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Libri sul tema "Modèle à variables latentes"

Autore: Grafiati
Pubblicato: 15 febbraio 2025

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1

Muthen, Linda K. Mplus: Statistical analysis with latent variables : user's guide. 4a ed. Los Angeles, CA: Muthén & Muthén, 2007.

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2

Hillmer, Matthias. Kausalanalyse makroökonomischer Zusammenhänge mit latenten Variablen: Mit einer empirischen Untersuchung des Transmissionsmechanismus monetärer Impulse. Heidelberg: Physica-Verlag, 1993.

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3

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

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4

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

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5

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

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6

Vermunt, Jeroen K. Log-linear event history analysis: A general approach with missing data, latent variables, and unobserved heterogeneity. Tilburg: Tilburg University Press, 1996.

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7

E, Duncan Terry, a cura di. An introduction to latent variable growth curve modeling: Concepts, issues, and applications. Mahwah, N.J: L. Erlbaum Associates, 1999.

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8

Ang, Andrew. A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables. Cambridge, MA: National Bureau of Economic Research, 2001.

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9

Bartholomew, David J. Latent variable models and factor analysis. 2a ed. London: Arnold, 1999.

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10

Bartholomew, David J. Latent variable models and factor analysis. London: C. Griffin, 1987.

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11

A, Marcoulides George, e Moustaki Irini, a cura di. Latent variable and latent structure models. Mahwah, N.J: Lawrence Earlbaum Publishers, 2002.

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12

C, Duncan Susan, e Strycker Lisa A, a cura di. An introduction to latent variable growth curve modeling: Concepts, issues, and applications. 2a ed. Mahwah, N.J: Lawrence Erlbaum Associates, 2006.

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13

Heckman, James J. Simple estimators for treatment parameters in a latent variable framework with an application to estimating the returns to schooling. Cambridge, MA: National Bureau of Economic Research, 2000.

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14

Loehlin, John C. Latent variable models: An introduction to factor, path, and structural analysis. 2a ed. Hillsdale, N.J: Lawrence Erlbaum Associates, 1992.

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15

Loehlin, John C. Latent variable models: An introduction to factor, path, and structural analysis. Hillsdale, N.J: L. Erlbaum Associates, 1987.

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16

Anders, Skrondal, e Skrondal Anders, a cura di. Multilevel and longitudinal modeling using stata. 2a ed. College Station, Tex: Stata Press Publication, 2008.

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17

Anders, Skrondal, a cura di. Multilevel and longitudinal modeling using Stata. 3a ed. College Station, Tex: Stata Press Publication, 2012.

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18

Multilevel and Longitudinal Modeling Using Stata Volumes I and II. Taylor & Francis Group, 2021.

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19

Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models. Chapman & Hall/CRC, 2004.

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20

Latent class analysis. Newbury Park: Sage Publications, 1987.

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21

Jackman, Simon. Measurement. A cura di Janet M. Box-Steffensmeier, Henry E. Brady e David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0006.

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Abstract (sommario):
This article shows that the words ‘behavioural’ and ‘behaviour’ turn out to be better measures as judged by tests of criterion and convergent validity. It specifically discusses measurement problems. Further, it pertains to statistical models that link latent variables and their observed indicators as measurement models. The success of measurement — the quality of the inferences provided by a measurement model — is usually assessed with reference to two key concepts: validity and reliability. The distinct uses of measures of latent variables are reported. The article then deals with the costs of ignoring measurement error. Additionally, a quick introduction to factor analysis, item-response models, and a very general class of latent variable models are briefly given. Moreover, it describes the inference for discrete latent variables and the measurement in a dynamic setting.
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22

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

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Abstract (sommario):
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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23

Loglinear models with Latent Variables. 1993.

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24

Loglinear models with latent variables. Newbury Park, Calif: Sage, 1993.

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25

Structural Equations with Latent Variables. Wiley & Sons Canada, Limited, John, 2014.

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26

Structural equations with latent variables. New York: Wiley, 1989.

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27

Bollen, Kenneth A. Structural Equations with Latent Variables. Wiley & Sons, Incorporated, John, 2014.

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28

Bollen, Kenneth A. Structural Equations with Latent Variables. Wiley & Sons, Incorporated, John, 2014.

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29

Hillmer, Matthias. Kausalanalyse Makroökonomischer Zusammenhänge Mit Latenten Variablen: Mit Einer Empirischen Untersuchung des Transmissionsmechanismus Monetärer Impulse. Physica-Verlag, 2013.

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30

Curran, Patrick J., e Kenneth A. Bollen. Latent Curve Models: A Structural Equation Perspective. Wiley & Sons Australia, Limited, John, 2005.

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31

Latent Curve Models. Wiley & Sons Canada, Limited, John, 2006.

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32

Edwards, Michael C., e Robert C. MacCallum. Current Topics in the Theory and Application of Latent Variable Models. Taylor & Francis Group, 2012.

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33

Edwards, Michael C., e Robert C. MacCallum. Current Topics in the Theory and Application of Latent Variable Models. Taylor & Francis Group, 2012.

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34

Current Topics in the Theory and Application of Latent Variable Models. Routledge, 2012.

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35

Current topics in the theory and application of latent variable models. New York: Psychology Press, 2013.

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36

Semialgebraic statistics and latent tree models. Boca Raton: CRC Press, Taylor & Francis Group, 2016.

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37

Dorff, Cassy, Shahryar Minhas e Michael D. Ward. Latent Networks and Spatial Networks in Politics. A cura di Jennifer Nicoll Victor, Alexander H. Montgomery e Mark Lubell. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190228217.013.11.

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Abstract (sommario):
Network analysis is a growing field in political science, with topics ranging from the study of individual actors in congressional networks to international war between countries. This chapter briefly summarizes the history of network analysis, the barriers facing previous approaches, and current innovations, with an emphasis on latent variable approaches. These approaches provide an organic link to the consideration of spatial networks, also discussed in detail. These innovations expand researchers’ ability to capture the many different facets of network-motivated questions, including how networks evolve or how spatial proximity determines network ties. The chapter concludes with a brief comparison of two major types of latent variable models and their relation to other network approaches commonly used in political science.
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38

Hoyle, Rick H. Applications of structural equation modelling in clinical and health psychology research. Oxford University Press, 2015. http://dx.doi.org/10.1093/med:psych/9780198527565.003.0020.

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Abstract (sommario):
This chapter discusses applications of structural equation modelling (SEM, or causal modelling) in clinical and health psychology research. It outlines path diagrams, measurement models, structural models, the inclusion of latent variables, validity (factorial and construct), and measurement invariance. Structural hypotheses are also explored, along with caveats for the use of SEM.
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39

Markon, Kristian E. From Comorbidity to Constructs. A cura di C. Steven Richards e Michael W. O'Hara. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199797004.013.014.

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Abstract (sommario):
Comorbidity models have become central to psychopathology theory and research, not only because they have clarified our understanding of how and why disorders co-occur but also because they have clarified our understanding of what the disorders are. This chapter reviews basic types of comorbidity models, recurring issues in comorbidity modeling, and discuss emerging issues in the area. Using recent epidemiological, repeated-measures data on depression and anxiety as an example, two different models of comorbidity are compared, one in which comorbidity arises due a shared liability dimension (i.e., a reflective or latent variable model) and another in which comorbidity arises as an epiphenomenon of correlated symptoms (i.e., a formative or network model). This comparison, relatively novel in the literature, illustrates a number of issues that are encountered in comorbidity modeling, and clearly demonstrates how questions pertaining to comorbidity can shape our understanding of psychopathology constructs.
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40

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

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Abstract (sommario):
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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41

Individual Latent Error Detection: Making Systems Safer. Taylor & Francis Group, 2023.

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42

Stanton, Neville A., e Justin R. E. Saward. Individual Latent Error Detection: Making Systems Safer. Taylor & Francis Group, 2018.

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43

Stanton, Neville A., e Justin R. E. Saward. Individual Latent Error Detection: Making Systems Safer. Taylor & Francis Group, 2018.

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44

Stanton, Neville A., e Justin R. E. Saward. Individual Latent Error Detection: Making Systems Safer. Taylor & Francis Group, 2018.

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45

Stanton, Neville A., e Justin R. E. Saward. Individual Latent Error Detection: Making Systems Safer. Taylor & Francis Group, 2018.

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46

Individual Latent Error Detection: Making Systems Safer. Taylor & Francis Group, 2018.

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47

Latent variable modeling using R: a step by step guide. Routledge, Taylor and Francis Group, 2014.

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48

Loehlin, John C., e A. Alexander Beaujean. Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis, Fifth Edition. Taylor & Francis Group, 2017.

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49

Loehlin, John C., e A. Alexander Beaujean. Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis, Fifth Edition. Taylor & Francis Group, 2016.

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50

Loehlin, John C., e A. Alexander Beaujean. Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis, Fifth Edition. Taylor & Francis Group, 2016.

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