A tutorial on aggregating evidence from conceptual replication studies using the product Bayes factor.

The product Bayes factor (PBF) synthesizes evidence for an informative hypothesis across heterogeneous replication studies. It can be used when fixed- or random effects meta-analysis fall short. For example, when effect sizes are incomparable and cannot be pooled, or when studies diverge significantly in the populations, study designs, and measures used. PBF shines as a solution for small sample meta-analyses, where the number of between-study differences is often large relative to the number of studies, precluding the use of meta-regression to account for these differences. Users should be mindful of the fact that the PBF answers a qualitatively different research question than other evidence synthesis methods. For example, whereas fixed-effect meta-analysis estimates the size of a population effect, the PBF quantifies to what extent an informative hypothesis is supported in all included studies. This tutorial paper showcases the user-friendly PBF functionality within the bain R-package. This new implementation of an existing method was validated using a simulation study, available in an Online Supplement. Results showed that PBF had a high overall accuracy, due to greater sensitivity and lower specificity, compared to random-effects meta-analysis, individual participant data meta-analysis, and vote counting. Tutorials demonstrate applications of the method on meta-analytic and individual participant data. The example datasets, based on published research, are included in bain so readers can reproduce the examples and apply the code to their own data. The PBF is a promising method for synthesizing evidence for informative hypotheses across conceptual replications that are not suitable for conventional meta-analysis.

Long-term trajectories of depressive symptoms in deployed military personnel: A 10-year prospective study.

BACKGROUND: Military missions, especially those involving combat exposure, are associated with an increased risk of depression. Understanding the long-term course of depressive symptoms post-deployment is important to improve decision-making regarding deployment and mental health policies in the military. This study investigates trajectories of depressive symptoms in the Dutch army, exploring the influence of factors such as demographics, early-life trauma, posttraumatic stress disorder (PTSD) symptoms, and deployment stressors. METHODS: A cohort of 1032 military men and women deployed to Afghanistan (2005-2008) was studied from pre- to 10 years post-deployment. Depressive and PTSD symptoms were assessed using the Symptom CheckList-90 and the Self-Rating Inventory for PTSD. Demographics, early trauma, and deployment experiences were collected at baseline and after deployment, respectively. Latent Class Growth Analysis was used to explore heterogeneity in trajectories of depressive symptoms over time. RESULTS: Four trajectories were found: resilient (65%), intermediate-stable (20%), symptomatic-chronic (9%), and late-onset-increasing (6%). The resilient group experienced fewer deployment stressors, while the symptomatic-chronic group reported more early life traumas. Trajectories with elevated depressive symptoms consistently demonstrated higher PTSD symptoms. LIMITATIONS: Potential nonresponse bias and missing information due to the longitudinal design and extensive follow-up times. CONCLUSIONS: This study identified multiple trajectories of depressive symptoms in military personnel up to 10 years post-deployment, associated with early trauma, deployment stressors, adverse life events and PTSD symptoms. The prevalence of the resilient trajectory suggests a substantial level of resilience among deployed military personnel. These findings provide valuable insights and a foundation for further research.

A theory-informed emotion regulation variability index: Bray-Curtis dissimilarity.

Emotion regulation (ER) variability refers to how individuals vary their use of ER strategies across time. It helps individuals to meet contextual needs, underscoring its importance in well-being. The theoretical foundation of ER variability recognizes two constituent processes: strategy switching (e.g., moving from distraction to social sharing) and endorsement change (e.g., decreasing the intensity of both distraction and social sharing). ER variability is commonly operationalized as the SD between strategies per observation (between-strategy SD) or within a strategy across time (within-strategy SD). In this article, we show that these SD-based approaches cannot sufficiently capture strategy switching and endorsement change, leading to ER variability indices with poor validity. We propose Bray-Curtis dissimilarity, a measure used in ecology to quantify biodiversity variability, as a theory-informed ER variability index. First, we demonstrate how Bray-Curtis dissimilarity is more sensitive than SD-based approaches in detecting ER variability through two simulation studies. Second, assuming that higher ER variability is adaptive in daily life, we test the relation between ER variability and negative affect in three experience sampling method data sets (total N = [70, 95, 200], number of moment-level observations = [5,040, 6,329, 14,098]). At both the moment level and person level, higher Bray-Curtis dissimilarity predicted lower negative affect more consistently than SD-based indices. We conclude that Bray-Curtis dissimilarity may better capture moment-level within-person ER variability and could have implications for studying variability in other multivariate dynamic processes. The article is accompanied by an R tutorial and practical recommendations for using Bray-Curtis dissimilarity with experience sampling method data. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Within-family linkages between parental monitoring and adolescents externalizing problems with autonomy support as a moderator.

Parents' monitoring efforts are thought to be effective in reducing children's future externalizing problems. Empirical evidence for this claim, however, is limited, as only few studies have unraveled the temporal ordering of these constructs. The present six-wave longitudinal study contributed to the existing literature by examining within-family linkages between monitoring efforts (behavioral control and solicitation) and adolescents' externalizing behaviors while controlling for between-family differences. In addition, it was assessed whether these associations differed when using child versus parent reports, differed for less versus more autonomy-supportive parents, and differed for fathers' versus mothers' monitoring efforts. Longitudinal data (six annual waves) of 497 adolescents (56.9% boys, Mage at T1 = 13.03, SD = 0.46), their mothers (N = 495, Mage at T1 = 44.41, SD = 4.45), and their fathers (N = 446, Mage at T1 = 46.74, SD = 5.10) of the Dutch study Research on Adolescent Development and Relationships (RADAR) were used. Results showed no evidence for the claim that parents' monitoring efforts predict future externalizing problems. In contrast, we found some evidence for the idea that parents' monitoring efforts change in reaction to changes in externalizing problems; when adolescents reported higher levels of externalizing problems than usual in 1 year, this predicted less behavioral control from mothers in the next year. Linkages between monitoring efforts and externalizing problems did not differ between less or more autonomy-supportive parents. Overall, our findings suggest that monitoring efforts are not effective, but also not damaging, in relation to adolescents' externalizing problems.

Anxiolytic effects of endocannabinoid enhancing compounds: A systematic review and meta-analysis.

The endocannabinoid system is a promising candidate for anxiolytic therapy, but translation to the clinic has been lagging. We meta-analyzed the evidence for anxiety-reduction by compounds that facilitate endocannabinoid signaling in humans and animals. To identify areas of specific potential, effects of moderators were assessed. Literature was searched in Pubmed and Embase up to May 2021. A placebo/vehicle-control group was required and in human studies, randomization. We excluded studies that co-administered other substances. Risk of bias was assessed with SYRCLE's RoB tool and Cochrane RoB 2.0. We conducted three-level random effects meta-analyses and explored sources of heterogeneity using Bayesian regularized meta-regression (BRMA). The systematic review yielded 134 studies. We analyzed 120 studies (114 animal, 6 human) that investigated cannabidiol (CBD, 61), URB597 (39), PF-3845 (6) and AM404 (14). Pooled effects on conditioned and unconditioned anxiety in animals (with the exception of URB597 on unconditioned anxiety) and on experimentally induced anxiety in humans favored the investigational drugs over placebo/vehicle. Publication year was negatively associated with effects of CBD on unconditioned anxiety. Compared to approach avoidance tests, tests of repetitive-compulsive behavior were associated with larger effects of CBD and URB597, and the social interaction test with smaller effects of URB597. Larger effects of CBD on unconditioned anxiety were observed when anxiety pre-existed. Studies reported few side effects at therapeutic doses. The evidence quality was low with indications of publication bias. More clinical trials are needed to translate the overall positive results to clinical applications.

Using machine learning to identify early predictors of adolescent emotion regulation development.

As 20% of adolescents develop emotion regulation difficulties, it is important to identify important early predictors thereof. Using the machine learning algorithm SEM-forests, we ranked the importance of (87) candidate variables assessed at age 13 in predicting quadratic latent trajectory models of emotion regulation development from age 14 to 18. Participants were 497 Dutch families. Results indicated that the most important predictors were individual differences (e.g., in personality), aspects of relationship quality and conflict behaviors with parents and peers, and internalizing and externalizing problems. Relatively less important were demographics, bullying, delinquency, substance use, and specific parenting practices-although negative parenting practices ranked higher than positive ones. We discuss implications for theory and interventions, and present an open source risk assessment tool, ERRATA.

Selecting relevant moderators with Bayesian regularized meta-regression.

When meta-analyzing heterogeneous bodies of literature, meta-regression can be used to account for potentially relevant between-studies differences. A key challenge is that the number of candidate moderators is often high relative to the number of studies. This introduces risks of overfitting, spurious results, and model non-convergence. To overcome these challenges, we introduce Bayesian Regularized Meta-Analysis (BRMA), which selects relevant moderators from a larger set of candidates by shrinking small regression coefficients towards zero with regularizing (LASSO or horseshoe) priors. This method is suitable when there are many potential moderators, but it is not known beforehand which of them are relevant. A simulation study compared BRMA against state-of-the-art random effects meta-regression using restricted maximum likelihood (RMA). Results indicated that BRMA outperformed RMA on three metrics: BRMA had superior predictive performance, which means that the results generalized better; BRMA was better at rejecting irrelevant moderators, and worse at detecting true effects of relevant moderators, while the overall proportion of Type I and Type II errors was equivalent to RMA. BRMA regression coefficients were slightly biased towards zero (by design), but its residual heterogeneity estimates were less biased than those of RMA. BRMA performed well with as few as 20 studies, suggesting its suitability as a small sample solution. We present free open source software implementations in the R-package pema (for penalized meta-analysis) and in the stand-alone statistical program JASP. An applied example demonstrates the use of the R-package.

Longitudinal Linkages Between Parent-Child Discrepancies in Reports on Parental Autonomy Support and Informants' Depressive Symptoms.

Although parent-child discrepancies in reports of parenting are known to be associated with child depressive symptoms, the direction of causality is unknown. To address this knowledge gap, this study contributes to existing literature by examining longitudinal within-family linkages between parent-child discrepancies in their reports on autonomy support and depressive symptoms of children, while also assessing these linkages with parents' depressive symptoms. In addition, this study explored whether these linkages differ for father- versus mother-child discrepancies. Longitudinal data (six annual waves) of 497 adolescents (56.9% boys, Mage at T1 = 13.03, SD = 0.46), their mothers (N = 495), and their fathers (N = 446) of the Dutch study Research on Adolescent Development and Relationships (RADAR) were used. Counter to expectations, the results of a Random Intercept Cross-Lagged Panel Model provided no evidence for within-family cross-lagged effects. Instead, stable differences between families explained linkages; in families where children reported on average higher levels of depressive symptoms, children also reported lower levels of autonomy support relative to their parents. There were no associations between parent-child discrepancies and parents' depressive symptoms. Thus, the findings suggest that depressive symptoms are neither a consequence, nor a predictor of parent-child discrepancies in adolescence. The hypotheses and analytical plan of this study were preregistered in a project on the Open Science Framework.

Longitudinal links between expressive flexibility and friendship quality in adolescence: The moderating effect of social anxiety.

INTRODUCTION: Expressive flexibility, or the ability to both up- and down-regulate emotional expressions in social interactions, is thought as an indicator and a consequence of healthy interpersonal relationships. The present longitudinal study examined bidirectional associations between expressive flexibility and friendship quality in early adolescence. Since prior research found inconsistent results regarding the adaptiveness of expressive flexibility, which indicated the necessity to consider individual variability in the process, we further tested the potential moderating effect of social anxiety in the links from expressive flexibility to friendship quality. METHODS: Participants from two junior high schools in eastern China (N = 274; 50.4% female; Mage = 13.56) were surveyed at three time points with 6-month intervals. Expressive flexibility, friendship quality, and social anxiety were all assessed via self-reported scales. RESULTS: According to the cross-lagged model results, friendship quality significantly predicted increased expressive flexibility over time. Conversely, the longitudinal association from expressive flexibility to friendship quality was not significant, but the interaction between expressive flexibility and social anxiety significantly predicted later friendship quality. Further analyses via the Johnson-Neyman technique revealed that expressive flexibility only positively predicted friendship quality for adolescents with lower levels of social anxiety. CONCLUSION: Our results suggest that expressive flexibility is not always socially adaptive, so practical interventions that aim to improve youths' social adjustment via expressive flexibility training might need to consider the role of individual characteristics.

A review of applications of the Bayes factor in psychological research.

The last 25 years have shown a steady increase in attention for the Bayes factor as a tool for hypothesis evaluation and model selection. The present review highlights the potential of the Bayes factor in psychological research. We discuss six types of applications: Bayesian evaluation of point null, interval, and informative hypotheses, Bayesian evidence synthesis, Bayesian variable selection and model averaging, and Bayesian evaluation of cognitive models. We elaborate what each application entails, give illustrative examples, and provide an overview of key references and software with links to other applications. The article is concluded with a discussion of the opportunities and pitfalls of Bayes factor applications and a sketch of corresponding future research lines. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Maximizing Realism: Mapping Plastic Particles at the Ocean Surface Using Mixtures of Normal Distributions.

Current methods of characterizing plastic debris use arbitrary, predetermined categorizations and assume that the properties of particles are independent. Here we introduce Gaussian mixture models (GMM), a technique suitable for describing non-normal multivariate distributions, as a method to identify mutually exclusive subsets of floating macroplastic and microplastic particles (latent class analysis) based on statistically defensible categories. Length, width, height and polymer type of 6,942 particles and items from the Atlantic Ocean were measured using infrared spectroscopy and image analysis. GMM revealed six underlying normal distributions based on length and width; two within each of the lines, films, and fragments categories. These classes differed significantly in polymer types. The results further showed that smaller films and fragments had a higher correlation between length and width, indicating that they were about the same size in two dimensions. In contrast, larger films and fragments showed low correlations of height with length and width. This demonstrates that larger particles show greater variability in shape and thus plastic fragmentation is associated with particle rounding. These results offer important opportunities for refinement of risk assessment and for modeling the fragmentation and distribution of plastic in the ocean. They further illustrate that GMM is a useful method to map ocean plastics, with advantages over approaches that use arbitrary categorizations and assume size independence or normal distributions.

Perceived Teacher Discrimination and Depressive Feelings in Adolescents: The Role of National, Regional, and Heritage Identities in Flemish Schools.

Adolescents' identities are multiple, yet there is very little research that investigates the importance of intersecting identities, especially in relationship to teacher ethnic/racial discrimination and mental health. Multiplicity is often approached bi-dimensional (heritage and national identities) yet this study highlights the importance of regional identity. Regions are distinct socio-political contexts in relation to migration and integration dynamics. Hence, this study investigates for different combinations of national, heritage and regional identities (i.e. Flemish, Belgian and Turkish or Moroccan) the relationship between students' experiences with teacher ethnic/racial discrimination and students' depressive feelings. Latent Class Analysis of survey data involving a sample of 439 adolescents (Mage = 18, SD = 0.93; Girls = 49%) with Turkish (41%) or Moroccan origin in Flanders, shows three identification classes: full integration (35%), national integration (40%) and (weak) separation (24%). All these identity profiles had in common that heritage identification was high, yet they were highly distinct due to variation in national and regional identification. Additional, multilevel modelling showed that nationally integrated adolescents were less depressed than fully integrated adolescents. This finding illustrates the importance of adolescents' identity multiplicity for understanding their resilience in relation to teacher discrimination.

Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic.

Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine-learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection-prevention behavior in 56,072 participants across 28 countries, administered in March to May 2020. The machine-learning model predicted 52% of the variance in infection-prevention behavior in a separate test sample-exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individual-level injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior-and some theoretically derived predictors were relatively unimportant.

Characteristics of suicide attempts associated with lethality and method: A latent class analysis of the Military Suicide Research Consortium.

While suicide prevention is a national priority, particularly among service members and veterans (SMVs), understanding of suicide-related outcomes remains poor. Person-centered approaches (e.g., latent class analysis) have promise to identify unique risk profiles and subgroups in the larger population. The current study identified latent subgroups characterized by prior self-directed violence history and proximal risk factors for suicide among suicide attempt survivors, and compared subgroups on demographics and most-lethal attempt characteristics. Participants included civilians and SMVs reporting lifetime suicide attempt(s) (n = 2643) from the Military Suicide Research Consortium. Two classes emerged from Common Data Elements: suicide attempt and non-suicidal self-injury frequency, suicide attempt method, perceived likelihood of future suicide, suicide disclosure, suicide intent, and perceived and actual lethality of attempt. A Higher-Risk History class was characterized by greater intent to die, certainty about attempt fatality and method lethality, belief injury would be medically unfixable, and likelihood of prior non-suicidal self-injury. A Lower-Risk History class was characterized by greater ambivalence toward death and methods. Higher-Risk class members were more likely to be male, older, SMVs, have less formal education, use firearms as most-lethal attempt method, and require a higher degree of medical attention. Lower-Risk class members were more likely to be female, civilian, use cutting as most-lethal attempt method, and require less medical attention for attempts. Findings have implications for risk assessments and highlight the importance of subjective perceptions about suicidal behavior. Further investigation of real-time individual-level is necessary, especially for SMVs who may be at greatest risk for potentially lethal suicidal behavior.

Predictors of adherence to public health behaviors for fighting COVID-19 derived from longitudinal data.

The present paper examines longitudinally how subjective perceptions about COVID-19, one's community, and the government predict adherence to public health measures to reduce the spread of the virus. Using an international survey (N = 3040), we test how infection risk perception, trust in the governmental response and communications about COVID-19, conspiracy beliefs, social norms on distancing, tightness of culture, and community punishment predict various containment-related attitudes and behavior. Autoregressive analyses indicate that, at the personal level, personal hygiene behavior was predicted by personal infection risk perception. At social level, social distancing behaviors such as abstaining from face-to-face contact were predicted by perceived social norms. Support for behavioral mandates was predicted by confidence in the government and cultural tightness, whereas support for anti-lockdown protests was predicted by (lower) perceived clarity of communication about the virus. Results are discussed in light of policy implications and creating effective interventions.

Cross-cultural variation in cooperation: A meta-analysis.

Impersonal cooperation among strangers enables societies to create valuable public goods, such as infrastructure, public services, and democracy. Several factors have been proposed to explain variation in impersonal cooperation across societies, referring to institutions (e.g., rule of law), religion (e.g., belief in God as a third-party punisher), cultural beliefs (e.g., trust) and values (e.g., collectivism), and ecology (e.g., relational mobility). We tested 17 preregistered hypotheses in a meta-analysis of 1,506 studies of impersonal cooperation in social dilemmas (e.g., the Public Goods Game) conducted across 70 societies (k = 2,271), where people make costly decisions to cooperate among strangers. After controlling for 10 study characteristics that can affect the outcome of studies, we found very little cross-societal variation in impersonal cooperation. Categorizing societies into cultural groups explained no variance in cooperation. Similarly, cultural, ancestral, and linguistic distance between societies explained little variance in cooperation. None of the cross-societal factors hypothesized to relate to impersonal cooperation explained variance in cooperation across societies. We replicated these conclusions when meta-analyzing 514 studies across 41 states and nine regions in the United States (k = 783). Thus, we observed that impersonal cooperation occurred in all societies-and to a similar degree across societies-suggesting that prior research may have overemphasized the magnitude of differences between modern societies in impersonal cooperation. We discuss the discrepancy between theory, past empirical research and the meta-analysis, address a limitation of experimental research on cooperation to study culture, and raise possible directions for future research. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Parent-child personality similarity and differential autonomy support toward siblings.

Since parental differential treatment is related to more adjustment difficulties over and above main effects of parental treatment, it is important to understand under what conditions differential parenting is likely to occur. Using a within-family design, this study focused on between-sibling differences in parent-child personality similarity as a potential predictor of differential autonomy support from fathers and mothers. Longitudinal data (6 annual waves) of 497 target adolescents (56.9% boys, Mage at T1 = 13.03), one of their siblings (N = 416, Mage at T1 = 14.92), their fathers (N = 446, Mage at T1 = 46.74), and their mothers (N = 495, Mage at T1 = 44.41) were used. Parent-child personality similarity was determined based on distinctive profile correlations using the Big Five personality inventory. Structural Equation Modeling showed that the association between sibling differences in mother-child similarity and maternal autonomy support was positive and significant at the between-family level, and not at the within-family level. This means that, in families where one sibling was relatively more similar to the mother, the sibling with closer resemblance to the mother received relatively more autonomy support. No significant effects were found for fathers' differential autonomy support. The present study highlights the importance of considering parent-child similarity in personality for understanding differences between siblings in maternal autonomy support. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Mapping Phenomena Relevant to Adolescent Emotion Regulation: A Text-Mining Systematic Review.

Adolescence is a developmentally sensitive period for emotion regulation with potentially lifelong implications for mental health and well-being. Although substantial empirical research has addressed this topic, the literature is fragmented across subdisciplines, and an overarching theoretical framework is lacking. The first step toward constructing a unifying framework is identifying relevant phenomena. This systematic review of 6305 articles used text mining to identify phenomena relevant to adolescents' emotion regulation. First, a baseline was established of relevant phenomena discussed in theory and recent narrative reviews. Then, article keywords and abstracts were analyzed using text mining, examining term frequency as an indicator of relevance and term co-occurrence as an indicator of association. The results reflected themes commonly featured in theory and narrative reviews, such as socialization and neurocognitive development, but also identified undertheorized themes, such as developmental disorders, physical health, external stressors, structural disadvantage, substance use, identity and moral development, and sexual development. The findings illustrate how text mining systematic reviews, a novel approach, may complement narrative reviews. Future theoretical work might integrate these undertheorized themes into an overarching framework, and empirical research might consider them as promising areas for future research, or as potential confounders in research on adolescents' emotion regulation. Supplementary Information: The online version contains supplementary material available at 10.1007/s40894-021-00160-7.

Placebo Response and Its Predictors in Attention Deficit Hyperactivity Disorder: A Meta-Analysis and Comparison of Meta-Regression and MetaForest.

BACKGROUND: High placebo response in attention deficit hyperactivity disorder (ADHD) can reduce medication-placebo differences, jeopardizing the development of new medicines. This research aims to (1) determine placebo response in ADHD, (2) compare the accuracy of meta-regression and MetaForest in predicting placebo response, and (3) determine the covariates associated with placebo response. METHODS: A systematic review with meta-analysis of randomized, placebo-controlled clinical trial investigating pharmacological interventions for ADHD was performed. Placebo response was defined as the change from baseline in ADHD symptom severity assessed according to the 18-item, clinician-rated, DSM-based rating scale. The effect of study design-, intervention-, and patient-related covariates in predicting placebo response was studied by means of meta-regression and MetaForest. RESULTS: Ninety-four studies including 6614 patients randomized to placebo were analyzed. Overall, placebo response was -8.9 points, representing a 23.1% reduction in the severity of ADHD symptoms. Cross-validated accuracy metrics for meta-regression were R2 = 0.0012 and root mean squared error = 3.3219 for meta-regression and 0.0382 and 3.2599 for MetaForest. Placebo response among ADHD patients increased by 63% between 2001 and 2020 and was larger in the United States than in other regions of the world. CONCLUSIONS: Strong placebo response was found in ADHD patients. Both meta-regression and MetaForest showed poor performance in predicting placebo response. ADHD symptom improvement with placebo has markedly increased over the last 2 decades and is greater in the United States than the rest of the world.

Evaluation of inequality constrained hypotheses using a generalization of the AIC.

In the social and behavioral sciences, it is often not interesting to evaluate the null hypothesis by means of a p-value. Researchers are often more interested in quantifying the evidence in the data (as opposed to using p-values) with respect to their own expectations represented by equality and/or inequality constrained hypotheses (as opposed to the null hypothesis). This article proposes an Akaike-type information criterion (AIC; Akaike, 1973, 1974) called the generalized order-restricted information criterion approximation (GORICA) that evaluates (in)equality constrained hypotheses under a very broad range of statistical models. The results of five simulation studies provide empirical evidence showing that the performance of the GORICA on selecting the best hypothesis out of a set of (in)equality constrained hypotheses is convincing. To illustrate the use of the GORICA, the expectations of researchers are investigated in a logistic regression, multilevel regression, and structural equation model. (PsycInfo Database Record (c) 2021 APA, all rights reserved).