformations (e.g., centering, interaction terms, polynomials) affect multicollinearity and the stability of regression estimates.
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This Excel file provides a set of practice worksheets in Excel for core data analysis skills—covering basics, computing averages, reverse scoring, data screening, correlations, and multiple regression.
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This Excel file calculates Cohen’s kappa from a classification matrix, providing a measure of interrater agreement adjusted for chance.
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This tool computes Steiger’s T test for comparing two dependent correlations (i.e., two correlations computed from the same data) that both involve the same outcome variable, allowing researchers to test whether one predictor correlates more strongly with the outcome than another.
Relevant publication: Steiger, J. H. (1980). Tests for comparing elements of a correlation matrix. Psychological Bulletin, 87, 245–251..
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This Excel file calculates the Standardized Root Mean Square Residual (SRMR) by comparing two correlation matrices, providing a quantitative index of model fit based on the average standardized difference between observed and predicted correlations.
Relevant publication: Bentler, P. M. (1995). EQS structural equations program manual. Encino, CA: Multivariate Software.
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