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Books on the topic 'Bayesian structural equation model'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

Lee, Sik-Yum. Basic and advanced structural equation models for medical and behavioural sciences. Hoboken: Wiley, 2012.

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2

Structural equation modeling: A Bayesian approach. Chichester, England: Wiley, 2007.

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3

Ghoshal, Sumantra. A structural equation model of scanning behavior of managers. Cambridge, Mass: Massachusetts Institute of Technology, Alfred P. Sloan School of Management, 1985.

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4

Hui-xin, Ke. Software system for the analysis of linear structural equation model. Fukuoka, Japan: Kyushu University, Research Institute of Fundamental Inforamtion Science, 1988.

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5

Jonsson, Fan Yang. Non-linear structural equation models: Simulation studies of the Kenny-Judd model. Uppsala, Sweden: Uppsala University, 1997.

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6

Mishler, William. What are the political consequences of trust?: A Russian structural equation model. Glasgow: University of Strathclyde, Centre for the Study of Public Policy, 2003.

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7

Amit, Gupta. Effect of service climate on service quality: Test of a model using structural equation modeling. Bangalore: Indian Institute of Management, 2002.

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8

Lee, Sik-Yum. Structural Equation Modeling: A Bayesian Approach. Wiley & Sons, Incorporated, John, 2007.

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9

Bollen, Kenneth A., Sophia Rabe‐Hesketh, and Anders Skrondal. Structural Equation Models. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0018.

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This article explains the use of factor analysis types of models to develop measures of latent concepts which were then combined with causal models of the underlying latent concepts. In particular, it offers an overview of the classic structural equation models (SEMs) when the latent and observed variables are continuous. Then it looks at more recent developments that include categorical, count, and other noncontinuous variables as well as multilevel structural equation models. The model specification, assumptions, and notation are covered. This is followed by addressing implied moments, identification, estimation, model fit, and respecification. The penetration of SEMs has been high in disciplines such as sociology, psychology, educational testing, and marketing, but lower in economics and political science despite the large potential number of applications. Today, SEMs have begun to enter the statistical literature and to re-enter biostatistics, though often under the name ‘latent variable models’ or ‘graphical models’.

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10

Miksza, Peter, and Kenneth Elpus. Structural Equation Modeling. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199391905.003.0014.

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This chapter presents structural equation modeling as a tool for conducting research regarding how collections of variables may be related to each other as well as to a particular outcome or even multiple outcomes. Structural equation modeling refers to a collection of analytical techniques that can be used to model complex patterns of predictive relationships among a collection of both measured and latent variables. As a statistical tool, structural equation modeling combines the features of regression and factor analysis. The chapter offers conceptual illustrations and practical steps for carrying out structural equation modeling by describing mediation and moderation analyses in the context of music education research.

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11

Lee, Sik-Yum. Structural Equation Modelling: A Bayesian Approach (Wiley Series in Probability and Statistics). Wiley, 2007.

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12

Lee, Sik-Yum, Xin-Yuan Song, and Xin-Yuan Song. Basic and Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences. Wiley & Sons, Incorporated, John, 2012.

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13

Lee, Sik-Yum, and Xin-Yuan Song. Basic and Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences. Wiley & Sons, Incorporated, John, 2012.

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14

Collective efficacy and cohesion of intercollegiate basketball athletes: A structural equation model. 2002.

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15

Amlung, Stephanie Rockwern. A SECONDARY DATA ANALYSIS OF THE HEALTH BELIEF MODEL USING STRUCTURAL EQUATION MODELING (BREAST CANCER, LISREL). 1996.

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16

Beasom, Lisa Ann. A cross-validated structural equation model of family and peer influences on adolescent American Indian drug use. 1995.

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17

Johnston, Ian. Turnover of junior Army officers: A test of the Mobley, Griffeth, Hand and Meglino model of personnel turnover, using structural equation techniques. 1988.

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18

Adamson, Gary, and Brendan Bunting. Some statistical and graphical strategies for exploring the effect of interventions in health research. Oxford University Press, 2015. http://dx.doi.org/10.1093/med:psych/9780198527565.003.0021.

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This chapter explores statistical and graphical strategies for exploring the effect of interventions in health research and presents a brief overview of some common and some not so common methods for assessing data from interventions and have pointed to some associated advantages and disadvantages. It discusses gain scores, analysis of variance models, structural equation modelling, and the latent growth model.

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19

Mauk, Marlene. Citizen Support for Democratic and Autocratic Regimes. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198854852.001.0001.

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The book takes a political-culture perspective on the struggle between democracy and autocracy by examining how these regimes fare in the eyes of their citizens. Taking a globally comparative approach, it studies both the levels as well as the individual- and system-level sources of political support in democracies and autocracies worldwide. The book develops an explanatory model of regime support which includes both individual- and system-level determinants and specifies not only the general causal mechanisms and pathways through which these determinants affect regime support but also spells out how these effects might vary between the two types of regimes. It empirically tests its propositions using multi-level structural equation modeling and a comprehensive dataset that combines recent public-opinion data from six cross-national survey projects with aggregate data from various sources for more than one hundred democracies and autocracies. It finds that both the levels and individual-level sources of regime support are the same in democracies and autocracies, but that the way in which system-level context factors affect regime support differs between the two types of regimes. The results enhance our understanding of what determines citizen support for fundamentally different regimes, help assessing the present and future stability of democracies and autocracies, and provide clear policy implications to those interested in strengthening support for democracy and/or fostering democratic change in autocracies.

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