The Bayesian reservoir model of psychological regulation.

IF 7.6 1区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY Psychological methods Pub Date : 2024-09-12 DOI:10.1037/met0000690
Mirinda M Whitaker,Cindy S Bergeman,Pascal R Deboeck
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Abstract

Social and behavioral scientists are increasingly interested the dynamics of the processes they study. Despite the wide array of processes studied, a fairly narrow set of models are applied to characterize dynamics within these processes. For social and behavioral research to take the next step in modeling dynamics, a wider variety of models need to be considered. The reservoir model is one model of psychological regulation that helps expand the models available (Deboeck & Bergeman, 2013). The present article implements the Bayesian reservoir model for both single time series and multilevel data. Simulation 1 compares the performance of the original version of the reservoir model fit using structural equation modeling (Deboeck & Bergeman, 2013) to the proposed Bayesian estimation approach. Simulation 2 expands this to a multilevel data scenario and compares this to the single-level version. The Bayesian estimation approach performs substantially better than the original estimation approach and produces low-bias estimates even with time series as short as 25 observations. Combining Bayesian estimation with a multilevel modeling approach allows for relatively unbiased estimation with sample sizes as small as 15 individuals and/or with time series as short as 15 observations. Finally, a substantive example is presented that applies the Bayesian reservoir model to perceived stress, examining how the model parameters relate to psychological variables commonly expected to relate to resilience. The current expansion of the reservoir model demonstrates the benefits of leveraging the combined strengths of Bayesian estimation and multilevel modeling, with new dynamic models that have been tailored to match the process of psychological regulation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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心理调节的贝叶斯水库模型。
社会和行为科学家对其研究过程的动态越来越感兴趣。尽管所研究的过程种类繁多,但用于描述这些过程内部动态特征的模型却相当狭窄。为了使社会和行为研究在动态建模方面更上一层楼,需要考虑更广泛的模型。水库模型是心理调节的一种模型,有助于扩展现有模型(Deboeck & Bergeman,2013 年)。本文针对单一时间序列和多层次数据实施了贝叶斯水库模型。模拟 1 比较了使用结构方程建模(Deboeck 和 Bergeman,2013 年)拟合的原始水库模型与所提出的贝叶斯估计方法的性能。模拟 2 将其扩展到多层次数据情景,并与单层次版本进行比较。贝叶斯估计方法的性能大大优于原始估计方法,即使时间序列短至 25 个观测值,也能产生低偏差估计值。将贝叶斯估计法与多层次建模法相结合,可以在样本量小到 15 个个体和/或时间序列短到 15 个观测值的情况下进行相对无偏的估计。最后,介绍了一个将贝叶斯水库模型应用于感知压力的实例,研究了模型参数与通常预期与复原力相关的心理变量之间的关系。目前对水库模型的扩展表明,利用贝叶斯估计和多层次建模的综合优势,以及为匹配心理调节过程而定制的新动态模型,是非常有益的。(PsycInfo Database Record (c) 2024 APA, 版权所有)。
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来源期刊
Psychological methods
Psychological methods PSYCHOLOGY, MULTIDISCIPLINARY-
CiteScore
13.10
自引率
7.10%
发文量
159
期刊介绍: Psychological Methods is devoted to the development and dissemination of methods for collecting, analyzing, understanding, and interpreting psychological data. Its purpose is the dissemination of innovations in research design, measurement, methodology, and quantitative and qualitative analysis to the psychological community; its further purpose is to promote effective communication about related substantive and methodological issues. The audience is expected to be diverse and to include those who develop new procedures, those who are responsible for undergraduate and graduate training in design, measurement, and statistics, as well as those who employ those procedures in research.
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