Document Type

Article

Publication Date

9-1-2021

School

Psychology

Abstract

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.

Comments

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s40817-020-00095-2

Publication Title

Journal of Pediatric Neuropsychology

Volume

7

First Page

102

Last Page

112

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