A Multilevel CFA-MTMM Model for Nested Structurally Different Methods

Research output: Journal contributionsJournal articlesResearchpeer-review

Standard

A Multilevel CFA-MTMM Model for Nested Structurally Different Methods. / Koch, Tobias; Schultze, Martin; Burrus, Jeremy et al.
In: Journal of Educational and Behavioral Statistics, Vol. 40, No. 5, 01.10.2015, p. 477-510.

Research output: Journal contributionsJournal articlesResearchpeer-review

Harvard

APA

Vancouver

Koch T, Schultze M, Burrus J, Roberts RD, Eid M. A Multilevel CFA-MTMM Model for Nested Structurally Different Methods. Journal of Educational and Behavioral Statistics. 2015 Oct 1;40(5):477-510. doi: 10.3102/1076998615606109

Bibtex

@article{d4003f9d59d547a6bfbde3d08c166da6,
title = "A Multilevel CFA-MTMM Model for Nested Structurally Different Methods",
abstract = "The numerous advantages of structural equation modeling (SEM) for the analysis of multitrait–multimethod (MTMM) data are well known. MTMM-SEMs allow researchers to explicitly model the measurement error, to examine the true convergent and discriminant validity of the given measures, and to relate external variables to the latent trait as well as the latent method factors in the model. According to Eid et al. (2008) different MTMM measurement designs require different types of MTMM-SEMs. Eid et al. (2008) proposed three different MTMM-SEMs for measurement designs with (a) structurally different methods, (b) interchangeable methods, and (c) a combination of both types of methods. In the present work, we extend this taxonomy to a multilevel correlated traits–correlated methods minus one [CTC(M−1)] model for nested structurally different methods. The new model enables researchers to study method effects on both measurement levels (i.e., within and between clusters, classes, schools, etc.) and evaluate the convergent and discriminant validity of the measures. The statistical performance of the model is examined by a simulation study, and recommendations for the application of the model are given.",
keywords = "Sociology, MTMM analysis, multilevel structural equation modeling, structurally different and interchangeable methods",
author = "Tobias Koch and Martin Schultze and Jeremy Burrus and Roberts, {Richard D.} and Michael Eid",
year = "2015",
month = oct,
day = "1",
doi = "10.3102/1076998615606109",
language = "English",
volume = "40",
pages = "477--510",
journal = "Journal of Educational and Behavioral Statistics",
issn = "1076-9986",
publisher = "SAGE Publications Inc.",
number = "5",

}

RIS

TY - JOUR

T1 - A Multilevel CFA-MTMM Model for Nested Structurally Different Methods

AU - Koch, Tobias

AU - Schultze, Martin

AU - Burrus, Jeremy

AU - Roberts, Richard D.

AU - Eid, Michael

PY - 2015/10/1

Y1 - 2015/10/1

N2 - The numerous advantages of structural equation modeling (SEM) for the analysis of multitrait–multimethod (MTMM) data are well known. MTMM-SEMs allow researchers to explicitly model the measurement error, to examine the true convergent and discriminant validity of the given measures, and to relate external variables to the latent trait as well as the latent method factors in the model. According to Eid et al. (2008) different MTMM measurement designs require different types of MTMM-SEMs. Eid et al. (2008) proposed three different MTMM-SEMs for measurement designs with (a) structurally different methods, (b) interchangeable methods, and (c) a combination of both types of methods. In the present work, we extend this taxonomy to a multilevel correlated traits–correlated methods minus one [CTC(M−1)] model for nested structurally different methods. The new model enables researchers to study method effects on both measurement levels (i.e., within and between clusters, classes, schools, etc.) and evaluate the convergent and discriminant validity of the measures. The statistical performance of the model is examined by a simulation study, and recommendations for the application of the model are given.

AB - The numerous advantages of structural equation modeling (SEM) for the analysis of multitrait–multimethod (MTMM) data are well known. MTMM-SEMs allow researchers to explicitly model the measurement error, to examine the true convergent and discriminant validity of the given measures, and to relate external variables to the latent trait as well as the latent method factors in the model. According to Eid et al. (2008) different MTMM measurement designs require different types of MTMM-SEMs. Eid et al. (2008) proposed three different MTMM-SEMs for measurement designs with (a) structurally different methods, (b) interchangeable methods, and (c) a combination of both types of methods. In the present work, we extend this taxonomy to a multilevel correlated traits–correlated methods minus one [CTC(M−1)] model for nested structurally different methods. The new model enables researchers to study method effects on both measurement levels (i.e., within and between clusters, classes, schools, etc.) and evaluate the convergent and discriminant validity of the measures. The statistical performance of the model is examined by a simulation study, and recommendations for the application of the model are given.

KW - Sociology

KW - MTMM analysis

KW - multilevel structural equation modeling

KW - structurally different and interchangeable methods

UR - http://www.scopus.com/inward/record.url?scp=84945303884&partnerID=8YFLogxK

U2 - 10.3102/1076998615606109

DO - 10.3102/1076998615606109

M3 - Journal articles

AN - SCOPUS:84945303884

VL - 40

SP - 477

EP - 510

JO - Journal of Educational and Behavioral Statistics

JF - Journal of Educational and Behavioral Statistics

SN - 1076-9986

IS - 5

ER -

DOI

Recently viewed

Publications

  1. On the Power and Performance of a Doubly Latent Residual Approach to Explain Latent Specific Factors in Multilevel-Bifactor-(S-1) Models
  2. Building a process layer for business applications using the blackboard pattern
  3. A discrete approximate solution for the asymptotic tracking problem in affine nonlinear systems
  4. Global text processing in CSCL with learning protocols
  5. Performance and Comfort when Using Motion-Controlled Tools in Complex Tasks
  6. Neural network-based adaptive fault-tolerant control for strict-feedback nonlinear systems with input dead zone and saturation
  7. N3 - A collection of datasets for named entity recognition and disambiguation in the NLP interchange format
  8. Comparing the Sensitivity of Social Networks, Web Graphs, and Random Graphs with Respect to Vertex Removal
  9. Optimal trajectory generation using MPC in robotino and its implementation with ROS system
  10. Multi-Parallel Sending Coils for Movable Receivers in Inductive Charging Systems
  11. On the Nonlinearity Compensation in Permanent Magnet Machine Using a Controller Based on a Controlled Invariant Subspace
  12. Paraphrasing Method for Controlling a Robotic Arm Using a Large Language Model
  13. Anomaly detection in formed sheet metals using convolutional autoencoders
  14. A Multilevel CFA-MTMM Model for Nested Structurally Different Methods
  15. Selection and Recognition of Statistically Defined Signals in Learning Systems
  16. Linux-based Embedded System for Wavelet Denoising and Monitoring of sEMG Signals using an Axiomatic Seminorm
  17. Neural Combinatorial Optimization on Heterogeneous Graphs
  18. Constructions and Reconstructions. The Architectural Image between Rendering and Photography
  19. Analyzing different types of moderated method effects in confirmatory factor models for structurally different methods
  20. Using the flatness of DC-Drives to emulate a generator for a decoupled MPC using a geometric approach for motion control in Robotino
  21. Dynamic Lot Size Optimization with Reinforcement Learning
  22. Latent structure perceptron with feature induction for unrestricted coreference resolution
  23. Intersection tests for the cointegrating rank in dependent panel data
  24. Dispatching rule selection with Gaussian processes
  25. Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa
  26. Optimizing sampling of flying insects using a modified window trap
  27. Finding Similar Movements in Positional Data Streams
  28. Exploration strategies, performance, and error consequences when learning a complex computer task
  29. The Use of Genetic Algorithm for PID Controller Auto-Tuning in ARM CORTEX M4 Platform
  30. Lyapunov stability analysis to set up a PI controller for a mass flow system in case of a non-saturating input
  31. Empowering materials processing and performance from data and AI
  32. Multidimensional Cross-Recurrence Quantification Analysis (MdCRQA)–A Method for Quantifying Correlation between Multivariate Time-Series