**1. Introduction**

Over the past few decades, various survey studies have been conducted using different types of survey designs. Many of them used cross-sectional survey design that is able to measure variation in the individuals of a population [1–4] at one point in time. However, in recent years, the development from cross-sectional to panel survey studies can be seen to escalate [5–7] in longitudinal studies. The panel survey data has been used widely in several areas such as education, medicine, psychology, behavior, and many more [8–10]. This type of study allows the researcher to measure variation at the individual level repeatedly on the same sample of units at different points of time. Through panel survey data, the trend and factors influencing those changes can also be observed.

From a methodological perspective, there are several methods that can be used to analyze cross-sectional survey data types. The most commonly used methods are based on Structural Equation Modelling (SEM). SEM offers two methods which are (i) covariancebased SEM (known as CB-SEM), and (ii) variance-based SEM (known as Partial Least Square (PLS-SEM)) method. These methods are often used to identify multiple statistical

**Citation:** Mohd Ghazali, Z.; Wan Yaacob, W.F.; Wan Omar, W.M. LGCM and PLS-SEM in Panel Survey Data: A Systematic Review and Bibliometric Analysis. *Data* **2023**, *8*, 32. https://doi.org/10.3390/ data8020032

Academic Editors: María del Carmen Valls Martínez, José-María Montero and Pedro Antonio Martín Cervantes

Received: 13 December 2022 Revised: 19 January 2023 Accepted: 22 January 2023 Published: 30 January 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

relationships simultaneously through visualization and model validation. It is more suitable for complex models compared to the traditional method such as Multiple Linear Regression and Analysis of Variance (ANOVA). CB-SEM and PLS-SEM have their own strengths and weaknesses depending on the data structures and assumptions of the methods.

While in the panel survey data, a method based on the CB-SEM framework known as Latent Growth Curve Model (LGCM) is commonly being used compared to the PLS-SEM. The LGCM has gained its popularity largely in behavioral sciences research. This method is widely used in many areas including social behavioral research, psychology, clinical, developmental, educational research, learning and memory, and personality [11–16]. The LGCM has the advantage of analyzing the developmental trajectory of a single person and capturing individual variations over time. This means the method can assess the changes in intra-individual (within the individual) as well as inter-individual (between individuals) variation. It can also identify the important predictor variables that contribute to the individual's growth change over time. Although LGCM listed several advantages, this method can still be improved as highlighted by [11,17,18]. Among the issues concerned are statistical assumptions, reliable sample size, number of repeated measures, and missing data. Despite its limitation, this method is still the choice of researchers for analyzing panel survey data compared to the PLS-SEM.

On the other hand, PLS-SEM only gained popularity in analyzing cross-sectional survey data but not in panel survey data. In a cross-sectional survey study, the PLS-SEM showed good performance in handling non-normal data and small sample size. According to [9], PLS-SEM showed higher robustness in situations of non-normal data and small sample size. It also shows a better result with a small sample size when a model has many constructs and a large number of items [19–21]. However, the use of PLS-SEM in panel survey data seems to be seemingly underrated as cross-sectional survey data, even though the PLS-SEM is good in handling several highlighted issues such as statistical assumptions and reliable sample size in LGCM. The previous approaches for panel survey data using PLS-SEM were unable to establish one structural model to represent the whole changes in the repeated measure. Current approaches also cannot capture the individual trajectory, mean of the trajectory of the sample or entire group, the evaluation of individual differences in trajectories, and assess the potential incorporation of predictors of individual differences in trajectories. A review of the previous approaches of these path analyses in longitudinal studies does not consider systematic literature review methodology [22]. Thus, there is a need to review the current and existing PLS-SEM approaches using SLR and Bibliometric analysis for panel survey data in identifying the gap in existing methods for improvement.

Hence, this study aimed to explore 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. It focuses on answering the following research questions; (i) What is the distribution and trend of LGCM and PLS-SEM in a panel survey study? (ii) What are the reasons for the lack of application of PLS-SEM in panel survey data? and (iii) What is the existing framework or procedure of PLS-SEM in analyzing the panel survey data? This study employs the integrated bibliometric analysis and systematic review because the way of reviewing the existing literature is more systematic, and more comprehensive compared to the classical literature review [23–25]. Through a systematic review, further investigation and identification of the reasons for the lack of application for PLS-SEM in panel survey data can be discovered. Exploration of the existing framework or procedure of PLS-SEM could help the researcher to identify the method for improvement in analyzing the panel survey data.

#### **2. Related Work**

#### *2.1. Panel Survey Data*

A panel survey is a type of survey method that involves the process of gathering data from the same sample over a period of time. It is one of the longitudinal study types that is conducted over an extended period of time. The data collected from this panel survey are referred to as panel survey data. Panel survey data are commonly used to measure the behavior of people over time including their thoughts, attitudes, feelings, emotions, and many more [26–28]. It can measure the changes in behavior over time and examine the factors that influence that change. In the context of statistical methods, the LGCM and PLS-SEM are two methods that are used for analyzing the panel survey data. These two methods can handle this type of data since both can assess the measurement model (reliability and validity) and structural model. This is because these methods used Structural Equation Modeling as a basis of the framework and followed its criteria.

#### *2.2. Latent Growth Curve Model*

The latent Growth Curve Model (LGCM) created by [29,30] has grown to be a better method for addressing issues about individual behavior change and assessing the factors that contributed to the change simultaneously. The LGCM is a combination of the growth curve model (GCM) and structural equation modeling (CB-SEM). According to [31], the LGCM is a special case of confirmatory factor analysis (CFA) in CB-SEM and followed all underlying assumptions of CB-SEM. The growth of LGCM has become more popular in panel survey study since it can measure the changes in individuals and groups (known as trajectory) over time. Furthermore, it can also assess the factors that influence the trajectory.
