Automated Fitting of Nonstandard Models

Browne, Michael W.; Toit, S. H. C. du

doi:10.1207/s15327906mbr2702_13

Cited by 140 publications

(125 citation statements)

References 32 publications

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“…A confidence interval including 0.05 means that the two models are close in misfit. In this case, although there is a significant difference in fit between the two models, we can say that they fit the data almost equally well (Browne & DuToit, 1992). When not controlling for age we obtained a cRMSEA of 0.098 with a 95% CI of [0.080-0.117].…”

Section: Multivariate Lgmsmentioning

confidence: 62%

Exploring structural dynamics within and between sensory and intellectual functioning in old and very old age: Longitudinal evidence from the Berlin Aging Study

Ghisletta

Lindenberger

2005

Intelligence

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Cross-sectional and longitudinal analyses of age-heterogeneous samples have revealed correlational links between and within intellectual, sensory, and sensorimotor domains. Due to basic limitations of cross-sectional designs and a reluctance to disentangle antecedent-consequent relations in longitudinal designs, the functional significance and dynamics of these links have remained unclear. Advanced structural equation models allow representing multivariate longitudinal changes as a function of time-based and directed relations. We applied this methodology to longitudinal data from the Berlin Aging Study (at inception total N = 516; age range = 70-103 years) to explore the structural dynamics among perceptual speed, verbal knowledge, close visual acuity, and distance visual acuity. We found reliable, occasion-based, age-partialed latent regression paths that influence longitudinal changes within and across intellectual and sensory domains. We conclude that intellectual and sensory domains are dynamically linked in old and very old age, and discuss implications of this finding for theories of cognitive aging. D

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Section: Multivariate Lgmsmentioning

confidence: 62%

Exploring structural dynamics within and between sensory and intellectual functioning in old and very old age: Longitudinal evidence from the Berlin Aging Study

Ghisletta

Lindenberger

2005

Intelligence

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“…According to Ying and Fan (2003), increasing the constraints on a model leads to poorer model fit and, therefore, the different constraint models studied should be compared with a baseline model in order to assess the effect of these invariance constraints on the fit/misfit model. In this context other authors such as Browne and Du Toit (1992) and Cheung and Rensvold (2002) have suggested, respectively, using the root deterioration per restriction (RDR) statistic and changes in the comparative fit index (CFI).…”

Section: Hypothesis Ii: Testing For the Invariant Pattern Of Factor Lmentioning

confidence: 99%

Measurement Invariance Study of the Training Satisfaction Questionnaire (TSQ)

et al. 2013

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This article presents an empirical measurement invariance study in the substantive area of satisfaction evaluation in training programmes. Specifically, it (I) provides an empirical solution to the lack of explicit measurement models of satisfaction scales, offering a way of analysing and operationalizing the substantive theoretical dimensions; (II) outlines and discusses the analytical consequences of considering the effects of categorizing supposedly continuous variables, which are not usually taken into account; (III) presents empirical results from a measurement invariance study based on 5,272 participants' responses to a training satisfaction questionnaire in three different organizations and in two different training methods, taking into account the factor structure of the measured construct and the ordinal nature of the recorded data; and (IV) describes the substantive implications in the area of training satisfaction evaluation, such as the usefulness of the training satisfaction questionnaire to measure satisfaction in different organizations and different training methods. It also discusses further research based on these findings.

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“…We propose using one step of the Fisher-Scoring algorithm with step-halving (see e.g., Lee & Jennrich, 1979). This gives a point that satisfies (9). Our choice of the Fisher-scoring step is also motivated by the fact that existing programs and recent theoretical discussions (see e.g., …”

Section: The Em and Gem Algorithmsmentioning

confidence: 99%

“…A summary of this statistical theory can be found, for example, in Satorra (1992) and Browne and Arminger (1995). Effective computational procedures for implementing this theory exist in various standard computer programs such as EQS (Bentler, 1995), LISREL (Jöreskog & Sörbom, 1988), MECOSA (Schepers & Arminger, 1992), and SEPATH (Steiger, 1994), and have recently been discussed by Arminger (1994), Browne and Du Toit (1992), and Cudeck, Klebe, and Henly (1993).…”

Section: Introductionmentioning

confidence: 99%

ML Estimation of Mean and Covariance Structures with Missing Data Using Complete Data Routines

Jamshidian¹,

Bentler²

1999

Journal of Educational and Behavioral Statistics

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We consider maximum likelihood (ML) estimation of mean and covariance structure models when data are missing. Expectation maximization (EM), generalized expectation maximization (GEM), Fletcher-Powell, and Fisherscoring algorithms are described for parameter estimation. It is shown how the machinery within a software that handles the complete data problem can be utilized to implement each algorithm. A numerical differentiation method for obtaining the observed information matrix and the standard errors is given. This method too uses the complete data program machinery. The likelihood ratio test is discussed for testing hypotheses. Three examples are used to compare the cost of the four algorithms mentioned above, as well as to illustrate the standard error estimation and the test of hypothesis considered.The sensitivity of the ML estimates as well as the mean imputed and listwise deletion estimates to missing data mechanisms is investigated using three artificial data sets that are missing completely at random (MCAR), missing at random (MAR), and neither MCAR nor MAR.

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Automated Fitting of Nonstandard Models

Cited by 140 publications

References 32 publications

Exploring structural dynamics within and between sensory and intellectual functioning in old and very old age: Longitudinal evidence from the Berlin Aging Study

Exploring structural dynamics within and between sensory and intellectual functioning in old and very old age: Longitudinal evidence from the Berlin Aging Study

Measurement Invariance Study of the Training Satisfaction Questionnaire (TSQ)

ML Estimation of Mean and Covariance Structures with Missing Data Using Complete Data Routines

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