The Metacognition in Self-Control Scale (MISCS)

Metacognition is a well-researched construct important to successful learning. Recent studies show that state-level metacognition regarding self-control conflicts is also important for successfully resolving these conflicts. Because there exists no scale to assess trait-level metacognition in self-control and because of limitations of commonly used measures in self-control research, we adapted a scale that is widely used to assess trait-level metacognition in self-regulated learning, the Metacognitive Awareness Inventory (MAI). In two studies (N = 315 and N = 503), we constructed the 12-item Metacognition in Self-Control Scale (MISCS), which loaded on the two factors metacognitive knowledge and metacognitive regulation. The MISCS showed a good fit with good internal consistencies. In the 10-day experience sampling part of study 2, which included 9639 reports of self-control conflicts, higher trait-levels of metacognition as measured with the MISCS predicted higher state-levels of success in resolving these conflicts, as well as higher state-levels of the subcomponents of metacognition, namely metacognitive knowledge, planning, monitoring, and evaluation. Most of these associations persisted when controlling for trait self-control, supporting the usefulness of the scale beyond the most commonly used scale in self-control research. The MISCS showed adequate test-retest reliability. Correlations with other scales, limitations, and future directions are discussed

Longitudinal relationship between posttraumatic cognitions and internalising symptoms in children and adolescents

Background: Little is known about the naturalistic course of posttraumatic cognitions (PTCs) after exposure to a potentially traumatic event (PTE) in children and adolescents. Moreover, previous studies on the longitudinal associations of PTCs with internalising symptoms yielded mixed results. Objective: To explore the naturalistic courses and longitudinal associations of dysfunctional PTCs and functional PTCs with posttraumatic stress symptoms (PTSS), depression, and anxiety. Method: A total of 115 children and adolescents, aged 7–15 years, were assessed within 1 month, 3 months, and 6 months after exposure to an acute accidental PTE. Repeated measures analyses of variance were conducted to capture the naturalistic courses of PTCs and internalising symptoms. Cross-lagged panel analyses were applied to explore the longitudinal relationship between dysfunctional and functional PTCs, along with their longitudinal associations with PTSS, depression, and anxiety. Results: Dysfunctional PTCs and internalising symptoms decreased, whereas functional PTCs increased over time. Dysfunctional and functional PTCs were moderately inversely related, but no significant cross-lagged paths emerged among them. Dysfunctional PTCs were moderately to strongly associated with internalising symptoms, while functional PTCs were weakly to moderately inversely associated with internalising symptoms. Initial PTSS predicted later dysfunctional PTCs (β = .31, p < .05), but not vice versa. Conclusions: Dysfunctional PTCs, functional PTCs, and internalising symptoms were entangled over time. Our findings support the cognitive scar model with initial PTSS predicting later dysfunctional PTCs. Future research complementing between-subject with within-subject analyses could offer additional insights into the longitudinal relationship between dysfunctional PTCs, functional PTCs, and psychological symptoms

Multiple imputation of incomplete ordinary and overdispersed count data

Kleinke K, de Jong R, Spiess M, Reinecke J. Multiple imputation of incomplete ordinary and overdispersed count data.; 2011.Throughout the last couple of years multiple imputation (MI) has become a popular and widely accepted method to address the missing data problem. However, MI solutions for incomplete count data are still not available in most statistical packages. We present count data imputation add-ons for the popular mice software in R (van Buuren & Groothuis-Oudshoorn, 2011). Our add-on functions allow to create multiple imputations of incomplete ordinary and overdispersed count data following the chained equations approach of creating multiple imputations (cf. Raghunathan, Lepkowski, van Hoewyk, & Solenberger, 2001; van Buuren & Groothuis-Oudshoorn, 2011). We furthermore present evaluations of these solutions regarding their ability to produce unbiased parameter estimates and standard errors as well as their ability to cope with missing not at random mechanisms

Effect of Life Review Therapy for Holocaust Survivors: A randomized controlled trial

Despite the therapeutic needs of aging Holocaust survivors, no randomized controlled trial (RCT) of psychotherapy exists for this population, with very few on older adults in general. This RCT aimed to compare the efficacy of Life Review Therapy for Holocaust survivors (LRT‐HS) relative to a supportive control group. Holocaust survivors with a probable diagnosis of full or subsyndromal posttraumatic stress disorder (PTSD) or depressive disorder were included. Exclusion criteria were probable dementia, acute psychotic disorder, and acute suicidality. The predefined primary endpoint was the course of PTSD symptom scores. In total, 49 of 79 consecutive individuals assessed for eligibility were randomized and included in the intent‐to‐treat analyses (LRT‐HS: n = 24, control: n = 25; M$_{age}$ = 81.5 years, SD = 4.81, 77.6% female). Linear mixed models revealed no statistically significant superiority of LRT‐HS for PTSD symptoms at posttreatment, with moderate effect sizes, Time x Condition interaction: t(75) = 1.46, p = .148, d$_{within}$ = 0.70, d$_{between}$ = 0.41, but analyses were significant at follow‐up, with large effect sizes, t(79) = 2.89, p = .005, d$_{within}$ = 1.20, d$_{between}$ = 1.00. LRT‐HS superiority for depression was observed at posttreatment, t(73) = 2.58, p = .012, but not follow‐up, t(76) = 1.08, p = .282, with moderate effect sizes, d$_{within}$ = 0.46–0.60, d$_{between}$ = 0.53–0.70. The findings show that even in older age, PTSD and depression following exposure to multiple traumatic childhood events can be treated efficaciously using an age‐appropriate treatment that includes structured life review and narrative exposure

Efficient multiple imputation of complex data structures (like multilevel data, zero-inflated count data and multilevel count data)

Kleinke K. Efficient multiple imputation of complex data structures (like multilevel data, zero-inflated count data and multilevel count data). Bielefeld; 2013

Multiple Imputation by Predictive Mean Matching When Sample Size Is Small

Kleinke K. Multiple Imputation by Predictive Mean Matching When Sample Size Is Small. METHODOLOGY-EUROPEAN JOURNAL OF RESEARCH METHODS FOR THE BEHAVIORAL AND SOCIAL SCIENCES. 2018;14(1):3-15.Predictive mean matching (PMM) is a state-of-the-art hot deck multiple imputation (MI) procedure. The quality of its results depends, inter alia, on the availability of suitable donor cases. Applying PMM in small sample scenarios often found in psychological or medical research could be problematic, as there might not be many (or any) suitable donor cases in the data set. So far, there has not been any systematic research that examined the performance of PMM, when sample size is small. The present study evaluated PMM in various multiple regression scenarios, where sample size, missing data percentages, the size of the regression coefficients, and PMM's donor selection strategy were systematically varied. Results show that PMM could be used in most scenarios, however results depended on the donor selection strategy: overall, PMM using either automatic distance-aided selection of donors (Gaffert, Meinfelder, & Bosch, 2016) or using the nearest neighbor produced the best results

Can we have both simplicity and quality? - A systematic evaluation of the assumed robustness of normal model multiple imputation

Kleinke K. Can we have both simplicity and quality? - A systematic evaluation of the assumed robustness of normal model multiple imputation. University of Hagen, Insitute of Psychology; 2015

Multiple imputation under violated distributional assumptions: A systematic evaluation of the assumed robustness of predictive mean matching

Kleinke K. Multiple imputation under violated distributional assumptions: A systematic evaluation of the assumed robustness of predictive mean matching. Journal of Educational and Behavioral Statistics. 2017;42(4):371-404

Multiple Imputation of Zero-Inflated and Overdispersed Multilevel Count Data

Kleinke K, Reinecke J. Multiple Imputation of Zero-Inflated and Overdispersed Multilevel Count Data. University of Bielefeld, Faculty of Sociology and Centre for Statistics; 2014