New developments in latent variable panel analyses of longitudinal data

2007 ◽  
Vol 31 (4) ◽  
pp. 357-365 ◽  
Author(s):  
Todd D. Little ◽  
Kristopher J. Preacher ◽  
James P. Selig ◽  
Noel A. Card
Keyword(s):  
Structural Equation Modeling ◽  
Longitudinal Data ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Null Model ◽  
Factorial Invariance ◽  
Equation Modeling ◽  
New Developments ◽  
Mediated Effects

We review fundamental issues in one traditional structural equation modeling (SEM) approach to analyzing longitudinal data — cross-lagged panel designs. We then discuss a number of new developments in SEM that are applicable to analyzing panel designs. These issues include setting appropriate scales for latent variables, specifying an appropriate null model, evaluating factorial invariance in an appropriate manner, and examining both direct and indirect (mediated), effects in ways better suited for panel designs. We supplement each topic with discussion intended to enhance conceptual and statistical understanding.

scholarly journals Structural Equation Modeling for the Effect of Main Factors on Abortion Issue

2019 ◽  
Vol 7 (1) ◽  
pp. 1-13
Author(s):  
Aras Jalal Mhamad ◽  
Renas Abubaker Ahmed
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Close Contact ◽  
Maternity Hospital ◽  
Equation Modeling ◽  
Research Model ◽  
Statistical Tools ◽  
Main Factors

       Based on medical exchange and medical information processing theories with statistical tools, our study proposes and tests a research model that investigates main factors behind abortion issue. Data were collected from the survey of Maternity hospital in Sulaimani, Kurdistan-Iraq. Structural Equation Modelling (SEM) is a powerful technique as it estimates the causal relationship between more than one dependent variable and many independent variables, which is ability to incorporate quantitative and qualitative data, and it shows how all latent variables are related to each other. The dependent latent variable in SEM which have one-way arrows pointing to them is called endogenous variable while others are exogenous variables. The structural equation modeling results reveal is underlying mechanism through which statistical tools, as relationship between factors; previous disease information, food and drug information, patient address, mother’s information, abortion information, which are caused abortion problem. Simply stated, the empirical data support the study hypothesis and the research model we have proposed is viable. The data of the study were obtained from a survey of Maternity hospital in Sulaimani, Kurdistan-Iraq, which is in close contact with patients for long periods, and it is number one area for pregnant women to obtain information about the abortion issue. The results shows arrangement about factors effectiveness as mentioned at section five of the study. This gives the conclusion that abortion problem must be more concern than the other pregnancy problem.

scholarly journals Regularized Structural Equation Modeling with Stability Selection

2020 ◽  
Author(s):  
Xiaobei Li ◽  
Ross Jacobucci
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Equation Modeling ◽  
High Dimensional ◽  
Free Lunch ◽  
Stability Selection ◽  
Selection Operator ◽  
No Free Lunch

Regularization methods such as the least absolute shrinkage and selection operator (LASSO) are commonly used in high dimensional data to achieve sparser solutions. They are also becoming increasingly popular in social and behavioral research. Recently methods such as regularized structural equation modeling (SEM) and penalized likelihood SEM have been proposed, trying to transfer the benefits of regularization to models with latent variables involved. However, some drawbacks of the LASSO such as high false positive rates (FPRs) and inconsistency in selection results persist at the same time. We propose the use of stability selection (Meinshausen & Bu ̈hlmann, 2010) as a mechanism to overcome these limitations, demonstrating simulation conditions in which it improves performance, and simulation conditions in which it does not. In this paper, we point out that there is no free lunch, and researchers should be aware of those problems when applying regularization to latent variable models, concluding with an empirical example and further discussion of the application of regularization to SEM.

scholarly journals Structural Equation Modeling Multi-group of Science Process Skills and Cognitive in PjBL Integrated STEAM Learning

2021 ◽  
Vol 9 (3) ◽  
pp. 512-527
Author(s):  
Anik Anekawati* ◽  
Jefri Nur Hidayat ◽  
Nabila Abdullah ◽  
Helliyatul Matlubah
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Significance Test ◽  
Project Based Learning ◽  
Partial Least Square ◽  
Equation Modeling ◽  
Process Skills ◽  
Science Process Skills

In Science, students tend to use the ability, which is dominantly controlled by their left-hemisphere brain. This study explores how science process skills (SPS) affect cognitive learning achievement (CLA) of dominantly right-brained and left-brained students. By applying project-based learning on the topic that integrates STEAM elements, this research examines the differences of the effects among those groups. The respondents were 32 8th-grade students from two randomly selected intact classes. This study employed a test to measure exogenous (SPS) and endogenous (CLA) latent variables. The partial least square - structural equation modeling (PLS-SEM) and multi-group PLS-SEM were employed to analyze the results. The evaluation of the outer model shows that both latent variables were valid and reliable. The factor loading value for all indicators of each latent variable was over 0.7. The cross-loading value indicates a higher correlation between the latent variable and its indicators compared to the other variables' indicators. The composite reliability and Cronbach's alpha values were over 0.7. The significance test shows that all indicators of each latent variable were valid. The evaluation of the inner model through the significance test (α=5%) suggests that the science process skill influenced CLA with a coefficient of 0.907. Meanwhile, the 0.822 R-square value demonstrates the variability of the SPS can explain the variability of CLA of 82.2%. The multi-group-SEM test reveals a difference in the effect of SPS toward CLA among dominantly right-brained and left-brained students. While the path coefficient for the former was 0.94, the latter was 0.881.

scholarly journals Latent Variable Structural Equation Modeling: A Flexible Tool for Establishing Validity and More

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Drew Altschul
Keyword(s):  
Social Sciences ◽  
Structural Equation Modeling ◽  
Construct Validity ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Equation Modeling ◽  
Statistical Techniques ◽  
Flexible Tool ◽  
The Past

Petrinovich highlighted many salient issues in the behavioral and social sciences that are of concern to this day, such as insufficient attention to construct validity. Structural equation modeling, particularly with regard to latent variables, is introduced and discussed in this context. Though conceptual issues remain, analytic and statistical techniques have made immense strides in the past three decades since the article was written, and properly used, offer solutions to many problems Petrinovich identified.

Multilevel Structural Equation Modeling for Intensive Longitudinal Data: A Practical Guide for Personality Researchers

2019 ◽  
Author(s):  
Gentiana Sadikaj ◽  
Aidan G.C. Wright ◽  
David Dunkley ◽  
David Zuroff ◽  
D.S. Moskowitz
Keyword(s):  
Structural Equation Modeling ◽  
Longitudinal Data ◽  
Latent Variable ◽  
Structural Equation ◽  
Longitudinal Research ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Intensive Longitudinal Data ◽  
Mixed Effects Modeling ◽  
Multilevel Structural Equation

Intensive longitudinal research designs are increasingly used to study personality processes. The resulting data can be highly informative in ways that other data cannot, but these data also pose statistical challenges. Most often a multilevel or mixed effects modeling approach is adopted which is appropriate but may not be optimal. Surprisingly little attention is given to reliability of measurement, and the models often lack adequate complexity to test theoretical questions of interest. These limitations can be addressed with multilevel structural equation modeling (MSEM), which weds the ability to deal with nested data structures with the strengths of structural equation modeling (e.g., latent variable models, multiple outcomes and mediators). This article provides a gentle introduction to MSEM for personality researchers. Following an initial review of the relevant challenges facing researchers interested in studying personality using intensive longitudinal data, basic issues in MSEM are summarized, and a series of example models are presented. The online supplementary material provides Mplus syntax for the models presented.

scholarly journals Multiple skills underlie arithmetic performance: A large-scale structural equation modeling analysis

2017 ◽  
Vol 3 (2) ◽  
pp. 496-515 ◽  
Author(s):  
Sarit Ashkenazi ◽  
Sarit Silverman
Keyword(s):  
Structural Equation Modeling ◽  
Direct Effect ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Large Scale ◽  
Mathematics Performance ◽  
Equation Modeling ◽  
Theoretical Approaches ◽  
Wide Range

Current theoretical approaches point to the importance of several cognitive skills not specific to mathematics for the etiology of mathematics disorders (MD). In the current study, we examined the role of many of these skills, specifically: rapid automatized naming, attention, reading, and visual perception, on mathematics performance among a large group of college students (N = 1,322) with a wide range of arithmetic proficiency. Using factor analysis, we discovered that our data clustered to four latent variables 1) mathematics, 2) perception speed, 3) attention and 4) reading. In subsequent structural equation modeling, we found that the latent variable perception speed had a strong and meaningful effect on mathematics performance. Moreover, sustained attention, independent from the effect of the latent variable perception speed, had a meaningful, direct effect on arithmetic fact retrieval and procedural knowledge. The latent variable reading had a modest effect on mathematics performance. Specifically, reading comprehension, independent from the effect of the latent variable reading, had a meaningful direct effect on mathematics, and particularly on number line knowledge. Attention, tested by the attention network test, had no effect on mathematics, reading or perception speed. These results indicate that multiple factors can affect mathematics performance supporting a heterogeneous approach to mathematics. These results have meaningful implications for the diagnosis and intervention of pure and comorbid learning disorders.

scholarly journals PLS-based SEM Algorithms: The Good Neighbor Assumption, Collinearity, and Nonlinearity

2015 ◽  
Vol 7 (2) ◽  
pp. 113-130 ◽  
Author(s):  
Ned Kock
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Model ◽  
Measurement Model ◽  
Equation Modeling ◽  
Systems Research ◽  
Inner Model ◽  
Good Neighbor

The partial least squares (PLS) method has been extensively used in information systems research, particularly in the context of PLS-based structural equation modeling (SEM). Nevertheless, our understanding of PLS algorithms and their properties is still progressing. With the goal of improving that understanding, we provide a discussion on the treatment of reflective and formative latent variables in the context of three main algorithms used in PLS-based SEM analyses –PLS regression, PLS Mode A, and PLS Mode B. Two illustrative examples based on actual data are presented. It is shown that the “good neighbor” assumption underlying modes A and B has several consequences, including the following: the inner model influences the outer model in a way that increases inner model coefficients of association and collinearity levels in tandem, and makes measurement model analysis tests dependent on structural model links; instances of Simpson’s paradox tend to occur with Mode B at the latent variable level; and nonlinearity is improperly captured. In spite of these mostly detrimental outcomes, it is argued that modes A and B may have important and yet unexplored roles to play in PLS-based structural equation modeling analyses.

Meta-Accuracy and Perceived Reciprocity From the Perception-Meta-Perception Social Relations Model

2019 ◽  
Vol 50 (1) ◽  
pp. 24-37
Author(s):  
Ben Porter ◽  
Camilla S. Øverup ◽  
Julie A. Brunson ◽  
Paras D. Mehta
Keyword(s):  
Structural Equation Modeling ◽  
Social Relations ◽  
Latent Variables ◽  
Structural Equation ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Complete Understanding ◽  
Social Relations Model ◽  
Flexible Framework ◽  
Multilevel Structural Equation

Abstract. Meta-accuracy and perceptions of reciprocity can be measured by covariances between latent variables in two social relations models examining perception and meta-perception. We propose a single unified model called the Perception-Meta-Perception Social Relations Model (PM-SRM). This model simultaneously estimates all possible parameters to provide a more complete understanding of the relationships between perception and meta-perception. We describe the components of the PM-SRM and present two pedagogical examples with code, openly available on https://osf.io/4ag5m . Using a new package in R (xxM), we estimated the model using multilevel structural equation modeling which provides an approachable and flexible framework for evaluating the PM-SRM. Further, we discuss possible expansions to the PM-SRM which can explore novel and exciting hypotheses.

Building a Model of Family Caregiving for Children with Emotional Disorders

1997 ◽  
Vol 5 (3) ◽  
pp. 138-148 ◽  
Author(s):  
Thomas P. Mcdonald ◽  
Thomas K. Gregoire ◽  
John Poertner ◽  
Theresa J. Early
Keyword(s):  
Structural Equation Modeling ◽  
Emotional Disorders ◽  
Problem Behaviors ◽  
Family Caregiving ◽  
Latent Variables ◽  
Structural Equation ◽  
Empirical Literature ◽  
Equation Modeling ◽  
Special Challenge ◽  
And Coping

In this article we describe the results of an ongoing effort to better understand the caregiving process in families of children with severe emotional problems. We make two assumptions. First, we assume that these families are essentially like other families but are faced with a special challenge in raising and caring for their special children while at the same time performing the multiple tasks and demands faced by all families. Second, we assume that public policy and programs must be supportive of the care of these children in their own homes and communities whenever possible. The purpose of this article is to present a model of family caregiving that draws broadly from available theory and empirical literature in multiple fields and to subject this model to empirical testing. We use structural equation modeling with latent variables to estimate an empirical model based on the theoretical model. Results of the model testing point to the importance of the child's external problem behaviors and the family's socioeconomic status and coping strategies as determinants of caregiver stress. Other findings highlight difficulties in measuring and modeling the complex mediating process, which includes formal and informal supports, perceptions, and coping behaviors. The use of structural equation modeling can benefit our efforts to support families by making explicit our theories about the important dimensions of this process and the relationship between these dimensions, which can then be subjected to measurement and validation.

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