Suppressors are third variables that increase the predictive power of one or more predictors by suppressing their irrelevant variance when included in a regression model. Although theoretically and statistically useful, no research has addressed the frequency or interpretation of statistical suppression (SS) in the psychological literature. Two studies explored the nature and interpretation of SS. In the first study, regression analyses were reviewed to determine the frequency with which SS occurs in psychological articles published in 2017. Results indicate that approximately one-third of articles showed evidence of SS, although researchers did not acknowledge or attempt to interpret the SS. The second study reviewed articles containing the keyword 'suppression' to assess the interpretations provided by researchers that identified SS.Results indicate that most researchers do not attempt to classify or interpret SS. Therefore, although SS is common in psychology, scarcely any attempts are made to identify, classify, and/or interpret it.

Suppressors are third variables that increase the predictive power of one or more predictors by suppressing their irrelevant variance when included in a regression model. Although theoretically and statistically useful, no research has addressed the frequency or interpretation of statistical suppression (SS) in the psychological literature. Two studies explored the nature and interpretation of SS. In the first study, regression analyses were reviewed to determine the frequency with which SS occurs in psychological articles published in 2017. Results indicate that approximately one-third of articles showed evidence of SS, although researchers did not acknowledge or attempt to interpret the SS. The second study reviewed articles containing the keyword 'suppression' to assess the interpretations provided by researchers that identified SS.Results indicate that most researchers do not attempt to classify or interpret SS. Therefore, although SS is common in psychology, scarcely any attempts are made to identify, classify, and/or interpret it. Statistical Suppression in Psychological Research 3 Incidence and Interpretation of Statistical Suppression in Psychological ResearchSuppressors are variables that remove irrelevant variance from other predictors included in a model, thereby increasing the predictive validity of the variable(s) which have had their irrelevant variance suppressed (Conger, 1974). In other words, suppressors unmask relationships between predictor(s) and outcomes, increasing each suppressed variable's predictive power.Despite their usefulness, Pandey and Elliot (2010) note that statistical suppression (SS) remains misunderstood and underreported. However, there are no previous studies that have investigated the frequency of SS within psychology or, subsequently, its interpretation (or lack thereof). Thus, the purpose of this study is to report on the occurrence and interpretation of SS within psychological research. Given the paucity of research on SS, a better understanding of the frequency with which SS occurs and the nature of the interpretations of SS provided by researchers will help clarify the extent to which SS is an issue warranting further investigation. Quantifying Statistical SuppressionThere are a variety of statistical models used in the field of psychology (e.g., multiple regression, structural equation modeling, hierarchical linear modeling), with most being able to easily produce the statistical information necessary to identify the presence of SS. For example, imagine a researcher interested in the partial effects of predictors X1 and X2 on outcome y who adopts the model:where yi is the outcome value for individual i, b0 is the predicted value of yi when X1 and X2 are 0, b1 and b2 are the partial regression coefficients (slopes) for predicting y from X1 and X2, respectively, and ei is the portion of yi not explained by b0 + b1X1i + b2X2i.

Reporting and interpreting effect sizes (ESs) has been recommended by all major bodies within the field of psychology. In this systematic review, we investigated the reporting of effect sizes in six social-personality psychology journals from 2018, given that this area has been at the center of psychology's replication crisis. Our results highlight that although ES reporting is near perfect (even for follow-up tests), interpreting the magnitude of ESs, including confidence intervals for ESs, and interpreting the precision of the confidence intervals needs development.We also highlight widespread confusion regarding the interpretations of the magnitude of ESs within the context of the research.

Equivalence testing (ET) is a framework to determine if an effect is small enough to be considered meaningless, wherein meaningless is expressed as an equivalence interval (EI). Although traditional effect sizes (ESs) are important accompaniments to ET, these measures exclude information about the EI. Incorporating the EI is valuable for quantifying how far the effect is from the EI bounds. An ES measure we propose is the proportional distance (PD) from an observed effect to the smallest effect that would render it meaningful. We conducted two Monte Carlo simulations to evaluate the PD when applied to (1) mean differences and (2) correlations. The coverage rate and bias of the PD were excellent within the investigated conditions. We also applied the PD to two recent psychological studies. These applied examples revealed the beneficial properties of the PD, namely its ability to supply information above and beyond other statistical tests and ESs.