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

Whoever has will be given more? How to use the intercept-slope correlation in improving our understanding of replicability, heterogeneity, and theory development.

Multi-lab projects are large scale collaborations between participating data collection sites that gather empirical evidence and (usually) analyze that evidence using meta-analyses. They are a valuable form of scientific collaboration, produce outstanding data sets and are a great resource for third-party researchers. Their data may be reanalyzed and used in research synthesis. Their repositories and code could provide guidance to future projects of this kind. But, while multi-labs are similar in their structure and aggregate their data using meta-analyses, they deploy a variety of different solutions regarding the storage structure in the repositories, the way the (analysis) code is structured and the file-formats they provide. Continuing this trend implies that anyone who wants to work with data from multiple of these projects, or combine their datasets, is faced with an ever-increasing complexity. Some of that complexity could be avoided. Here, we introduce MetaPipeX, a standardized framework to harmonize, document, analyze and multi-lab data. It features a pipeline conceptualization of the analysis and documentation process, an R-package that implements both and a Shiny App that allows users to explore and visualize these data sets. We introduce the framework by describing its components and applying it to a practical example. Engaging with this form of collaboration and integrating it further into research practice will certainly be beneficial to quantitative sciences and we hope the framework provides a structure and tools to reduce effort for anyone who creates, re-uses, harmonizes or learns about multi-lab replication projects

Reduce, Reuse, Recycle: Introducing MetaPipeX, a Framework for Analyses of Multi-Lab Data

Multi-lab projects are large scale collaborations between participating data collection sites that gather empirical evidence and (usually) analyze that evidence using meta-analyses. They are a valuable form of scientific collaboration, produce outstanding data sets and are a great resource for third-party researchers. Their data may be reanalyzed and used in research synthesis. Their repositories and code could provide guidance to future projects of this kind. But, while multi-labs are similar in their structure and aggregate their data using meta-analyses, they deploy a variety of different solutions regarding the storage structure in the repositories, the way the (analysis) code is structured and the file-formats they provide. Continuing this trend implies that anyone who wants to work with data from multiple of these projects, or combine their datasets, is faced with an ever-increasing complexity. Some of that complexity could be avoided. Here, we introduce MetaPipeX, a standardized framework to harmonize, document and analyze multi-lab data. It features a pipeline conceptualization of the analysis and documentation process, an R-package that implements both and a Shiny App (https://www.apps.meta-rep.lmu.de/metapipex/) that allows users to explore and visualize these data sets. We introduce the framework by describing its components and applying it to a practical example. Engaging with this form of collaboration and integrating it further into research practice will certainly be beneficial to quantitative sciences and we hope the framework provides a structure and tools to reduce effort for anyone who creates, re-uses, harmonizes or learns about multi-lab replication projects

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

This paper set out to identify the most important early early predictors of emotion regulation development from many (87) candidates using the machine learning algorithm SEM forests. Candidate predictors were selected based on a prior text mining systematic review of 6305 papers. Participants were 497 Dutch adolescents. Predictors were assessed at age 13, and emotion regulation development from age 14-18 was described using a quadratic latent trajectory model. SEM-forests were used to group adolescents with similar trajectories, based on their scores on the predictors. This person-centered approach not only identifies groups of adolescents with similar trajectories, but also indicates why some adolescents follow similar trajectories. The results indicated that individual differences (e.g., in personality) and conflict behaviors in relationships with parents and friends, and internalizing and externalizing were important early predictors. Less important were parenting behaviors, except autonomy support and invasive parenting, bullying and victimization, delinquent peers, and substance use. The implications of these findings for theory formation and intervention are discussed, and we present an open source risk assessment tool, ERRATA