Regression among factor scores

Skrondal, Anders; Laake, Petter

doi:10.1007/bf02296196

Cited by 405 publications

(340 citation statements)

References 25 publications

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“…Для первого тестирования все переменные, измеренные с помощью шкал, были опера-ционализированы как значения факторов (factor scores), основанные на объясня-ющем факторном анализе. С целью полу-чения надежных оценок латентных пере-менных значения факторов для независи-мых переменных были рассчитаны методом регрессии (regression method), а для зави-симой переменной -методом Бартлетта (Bartlett method) [Skrondal, Laake, 2001]. Результаты регрессионного анализа полу-ченных значений факторов латентных пере-менных подтвердили все первоначальные результаты анализа данных.…”

Section: тестирование результатов на устойчивостьunclassified

Firm Performance During Economic Crisis: The Role of Strategic Orientations and Financial Capital

Beliaeva¹,

Shirokova²,

Гаффорова³

2017

RMJ

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Section: тестирование результатов на устойчивостьunclassified

Firm Performance During Economic Crisis: The Role of Strategic Orientations and Financial Capital

Beliaeva¹,

Shirokova²,

Гаффорова³

2017

RMJ

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“…If this assumption is appropriate, it would generally be problematic to substitute sumscores or factor scores for the latent variables (Skrondal & Laake, 2001), an approach often adopted in multilevel modeling.…”

Section: Latent Covariates (Contexts) Measured By Lower-level Itemsmentioning

confidence: 99%

“…However, using such constructed scores as proxies for latent variables has been demonstrated to be highly problematic, leading to biased standard errors and often to inconsistent parameter estimates (Skrondal & Laake, 2001). Hence, we use multilevel factor models with a logit link for the dichotomous items.…”

Section: Psychometrikamentioning

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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“…Skrondal and Laake (2001) provide the most sophisticated approach based on factor score estimates, but it is limited to three groups of factors and allows no higher-order factors. In the context of partial least squares (PLS) estimation of formative measurement models, such substitution of estimates for the true LV is routinely done but with the consequence that "in general, not all parameters will be estimated consistently" (Dijkstra, 2010, p. 37).…”

Section: Factor Score Predictors and Consistency At Largementioning

confidence: 99%

Formative Constructs Implemented via Common Factors

Treiblmaier

Bentler

Mair

2011

Structural Equation Modeling: A Multidisciplinary Journal

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Recently there has been a renewed interest in formative measurement and its role in properly specified models. Formative measurement models are difficult to identify, and hence to estimate and test. Existing solutions to the identification problem are shown to not adequately represent the formative constructs of interest. We propose a new two-step approach to operationalize a formatively measured construct that allows a closely-matched common factor equivalent to be included in any structural equation model. We provide an artificial example and an original empirical study of privacy to illustrate our approach. Detailed proofs are given in an appendix.

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Regression among factor scores

Abstract: regression, factor scores, structural equation models, latent variables, factor score regression,

Cited by 405 publications

References 25 publications

Firm Performance During Economic Crisis: The Role of Strategic Orientations and Financial Capital

Firm Performance During Economic Crisis: The Role of Strategic Orientations and Financial Capital

Generalized multilevel structural equation modeling

Formative Constructs Implemented via Common Factors

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