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Books on the topic 'Structural model equation'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

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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2

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

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3

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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4

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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5

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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6

Westland, J. Christopher. Structural Equation Models. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12508-0.

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7

Westland, J. Christopher. Structural Equation Models. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16507-3.

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8

van Montfort, Kees, Johan Oud, and Albert Satorra, eds. Recent Developments on Structural Equation Models. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-1-4020-1958-6.

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9

1965-, Curran Patrick J., ed. Latent curve models: A structural equation perspective. Hoboken, NJ: John Wiley & Sons, 2005.

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10

McArdle, John J., and John R. Nesselroade. Longitudinal data analysis using structural equation models. Washington: American Psychological Association, 2014. http://dx.doi.org/10.1037/14440-000.

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11

Bollen, Kenneth A. Latent curve models: A structural equation perspective. Hoboken, NJ: Wiley Interscience, 2005.

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12

Bollen, Kenneth A. Latent curve models: A structural equation perspective. Hoboken, N.J: Wiley-Interscience, 2006.

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13

Structural equation modeling: A second course. 2nd ed. Charlotte, NC: Information Age Publishing, Inc., 2013.

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14

Structural equation modeling: Foundations and extensions. 2nd ed. Thousand Oaks: Sage Publications, 2009.

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15

Structural equation modeling for social and personality psychology. London: SAGE, 2011.

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16

Heinen, Ton. Discrete latent variable models. Tilburg, Netherlands: Tilburg University Press, 1993.

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17

Krishnakumar, Jayalakshmi. Estimation of simultaneous equation models with error components structure. Berlin: Springer Verlag, 1988.

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18

Krishnakumar, Jayalakshmi. Estimation of simultaneous equation models with error components structure. Berlin: Springer-Verlag, 1988.

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19

Krishnakumar, Jayalakshmi. Estimation of Simultaneous Equation Models with Error Components Structure. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-45647-3.

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20

Heckman, James J. Structural equations, treatment effects, and econometric policy evaluation. Cambridge, MA: National Bureau of Economic Research, 2005.

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21

Heckman, James J. Structural equations, treatment effects, and econometric policy evaluation. Cambridge, MA: National Bureau of Economic Research, 2005.

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22

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

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23

Lindén, Lena. Developmental change and linear structural equations: Applications of LISREL models. Stockholm: Almqvist & Wiksell International, 1986.

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24

Latent variable models: An introduction to factor, path, and structural equation analysis. 4th ed. Mahwah, N.J: L. Erlbaum Associates, 2004.

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25

Jyoti, Savla, ed. Statistical power analysis with missing data: A structural equation modeling approach. New York: Routledge, 2010.

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26

Principles and practice of structural equation modeling. 2nd ed. New York: Guilford Press, 2005.

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27

Principles and practice of structural equation modeling. New York: Guilford Press, 1998.

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28

Davey, Adam. Statistical power analysis with missing data: A structural equation modeling approach. New York: Routledge, 2010.

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29

Davey, Adam. Statistical power analysis with missing data: A structural equation modeling approach. New York: Routledge, 2010.

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30

Bhattacharya, Joysankar. Women empowerment as multidimensional capability enhancement: An application of structural-equation-modelling. Kolkata: Institute of Development Studies, 2012.

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31

Gent, M. R. A. van. Numerical model for wave action on and in coastal structures. [Delft]: Faculty of Civil Engineering, Delft University of Technology, 1992.

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32

Ziebart, David Allen. The information content of annual accounting data: An empirical modeling approach using structural equation techniques. [Urbana]: College of Commerce and Business Administration,University of Illinois at Urbana-Champaign, 1986.

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33

Forbes, P. D. A dynamic matrix reduction process which facilitates modal frequency response analysis with the MARC system. East Kilbride: National Engineering Laboratory, 1991.

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34

Grossman, Robert. Querying databases of trajectories of differential equations: Data structures for trajectories. Chicago, Ill: University of Illinois at Chicago, 1989.

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35

Seiichi, Katayama. Estimation of structural change in the import and export equations: An international comparison. Kobe, Japan: Institute of Economic Research, Kobe University of Commerce, 1985.

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36

1963-, Ruan Shigui, ed. Center manifolds for semilinear equations with non-dense domain and applications to Hopf bifurcation in age structured models. Providence, R.I: American Mathematical Society, 2009.

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37

Iacobucci, Dawn. Mediation analysis. Los Angeles: Sage, 2008.

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38

Iacobucci, Dawn. Mediation analysis. Los Angeles: Sage, 2008.

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39

Hodoshima, Jiro. Identification and estimation in linear simultaneous equations models with structural change under limited information: Gains by homoskedasticity. Louvain-la-Neuve: CORE, 1985.

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40

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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41

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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42

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

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43

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

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44

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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45

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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46

McArdle, John J., and Kelly M. Kadlec. Structural Equation Models. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199934898.013.0015.

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47

A, Bollen Kenneth, and Long J. Scott, eds. Testing structural equation models. Newbury Park: Sage Publications, 1993.

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48

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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49

Solymar, L., D. Walsh, and R. R. A. Syms. The band theory of solids. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198829942.003.0007.

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The solution of Schrodinger’s equation is discussed for a model in which atoms are represented by potential wells, from which the band structure follows. Three further models are discussed, the Ziman model (which is based on the effect of Bragg reflection upon the wave functions), and the Feynman model (based on coupled equations), and the tight binding model (based on a more realistic solution of the Schrödinger equation). The concept of effective mass is introduced, followed by the effective number of electrons. The difference between metals and insulators based on their band structure is discussed. The concept of holes is introduced. The band structure of divalent metals is explained. For finite temperatures the Fermi–Dirac function is combined with band theory whence the distinction between insulators and semiconductors is derived.

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50

Curran, Patrick J., and Kenneth A. Bollen. Latent Curve Models: A Structural Equation Perspective. Wiley & Sons, Incorporated, John, 2006.

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