Multilevel random coefficient analyses of event- and interval-contingent data in social and personality psychology research
Multilevel random coefficient analyses of event- and interval-contingent data in social and personality psychology research
复制标题
DOI：
10.1177/0146167201277001
复制
发表时间：
2001-07-01
影响因子：
4
通讯作者：
Nezlek, JB
中科院分区：
文献类型：
--
作者：
Nezlek, JB
社会心理学家和人格心理学家越来越多地进行一些研究，在这些研究中，数据在多个层面同时收集，并且假设涉及多个层面的分析所产生的影响。在对自然发生的社会互动的研究中，描述人和他们的社会互动的数据是同时收集的。本文讨论了如何使用随机系数模型来分析这类数据。分析描述日常社会互动的数据被用来说明对事件依存数据（当特定事件触发或组织数据收集时）的分析，分析描述对日常事件反应的数据被用来说明对区间依存数据（当按时间间隔收集数据时）的分析。文中介绍了不同的分析策略，描述了普通最小二乘法分析的缺点，并详细讨论了多层随机系数模型的使用。还考虑了不同的建模技术、提出和检验假设的细节以及固定效应和随机效应之间的差异。
Increasingly, social and personality psychologists are conducting studies in which data are collected simultaneously at multiple levels, with hypotheses concerning effects that involve multiple levels of analysis. In studies of naturally occurring social interaction, data describing people and their social interactions are collected simultaneously. This article discuses how to analyze such data using random coefficient modeling. Analyzing data describing day-to-day social interaction is used to illustrate the analysis of event-contingent data (when specific events trigger or organize data collection), and analyzing data describing reactions to daily events is used to illustrate the analysis of interval-contingent data (when data are collected at intervals). Different analytic strategies are presented, the shortcomings of ordinary least squares analyses are described, and the use of multilevel random coefficient modeling is discussed in detail. Different modeling techniques, the specifics of formulating and testing hypotheses, and the differences between fixed and random effects are also considered.