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Handbook of structural equation modeling
01 Jan 2012-
TL;DR: This work focuses on the implementation of Structural Equation Modeling in R with the sem and OpenMx Packages and on the development of scale construction and development models for this and other applications.
Abstract: Part 1. Background. R. Hoyle, Introduction and Overview. R. Matsueda, Key Advances in the History of Structural Equation Modeling. M. Ho, S. Stark, O. Chernyshenko, Graphical Representation of Structural Equation Models Using Path Diagrams. K. Bollen, R. Hoyle, Latent Variables in Structural Equation Modeling. J. Pearl, The Causal Foundations of Structural Equation Modeling. D. Bandalos, P. Gagne, Simulation Methods in Structural Equation Modeling. Part 2. Fundamentals. R. Kline, Assumptions in Structural Equation Modeling. R. Hoyle, Model Specification in Structural Equation Modeling. D. Kenny, S. Milan, Identification: A Nontechnical Discussion of a Technical Issue. P. Lei, Q. Wu, Estimation in Structural Equation Modeling. T. Lee, L. Cai, R. MacCallum, Power Analysis for Tests of Structural Equation Models. M. Edwards, R. Wirth, C. Houts, N. Xi, Categorical Data in the Structural Equation Modeling Framework. S. West, A. Taylor, W. Wu, Model Fit and Model Selection in Structural Equation Modeling. C. Chou, J. Huh, Model Modification in Structural Equation Modeling. L. Williams, Equivalent Models: Concepts, Problems, Alternatives. Part 3. Implementation. P. Malone, J. Lubansky, Preparing Data for Structural Equation Modeling: Doing Your Homework. J. Graham, D. Coffman, Structural Equation Modeling with Missing Data. G. Hancock, M. Liu, Bootstrapping Standard Errors and Data-Model Fit Statistics in Structural Equation Modeling. B. Byrne, Choosing Structural Equation Modeling Computer Software: Snapshots of LISREL, EQS, Amos, and Mplus. J. Fox, J. Byrnes, S. Boker, M. Neale, Structural Equation Modeling in R with the sem and OpenMx Packages. A. Boomsma, R. Hoyle, A. Panter, The Structural Equation Modeling Research Report. Part 4. Basic Applications. T. Brown, M. Moore, Confirmatory Factor Analysis. R. Millsap, M. Olivera-Aguilar, Investigating Measurement Invariance Using Confirmatory Factor Analysis. S. Green, M. Thompson, A Flexible Structural Equation Modeling Approach for Analyzing Means. J. Cheong, D. MacKinnon, Mediation/Indirect Effects in Structural Equation Modeling. H. Marsh, Z. Wen, B. Nagengast, K. Hau, Structural Equation Models of Latent Interaction. J. Biesanz, Autoregressive Longitudinal Models. T. Raykov, Scale Construction and Development Using Structural Equation Modeling. Part 5. Advanced Applications. J. Bovaird, N. Koziol, Measurement Models for Ordered-Categorical Indicators. S. Rabe-Hesketh, A. Skrondal, X. Zheng, Multilevel Structural Equation Modeling. M. Shiyko, N. Ram, K. Grimm, An Overview of Growth Mixture Modeling: A Simple Nonlinear Application in OpenMx. J. McArdle, Latent Curve Modeling of Longitudinal Growth Data. P. Wood, Dynamic Factor Models for Longitudinally Intensive Data: Description and Estimation via Parallel Factor Models of Cholesky Decomposition. D. Cole, Latent Trait-State Models. E. Ferrer, H. Song, Longitudinal Structural Models for Assessing Dynamics in Dyadic Interactions. S. Franic, C. Dolan, D. Borsboom, D. Boomsma, Structural Equation Modeling in Genetics. A. McIntosh, A. Protzner, Structural Equation Models of Imaging Data. D.Kaplan, S. Depaoli, Bayesian Structural Equation Modeling. M. Wall, Spatial Structural Equation Modeling. G. Marcoulides, M. Ing, Automated Structural Equation Modeling Strategies.
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TL;DR: Findings support the pivotal role of phonemic awareness as a predictor of individual differences in reading development and whether such a relationship is a causal one and the implications of research in this area for current approaches to the teaching of reading and interventions for children with reading difficulties.
Abstract: The authors report a systematic meta-analytic review of the relationships among 3 of the most widely studied measures of children's phonological skills (phonemic awareness, rime awareness, and verbal short-term memory) and children's word reading skills The review included both extreme group studies and correlational studies with unselected samples (235 studies were included, and 995 effect sizes were calculated) Results from extreme group comparisons indicated that children with dyslexia show a large deficit on phonemic awareness in relation to typically developing children of the same age (pooled effect size estimate: -137) and children matched on reading level (pooled effect size estimate: -057) There were significantly smaller group deficits on both rime awareness and verbal short-term memory (pooled effect size estimates: rime skills in relation to age-matched controls, -093, and reading-level controls, -037; verbal short-term memory skills in relation to age-matched controls, -071, and reading-level controls, -009) Analyses of studies of unselected samples showed that phonemic awareness was the strongest correlate of individual differences in word reading ability and that this effect remained reliable after controlling for variations in both verbal short-term memory and rime awareness These findings support the pivotal role of phonemic awareness as a predictor of individual differences in reading development We discuss whether such a relationship is a causal one and the implications of research in this area for current approaches to the teaching of reading and interventions for children with reading difficulties
865 citations
TL;DR: The HLQ covers 9 conceptually distinct areas of health literacy to assess the needs and challenges of a wide range of people and organisations and is likely to be useful in surveys, intervention evaluation, and studies of theneeds and capabilities of individuals.
Abstract: Health literacy has become an increasingly important concept in public health. We sought to develop a comprehensive measure of health literacy capable of diagnosing health literacy needs across individuals and organisations by utilizing perspectives from the general population, patients, practitioners and policymakers. Using a validity-driven approach we undertook grounded consultations (workshops and interviews) to identify broad conceptually distinct domains. Questionnaire items were developed directly from the consultation data following a strict process aiming to capture the full range of experiences of people currently engaged in healthcare through to people in the general population. Psychometric analyses included confirmatory factor analysis (CFA) and item response theory. Cognitive interviews were used to ensure questions were understood as intended. Items were initially tested in a calibration sample from community health, home care and hospital settings (N=634) and then in a replication sample (N=405) comprising recent emergency department attendees. Initially 91 items were generated across 6 scales with agree/disagree response options and 5 scales with difficulty in undertaking tasks response options. Cognitive testing revealed that most items were well understood and only some minor re-wording was required. Psychometric testing of the calibration sample identified 34 poorly performing or conceptually redundant items and they were removed resulting in 10 scales. These were then tested in a replication sample and refined to yield 9 final scales comprising 44 items. A 9-factor CFA model was fitted to these items with no cross-loadings or correlated residuals allowed. Given the very restricted nature of the model, the fit was quite satisfactory: χ
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WLSMV(866 d.f.) = 2927, p<0.000, CFI = 0.936, TLI = 0.930, RMSEA = 0.076, and WRMR = 1.698. Final scales included: Feeling understood and supported by healthcare providers; Having sufficient information to manage my health; Actively managing my health; Social support for health; Appraisal of health information; Ability to actively engage with healthcare providers; Navigating the healthcare system; Ability to find good health information; and Understand health information well enough to know what to do. The HLQ covers 9 conceptually distinct areas of health literacy to assess the needs and challenges of a wide range of people and organisations. Given the validity-driven approach, the HLQ is likely to be useful in surveys, intervention evaluation, and studies of the needs and capabilities of individuals.
794 citations
TL;DR: Under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results are shown, and guidelines on how to report on Bayesian statistics are provided.
Abstract: Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First, the ingredients underlying Bayesian methods are introduced using a simplified example. Thereafter, the advantages and pitfalls of the specification of prior knowledge are discussed. To illustrate Bayesian methods explained in this study, in a second example a series of studies that examine the theoretical framework of dynamic interactionism are considered. In the Discussion the advantages and disadvantages of using Bayesian statistics are reviewed, and guidelines on how to report on Bayesian statistics are provided.
540 citations
Cites background or methods from "Handbook of structural equation mod..."
...Advantages of Bayesian statistics over frequentist statistics are well documented in the literature (Jaynes, 2003; Kaplan & Depaoli, 2012, 2013; Kruschke, 2011a, 2011b; Lee & Wagenmakers, 2005; Van de Schoot, Verhoeven, & Hoijtink, 2012; Wagenmakers, 2007) and we will just highlight some of those…...
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...A complete discussion of Bayesian model evaluation is beyond the scope of this study; we refer the interested reader to Kaplan and Depaoli (2012, 2013)....
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...Of specific concern to substantive researchers, the Bayesian paradigm offers a very different view of hypothesis testing (e.g., Kaplan & Depaoli, 2012, 2013; Walker, Gustafson, & Frimer, 2007; Zhang, Hamagami, Wang, Grimm, & Nesselroade, 2007)....
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...…methods in popular software packages such as Amos (Arbuckle, 2006), Mplus v6 (Muth en & Muth en, 1998–2012; for the Bayesian methods in Mplus see Kaplan & Depaoli, 2012; Muth en & Asparouhov, 2012), WinBUGS (Lunn, Thomas, Best, & Spiegelhalter, 2000), and a large number of packages within the…...
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01 Jan 2013
TL;DR: In this paper, structural equation models (SEMs) and their role in causal analysis have been discussed and a variety of misunderstandings and myths about the nature of SEMs have emerged, and their repetition has led some to believe they are true.
Abstract: Causality was at the center of the early history of structural equation models (SEMs) which continue to serve as the most popular approach to causal analysis in the social sciences. Through decades of development, critics and defenses of the capability of SEMs to support causal inference have accumulated. A variety of misunderstandings and myths about the nature of SEMs and their role in causal analysis have emerged, and their repetition has led some to believe they are true. Our chapter is organized by presenting eight myths about causality and SEMs in the hope that this will lead to a more accurate understanding. More specifically, the eight myths are the following: (1) SEMs aim to establish causal relations from associations alone, (2) SEMs and regression are essentially equivalent, (3) no causation without manipulation, (4) SEMs are not equipped to handle nonlinear causal relationships, (5) a potential outcome framework is more principled than SEMs, (6) SEMs are not applicable to experiments with randomized treatments, (7) mediation analysis in SEMs is inherently noncausal, and (8) SEMs do not test any major part of the theory against the data. We present the facts that dispel these myths, describe what SEMs can and cannot do, and briefly present our critique of current practice using SEMs. We conclude that the current capabilities of SEMs to formalize and implement causal inference tasks are indispensible; its potential to do more is even greater.
495 citations
19 Sep 2017
326 citations