Adequately addressing missing data is a pervasive issue in the social sciences. Failure to correctly address missing data can lead to biased or inefficient estimation of parameters, confidence intervals, and significance tests. Multiple imputation is a statistical technique for handling missing data that involves using existing data to generate multiple datasets of plausible values for missing data that each incorporate random components to reflect their uncertainty. Each dataset is analyzed individually and identically, and parameter estimates are pooled into one set of estimates, variances, and confidence intervals. Although this technique is widely used, there is little consensus on what constitutes best practices in multiple imputation, including with regard to assessing the extent of missing data bias and reporting multiple imputation procedures in publications. This decision tree was crowdsourced at the 2021 annual meeting of the Society for the Improvement of Psychological Science (SIPS) and revised thereafter. This document is intended to provide practical guidelines for researchers to follow when examining their data for missingness and making decisions about how to handle that missingness. We primarily offer recommendations for multiple imputation, but also indicate where the same decisional guidelines are appropriate for other types of missing data procedures such as full imputation maximum likelihood (FIML).

The study aims to examine the mediation effects of physical literacy and physical activity behavior in a relationship between psychological distress and life satisfaction among Chinese college students during the real-life Coronavirus disease pandemic (COVID-19) circumstance. This study implemented a cross-sectional design, and 1,516 participants from 12 universities participated in this study. Structural equation modeling was used to examine a hypothesized model. The findings indicated an acceptable model fit ( X²[61] = 508.2, Comparative Fit Index [CFI] = 0.958, Tucker Lewis Index [TLI] = 0.946, Root Mean Square Error of Approximation [RMSEA] = 0.076, 90% CI [0.070, 0.082], Standardized Root Mean Square Residual [SRMR] = 0.047). The results indicated that college students with low participation in physical activity could experience less than healthy living conditions. The findings offered empirical support to the theory that physical literacy could advance individuals’ healthy living by promoting physical activity participation. The study suggested that educational institutions and physical activity programs should cultivate individuals’ physical literacy in order to promote lifelong healthy living.

This study sought to identify the race differences in perceived access to health and community services and self-rated health (SRH) among White and Black older adult participants of an age-friendly community assessment. Responses (n = 313) to a baseline assessment of Columbus, Ohio, residents aged ≥50 years were analyzed. Significant differences were found between White and Black older adults regarding SRH, with Black older adults reporting lower SRH. Black older adults reported significantly lower perceived access to 11 out of the 13 health and community services. There were no significant differences by race regarding ratings of Columbus and personal neighborhoods as a place for people to live as they age. Regression analyses found income was a significant predictor of SRH for both White and Black older adults. Opportunities to increase perceived access and knowledge of health and community services for older adults through targeted, equitable interventions are warranted.