To deal with missing data that arise due to participant nonresponse or attrition, methodologists have recommended an "inclusive" strategy where a large set of auxiliary variables are used to inform the missing data process. In practice, the set of possible auxiliary variables is often too large. We propose using principal components analysis (PCA) to reduce the number of possible auxiliary variables to a manageable number. A series of Monte Carlo simulations compared the performance of the inclusive strategy with eight auxiliary variables (inclusive approach) to the PCA strategy using just one principal component derived from the eight original variables (PCA approach). We examined the influence of four independent variables: magnitude of correlations, rate of missing data, missing data mechanism, and sample size on parameter bias, root mean squared error, and confidence interval coverage. Results indicate that the PCA approach results in unbiased parameter estimates and potentially more accuracy than the inclusive approach. We conclude that using the PCA strategy to reduce the number of auxiliary variables is an effective and practical way to reap the benefits of the inclusive strategy in the presence of many possible auxiliary variables.

This study explores the pathways through which school-based mentoring relationships are associated with improvements in elementary and high school students’ socio-emotional, academic, and behavioral outcomes. Participants in the study (N=526) were part of a national evaluation of the Big Brothers Big Sisters school-based mentoring programs, all of whom had been randomly assigned to receive mentoring at their schools over the course of one academic year. Students were assessed at the beginning and end of the school year. The results of structural equation modeling showed that mentoring relationship quality, as measured by the Youth-Centered Relationship scale and the Youth’s Emotional Engagement scale, was significantly associated with positive changes in youths’ relationships with parents and teachers, as measured by subscales of the Inventory of Parent and Peer Attachment, the Teacher Relationship Quality scale, and the Hemingway Measure of Adolescent Connectedness. Higher quality relationships with parents and teachers, in turn, were significantly associated with better youth outcomes, including self-esteem, academic attitudes, prosocial behaviors, and misconduct. The effect sizes of the associations ranged from 0.12 to 0.52. Mediation analysis found that mentoring relationship quality was indirectly associated with some of the outcomes through its association with improved parent and teacher relationships. Implications of the findings for theory and research are discussed.

Access to a CCMS generally improved health care quality, but was not associated with changes in child functional status or hospital-based utilization, and increased overall health care costs among CMC.

Inherent in applied developmental sciences is the threat to validity and generalizability due to missing data as a result of participant dropout. The current paper provides an overview of how attrition should be reported, which tests can examine the potential of bias due to attrition (e.g., t-tests, logistic regression, Little's MCAR test, sensitivity analysis), and how it is best corrected through modern missing data analyses. To amend this discussion of best practices in managing and reporting attrition, an assessment of how developmental sciences currently handle attrition was conducted. Longitudinal studies (n ¼ 541) published from 2009-2012 in major developmental journals were reviewed for attrition reporting practices and how authors handled missing data based on recommendations in the Publication Manual of the American Psychological Association (APA, 2010). Results suggest attrition reporting is not following APA recommendations, quality of reporting did not improve since the APA publication, and a low proportion of authors provided sufficient information to convey that data properly met the MAR assumption. An example based on simulated data demonstrates bias that may result from various missing data mechanisms in longitudinal data, the utility of auxiliary variables for the MAR assumption, and the need for viewing missingness along a continuum from MAR to MNAR.

Theoretical models of pediatric chronic pain propose longitudinal associations between children's pain experiences and parent and family factors. A large body of cross-sectional research supports these models, demonstrating that greater parent distress and maladaptive parenting behaviors are associated with greater child disability. Family-based cognitive-behavioral therapy interventions have been developed for youth with chronic pain which aim to improve child disability and reduce maladaptive parenting behaviors. However, little is known about temporal, longitudinal associations between parent and child functioning in this population. In the present study, we conducted a secondary analysis of data from 138 families of youth with chronic pain aged 11 to 17 years old who received family-based cognitive-behavioral therapy delivered through the Internet as part of a randomized controlled trial. Measures of child disability, parent protective behavior, and parent distress were obtained at pretreatment, immediate posttreatment, 6-month follow-up, and 12-month follow-up. Latent growth modeling indicated that child disability, parent protective behavior, and parent distress improved with treatment over the 12-month study period. Latent growth modeling for parallel processes indicated that higher parent distress at pretreatment predicted less improvement in child disability over 12 months. No other predictive paths between parent and child functioning were significant. These findings indicate that parent distress may increase the risk of poor response to psychological pain treatment among youth with chronic pain. At present, parent distress is not routinely targeted in psychological interventions for pediatric chronic pain. Research is needed to determine optimal strategies for targeting parent and family factors in the treatment of pediatric chronic pain.