Author
Xinyuan Song
Other affiliations: Sun Yat-sen University
Bio: Xinyuan Song is an academic researcher from The Chinese University of Hong Kong. The author has contributed to research in topics: Latent variable & Structural equation modeling. The author has an hindex of 32, co-authored 175 publications receiving 3561 citations. Previous affiliations of Xinyuan Song include Sun Yat-sen University.
Papers published on a yearly basis
Papers
More filters
TL;DR: The conclusion is: for data that are normally distributed, the Bayesian approach can be used with small sample sizes, whilst ML cannot.
Abstract: The main objective of this article is to investigate the empirical performances of the Bayesian approach in analyzing structural equation models with small sample sizes. The traditional maximum likelihood (ML) is also included for comparison. In the context of a confirmatory factor analysis model and a structural equation model, simulation studies are conducted with the different magnitudes of parameters and sample sizes n = da, where d = 2, 3, 4 and 5, and a is the number of unknown parameters. The performances are evaluated in terms of the goodness-of-fit statistics, and various measures on the accuracy of the estimates. The conclusion is: for data that are normally distributed, the Bayesian approach can be used with small sample sizes, whilst ML cannot.
378 citations
TL;DR: In this article, the authors considered a semivarying coefficient model which is an extension of the varying coefficient model, and developed procedures for estimation of the linear part and the nonparametric part and their associated statistical properties.
267 citations
Book•
17 Sep 2012
TL;DR: Basic and Advanced Bayesian Structural Equation Modeling introduces basic and advanced SEMs for analyzing various kinds of complex data, such as ordered and unordered categorical data, multilevel data, mixture data, longitudinal data, highly non-normal data, as well as some of their combinations.
Abstract: Basic and Advanced Bayesian Structural Equation Modeling introduces basic and advanced SEMs for analyzing various kinds of complex data, such as ordered and unordered categorical data, multilevel data, mixture data, longitudinal data, highly non-normal data, as well as some of their combinations. In addition, Bayesian semiparametric SEMs to capture the true distribution of explanatory latent variables are introduced, whilst SEM with a nonparametric structural equation to assess unspecified functional relationships among latent variables are also explored.
168 citations
TL;DR: Change in mood in the postacute phase of stroke recovery is the most significant determinant of change in HRQOL and more attention should be paid to the detection and management of poststroke depression.
Abstract: Background and Purpose— For the survivors, activities of daily living, handicap, and depression have a significant impact on health-related quality of life (HRQOL). How the dynamic changes of these variables relate to HRQOL over time in the subacute phase of stroke recovery has not been investigated. The objective of this study was to study longitudinal behaviors of HRQOL of the stroke survivors in relation to the changes in activities of daily living, handicap, and depression after stroke. Methods— This was a prospective cohort study of first disabling patients with stroke. Subjects were interviewed at 3, 6, and 12 months after stroke for modified Barthel Index, London Handicap Scale, Geriatric Depression Scale, and the World Health Organization Quality of Life questionnaire (abbreviated Hong Kong version). A latent curve model was developed to analyze how the dynamic changes in activities of daily living, handicap, and depressive mood related to the changes in HRQOL. Results— Two hundred forty-seven of ...
98 citations
TL;DR: This article reviews, elaborates and compares several approaches for analyzing nonlinear models with interaction and/or quadratic effects, and finds that whilst the Bayesian and the exact ML approaches produce satisfactory results in all the settings under consideration, they can only produce reasonable results in simple models with large sample sizes.
Abstract: Various approaches using the maximum likelihood (ML) option of the LISREL program and products of indicators have been proposed to analyze structural equation models with non-linear latent effects on the basis of Kenny and Judd's formulation. Recently, some methods based on the Bayesian approach and the exact ML approaches have been developed. This article reviews, elaborates and compares several approaches for analyzing nonlinear models with interaction and/or quadratic effects. A total of four approaches are examined, including the product indicator ML approaches proposed by Jaccard and Wan (1995) and Joreskog and Yang (1996), a Bayesian approach and an exact ML approach. The empirical performances of these approaches are assessed using simulation studies in terms of their capabilities in producing reliable parameter and standard error estimates. It is found that whilst the Bayesian and the exact ML approaches produce satisfactory results in all the settings under consideration, and are in general very reliable; the product indicator ML approaches can only produce reasonable results in simple models with large sample sizes.
84 citations
Cited by
More filters
3,152 citations
TL;DR: This work presents an integrative 2-level MSEM mathematical framework that subsumes new and existing multilevel mediation approaches as special cases and uses several applied examples to illustrate the flexibility of this framework.
Abstract: Several methods for testing mediation hypotheses with 2-level nested data have been proposed by researchers using a multilevel modeling (MLM) paradigm. However, these MLM approaches do not accommodate mediation pathways with Level-2 outcomes and may produce conflated estimates of between- and within-level components of indirect effects. Moreover, these methods have each appeared in isolation, so a unified framework that integrates the existing methods, as well as new multilevel mediation models, is lacking. Here we show that a multilevel structural equation modeling (MSEM) paradigm can overcome these 2 limitations of mediation analysis with MLM. We present an integrative 2-level MSEM mathematical framework that subsumes new and existing multilevel mediation approaches as special cases. We use several applied examples and accompanying software code to illustrate the flexibility of this framework and to show that different substantive conclusions can be drawn using MSEM versus MLM.
2,595 citations
1,484 citations
TL;DR: A review of recent papers suggests that ecological theory is rarely explicitly considered as mentioned in this paper, and that current theory and results support species responses to environmental variables to be unimodal and often skewed though process-based theory is often lacking.
1,358 citations
01 Jan 1996
TL;DR: Exploring and identifying structure is even more important for multivariate data than univariate data, given the difficulties in graphically presenting multivariateData and the comparative lack of parametric models to represent it.
Abstract: Exploring and identifying structure is even more important for multivariate data than univariate data, given the difficulties in graphically presenting multivariate data and the comparative lack of parametric models to represent it. Unfortunately, such exploration is also inherently more difficult.
920 citations