King_Open_Practices_Disclosure – Supplemental material for Negative Urgency Is Correlated With the Use of Reflexive and Disengagement Emotion Regulation Strategies

<p>Supplemental material, King_Open_Practices_Disclosure for Negative Urgency Is Correlated With the Use of Reflexive and Disengagement Emotion Regulation Strategies by Kevin M. King, Madison C. Feil and Max Halvorson in Clinical Psychological Science</p

State-level impulsivity, affect, and alcohol: a psychometric evaluation of the Momentary Impulsivity Scale across two intensive longitudinal samples.

We reexamined the psychometric properties of the Momentary Impulsivity Scale (MIS) in two young adult samples using daily diary (=77) and ecological momentary assessment (=147). A one-factor between- and within-person structure was supported, though "I felt impatient" loaded poorly within-person. MIS scores consistently related to emotion-driven trait impulsivity; however, MSSDs of MIS scores were unrelated to outcomes after accounting for aggregate MIS scores. We observed positive, within-person correlations with negative, but not positive, affect. Between-person MIS scores correlated with alcohol problems, though within-person MIS-alcohol relations were inconsistent. MIS scores were unrelated to laboratory-based impulsivity tasks. Findings inform the assessment of state-level impulsivity in young adults. Future research should prioritize expanding the MIS to capture the potential multidimensionality of state-level impulsivity

Best practices for addressing missing data through multiple imputation

A common challenge in developmental research is the amount of incomplete and missing data that occurs from respondents failing to complete tasks or questionnaires, as well as from disengaging from the study (i.e., attrition). This missingness can lead to biases in parameter estimates and, hence, in the interpretation of findings. These biases can be addressed through statistical techniques that adjust for missing data, such as multiple imputation. Although multiple imputation is highly effective, it has not been widely adopted by developmental scientists given barriers such as lack of training or misconceptions about imputation methods. Utilizing default methods within statistical software programs like listwise deletion is common but may introduce additional bias. This manuscript is intended to provide practical guidelines for developmental researchers to follow when examining their data for missingness, making decisions about how to handle that missingness and reporting the extent of missing data biases and specific multiple imputation procedures in publications

Implementing stakeholder engagement to explore alternative models of consent: An example from the PREP-IT trials

Introduction: Cluster randomized crossover trials are often faced with a dilemma when selecting an optimal model of consent, as the traditional model of obtaining informed consent from participant's before initiating any trial related activities may not be suitable. We describe our experience of engaging patient advisors to identify an optimal model of consent for the PREP-IT trials. This paper also examines surrogate measures of success for the selected model of consent. Methods: The PREP-IT program consists of two multi-center cluster randomized crossover trials that engaged patient advisors to determine an optimal model of consent. Patient advisors and stakeholders met regularly and reached consensus on decisions related to the trial design including the model for consent. Patient advisors provided valuable insight on how key decisions on trial design and conduct would be received by participants and the impact these decisions will have. Results: Patient advisors, together with stakeholders, reviewed the pros and cons and the requirements for the traditional model of consent, deferred consent, and waiver of consent. Collectively, they agreed upon a deferred consent model, in which patients may be approached for consent after their fracture surgery and prior to data collection. The consent rate in PREP-IT is 80.7%, and 0.67% of participants have withdrawn consent for participation. Discussion: Involvement of patient advisors in the development of an optimal model of consent has been successful. Engagement of patient advisors is recommended for other large trials where the traditional model of consent may not be optimal