A longitudinal multilevel CFA-MTMM model for interchangeable and structurally different methods

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A longitudinal multilevel CFA-MTMM model for interchangeable and structurally different methods. / Koch, Tobias; Schultze, Martin; Eid, Michael et al.
in: Frontiers in Psychology, Jahrgang 5, 311, 17.04.2014.

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

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Koch T, Schultze M, Eid M, Geiser C. A longitudinal multilevel CFA-MTMM model for interchangeable and structurally different methods. Frontiers in Psychology. 2014 Apr 17;5:311. doi: 10.3389/fpsyg.2014.00311

Bibtex

@article{a21a6f37885b40b3847c198cf8d6fd35,
title = "A longitudinal multilevel CFA-MTMM model for interchangeable and structurally different methods",
abstract = "One of the key interests in the social sciences is the investigation of change and stability of a given attribute. Although numerous models have been proposed in the past for analyzing longitudinal data including multilevel and/or latent variable modeling approaches, only few modeling approaches have been developed for studying the construct validity in longitudinal multitrait-multimethod (MTMM) measurement designs. The aim of the present study was to extend the spectrum of current longitudinal modeling approaches for MTMM analysis. Specifically, a new longitudinal multilevel CFA-MTMM model for measurement designs with structurally different and interchangeable methods (called Latent-State-Combination-Of-Methods model, LS-COM) is presented. Interchangeable methods are methods that are randomly sampled from a set of equivalent methods (e.g., multiple student ratings for teaching quality), whereas structurally different methods are methods that cannot be easily replaced by one another (e.g., teacher, self-ratings, principle ratings). Results of a simulation study indicate that the parameters and standard errors in the LS-COM model are well recovered even in conditions with only five observations per estimated model parameter. The advantages and limitations of the LS-COM model relative to other longitudinal MTMM modeling approaches are discussed.",
keywords = "Sociology, multilevel structural equation modeling, longitudinal modeling, MTMM modeling, multirater data, rater bias, method effects, simulation study",
author = "Tobias Koch and Martin Schultze and Michael Eid and Christian Geiser",
year = "2014",
month = apr,
day = "17",
doi = "10.3389/fpsyg.2014.00311",
language = "English",
volume = "5",
journal = "Frontiers in Psychology",
issn = "1664-1078",
publisher = "Frontiers Research Foundation",

}

RIS

TY - JOUR

T1 - A longitudinal multilevel CFA-MTMM model for interchangeable and structurally different methods

AU - Koch, Tobias

AU - Schultze, Martin

AU - Eid, Michael

AU - Geiser, Christian

PY - 2014/4/17

Y1 - 2014/4/17

N2 - One of the key interests in the social sciences is the investigation of change and stability of a given attribute. Although numerous models have been proposed in the past for analyzing longitudinal data including multilevel and/or latent variable modeling approaches, only few modeling approaches have been developed for studying the construct validity in longitudinal multitrait-multimethod (MTMM) measurement designs. The aim of the present study was to extend the spectrum of current longitudinal modeling approaches for MTMM analysis. Specifically, a new longitudinal multilevel CFA-MTMM model for measurement designs with structurally different and interchangeable methods (called Latent-State-Combination-Of-Methods model, LS-COM) is presented. Interchangeable methods are methods that are randomly sampled from a set of equivalent methods (e.g., multiple student ratings for teaching quality), whereas structurally different methods are methods that cannot be easily replaced by one another (e.g., teacher, self-ratings, principle ratings). Results of a simulation study indicate that the parameters and standard errors in the LS-COM model are well recovered even in conditions with only five observations per estimated model parameter. The advantages and limitations of the LS-COM model relative to other longitudinal MTMM modeling approaches are discussed.

AB - One of the key interests in the social sciences is the investigation of change and stability of a given attribute. Although numerous models have been proposed in the past for analyzing longitudinal data including multilevel and/or latent variable modeling approaches, only few modeling approaches have been developed for studying the construct validity in longitudinal multitrait-multimethod (MTMM) measurement designs. The aim of the present study was to extend the spectrum of current longitudinal modeling approaches for MTMM analysis. Specifically, a new longitudinal multilevel CFA-MTMM model for measurement designs with structurally different and interchangeable methods (called Latent-State-Combination-Of-Methods model, LS-COM) is presented. Interchangeable methods are methods that are randomly sampled from a set of equivalent methods (e.g., multiple student ratings for teaching quality), whereas structurally different methods are methods that cannot be easily replaced by one another (e.g., teacher, self-ratings, principle ratings). Results of a simulation study indicate that the parameters and standard errors in the LS-COM model are well recovered even in conditions with only five observations per estimated model parameter. The advantages and limitations of the LS-COM model relative to other longitudinal MTMM modeling approaches are discussed.

KW - Sociology

KW - multilevel structural equation modeling

KW - longitudinal modeling

KW - MTMM modeling

KW - multirater data

KW - rater bias

KW - method effects

KW - simulation study

UR - http://www.scopus.com/inward/record.url?scp=84899697394&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/b5a6655a-b20e-30b1-9df1-6917599916df/

U2 - 10.3389/fpsyg.2014.00311

DO - 10.3389/fpsyg.2014.00311

M3 - Journal articles

C2 - 24860515

AN - SCOPUS:84899697394

VL - 5

JO - Frontiers in Psychology

JF - Frontiers in Psychology

SN - 1664-1078

M1 - 311

ER -

DOI