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Exploring factor model parameters across continuous variables with local structural equation models. / Hildebrandt, Andrea; Lüdtke, Oliver; Robitzsch, Alexander et al.

In: Multivariate Behavioral Research, Vol. 51, No. 2-3, 2016, p. 257-258.

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Harvard

Hildebrandt, A, Lüdtke, O, Robitzsch, A, Sommer, C & Wilhelm, O 2016, 'Exploring factor model parameters across continuous variables with local structural equation models', Multivariate Behavioral Research, vol. 51, no. 2-3, pp. 257-258. https://doi.org/10.1080/00273171.2016.1142856

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Vancouver

Author

Hildebrandt, Andrea ; Lüdtke, Oliver ; Robitzsch, Alexander et al. / Exploring factor model parameters across continuous variables with local structural equation models. In: Multivariate Behavioral Research. 2016 ; Vol. 51, No. 2-3. pp. 257-258.

BibTeX

@article{7a4b1d3b31954f31bebdf4a221845343,
title = "Exploring factor model parameters across continuous variables with local structural equation models",
abstract = "Using an empirical data set, we investigated variation in factor model parameters across a continuous moderator variable and demonstrated three modeling approaches: multiple-group mean and covariance structure (MGMCS) analyses, local structural equation modeling (LSEM), and moderated factor analysis (MFA). We focused on how to study variation in factor model parameters as a function of continuous variables such as age, socioeconomic status, ability levels, acculturation, and so forth. Specifically, we formalized the LSEM approach in detail as compared with previous work and investigated its statistical properties with an analytical derivation and a simulation study. We also provide code for the easy implementation of LSEM. The illustration of methods was based on cross-sectional cognitive ability data from individuals ranging in age from 4 to 23 years. Variations in factor loadings across age were examined with regard to the age differentiation hypothesis. LSEM and MFA converged with respect to the conclusions. When there was a broad age range within groups and varying relations between the indicator variables and the common factor across age, MGMCS produced distorted parameter estimates. We discuss the pros of LSEM compared with MFA and recommend using the two tools as complementary approaches for investigating moderation in factor model parameters.",
keywords = "Methodological research and development, Local structural equation model, WJ-III tests of cognitive abilities, age differentiation of cognitive abilities, multiple-group mean and covariance structures",
author = "Andrea Hildebrandt and Oliver L{\"u}dtke and Alexander Robitzsch and Christopher Sommer and Oliver Wilhelm",
year = "2016",
doi = "10.1080/00273171.2016.1142856",
language = "English",
volume = "51",
pages = "257--258",
journal = "Multivariate Behavioral Research",
issn = "0027-3171",
publisher = "Taylor and Francis Ltd.",
number = "2-3",

}

RIS

TY - JOUR

T1 - Exploring factor model parameters across continuous variables with local structural equation models

AU - Hildebrandt, Andrea

AU - Lüdtke, Oliver

AU - Robitzsch, Alexander

AU - Sommer, Christopher

AU - Wilhelm, Oliver

PY - 2016

Y1 - 2016

N2 - Using an empirical data set, we investigated variation in factor model parameters across a continuous moderator variable and demonstrated three modeling approaches: multiple-group mean and covariance structure (MGMCS) analyses, local structural equation modeling (LSEM), and moderated factor analysis (MFA). We focused on how to study variation in factor model parameters as a function of continuous variables such as age, socioeconomic status, ability levels, acculturation, and so forth. Specifically, we formalized the LSEM approach in detail as compared with previous work and investigated its statistical properties with an analytical derivation and a simulation study. We also provide code for the easy implementation of LSEM. The illustration of methods was based on cross-sectional cognitive ability data from individuals ranging in age from 4 to 23 years. Variations in factor loadings across age were examined with regard to the age differentiation hypothesis. LSEM and MFA converged with respect to the conclusions. When there was a broad age range within groups and varying relations between the indicator variables and the common factor across age, MGMCS produced distorted parameter estimates. We discuss the pros of LSEM compared with MFA and recommend using the two tools as complementary approaches for investigating moderation in factor model parameters.

AB - Using an empirical data set, we investigated variation in factor model parameters across a continuous moderator variable and demonstrated three modeling approaches: multiple-group mean and covariance structure (MGMCS) analyses, local structural equation modeling (LSEM), and moderated factor analysis (MFA). We focused on how to study variation in factor model parameters as a function of continuous variables such as age, socioeconomic status, ability levels, acculturation, and so forth. Specifically, we formalized the LSEM approach in detail as compared with previous work and investigated its statistical properties with an analytical derivation and a simulation study. We also provide code for the easy implementation of LSEM. The illustration of methods was based on cross-sectional cognitive ability data from individuals ranging in age from 4 to 23 years. Variations in factor loadings across age were examined with regard to the age differentiation hypothesis. LSEM and MFA converged with respect to the conclusions. When there was a broad age range within groups and varying relations between the indicator variables and the common factor across age, MGMCS produced distorted parameter estimates. We discuss the pros of LSEM compared with MFA and recommend using the two tools as complementary approaches for investigating moderation in factor model parameters.

KW - Methodological research and development

KW - Local structural equation model

KW - WJ-III tests of cognitive abilities

KW - age differentiation of cognitive abilities

KW - multiple-group mean and covariance structures

U2 - 10.1080/00273171.2016.1142856

DO - 10.1080/00273171.2016.1142856

M3 - Journal article

VL - 51

SP - 257

EP - 258

JO - Multivariate Behavioral Research

JF - Multivariate Behavioral Research

SN - 0027-3171

IS - 2-3

ER -

ID: 622812