Estimating Group Differences in Network Models using Moderation Analysis

التفاصيل البيبلوغرافية
العنوان: Estimating Group Differences in Network Models using Moderation Analysis
المؤلفون: Haslbeck Jmb
بيانات النشر: Center for Open Science, 2020.
سنة النشر: 2020
مصطلحات موضوعية: Computer science, business.industry, bepress|Social and Behavioral Sciences|Psychology|Quantitative Psychology, Moderation, Machine learning, computer.software_genre, PsyArXiv|Social and Behavioral Sciences, Text mining, Group differences, bepress|Social and Behavioral Sciences, PsyArXiv|Social and Behavioral Sciences|Quantitative Methods|Statistical Methods, PsyArXiv|Social and Behavioral Sciences|Quantitative Methods, Artificial intelligence, business, computer, Network model
الوصف: Statistical network models such as the Gaussian Graphical Model and the Ising model have become popular tools to analyze multivariate psychological data sets. In many applications the goal is to compare such network models across groups. In this paper I introduce a method to estimate differences in network models across groups that is based on moderation analysis. This method is attractive because it allows to make comparisons across more than two groups within a single model, and because it is implemented for all commonly used cross-sectional network models. Next to introducing the method, I evaluate the performance of the proposed method and existing approaches in a simulation study. Finally, I provide a fully reproducible tutorial on how to use the moderation method to compare a network model across three groups using the R-package mgm.
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3cc1a27a1414e0dcf4dbcd57be344ce7
https://doi.org/10.31234/osf.io/926pv
حقوق: OPEN
رقم الأكسشن: edsair.doi.dedup.....3cc1a27a1414e0dcf4dbcd57be344ce7
قاعدة البيانات: OpenAIRE