The ability to regulate behavior in service of long-term goals is a widely studied psychological construct known as self-regulation. This wide interest is in part due to the putative relations between self-regulation and a range of real-world behaviors. Selfregulation is generally viewed as a trait, and individual differences are quantified using a diverse set of measures, including selfreport surveys and behavioral tasks. Accurate characterization of individual differences requires measurement reliability, a property frequently characterized in self-report surveys, but rarely assessed in behavioral tasks. We remedy this gap by (i) providing a comprehensive literature review on an extensive set of self-regulation measures and (ii) empirically evaluating test-retest reliability of this battery in a new sample. We find that dependent variables (DVs) from self-report surveys of self-regulation have high testretest reliability, while DVs derived from behavioral tasks do not. This holds both in the literature and in our sample, although the test-retest reliability estimates in the literature are highly variable. We confirm that this is due to differences in between-subject variability. We also compare different types of task DVs (e.g., model parameters vs. raw response times) in their suitability as individual difference DVs, finding that certain model parameters are as stable as raw DVs. Our results provide greater psychometric footing for the study of self-regulation and provide guidance for future studies of individual differences in this domain.self-regulation | retest reliability | individual differences These data were previously presented as a poster at

The ability to regulate behavior in service of long-term goals is a widely studied psychological construct known as self-regulation. This wide interest is in part due to the putative relations between self-regulation and a range of real-world behaviors. Self-regulation is generally viewed as a trait, and individual differences are quantified using a diverse set of measures including self-report surveys and behavioral tasks. Accurate characterization of individual differences requires measurement reliability, a property frequently characterized in self-report surveys, but rarely assessed in behavioral tasks. We remedy this gap by (1) providing a comprehensive literature review on an extensive set of self-regulation measures, and (2) empirically evaluating retest reliability in this battery of measures in a new sample. We find that self-report survey measures of self-regulation have high test-retest reliability while measures derived from behavioral tasks do not. This holds both in the literature and in our sample. We confirm that this is due to differences in between-subjects variability. We also compare different types of task measures (e.g., model parameters vs. raw response times) in their suitability as individual difference measures, finding that certain model parameters are as stable as raw measures. Our results provide greater psychometric footing for the study of self-regulation and provide guidance for future studies of individual differences in this domain.

IMPORTANCE Electronic systems that facilitate patient-reported outcome (PRO) surveys for patients with cancer may detect symptoms early and prompt clinicians to intervene. OBJECTIVE To evaluate whether electronic symptom monitoring during cancer treatment confers benefits on quality-of-life outcomes. DESIGN, SETTING, AND PARTICIPANTS Report of secondary outcomes from the PRO-TECT (Alliance AFT-39) cluster randomized trial in 52 US community oncology practices randomized to electronic symptom monitoring with PRO surveys or usual care. Between October 2017 and March 2020, 1191 adults being treated for metastatic cancer were enrolled, with last follow-up on May 17, 2021. INTERVENTIONSIn the PRO group, participants (n = 593) were asked to complete weekly surveys via an internet-based or automated telephone system for up to 1 year. Severe or worsening symptoms triggered care team alerts. The control group (n = 598) received usual care. MAIN OUTCOMES AND MEASURESThe 3 prespecified secondary outcomes were physical function, symptom control, and health-related quality of life (HRQOL) at 3 months, measured by the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (QLQ-C30; range, 0-100 points; minimum clinically important difference [MCID], 2-7 for physical function; no MCID defined for symptom control or HRQOL). Results on the primary outcome, overall survival, are not yet available. RESULTS Among 52 practices, 1191 patients were included (mean age, 62.2 years; 694 [58.3%] women); 1066 (89.5%) completed 3-month follow-up. Compared with usual care, mean changes on the QLQ-C30 from baseline to 3 months were significantly improved in the PRO group for physical function (PRO, from 74.27 to 75.81 points; control, from 73.54 to 72.61 points; mean difference, 2.47 [95% CI,]; P = .02), symptom control (PRO, from 77.67 to 80.03 points; control, from 76.75 to 76.55 points; mean difference, 2.56 [95% CI, 0.95-4.17]; P = .002), and HRQOL (PRO, from 78.11 to 80.03 points; control, from 77.00 to 76.50 points; mean difference, 2.43 [95% CI, 0.90-3.96]; P = .002). Patients in the PRO group had significantly greater odds of experiencing clinically meaningful benefits vs usual care for physical function (7.7% more with improvements of Ն5 points and 6.1% fewer with worsening of Ն5 points; odds ratio [OR], 1.35 [95% CI, 1.08-1.70];

Often when participants have missing scores on one or more of the items comprising a scale, researchers compute prorated scale scores by averaging the available items. Methodologists have cautioned that proration may make strict assumptions about the mean and covariance structures of the items comprising the scale (Schafer & Graham, 2002; Graham, 2009; Enders, 2010). We investigated proration empirically and found that it resulted in bias even under a missing completely at random (MCAR) mechanism. To encourage researchers to forgo proration, we describe an FIML approach to item-level missing data handling that mitigates the loss in power due to missing scale scores and utilizes the available item-level data without altering the substantive analysis. Specifically, we propose treating the scale score as missing whenever one or more of the items are missing and incorporating items as auxiliary variables. Our simulations suggest that item-level missing data handling drastically increases power relative to scale-level missing data handling. These results have important practical implications, especially when recruiting more participants is prohibitively difficult or expensive. Finally, we illustrate the proposed method with data from an online chronic pain management program.

We examined how the development of familism values from 5th to 10th grade relates to 12th-grade prosocial tendencies (after controlling for 10th-grade prosocial tendencies) using a stratified random sample of 749 Mexican American adolescents (M = 10.42 years of age at 5th grade; 48.9% girls) from 35 culturally and economically diverse neighborhoods. Most of the families (44.3%) were at or below $25,000 in annual income. A 2nd-order linear growth model represented adolescents' familism values at 5th grade (intercepts) and change in familism values from 5th to 10th grade (slopes), with the vast majority of slopes being negative. Higher intercepts predicted greater compliant and emotional prosocial tendencies, and higher (i.e., more positive or less negative) slopes predicted greater dire (female adolescents only) and public prosocial tendencies at 12th grade. The results underscore the important role of familism values in prosocial development among Mexican American adolescents. (PsycINFO Database Record