Interpreting LISREL Estimates of Explained Variance in Nonrecursive Structural Equation Models

1986 ◽  
Vol 23 (2) ◽  
pp. 164-168 ◽  
Author(s):  
Jesse E. Teel ◽  
William O. Bearden ◽  
Subhash Sharma
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Feedback Loops ◽  
Explained Variance ◽  
Computational Procedures ◽  
Variance Estimates

The LISREL method of computing explained variance for nonrecursive structural equation models is compared with alternative computational procedures. Explained variance estimates produced by LISREL are shown to be influenced by equation disturbance terms in nonrecursive models. The analysis demonstrates the need for caution in interpreting explained variance estimates in models with reciprocal feedback loops and/or errors correlated across equations.

scholarly journals ICT-related variables as predictors of ICT literacy beyond intelligence and prior achievement

2021 ◽  
Author(s):  
Martin Senkbeil
Keyword(s):  
Information And Communication Technologies ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Ict Literacy ◽  
Cross Sectional ◽  
Motivational Constructs ◽  
Self Confidence ◽  
Relative Contribution ◽  
Prior Achievement ◽  
Explained Variance

AbstractThis study examined the incremental validity of different information and communication technologies (ICT)-related person characteristics over and above intelligence and and prior achievement when predicting ICT literacy across a period of three years. Relative weights analyses were performed to determine the relative contribution of each predictor towards explaining variance in ICT literacy. We used data from German NEPS that tracks representative samples of German students across their school careers. The sample consisted of 14,436 fifteen-year-old German students who provided self-reports on several ICT-related variables: self-confidence, usage motives, breadth of usage, access, experience, usage at home and at school. Data were analyzed cross-sectionally and longitudinally with structural equation models and path analyses, respectively. Cross-sectionally, all ICT-related variables incrementally predicted ICT literacy after controlling for intelligenc (explained variance: 0.4%–14.1%). Longitudinally, ICT self-confidence, ICT-related usage motives, breadth of ICT usage, ICT usage at school, and ICT experience incrementally predict ICT literacy after controlling for intelligence and prior achievement.three years later (explained variance: 0.3%–8.1%). Relative weights providing estimates of relative importance of each predictor showed that intelligence (cross-sectional) and prior achievement and intelligence, respectively (longitudinal) explained the largest portion of variance in ICT literacy, followed by ICT self-confidence, and ICT usage motives as the strongest ICT-related variables. These results emphasize that ICT-related motivational constructs play an important role in the development of ICT literacy.

Measuring the Big Five in Self-Report and Other Ratings

2000 ◽  
Vol 16 (1) ◽  
pp. 31-43 ◽  
Author(s):  
Claudio Barbaranelli ◽  
Gian Vittorio Caprara
Keyword(s):  
Big Five ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Error Variance ◽  
The Self ◽  
Self Report ◽  
Sources Of Information ◽  
Third Person ◽  
Fit Indexes ◽  
Response Modes

Summary: The aim of the study is to assess the construct validity of two different measures of the Big Five, matching two “response modes” (phrase-questionnaire and list of adjectives) and two sources of information or raters (self-report and other ratings). Two-hundred subjects, equally divided in males and females, were administered the self-report versions of the Big Five Questionnaire (BFQ) and the Big Five Observer (BFO), a list of bipolar pairs of adjectives ( Caprara, Barbaranelli, & Borgogni, 1993 , 1994 ). Every subject was rated by six acquaintances, then aggregated by means of the same instruments used for the self-report, but worded in a third-person format. The multitrait-multimethod matrix derived from these measures was then analyzed via Structural Equation Models according to the criteria proposed by Widaman (1985) , Marsh (1989) , and Bagozzi (1994) . In particular, four different models were compared. While the global fit indexes of the models were only moderate, convergent and discriminant validities were clearly supported, and method and error variance were moderate or low.

Collective Efficacy and Organizational Commitment in an Italian City Hall

2009 ◽  
Vol 14 (4) ◽  
pp. 363-371 ◽  
Author(s):  
Laura Borgogni ◽  
Silvia Dello Russo ◽  
Laura Petitta ◽  
Gary P. Latham
Keyword(s):  
Organizational Commitment ◽  
Collective Efficacy ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Practical Significance ◽  
City Hall ◽  
Italian City ◽  
Public Sector Managers ◽  
The Individual ◽  
Level Group

Employees (N = 170) of a City Hall in Italy were administered a questionnaire measuring collective efficacy (CE), perceptions of context (PoC), and organizational commitment (OC). Two facets of collective efficacy were identified, namely group and organizational. Structural equation models revealed that perceptions of top management display a stronger relationship with organizational collective efficacy, whereas employees’ perceptions of their colleagues and their direct superior are related to collective efficacy at the group level. Group collective efficacy had a stronger relationship with affective organizational commitment than did organizational collective efficacy. The theoretical significance of this study is in showing that CE is two-dimensional rather than unidimensional. The practical significance of this finding is that the PoC model provides a framework that public sector managers can use to increase the efficacy of the organization as a whole as well as the individual groups that compose it.

A Cautionary Note on Incremental Fit Indices Reported by LISREL

Methodology ◽  
2005 ◽  
Vol 1 (2) ◽  
pp. 81-85 ◽  
Author(s):  
Stefan C. Schmukle ◽  
Jochen Hardt
Keyword(s):  
Factor Analysis ◽  
Maximum Likelihood ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Null Model ◽  
Likelihood Estimation ◽  
Parameter Estimates ◽  
Fit Indices ◽  
Cautionary Note ◽  
Confirmatory Factor

Abstract. Incremental fit indices (IFIs) are regularly used when assessing the fit of structural equation models. IFIs are based on the comparison of the fit of a target model with that of a null model. For maximum-likelihood estimation, IFIs are usually computed by using the χ2 statistics of the maximum-likelihood fitting function (ML-χ2). However, LISREL recently changed the computation of IFIs. Since version 8.52, IFIs reported by LISREL are based on the χ2 statistics of the reweighted least squares fitting function (RLS-χ2). Although both functions lead to the same maximum-likelihood parameter estimates, the two χ2 statistics reach different values. Because these differences are especially large for null models, IFIs are affected in particular. Consequently, RLS-χ2 based IFIs in combination with conventional cut-off values explored for ML-χ2 based IFIs may lead to a wrong acceptance of models. We demonstrate this point by a confirmatory factor analysis in a sample of 2449 subjects.

Forced Zero Cross-Loading Misspecifications in Measurement Component of Structural Equation Models

Methodology ◽  
2014 ◽  
Vol 10 (4) ◽  
pp. 138-152 ◽  
Author(s):  
Hsien-Yuan Hsu ◽  
Susan Troncoso Skidmore ◽  
Yan Li ◽  
Bruce Thompson
Keyword(s):  
Structural Equation ◽  
Structural Model ◽  
Structural Equation Models ◽  
Equation Model ◽  
Model Fit ◽  
Model Specification ◽  
Simulation Design ◽  
Measurement Models ◽  
Fit Statistics ◽  
Path Coefficients

The purpose of the present paper was to evaluate the effect of constraining near-zero parameter cross-loadings to zero in the measurement component of a structural equation model. A Monte Carlo 3 × 5 × 2 simulation design was conducted (i.e., sample sizes of 200, 600, and 1,000; parameter cross-loadings of 0.07, 0.10, 0.13, 0.16, and 0.19 misspecified to be zero; and parameter path coefficients in the structural model of either 0.50 or 0.70). Results indicated that factor pattern coefficients and factor covariances were overestimated in measurement models when near-zero parameter cross-loadings constrained to zero were higher than 0.13 in the population. Moreover, the path coefficients between factors were misestimated when the near-zero parameter cross-loadings constrained to zero were noteworthy. Our results add to the literature detailing the importance of testing individual model specification decisions, and not simply evaluating omnibus model fit statistics.

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