Multilevel Modeling of Interval-Contingent Data in Neuropsychology Research Using the lmerTest Package in R

被引:3
作者
Pond, Richard S. [1 ]
McCool, Matison W. [1 ]
Bulla, Brian A. [2 ]
机构
[1] Univ N Carolina, Dept Psychol, Teaching Lab 2080, Wilmington, NC 28403 USA
[2] Univ Southern Mississippi, Dept Psychol, Hattiesburg, MS 39406 USA
关键词
Statistical methods; Multilevel modeling; Hierarchical linear modeling; Linear mixed-effects modeling; R; APPROACH MOTIVATION; SOCIAL-INTERACTION; LINE BISECTION; ANGER; AGGRESSION; AGREEABLENESS; REGRESSION; ASYMMETRY; EMOTION; TRAIT;
D O I
10.1007/s40817-020-00095-2
中图分类号
B849 [应用心理学];
学科分类号
040203 ;
摘要
Intensive longitudinal research designs are becoming more common in the field of neuropsychology. They are a powerful approach to studying development and change in naturally occurring phenomena. However, to fully capitalize on the wealth of data yielded by these designs, researchers have to understand the nature of multilevel data structures. The purpose of the present article is to describe some of the basic concepts and techniques involved in modeling multilevel data structures. In addition, this article serves as a step-by-step tutorial to demonstrate how neuropsychologists can implement basic multilevel modeling techniques with real data and the R package, lmerTest. R may be an ideal option for some empirical scientists, applied statisticians, and clinicians, because it is a free and open-source programming language for statistical computing and graphics that offers a flexible and powerful set of tools for analyzing data. All data and code described in the present article have been made publicly available.
引用
收藏
页码:102 / 112
页数:11
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