纵向数据
心理学
纵向研究
优势和劣势
光学(聚焦)
认知心理学
计算机科学
社会心理学
数据挖掘
统计
数学
光学
物理
作者
Masumi Iida,Andrea Savord,Thomas Ledermann
摘要
Abstract In this review, we discuss the most commonly used models to analyze dyadic longitudinal data. We start the review with a definition of dyadic longitudinal data that allows relationship researchers to identify when these models might be appropriate. Then, we go on to describe the three major models commonly used when one has dyadic longitudinal data: the dyadic growth curve model (DGCM), the actor–partner interdependence model (APIM), and the common fate growth model (CFGM). We discuss when each model might be used and strengths and weaknesses of each model. We end with additional thoughts that focus on extensions to new methods being discussed in the literature, along with some of the challenges of collecting and analyzing dyadic longitudinal data that might be helpful for future dyadic researchers.
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