Journal article
Applying the hierarchical linear model to longitudinal data / La aplicación del modelo lineal jerárquico a datos longitudinales
Cultura y Educación: Analytic Strategies in Education/ Estrategias de análisis avanzadas en educación, Vol.29(3), pp.666-701
07/03/2017
DOI: 10.1080/11356405.2017.1367168
Abstract
Educational researchers and school administrators frequently evaluate academic outcomes collected from cross-sectional sampling designs with overt nested structures, such as when students are nested within schools. More recently, interest has focused on the longitudinal collection of academic outcomes to evaluate a student's growth across time. In a longitudinal context, the repeatedly measured academic outcomes are nested within a student. Proper analysis of longitudinal data requires the hierarchical linear model to quantify the extra correlations within students created by the nested sampling structure. In this article, we introduce the hierarchical linear model used to quantify and predict between-student differences in a repeatedly measured continuous maths achievement outcome. This introduction is presented as a conversation representative of those we have frequently with individuals who lack statistical training in hierarchical linear models for longitudinal data. Specifically, we cover why repeated-measures ANOVA may not always be appropriate, how the hierarchical linear model can be used to quantify between-student differences in change and how student- and occasion-level predictors can be properly modelled and interpreted.
Details
- Title: Subtitle
- Applying the hierarchical linear model to longitudinal data / La aplicación del modelo lineal jerárquico a datos longitudinales
- Creators
- Ryan W Walters - Creighton UniversityLesa Hoffman - University of Kansas
- Resource Type
- Journal article
- Publication Details
- Cultura y Educación: Analytic Strategies in Education/ Estrategias de análisis avanzadas en educación, Vol.29(3), pp.666-701
- Publisher
- Routledge
- DOI
- 10.1080/11356405.2017.1367168
- ISSN
- 1135-6405
- eISSN
- 1578-4118
- Language
- English
- Date published
- 07/03/2017
- Academic Unit
- Psychological and Quantitative Foundations
- Record Identifier
- 9983993325002771
Metrics
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