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Korean Journal of Psychology: General

ISSN : 1229-067X

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Vol.37 No.1
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The Effects of Factorial Invariance and Factor Scaling on Model Fit and Parameter Estimates in the Multiple-Indicator Latent Growth Model

Korean Journal of Psychology: General / Korean Journal of Psychology: General, (P)1229-067X; (E)2734-1127
2018, v.37 no.1, pp.153-183
https://doi.org/10.22257/kjp.2018.03.37.1.153


& (2018). The Effects of Factorial Invariance and Factor Scaling on Model Fit and Parameter Estimates in the Multiple-Indicator Latent Growth Model. Korean Journal of Psychology: General, 37(1), 153-183, https://doi.org/10.22257/kjp.2018.03.37.1.153
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Abstract

The multiple-indicator latent growth model (MI-LGM) is a second-order confirmatory factor model that analyzes latent trajectories of a factor measured by multiple indicators over time. Although MI-LGM can test the factorial invariance of indicators and estimate trajectories of a latent variable controlling measurement error, model fit and parameter estimates of the model may vary depending on factor scaling methods. The purpose of this study is to investigate how factor scaling methods, given a specified level of factorial invariance, change the meaning of the factor mean and thus affects the model fit and parameter estimates of MI-LGM. The authors first explored how factorial invariance and factor scaling affect the definition of factor means and the model fit in longitudinal factor analysis models. Next, they showed that constraining the sum of the indicator’ intercepts to zero creates a clear definition of the factor mean and the constraint provides consistent results and interpretation of the means of growth factors in the MI-LGM even under the weak factorial invariance. An analysis of actual panel data then illustrated such characteristics of the MI-LGM. Finally, the authors discussed the importance of factorial invariance and factor scaling in the analysis of mean and covariance structure models and that of using the strong factorial invariance when modeling the MI-LGM.

keywords
다지표 잠재성장모형, 요인척도 설정, 요인동일성, 평균구조, 모형 적합도, multiple-indicator latent growth model, factor scaling, factorial invariance, mean structure, model fit

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