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Book ChapterDOI

Evaluating Model Fit

Mary C. Hill, +1 more
- pp 93-123
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The article was published on 2005-12-19. It has received 1788 citations till now. The article focuses on the topics: Method of mean weighted residuals.

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Citations
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Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures.

TL;DR: Some guidelines that should help applied researchers to evaluate the adequacy of a given structural equation model are provided.
Journal ArticleDOI

An assessment of the use of partial least squares structural equation modeling in marketing research

TL;DR: An extensive search in the 30 top ranked marketing journals allowed us to identify 204 PLS-SEM applications published in a 30-year period (1981 to 2010), and a critical analysis of these articles addresses the following key methodological issues: reasons for using PLS, data and model characteristics, outer and inner model evaluations, and reporting.
Journal ArticleDOI

An examination of the effects of motivation and satisfaction on destination loyalty: a structural model

TL;DR: In this paper, an integrated approach to understand tourist motivation and attempts to extend the theoretical and empirical evidence on the causal relationships among the push and pull motivations, satisfaction, and destination loyalty is presented.
Journal ArticleDOI

A Brief Guide to Structural Equation Modeling

TL;DR: In this article, the authors present a consumer's guide to structural equation modeling (SEM) practice and principles by Martens and by Quintana and Maxwell, using an example derived from theory and research on vocational psychology.
Journal ArticleDOI

Positive Youth Development, Participation in Community Youth Development Programs, and Community Contributions of Fifth-Grade Adolescents: Findings From the First Wave Of the 4-H Study of Positive Youth Development

TL;DR: The 4-H Study of Positive Youth Development (PYD) as mentioned in this paper, a longitudinal investigation of a diverse sample of 1,700 fifth graders and 1,117 of their parents, tests developmental contextual ideas linking PYD, youth contributions, and participation in community youth development (YD) programs.
References
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Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures.

TL;DR: Some guidelines that should help applied researchers to evaluate the adequacy of a given structural equation model are provided.
Journal ArticleDOI

An assessment of the use of partial least squares structural equation modeling in marketing research

TL;DR: An extensive search in the 30 top ranked marketing journals allowed us to identify 204 PLS-SEM applications published in a 30-year period (1981 to 2010), and a critical analysis of these articles addresses the following key methodological issues: reasons for using PLS, data and model characteristics, outer and inner model evaluations, and reporting.
Journal ArticleDOI

A Brief Guide to Structural Equation Modeling

TL;DR: In this article, the authors present a consumer's guide to structural equation modeling (SEM) practice and principles by Martens and by Quintana and Maxwell, using an example derived from theory and research on vocational psychology.
Journal ArticleDOI

Positive Youth Development, Participation in Community Youth Development Programs, and Community Contributions of Fifth-Grade Adolescents: Findings From the First Wave Of the 4-H Study of Positive Youth Development

TL;DR: The 4-H Study of Positive Youth Development (PYD) as mentioned in this paper, a longitudinal investigation of a diverse sample of 1,700 fifth graders and 1,117 of their parents, tests developmental contextual ideas linking PYD, youth contributions, and participation in community youth development (YD) programs.
Journal ArticleDOI

Effect of the Number of Variables on Measures of Fit in Structural Equation Modeling.

TL;DR: In this article, the effect of the number of variables on model fit in structural equation modeling is investigated, and it is shown that root mean squared error of approximation (RMSEA) tends to improve regardless of the type of specification error.
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