Exploring the role of OXTR gene methylation in attachment development: A longitudinal study.

The current study explored longitudinally whether oxytocin receptor gene methylation (OXTRm) changes moderated the association between parental sensitivity changes and children's attachment changes over three waves. Six hundred six Flemish children (10-12 years, 42.8%-44.8% boys) completed attachment measures and provided salivary OXTRm data on seven CpG sites. Their parents reported their sensitive parenting. Results suggest that OXTRm changes hardly link to attachment (in)security changes after the age of 10. Some support was found for interaction effects between parental sensitivity changes and OXTRm changes on attachment changes over time. Effects suggest that for children with increased OXTRm in the promotor region and decreased methylation in the inhibitor region over time, increased parental sensitivity was associated with increased secure attachment and decreased insecure attachment over time.

What do meta-analysts need in primary studies? Guidelines and the SEMI checklist for facilitating cumulative knowledge.

Meta-analysis is often recognized as the highest level of evidence due to its notable advantages. Therefore, ensuring the precision of its findings is of utmost importance. Insufficient reporting in primary studies poses challenges for meta-analysts, hindering study identification, effect size estimation, and meta-regression analyses. This manuscript provides concise guidelines for the comprehensive reporting of qualitative and quantitative aspects in primary studies. Adhering to these guidelines may help researchers enhance the quality of their studies and increase their eligibility for inclusion in future research syntheses, thereby enhancing research synthesis quality. Recommendations include incorporating relevant terms in titles and abstracts to facilitate study retrieval and reporting sufficient data for effect size calculation. Additionally, a new checklist is introduced to help applied researchers thoroughly report various aspects of their studies.

Cumulative family stress and externalizing problems: Secure base script knowledge as a protective factor.

The current study examined whether secure base script knowledge can buffer against higher concurrent externalizing problems and against relative increases in externalizing problems associated with cumulative family stress. We conducted a one-year longitudinal study with two waves between 2017 and 2019 in which 272 Dutch-speaking Western European children from Flanders (47.8% boys, M age = 10.20 , SD age = 0.60 ) participated. Secure base script knowledge was associated with lower concurrent externalizing problems (f2  = 0.03). High levels of secure base script knowledge also buffered against relative increases in externalizing problems associated with cumulative family risk (f2  = 0.02). These findings suggest that secure base script knowledge can mitigate the negative effects of a stressful family environment on externalizing problems.

Perceived Parental Support and Psychological Control, DNA Methylation, and Loneliness: Longitudinal Associations Across Early Adolescence.

A broad range of factors have been associated with the development of adolescent loneliness. In the family context, a lack of parental support and high levels of parental psychological control have systematically been linked to loneliness. On the biological level, DNA methylation (which is an epigenetic process that suppresses gene expression) is believed to play a role in the development of loneliness. Specifically, high levels of DNA methylation in genes that play an important role in the functioning of the human stress response system are believed to elevate the risk of loneliness. Moreover, DNA methylation levels in these stress-related genes can be influenced by stressful environmental factors, suggesting a potential mediating role of DNA methylation in the association between parenting behaviors and loneliness. The current 3-year longitudinal study is the first study to examine the potential bidirectional longitudinal associations between loneliness, DNA methylation in stress-related genes, and both perceived parental support and psychological control. Furthermore, we explored the potential mediating role of DNA methylation in stress-related genes in the associations between perceived parenting and loneliness. The sample comprised 622 early adolescents (55% girls, Mage T1 = 10.77 years, SDage T1 = 0.48) who were followed from Grade 5 to 7. Parental support, psychological control, and loneliness were assessed annually by adolescent self-report questionnaires and DNA methylation was determined from saliva samples. Cross-Lagged Panel Models (CLPM) revealed that higher levels of loneliness predicted lower perceived parental support and higher perceived psychological control over time, as well as higher DNA methylation in some stress-related genes, that is, the glucocorticoid receptor gene (NR3C1) and the brain-derived neurotrophic factor (BDNF). In addition, higher NR3C1 methylation was predictive of lower perceived parental support and higher psychological control over time. No evidence was found for a mediating role of DNA methylation. Overall, our longitudinal findings challenge the current focus on DNA methylation and parenting behaviors as risk factors for adolescent loneliness. Instead, they suggest that the less considered direction of effects, which implies that loneliness predicts DNA methylation and aspects of parenting such as support and psychological control, should receive greater attention in future research.

Network meta-analysis in psychology and educational sciences: A systematic review of their characteristics.

Network meta-analysis (NMA) allows the combination of evidence on the effectiveness of several interventions. NMA has mainly been applied in the medical science field, whereas in the domain of psychology and educational sciences its use is less frequent. Consequently, systematic reviews that describe the characteristics of published NMAs are limited to the field of medicine, and nothing is known about the characteristics of NMAs published in the psychology and educational sciences field. However, this information is still relevant for the design of future simulation studies and for detecting good and bad research practices. Thus, this study describes the features of the meta-analytic datasets of NMAs published in the field of psychology and educational sciences, as well as their methodological characteristics, and compares them to those observed in the medical domain. Results show that the number of studies included is larger in NMAs from psychology and educational sciences, the most commonly used effect size is the standardized mean difference (unlike the odds ratio in medicine), the sample size is smaller, more intervention groups are included, and inconsistent effects are observed more often. These results can be used in future simulation studies to generate realistic datasets. Finally, we warn about the poor quality of reporting of some technical aspects of the NMA, such as the statistical model used.

Comparing the prediction performance of item response theory and machine learning methods on item responses for educational assessments.

To obtain more accurate and robust feedback information from the students' assessment outcomes and to communicate it to students and optimize teaching and learning strategies, educational researchers and practitioners must critically reflect on whether the existing methods of data analytics are capable of retrieving the information provided in the database. This study compared and contrasted the prediction performance of an item response theory method, particularly the use of an explanatory item response model (EIRM), and six supervised machine learning (ML) methods for predicting students' item responses in educational assessments, considering student- and item-related background information. Each of seven prediction methods was evaluated through cross-validation approaches under three prediction scenarios: (a) unrealized responses of new students to existing items, (b) unrealized responses of existing students to new items, and (c) missing responses of existing students to existing items. The results of a simulation study and two real-life assessment data examples showed that employing student- and item-related background information in addition to the item response data substantially increases the prediction accuracy for new students or items. We also found that the EIRM is as competitive as the best performing ML methods in predicting the student performance outcomes for the educational assessment datasets.

Multilevel Meta-Analysis of Individual Participant Data of Single-Case Experimental Designs: One-Stage versus Two-Stage Methods.

To conduct a multilevel meta-analysis of multiple single-case experimental design (SCED) studies, the individual participant data (IPD) can be analyzed in one or two stages. In the one-stage approach, a multilevel model is estimated based on the raw data. In the two-stage approach, an effect size is calculated for each participant and these effect sizes and their sampling variances are subsequently combined to estimate a meta-analytic multilevel model. The multilevel model in the two-stage approach has fewer parameters to estimate, in exchange for the reduction of information of the raw data to effect sizes. In this paper we explore how the one-stage and two-stage IPD approaches can be applied in the context of meta-analysis of single-case designs. Both approaches are compared for several single-case designs of increasing complexity. Through a simulation study we show that the two-stage approach obtains better convergence rates for more complex models, but that model estimation does not necessarily converge at a faster speed. The point estimates of the fixed effects are unbiased for both approaches across all models, as such confirming results from methodological research on IPD meta-analysis of group-comparison designs. In light of these results, we discuss the implementation of both methods in R.

Estimating outcome-specific effects in meta-analyses of multiple outcomes: A simulation study.

In meta-analysis, primary studies often include multiple, dependent effect sizes. Several methods address this dependency, such as the multivariate approach, three-level models, and the robust variance estimation (RVE) method. As for today, most simulation studies that explore the performance of these methods have focused on the estimation of the overall effect size. However, researchers are sometimes interested in obtaining separate effect size estimates for different types of outcomes. A recent simulation study (Park & Beretvas, 2019) has compared the performance of the three-level approach and the RVE method in estimating outcome-specific effects when several effect sizes are reported for different types of outcomes within studies. The goal of this paper is to extend that study by incorporating additional simulation conditions and by exploring the performance of additional models, such as the multivariate model, a three-level model that specifies different study-effects for each type of outcome, a three-level model that specifies a common study-effect for all outcomes, and separate three-level models for each type of outcome. Additionally, we also tested whether the a posteriori application of the RV correction improves the standard error estimates and the 95% confidence intervals. Results show that the application of separate three-level models for each type of outcome is the only approach that consistently gives adequate standard error estimates. Also, the a posteriori application of the RV correction results in correct 95% confidence intervals in all models, even if they are misspecified, meaning that Type I error rate is adequate when the RV correction is implemented.

Investigating the interplay between parenting dimensions and styles, and the association with adolescent outcomes.

Research has indicated that a strictly dimensional or parental style approach does not capture the full complexity of parenting. To better understand this complexity, the current study combined these two approaches using a novel statistical technique, i.e., subspace K-means clustering. Four objectives were addressed. First, the study tried to identify meaningful groups of parents in longitudinal adolescent reports on parenting behaviour. Second, the dimensional structure of every cluster was inspected to uncover differences in parenting between and within clusters. Third, the parenting styles were compared on several adolescent characteristics. Fourth, to examine the impact of change in parenting style over time, we looked at the cluster membership over time. Longitudinal questionnaire data were collected at three annual waves, with 1,116 adolescents (mean age = 13.79 years) at wave 1. Based on five parenting dimensions (support and proactive, punitive, psychological and harsh control), subspace K-means clustering, analysed per wave separately, identified two clusters (authoritative and authoritarian parenting) in which parenting dimensions were interrelated differently. Authoritative parenting seemed to be beneficial for adolescent development (less externalising problem behaviour and higher self-concept). Longitudinal data revealed several parenting group trajectories which showed differential relations with adolescent outcomes. Change in membership from the authoritative cluster to the authoritarian cluster was associated with a decrease in self-concept and an increase in externalising problem behaviour, whereas changes from the authoritarian cluster to the authoritative cluster were associated with an increase in self-concept and a decrease in externalising problem behaviour.

A SNP, Gene, and Polygenic Risk Score Approach of Oxytocin-Vasopressin Genes in Adolescents' Loneliness.

Not much is known regarding underlying biological pathways to adolescents' loneliness. Insight in underlying molecular mechanisms could inform intervention efforts aimed at reducing loneliness. Using latent growth curve modeling, baseline levels and development of loneliness were studied in two longitudinal adolescent samples. Genes (OXTR, OXT, AVPR1A, AVPR1B) were examined using SNP-based, gene-based, and polygenic risk score (PRS) approaches. In both samples, SNP- and gene-based tests showed involvement of the OXTR gene in development of loneliness, though, significance levels did not survive correction for multiple testing. The PRS approach provided no evidence for relations with loneliness. We recommend alternative phenotyping methods, including environmental factors, to consider epigenetic studies, and to examine possible endophenotypes in relation to adolescents' loneliness.

Perspective taking, empathic concern, agreeableness, and parental support: Transactional associations across adolescence.

INTRODUCTION: Empathy consists of a cognitive and an affective component, of which it is thought that there are gender differences. Previous studies also suggest that maternal and paternal support play a more prominent role in the development of an adolescent's affective and cognitive empathy, respectively. Besides the environmental factor, that is parenting, adolescent personality, and more specifically, agreeableness, is closely linked to both empathy and support, but this interplay was not extensively investigated longitudinally. The present study investigated the transactional associations among parental support, adolescent agreeableness, and adolescent empathy. More specifically, we examined (a) whether maternal/paternal support is differentially associated with cognitive/affective empathy, while taking into account adolescent agreeableness and (b) whether adolescent agreeableness still predicts empathy, while taking into account parental support. METHODS: Data from 993 Belgian adolescents (MageT1 = 13.96 years; [12.6-18.4]) and their parents across four time points were used in a random intercept cross-lagged panel model. RESULTS: At the between-person level, maternal support was associated with affective, but not cognitive empathy, whereas agreeableness was associated with maternal and paternal support as well as with both types of empathy. At the within-person level, affective empathy predicted cognitive empathy one wave later. CONCLUSIONS: At a population level, agreeableness and support are both important in adolescent empathy development with limited evidence for the differential roles of mothers and fathers. Within participants, affective empathy, and not parental support or agreeableness, predicted cognitive empathy.

Comfort provided by parents versus strangers after eliciting stress in children with severe or profound intellectual disabilities: does it make a difference?

The relationships between children with severe or profound intellectual disabilities (ID) and their parents may fulfil attachment functions, such as regulating emotional responses to stress. This study examined the extent to which children with severe or profound ID differentiate between their parents and a stranger as a resource for stress-regulation. A home-based experimental paradigm was conducted and video-recorded in 38 families. Children (1-8 years) were exposed to four naturalistic stressors followed by comfort, randomly provided by the parents or the stranger. Emotional behaviour (arousal and valence) and the skin conductance level were simultaneously recorded. With regard to both emotional behaviour and skin conductance, children significantly differentiated between their parents as attachment figures and the stranger during stress and comfort, despite their impairments on various developmental domains. Behavioural observation and physiology show complementary manifestations of parent-child attachment in this population.

The application of meta-analytic (multi-level) models with multiple random effects: A systematic review.

In meta-analysis, study participants are nested within studies, leading to a multilevel data structure. The traditional random effects model can be considered as a model with a random study effect, but additional random effects can be added in order to account for dependent effects sizes within or across studies. The goal of this systematic review is three-fold. First, we will describe how multilevel models with multiple random effects (i.e., hierarchical three-, four-, five-level models and cross-classified random effects models) are applied in meta-analysis. Second, we will illustrate how in some specific three-level meta-analyses, a more sophisticated model could have been used to deal with additional dependencies in the data. Third and last, we will describe the distribution of the characteristics of multilevel meta-analyses (e.g., distribution of the number of outcomes across studies or which dependencies are typically modeled) so that future simulation studies can simulate more realistic conditions. Results showed that four- or five-level or cross-classified random effects models are not often used although they might account better for the meta-analytic data structure of the analyzed datasets. Also, we found that the simulation studies done on multilevel meta-analysis with multiple random factors could have used more realistic simulation factor conditions. The implications of these results are discussed, and further suggestions are given.

MultiSCED: A tool for (meta-)analyzing single-case experimental data with multilevel modeling.

The MultiSCED web application has been developed to assist applied researchers in behavioral sciences to apply multilevel modeling to quantitatively summarize single-case experimental design (SCED) studies through a user-friendly point-and-click interface embedded within R. In this paper, we offer a brief introduction to the application, explaining how to define and estimate the relevant multilevel models and how to interpret the results numerically and graphically. The use of the application is illustrated through a re-analysis of an existing meta-analytic dataset. By guiding applied researchers through MultiSCED, we aim to make use of the multilevel modeling technique for combining SCED data across cases and across studies more comprehensible and accessible.

Multilevel meta-analysis of multiple regression coefficients from single-case experimental studies.

The focus of the current study is on handling the dependence among multiple regression coefficients representing the treatment effects when meta-analyzing data from single-case experimental studies. We compare the results when applying three different multilevel meta-analytic models (i.e., a univariate multilevel model avoiding the dependence, a multivariate multilevel model ignoring covariance at higher levels, and a multivariate multilevel model modeling the existing covariance) to deal with the dependent effect sizes. The results indicate better estimates of the overall treatment effects and variance components when a multivariate multilevel model is applied, independent of modeling or ignoring the existing covariance. These findings confirm the robustness of multilevel modeling to misspecifying the existing covariance at the case and study level in terms of estimating the overall treatment effects and variance components. The results also show that the overall treatment effect estimates are unbiased regardless of the underlying model, but the between-case and between-study variance components are biased in certain conditions. In addition, the between-study variance estimates are particularly biased when the number of studies is smaller than 40 (i.e., 10 or 20) and the true value of the between-case variance is relatively large (i.e., 8). The observed bias is larger for the between-case variance estimates compared to the between-study variance estimates when the true between-case variance is relatively small (i.e., 0.5).

Measuring growth in students' proficiency in MOOCs: Two component dynamic extensions for the Rasch model.

Massive open online courses (MOOCs) are increasingly popular among students of various ages and at universities around the world. The main aim of a MOOC is growth in students' proficiency. That is why students, professors, and universities are interested in the accurate measurement of growth. Traditional psychometric approaches based on item response theory (IRT) assume that a student's proficiency is constant over time, and therefore are not well suited for measuring growth. In this study we sought to go beyond this assumption, by (a) proposing to measure two components of growth in proficiency in MOOCs; (b) applying this idea in two dynamic extensions of the most common IRT model, the Rasch model; (c) illustrating these extensions through analyses of logged data from three MOOCs; and (d) checking the quality of the extensions using a cross-validation procedure. We found that proficiency grows both across whole courses and within learning objectives. In addition, our dynamic extensions fit the data better than does the original Rasch model, and both extensions performed well, with an average accuracy of .763 in predicting students' responses from real MOOCs.

A demonstration and evaluation of the use of cross-classified random-effects models for meta-analysis.

It is common for the primary studies in meta-analyses to report multiple effect sizes, generating dependence among them. Hierarchical three-level models have been proposed as a means to deal with this dependency. Sometimes, however, dependency may be due to multiple random factors, and random factors are not necessarily nested, but rather may be crossed. For instance, effect sizes may belong to different studies, and, at the same time, effect sizes might represent the effects on different outcomes. Cross-classified random-effects models (CCREMs) can be used to model this nonhierarchical dependent structure. In this article, we explore by means of a simulation study the performance of CCREMs in comparison with the use of other meta-analytic models and estimation procedures, including the use of three- and two-level models and robust variance estimation. We also evaluated the performance of CCREMs when the underlying data were generated using a multivariate model. The results indicated that, whereas the quality of fixed-effect estimates is unaffected by any misspecification in the model, the standard error estimates of the mean effect size and of the moderator variables' effects, as well as the variance component estimates, are biased under some conditions. Applying CCREMs led to unbiased fixed-effect and variance component estimates, outperforming the other models. Even when a CCREM was not used to generate the data, applying the CCREM yielded sound parameter estimates and inferences.

Analysis of single-case experimental count data using the linear mixed effects model: A simulation study.

When (meta-)analyzing single-case experimental design (SCED) studies by means of hierarchical or multilevel modeling, applied researchers almost exclusively rely on the linear mixed model (LMM). This type of model assumes that the residuals are normally distributed. However, very often SCED studies consider outcomes of a discrete rather than a continuous nature, like counts, percentages or rates. In those cases the normality assumption does not hold. The LMM can be extended into a generalized linear mixed model (GLMM), which can account for the discrete nature of SCED count data. In this simulation study, we look at the effects of misspecifying an LMM for SCED count data simulated according to a GLMM. We compare the performance of a misspecified LMM and of a GLMM in terms of goodness of fit, fixed effect parameter recovery, type I error rate, and power. Because the LMM and the GLMM do not estimate identical fixed effects, we provide a transformation to compare the fixed effect parameter recovery. The results show that, compared to the GLMM, the LMM has worse performance in terms of goodness of fit and power. Performance in terms of fixed effect parameter recovery is equally good for both models, and in terms of type I error rate the LMM performs better than the GLMM. Finally, we provide some guidelines for applied researchers about aspects to consider when using an LMM for analyzing SCED count data.

Concealed correlations meta-analysis: A new method for synthesizing standardized regression coefficients.

The synthesis of standardized regression coefficients is still a controversial issue in the field of meta-analysis. The difficulty lies in the fact that the standardized regression coefficients belonging to regression models that include different sets of covariates do not represent the same parameter, and thus their direct combination is meaningless. In the present study, a new approach called concealed correlations meta-analysis is proposed that allows for using the common information that standardized regression coefficients from different regression models contain to improve the precision of a combined focal standardized regression coefficient estimate. The performance of this new approach was compared with that of two other approaches: (1) carrying out separate meta-analyses for standardized regression coefficients from studies that used the same regression model, and (2) performing a meta-regression on the focal standardized regression coefficients while including an indicator variable as a moderator indicating the regression model to which each standardized regression coefficient belongs. The comparison was done through a simulation study. The results showed that, as expected, the proposed approach led to more accurate estimates of the combined standardized regression coefficients under both random- and fixed-effect models.

An explanatory item response theory method for alleviating the cold-start problem in adaptive learning environments.

Electronic learning systems have received increasing attention because they are easily accessible to many students and are capable of personalizing the learning environment in response to students' learning needs. To that end, using fast and flexible algorithms that keep track of the students' ability change in real time is desirable. Recently, the Elo rating system (ERS) has been applied and studied in both research and practical settings (Brinkhuis & Maris, 2009; Klinkenberg, Straatemeier, & van der Maas in Computers & Education, 57, 1813-1824, 2011). However, such adaptive algorithms face the cold-start problem, defined as the problem that the system does not know a new student's ability level at the beginning of the learning stage. The cold-start problem may also occur when a student leaves the e-learning system for a while and returns (i.e., a between-session period). Because external effects could influence the student's ability level during the period, there is again much uncertainty about ability level. To address these practical concerns, in this study we propose alternative approaches to cold-start issues in the context of the e-learning environment. Particularly, we propose making the ERS more efficient by using an explanatory item response theory modeling to estimate students' ability levels on the basis of their background information and past trajectories of learning. A simulation study was conducted under various conditions, and the results showed that the proposed approach substantially reduces ability estimation errors. We illustrate the approach using real data from a popular learning platform.