LGCM and PLS-SEM in Panel Survey Data: A Systematic Review and Bibliometric Analysis

Zulkifli Mohd Ghazali, Wan Fairos Wan Yaacob () and Wan Marhaini Wan Omar
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Zulkifli Mohd Ghazali: Mathematical Sciences Studies, College of Computing, Informatics and Media, Universiti Teknologi MARA, Cawangan Perak, Kampus Tapah, Tapah Road 35400, Perak, Malaysia
Wan Fairos Wan Yaacob: Mathematical Sciences Studies, College of Computing, Informatics and Media, Universiti Teknologi MARA Cawangan Kelantan, Kampus Kota Bharu, Kota Bharu 15050, Kelantan, Malaysia
Wan Marhaini Wan Omar: Faculty of Business and Management, Universiti Teknologi MARA Cawangan Kelantan, Kampus Kota Bharu, Kota Bharu 15050, Kelantan, Malaysia

Data, 2023, vol. 8, issue 2, 1-24

Abstract: The application of Latent Growth Curve Model (LGCM) and Partial Least Square Structural Equation Modeling (PLS-SEM) has gained much attention in panel survey studies. This study explores the distributions and trends of LGCM, and PLS-SEM used in panel survey data. It highlights the gaps in the current and existing approaches of PLS-SEM practiced by researchers in analyzing panel survey data. The integrated bibliometric analysis and systematic review were employed in this study. Based on the reviewed articles, the LGCM and PLS-SEM showed an increasing trend of publication in the panel survey data. Though the popularity of LGCM was more outstanding than PLS-SEM for the panel survey data, LGCM has several limitations such as statistical assumptions, reliable sample size, number of repeated measures, and missing data. This systematic review identified five different approaches of PLS-SEM in analyzing the panel survey data namely pre- and post-approach with different constructs, a path comparison approach, a cross-lagged approach, pre- and post-approach with the same constructs, and an evaluation approach practiced by researchers. None of the previous approaches used can establish one structural model to represent the whole changes in the repeated measure. Thus, the findings of this paper could help researchers choose a more appropriate approach to analyzing panel survey data.

Keywords: bibliometric; SLR; panel survey data; longitudinal survey; Latent Growth Curve Model (LGCM); PLS-SEM (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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