Estimating Latent Growth Curve Models: An Introduction

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Author(s): K. A. S. Wickrama (The University of Georgia, USA)
Copyright: 2023
Pages: 12
Source title: Social Research Methodology and Publishing Results: A Guide to Non-Native English Speakers
Source Author(s)/Editor(s): Candauda Arachchige Saliya (Sri Lanka Institute of Information Technology, Sri Lanka)
DOI: 10.4018/978-1-6684-6859-3.ch013

Keywords: Information Science Reference / Library & Information Science / Research Methods

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Abstract

Although LGC modeling is gaining popularity in some disciplines, it has not been widely employed in social-epidemiological studies. This paper presents an introduction to the latent growth curve (LGC) technique within a structural equation modelling (SEM) framework as a powerful tool to analyze change in individual attributes over time (e.g., behaviors, attitudes, beliefs, and health) and potential correlates of such changes. The rationale for LGC analysis and subsequent elaboration of this statistical approach are discussed. For illustrations, Mplus (version 8, Muthén & Muthén, 2012) software and depressive symptoms as the individual outcomes attribute are used. The limitations of traditional analytical methods are also addressed. Particularly, the chapter considers socio-contextual factors as correlates of change in the outcomes variable, and examines the dynamic systematic relationship with the socioeconomic factors (however, these correlates can also be factors other than social-context).

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Estimating Latent Growth Curve Models: An Introduction

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