Estimation of a Model with Multiple Indicators and Multiple Causes of a Single Latent Variable

Jöreskog, Karl G.; Goldberger, Arthur S.

doi:10.1080/01621459.1975.10482485

Cited by 696 publications

(454 citation statements)

References 11 publications

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“…Following Jöreskog and Goldberger (1975) (see also Schoenberg and Arminger, 1989), let y 1 and y 2 denote two measurements of a latent variable X. Since the determinants of X (neglected in eq.…”

Section: Reliability and Validity Of Dce Resultsmentioning

confidence: 99%

Validity of discrete-choice experiments evidence for health risk reduction

Telser

Zweifel

2007

Applied Economics

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in

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Section: Reliability and Validity Of Dce Resultsmentioning

confidence: 99%

Validity of discrete-choice experiments evidence for health risk reduction

Telser

Zweifel

2007

Applied Economics

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“…A MIMIC model (e.g., Jöreskog & Goldberger, 1975) has the response model in (12) with structural model…”

mentioning

confidence: 99%

Generalized multilevel structural equation modeling

2004

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A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to incorporate factor structures in addition to random intercepts and coefficients. As in GLMMs, the data can have an arbitrary number of levels and can be highly unbalanced with different numbers of lower-level units in the higher-level units and missing data. A wide range of response processes can be modeled including ordered and unordered categorical responses, counts, and responses of mixed types. The structural model is similar to the structural part of a SEM except that it may include latent and observed variables varying at different levels. For example, unit-level latent variables (factors or random coefficients) can be regressed on cluster-level latent variables. Special cases of this framework are explored and data from the British Social Attitudes Survey are used for illustration. Maximum likelihood estimation and empirical Bayes latent score prediction within the GLLAMM framework can be performed using adaptive quadrature in gllamm, a freely available program running in Stata.

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“…MIMIC models typically consist of two simultaneously estimated equations within a structural equation modeling (SEM) framework (Jöreskog & Goldberger, 1975;Muthén, 1989). The first model typically defines one or more latent constructs and is analogous to a traditional CFA model.…”

Section: Supplemental Analysesmentioning

confidence: 99%

Proposing a Pedigree Risk Measurement Strategy: Capturing the Intergenerational Transmission of Antisocial Behavior in a Nationally Representative Sample of Adults

Schwartz¹,

Connolly

Beaver

et al. 2015

Twin Res Hum Genet

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An impressive literature has revealed that variation in virtually every measurable phenotype is the result of a combination of genetic and environmental influences. Based on these findings, studies that fail to use genetically informed modeling strategies risk model misspecification and biased parameter estimates. Twin-and adoption-based research designs have frequently been used to overcome this limitation. Despite the many advantages of such approaches, many available datasets do not contain samples of twins, siblings or adoptees, making it impossible to utilize these modeling strategies. The current study proposes a measurement strategy for estimating the intergenerational transmission of antisocial behavior (ASB) within a nationally representative sample of singletons using an extended pedigree risk approach that relies on information from first-and second-degree relatives. An evaluation of this approach revealed a pattern of findings that directly aligned with studies examining ASB using more traditional twin-and adoption-based research designs. While the proposed pedigree risk approach is not capable of effectively isolating genetic and environmental influences, this overall alignment in results provides tentative evidence suggesting that the proposed pedigree risk measure effectively captures genetic influences. Future replication studies are necessary as this observation remains preliminary. Whenever possible, more traditional quantitative genetic methodologies should be favored, but the presented strategy remains a viable alternative for more limited samples.

show abstract

Estimation of a Model with Multiple Indicators and Multiple Causes of a Single Latent Variable

Cited by 696 publications

References 11 publications

Validity of discrete-choice experiments evidence for health risk reduction

Validity of discrete-choice experiments evidence for health risk reduction

Generalized multilevel structural equation modeling

Proposing a Pedigree Risk Measurement Strategy: Capturing the Intergenerational Transmission of Antisocial Behavior in a Nationally Representative Sample of Adults

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