Perceived Statistical Knowledge Level and Self-Reported Statistical Practice Among Academic Psychologists.

Laura Badenes-Ribera,Dolores Frias-Navarro,Claudio Longobardi,Amparo Bonilla-Campos,Nathalie O Iotti

doi:10.3389/fpsyg.2018.00996

Laura Badenes-Ribera, Dolores Frias-Navarro + Show 3 more

Open Access

https://doi.org/10.3389/fpsyg.2018.00996

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Abstract

Introduction: Publications arguing against the null hypothesis significance testing (NHST) procedure and in favor of good statistical practices have increased. The most frequently mentioned alternatives to NHST are effect size statistics (ES), confidence intervals (CIs), and meta-analyses. A recent survey conducted in Spain found that academic psychologists have poor knowledge about effect size statistics, confidence intervals, and graphic displays for meta-analyses, which might lead to a misinterpretation of the results. In addition, it also found that, although the use of ES is becoming generalized, the same thing is not true for CIs. Finally, academics with greater knowledge about ES statistics presented a profile closer to good statistical practice and research design. Our main purpose was to analyze the extension of these results to a different geographical area through a replication study.Methods: For this purpose, we elaborated an on-line survey that included the same items as the original research, and we asked academic psychologists to indicate their level of knowledge about ES, their CIs, and meta-analyses, and how they use them. The sample consisted of 159 Italian academic psychologists (54.09% women, mean age of 47.65 years). The mean number of years in the position of professor was 12.90 (SD = 10.21).Results: As in the original research, the results showed that, although the use of effect size estimates is becoming generalized, an under-reporting of CIs for ES persists. The most frequent ES statistics mentioned were Cohen's d and R2/η2, which can have outliers or show non-normality or violate statistical assumptions. In addition, academics showed poor knowledge about meta-analytic displays (e.g., forest plot and funnel plot) and quality checklists for studies. Finally, academics with higher-level knowledge about ES statistics seem to have a profile closer to good statistical practices.Conclusions: Changing statistical practice is not easy.This change requires statistical training programs for academics, both graduate and undergraduate.

Highlights

Publications arguing against the null hypothesis significance testing (NHST) procedure and in favor of good statistical practices have increased
A recent survey conducted in Spain found that academic psychologists have poor knowledge about effect size statistics, confidence intervals, and graphic displays for meta-analyses, which might lead to a misinterpretation of the results
For this purpose, we elaborated an on-line survey that included the same items as the original research, and we asked academic psychologists to indicate their level of knowledge about effect size statistics (ES), their confidence intervals (CIs), and meta-analyses, and how they use them

Summary

Introduction

Publications arguing against the null hypothesis significance testing (NHST) procedure and in favor of good statistical practices have increased. The quest for the magical “p < 0.05” leads to bad statistical practices (e.g., p-hacking) that distort scientific knowledge and harm scientific progress (Fanelli, 2009; Gadbury and Allison, 2014; Wicherts et al, 2016; Agnoli et al, 2017) Given these misconceptions about the p-value and the misuse of the NHST procedure, the American Psychological Association (American Psychological Association 2010; Appelbaum et al, 2018), the American Educational Research Association (2006), the American Statistician Association (ASA) (Wasserstein and Lazar, 2016), the Open Science Collaboration (2015), the new statistic approach (e.g., Cumming, 2012; Kline, 2013), and many editors of psychology journals (e.g., Eich, 2014) make several recommendations. Two main groups can be distinguished: ES that use the standardized group mean difference, such as Cohen’s d, Glass’s g, Hedges’s gu, and Cohen’s f ; and ES based on the percentage of explained variance or the correlations between variables, such as r, R2/r2, η2, w2 (Rosnow and Rosenthal, 2009)

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