Multivariate Latent Growth Modeling: Issues on Preliminary Data Analyses

Multivariate Latent Growth Modeling: Issues on Preliminary Data Analyses

Multivariate latent growth modeling (multivariate LGM) provides a flexible data analytic framework for representing and assessing cross-domain (i.e., between-constructs) relationships in intraindividual changes over time, which also allows incorporation of multiple levels of analysis. Using the chap...

Full description

Saved in:
Bibliographic Details
Main Author: CHAN, David
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2005
Subjects:
Quantitative Psychology
Online Access:https://ink.library.smu.edu.sg/soss_research/88
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.soss_research-1087
record_format dspace
spelling sg-smu-ink.soss_research-10872010-08-31T09:30:04Z Multivariate Latent Growth Modeling: Issues on Preliminary Data Analyses CHAN, David Multivariate latent growth modeling (multivariate LGM) provides a flexible data analytic framework for representing and assessing cross-domain (i.e., between-constructs) relationships in intraindividual changes over time, which also allows incorporation of multiple levels of analysis. Using the chapter by Cortina, Pant, and Smith-Darden (this volume) as a point of departure, this chapter discusses important preliminary data analysis and interpretation issues prior to performing multivariate LGM analyses. 2005-01-01T08:00:00Z text https://ink.library.smu.edu.sg/soss_research/88 info:doi/10.1016/s1475-9144(05)04014-2 Research Collection School of Social Sciences eng Institutional Knowledge at Singapore Management University Quantitative Psychology
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Quantitative Psychology
spellingShingle Quantitative Psychology
CHAN, David
Multivariate Latent Growth Modeling: Issues on Preliminary Data Analyses
description Multivariate latent growth modeling (multivariate LGM) provides a flexible data analytic framework for representing and assessing cross-domain (i.e., between-constructs) relationships in intraindividual changes over time, which also allows incorporation of multiple levels of analysis. Using the chapter by Cortina, Pant, and Smith-Darden (this volume) as a point of departure, this chapter discusses important preliminary data analysis and interpretation issues prior to performing multivariate LGM analyses.
format text
author CHAN, David
author_facet CHAN, David
author_sort CHAN, David
title Multivariate Latent Growth Modeling: Issues on Preliminary Data Analyses
title_short Multivariate Latent Growth Modeling: Issues on Preliminary Data Analyses
title_full Multivariate Latent Growth Modeling: Issues on Preliminary Data Analyses
title_fullStr Multivariate Latent Growth Modeling: Issues on Preliminary Data Analyses
title_full_unstemmed Multivariate Latent Growth Modeling: Issues on Preliminary Data Analyses
title_sort multivariate latent growth modeling: issues on preliminary data analyses
publisher Institutional Knowledge at Singapore Management University
publishDate 2005
url https://ink.library.smu.edu.sg/soss_research/88
_version_ 1770567953796825088

Similar Items