Toward customer-centric organizational science: A common language effect size indicator for multiple linear regressions and regressions with higher-order terms.

Krasikova, Dina; Le, Huy; Bachura, Eric

doi:10.1037/apl0000296

Cited by 8 publications

(7 citation statements)

References 41 publications

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“…At the current stage, the calculator will only produce alternative effect sizes based on two continuous variables. Future developments of the calculator will allow for the calculation of the combined and incremental validity of multiple predictors (e.g., Bridgeman et al, 2004 ; Krasikova et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Utility of Alternative Effect Size Statistics and the Development of a Web-Based Calculator: Shiny-AESC

Zhang

2018

Front. Psychol.

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Alternative displays of effect size statistics can enhance the understandability and impact of validity evidence in a variety of applied settings. Arguably, the proliferation of alternative effect size statistics has been limited due to the lack of user-friendly tools to create them. Common statistical packages do not readily produce these alternative effect sizes and existing tools are outdated and inaccessible. In this paper, I introduce a free-to-use web-based calculator (https://dczhang.shinyapps.io/expectancyApp/) for generating alternative effect size displays from empirical data. This calculator requires no mathematical or programming expertise, and therefore, is ideal for academics and practitioners. I also present results from an empirical study that demonstrates the benefits of alternative effect size displays for enhancing lay people's perceived understandability of validity information and attitudes toward the use of standardized testing for college admissions.

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Section: Discussionmentioning

confidence: 99%

Utility of Alternative Effect Size Statistics and the Development of a Web-Based Calculator: Shiny-AESC

Zhang

2018

Front. Psychol.

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“…Along with correctly interpreting estimates, we encourage researchers to calculate and discuss marginal effects with theoretically salient values, such as at the median and for a select few standard cases (Hoetker, 2007). Further, in multivariate models including a mix of continuous and categorical predictors, researchers should make use of tools to present the estimated marginal effects in plain language to make their results more accessible (Krasikova et al, 2018). We also encourage researchers to report exact p-values, standard errors, confidence intervals, and related parameters appropriate to the specified model.…”

Section: Interpretation and Inferencementioning

confidence: 99%

Editorial: Enhancing quantitative theory-testing entrepreneurship research

Anderson

Wennberg

McMullen

2019

Journal of Business Venturing

120

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The purpose of this editorial is to discuss methodological advancements to enhance quantitative theory-testing entrepreneurship research. As the impact of entrepreneurship scholarship accelerates and deepens, our methods must keep pace to continue shaping theory, policy, and practice. Like our sister fields in business, entrepreneurship is coming to terms with the replication and credibility crisis in the social sciences, forcing the field to revisit commonly-held assumptions that limit the promise and prospect of our scholarship. Thus, we provide suggestions for reviewers and editors to identify concerns in empirical work, and to guide authors in improving their analyses and research designs. We hope that our editorial provides useful and actionable guidance for entrepreneurship researchers submitting theory-testing papers to Journal of Business Venturing.

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“…CLES indicators offer a way to improve the clarity of reported effects and improve the ability to communicate findings to researchers and practitioners alike (Brooks et al, 2014; Krasikova et al, 2018). CLES indicators are typically calculated by converting a traditionally reported effect size into a number or figure that is easier to practically interpret.…”

Section: Common Language Effect Sizesmentioning

confidence: 99%

Results everyone can understand: A review of common language effect size indicators to bridge the research-practice gap.

Mastrich¹,

Hernandez²

2021

Health Psychology

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Objective: Health psychology, as an applied area, emphasizes bridging the gap between researchers and practitioners. While rigorous research relies on advanced statistics to illustrate an underlying psychological process or treatment effectiveness, these statistics have less immediate applicability to practitioners who require knowing the relative magnitude in practical benefits. One way to reduce this research-practice gap is to translate reported effects into nontechnical language whose focus is on the likelihood of benefiting an individual. Common Language Effect Size (CLES) indicators offer a more intuitive way to understand statistical results from research but may not be widely known to researchers. Method: This article synthesizes the literature of available CLES indicators and how they overcome limitations from traditional effect sizes. To promote adoption, we summarize all existing measures in a compact table, which includes their analogous effect size, context, interpretation, calculation, and citation. Results: We present evidence describing the effectiveness of CLES indicators at facilitating research interpretability compared to traditional effect size indicators. We discuss some limitations of CLES indicators and reasons that they are not used in psychology. Finally, this review offers some future directions for the use and study of CLES indicators moving forward. Conclusions: In general, CLES indicators are tools that can benefit health psychology because of their shared goals to aid practitioners in understanding research findings and making informed decisions.

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Toward customer-centric organizational science: A common language effect size indicator for multiple linear regressions and regressions with higher-order terms.

Cited by 8 publications

References 41 publications

Utility of Alternative Effect Size Statistics and the Development of a Web-Based Calculator: Shiny-AESC

Utility of Alternative Effect Size Statistics and the Development of a Web-Based Calculator: Shiny-AESC

Editorial: Enhancing quantitative theory-testing entrepreneurship research

Results everyone can understand: A review of common language effect size indicators to bridge the research-practice gap.

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