Structural Equation Modeling with Factors and Composites

2017 ◽  
Vol 13 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Ned Kock
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Factor Model ◽  
Common Factor ◽  
Equation Modeling ◽  
A Algorithm ◽  
Multivariate Statistical ◽  
Full Information Maximum Likelihood ◽  
Common Factor Model ◽  
Path Coefficients

Recent methodological developments building on partial least squares (PLS) techniques and related ideas have significantly contributed to bridging the gap between factor-based and composite-based structural equation modeling (SEM) methods. PLS-SEM is extensively used in the field of e-collaboration, as well as in many other fields where multivariate statistical analyses are employed. The author compares results obtained with four methods: covariance-based SEM with full information maximum likelihood (FIML), factor-based SEM with common factor model assumptions (FSEM1), factor-based SEM building on the PLS Regression algorithm (FSEM2), and PLS-SEM employing the Mode A algorithm (PLSA). The comparison suggests that FSEM1 yields path coefficients and loadings that are very similar to FIML's; and that FSEM2 yields path coefficients that are very similar to FIML's and loadings that are very similar to PLSA's.

Methodological research on partial least squares structural equation modeling (PLS-SEM)

2019 ◽  
Vol 29 (3) ◽  
pp. 407-429 ◽  
Author(s):  
Gohar F. Khan ◽  
Marko Sarstedt ◽  
Wen-Lung Shiau ◽  
Joseph F. Hair ◽  
Christian M. Ringle ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Factor Model ◽  
Common Factor ◽  
Knowledge Network ◽  
Equation Modeling ◽  
Content Type ◽  
Methodological Research

Purpose The purpose of this paper is to explore the knowledge infrastructure of methodological research on partial least squares structural equation modeling (PLS-SEM) from a network point of view. The analysis involves the structures of authors, institutions, countries and co-citation networks, and discloses trending developments in the field. Design/methodology/approach Based on bibliometric data downloaded from the Web of Science, the authors apply various social network analysis (SNA) and visualization tools to examine the structure of knowledge networks of the PLS-SEM domain. Specifically, the authors investigate the PLS-SEM knowledge network by analyzing 84 methodological studies published in 39 journals by 145 authors from 106 institutions. Findings The analysis reveals that specific authors dominate the network, whereas most authors work in isolated groups, loosely connected to the network’s focal authors. Besides presenting the results of a country level analysis, the research also identifies journals that play a key role in disseminating knowledge in the network. Finally, a burst detection analysis indicates that method comparisons and extensions, for example, to estimate common factor model data or to leverage PLS-SEM’s predictive capabilities, feature prominently in recent research. Originality/value Addressing the limitations of prior systematic literature reviews on the PLS-SEM method, this is the first study to apply SNA to reveal the interrelated structures and properties of PLS-SEM’s research domain.

On the Structure of Affect

2021 ◽  
Vol 229 (1) ◽  
pp. 24-37 ◽  
Author(s):  
Nadine Wedderhoff ◽  
Timo Gnambs ◽  
Oliver Wedderhoff ◽  
Tanja Burgard ◽  
Michael Bošnjak
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Factor Model ◽  
Self Report ◽  
Equation Modeling ◽  
Positive And Negative Affect ◽  
Correlation Matrices ◽  
The World ◽  
Best Fit ◽  
Cross National

Abstract. The Positive and Negative Affect Schedule (PANAS; Watson et al., 1988 ) is a popular self-report questionnaire that is administered all over the world. Though originally developed to measure two independent factors, different models have been proposed in the literature. Comparisons among alternative models as well as analyses concerning their robustness in cross-national research have left an inconclusive picture. Therefore, the present study evaluates the dimensionality of the PANAS and differences between English and translated versions of the PANAS using a meta-analytic structural equation modeling approach. Correlation matrices from 57 independent samples ( N = 54,043) were pooled across subsamples. For both English and non-English samples, a correlated two-factor model including correlated uniquenesses provided the best fit. However, measurement invariance analyses indicated differences in factor loadings between subsamples. Thus, cross-national application of the PANAS might only be justified if measurement equivalence was explicitly tested for the countries at hand.

A New Dimension of Organizational Justice: Procedural Voice

2009 ◽  
Vol 105 (2) ◽  
pp. 411-426 ◽  
Author(s):  
Denise Jepsen ◽  
John Rodwell
Keyword(s):  
Structural Equation Modeling ◽  
Organizational Justice ◽  
Latent Variable ◽  
Structural Equation ◽  
Factor Model ◽  
Model Fit ◽  
Equation Modeling ◽  
Wide Range

Dimensionality of the Colquitt justice measures was investigated across a wide range of service occupations. Structural equation modeling of data from 410 survey respondents found support for the 4-factor model of justice (procedural, distributive, interpersonal, and informational), although significant improvement of model fit was obtained by including a new latent variable, “procedural voice,” which taps employees' desire to express their views and feelings and influence results. The model was confirmed in a second sample ( N = 505) in the same organization six months later.

scholarly journals Validating the University Student Engagement Model through Structural Equation Modeling

2018 ◽  
Vol 5 (4) ◽  
pp. 318 ◽  
Author(s):  
Hafsa Mzee Mwita
Keyword(s):  
Structural Equation Modeling ◽  
Student Engagement ◽  
Structural Equation ◽  
Factor Model ◽  
University Student ◽  
Equation Modeling ◽  
Psychological Constructs ◽  
Confirmatory Factor ◽  
The University ◽  
Behavior And Cognition

<p><em>The main purpose </em><em>of this study is to investigate whether emotional, cognitive and behavioral engagements, represents three conceptually and empirically distinct psychological constructs when studied within the same domain. This paper reports part of the findings from a major study entitled “Predictors of Self-Handicapping Behavior among Muslim Students”. Testing for factorial equivalence of scores from a measuring instrument was carried-out through structural equation modeling by using AMOS version 16.</em><em> </em><em>Results of Confirmatory Factor Analysis of responses from 790 undergraduates prove that the SEM three factor model of University Student Engagement (USE) is empirically fit and reliable, which also supports the argument that emotion, behavior and cognition are the student engagement manifestations of an interrelated constellation of academic student engagement. </em></p>

Construct Validity of the Risk, Pain, and Injury Questionnaire

2005 ◽  
Vol 22 (4) ◽  
pp. 433-446 ◽  
Author(s):  
Stephan R. Walk ◽  
Lenny D. Wiersma
Keyword(s):  
Structural Equation Modeling ◽  
Construct Validity ◽  
West Coast ◽  
Content Validity ◽  
Structural Equation ◽  
Factor Model ◽  
Model Fit ◽  
Face Validity ◽  
Equation Modeling ◽  
Limited Support

Nixon’s (1994a; 1994b; 1996a; 1996b) research using a Risk, Pain, and Injury Questionnaire (RPIQ) is perhaps the most systematic in the risk, pain, and injury literature. The RPIQ is intended to measure the acceptance of dominant discourses on risk, pain, and injury among athletes and others. This article presents a face validity critique of the RPIQ and results of a subsequent content validity analysis based on a study of 171 athletes from a West Coast university. Structural equation modeling used to test Nixon’s original 3-factor model (M1) revealed poor model fit. Two alternate models (M2 and M3) tested reformulated subscale constructs and items. Whereas M2 demonstrated poor construct validity, limited support was found for items in M3. Further replications of this research are recommended.

Factor Replication of the Reduced Laffrey Health Conception Scale

2000 ◽  
Vol 8 (2) ◽  
pp. 105-116 ◽  
Author(s):  
Oi Saeng Hong ◽  
Sally L. Lusk ◽  
Laura Klem
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Factor Model ◽  
Covariance Matrices ◽  
Construction Workers ◽  
Equation Modeling ◽  
Factory Workers ◽  
Oblimin Rotation ◽  
Direct Oblimin

This study replicated the factor model for the Reduced Laffrey Health Conception Scale (RLHCS), which was originally developed by Laffrey (1986) and reduced by Lusk, Kerr, and Baer (1995). Two independent samples of construction workers (n - 697 and n = 510) were used. The samples were predominately Caucasian males (over 97%), with mean ages of 35 and 38 years, respectively. Principal components factor analysis with direct oblimin rotation and structural equation modeling were used to replicate factors and to test the equality of the three observed covariance matrices (factory workers and two groups of construction workers), respectively. Results replicated the two-factor structure (clinical health and overall wellness) found in the earlier study with factory workers (Lusk et al., 1995) and demonstrated factor in variance across different samples.

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