Welcome to CARMA’s Webcast Lecture

Feb. 10, 2023

Dr. Dina Krasikova, University of Texas at San Antonio

Interpreting Results with Practical Significance in Mind: An Overview of the Common Language Effect Size Indices

12:00 PM – 1:30 PM ET

Dr. Dina Krasikova is an associate professor in the Department of Management at The University of Texas at San Antonio. Her research interests include constructive and destructive forms of leading, deviant behavior in organizations and development and evaluation of statistical methods that can be used to address research questions about organizational processes and phenomena. Dr. Krasikova serves as a Representative-at-Large at the Research Methods Division of the Academy of Management. She also serves on the editorial boards of the Journal of Applied Psychology, Journal of Business and Psychology, Journal of Leadership and Organizational Studies and Organizational Research Methods. Her recent published intellectual contributions are as follows:

“Toward Customer-Centric Organizational Science: A Common Language Effect Size Indicator for Multiple Linear Regressions and Regressions with Higher-Order Terms,” with H. Le and E. Bachura, Journal of Applied Psychology, in press.
“Not Me, but Reflects Me: Validating a Simple Implicit Measure of Psychological Capital,” with P.D. Harms and F. Luthans, Journal of Personality Assessment, in press.
“Non-Independence, Within-Group Agreement, and the Reliability of Group Means: Implications for Multilevel Research,” with J. M. LeBreton, in S. E. Humphrey & J. M. LeBreton, eds., The Handbook of Multilevel Theory, Measurement, and Analysis, American Psychological Association, in press.

Abstract

In looking for ways to bridge a divide between science and practice, organizational scholars suggested that more efficient ways of conveying research results are needed to improve knowledge transfer from science to practice. One strategy to improve the communicability of effects obtained in organizational research is to use common language effect size indices (CL) that are intuitively interpretable. In this lecture, I will provide an overview of the existing CL indices — CL _gthat is used for group comparisons, CL _rthat supplements bivariate correlations_,and CL_β that is used with multiple regression models, including regressions with higher-order terms. I will demonstrate how these indices can be computed, interpreted, and used in conjunction with other metrics designed to interpret the size and patterns of predictor effects.

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