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Cited by 4,653Psychometric Methodologies and TestingStatistical Methods and Bayesian InferenceStatistical Methods and Applications

Structural Equation Modeling With AMOS: Basic Concepts, Applications, and Programming, Third Edition

Byrne, Barbara M.·2016·Routledge eBooks
DOI 10.4324/9781315757421
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Abstract

This bestselling text provides a practical guide to structural equation modeling (SEM) using the Amos Graphical approach. Using clear, everyday language, the text is ideal for those with little to no exposure to either SEM or Amos. The author reviews SEM applications based on actual data taken from her own research. Each chapter "walks" readers through the steps involved (specification, estimation, evaluation, and post hoc modification) in testing a variety of SEM models. Accompanying each application is: an explanation of the issues addressed and a schematic presentation of hypothesized model structure; Amos input and output with interpretations; use of the Amos toolbar icons and pull-down menus; and data upon which the model application was based, together with updated references pertinent to the SEM model tested. Thoroughly updated throughout, the new edition features: All new screen shots featuring Amos Version 23. Descriptions and illustrations of Amos’ new Tables View format which enables the specification of a structural model in spreadsheet form. Key concepts and/or techniques that introduce each chapter. Alternative approaches to model analyses when enabled by Amos thereby allowing users to determine the method best suited to their data. Provides analysis of the same model based on continuous and categorical data (Ch. 5) thereby enabling readers to observe two ways of specifying and testing the same model as well as compare results. All applications based on the Amos graphical mode interface accompanied by more "how to" coverage of graphical techniques unique to Amos. More explanation of key procedures and analyses that address questions posed by readers All application data files are available at www.routledge.com/9781138797031. The two introductory chapters in Section 1 review the fundamental concepts of SEM methodology and a general overview of the Amos program. Section 2 provides single-group analyses applications including two first-order confirmatory factor analytic (CFA) models, one second-order CFA model, and one full latent variable model. Section 3 presents multiple-group analyses applications with two rooted in the analysis of covariance structures and one in the analysis of mean and covariance structures. Two models that are increasingly popular with SEM practitioners, construct validity and testing change over time using the latent growth curve, are presented in Section 4. The book concludes with a review of the use of bootstrapping to address non-normal data and a review of missing (or incomplete) data in Section 5. An ideal supplement for graduate level courses in psychology, education, business, and social and health sciences that cover the fundamentals of SEM with a focus on Amos, this practical text continues to be a favorite of both researchers and practitioners. A prerequisite of basic statistics through regression analysis is recommended but no exposure to either SEM or Amos is required.

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  • No deviations from the normal distribution were found for the PSQ-Op items considering skewness (Sk < 3.0) and kurtosis (Ku ≤ 7.0) absolute values.

    “No deviations from the normal distribution were found considering skewness (Sk, < 3.0) and kurtosis (Ku, ≤ 7.0) absolute values (Byrne, 2016).”

    From Burnout and Stress Measurement in Police Officers: Literature Review and a Study With the Operational Police Stress Questionnaire (2020) · cites this paper as Byrne, 2016

  • Model 1 had mixed fit: good fit by Bollen-Stine bootstrap (p = .072), RMSEA (.049, 90% CI [.007, .091]), CFI (.997) and SRMR (.012), but poor fit by chi-square (χ2 = 8.12, df = 3, p = .044), which is sensitive to large sample sizes.

    “Model fit statistics were mixed, with good fit indicated by the Bollen‐Stine bootstrap (p = .072), RMSEA (.049, 90% CI [.007, .091]), CFI (.997) and SRMR (.012). Chi‐square was significant (χ 2 = 8.12, df = 3, p = .044) indicating poor fit, but this statistic is sensitive to large sample sizes (Byrne, 2016).”

    From Associations between psychological inflexibility processes, pre‐sleep arousal and sleep quality. (2025) · cites this paper as Byrne, 2016

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Authors
Byrne, B. M.
Corresponding author
Byrne, Barbara M.
Venue
Routledge eBooks
Publisher
Routledge
Published
2016-06-10
DOI
10.4324/9781315757421
OpenAlex
W4297983578
Cited by
4,653
References
0
Citations by year
2026: 760 · 2025: 809 · 2024: 600 · 2023: 514 · 2022: 464 · 2021: 378 · 2020: 319 · 2019: 246 · 2018: 163 · 2017: 90 · 2016: 46 · 2015: 38 · 2014: 46 · 2013: 33 · 2012: 30
FWCI
79.07
Type
book
Language
en
Open access
closed
Locations
Routledge eBooks (publishedVersion)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Keywords
structural equation modeling, confirmatory factor analysis, latent growth curve models, construct validity, latent variable models, multigroup analysis, bootstrapping, missing data, categorical data, second-order factor model, model modification, non-normal data

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