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Latent Class Growth Modelling: a tutorial

Latent Class Growth Modelling: a tutorial
Latent Class Growth Modelling: a tutorial
The present work is an introduction to Latent Class Growth Modelling (LCGM). LCGM is a semi‐parametric statistical technique used to analyze longitudinal data. It is used when the data follows a pattern of change in which both the strength and the direction of the relationship between the independent and dependent variables differ across cases. The analysis identifies distinct subgroups of individuals following a distinct pattern of change over age or time on a variable of interest. The aim of the present tutorial is to introduce readers to LCGM and provide a concrete example of how the analysis can be performed using a real‐world data set and the SAS software package with accompanying PROC TRAJ application. The advantages and limitations of this technique are also discussed.
1913-4126
11-24
Andruff, Heather
3dc9c223-1a61-47ad-ab0b-50d06cddf4f2
Carraro, Natasha
97a20bba-3080-4271-b589-07b0960a028b
Thompson, Amanda
9eedd867-ec29-45a1-80d9-41b03f3bc6d0
Gaudreau, Patrick
1481a277-3757-449c-b24a-285a3318b72b
Louvet, Benoît
3ae28e7f-2d68-4817-bd24-5ded079c894c
Andruff, Heather
3dc9c223-1a61-47ad-ab0b-50d06cddf4f2
Carraro, Natasha
97a20bba-3080-4271-b589-07b0960a028b
Thompson, Amanda
9eedd867-ec29-45a1-80d9-41b03f3bc6d0
Gaudreau, Patrick
1481a277-3757-449c-b24a-285a3318b72b
Louvet, Benoît
3ae28e7f-2d68-4817-bd24-5ded079c894c

Andruff, Heather, Carraro, Natasha, Thompson, Amanda, Gaudreau, Patrick and Louvet, Benoît (2009) Latent Class Growth Modelling: a tutorial. Tutorials in Quantitative Methods for Psychology, 5 (1), 11-24. (doi:10.20982/tqmp.05.1.p011).

Record type: Article

Abstract

The present work is an introduction to Latent Class Growth Modelling (LCGM). LCGM is a semi‐parametric statistical technique used to analyze longitudinal data. It is used when the data follows a pattern of change in which both the strength and the direction of the relationship between the independent and dependent variables differ across cases. The analysis identifies distinct subgroups of individuals following a distinct pattern of change over age or time on a variable of interest. The aim of the present tutorial is to introduce readers to LCGM and provide a concrete example of how the analysis can be performed using a real‐world data set and the SAS software package with accompanying PROC TRAJ application. The advantages and limitations of this technique are also discussed.

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Published date: 1 March 2009

Identifiers

Local EPrints ID: 429944
URI: https://eprints.soton.ac.uk/id/eprint/429944
ISSN: 1913-4126
PURE UUID: b4be6d84-1a02-4f23-929f-b5cbc7bbe0b2
ORCID for Heather Andruff: ORCID iD orcid.org/0000-0002-1071-8644

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Date deposited: 09 Apr 2019 16:30
Last modified: 11 May 2019 00:20

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Contributors

Author: Heather Andruff ORCID iD
Author: Natasha Carraro
Author: Amanda Thompson
Author: Patrick Gaudreau
Author: Benoît Louvet

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