https://doi.org/10.1177/0003122418806282 American Sociological Review 2018, Vol. 83(6) 1281–1283 © American Sociological Association 2018 DOI: 10.1177/0003122418806282 journals.sagepub.com/home/asr In the three years we have been editing ASR, we have been impressed with the method- ological breadth and depth of the submissions to the journal. Among the subset of papers that use primarily quantitative analytic strate- gies, an equally impressive range of methods and techniques is on display. The field has come a long way since any of the three of us were in graduate school and, indeed, many of the articles we have published in our role as editors represent the forefront of sophistica- tion in techniques as varied as fixed and ran- dom effects on the one end to web scraping and text analysis on the other end. In this editorial, we would like to focus on a set of issues that seem to come up repeatedly in the thousands of papers we have read. These are not errors per se, but fall in the category of gaps or lags between previously accepted practices among quantitative scholars in soci- ology and the state of the art consensus among quantitative methodologists. These issues happen with such frequency that we feel compelled to offer some recommenda- tions for future ASR submissions. P-VALUES AND ONE VERSUS TWO-TAILED TESTS Debates about the utility of p-values abound in the scientific literature. On the one hand, those concerned about replicability and stan- dards for new discovery argue that the thresh- old for statistical significance should be reduced below .05 (Benjamin et al. 2018). On the other hand, some argue that we should do away with p values and null hypothesis sig- nificance testing altogether (McShane et al. 2017). We will not take a stand in this debate except to say that, in general, p < .10 and one- tailed tests should only be used in rare, excep- tional circumstances with proper justification. Many papers attempt to justify use of p < .10 standards by pointing to “directionality” in their verbally stated hypotheses. Others use vague language of p < .10 indicating “border- line” or “suggestive” findings. We do not find the first rationale compelling. In terms of the second practice, ASR is our discipline’s top journal. We need to be publishing strong evi- dence rather than “suggestive” findings. TESTING MEDIATION We get many submissions to the journal attempt- ing to test mediation with a stripped down ver- sion of the Baron and Kenny (1986) steps. Authors usually proceed like this—they run one model with their key predictor plus controls and then a second model adding the mediator. If the coefficient of the key predictor is reduced or rendered nonsignificant, the authors conclude that the main effect has been mediated. There are several problems with this approach. Most commonly, authors fail to run a significance test for the difference in magnitude between coefficients. This step is necessary to determine whether mediation has occurred. The coefficient of the key predictor can be reduced or even rendered nonsignificant yet still be in the window of what could be considered to have occurred by chance alone. As Gelman and Stern (2006) note, changes in statistical significance may not themselves be significant. 806282ASR XX X 10.1177/0003122418806282American Sociological ReviewLizardo et al. 2018 a University of Notre Dame b University of California-Los Angeles Editors’ Comment: A Few Guidelines for Quantitative Submissions Sarah A. Mustillo, a Omar A. Lizardo, b and Rory M. McVeigh a