Data-Generating Mechanisms Versus Constructively Defined Latent Variables in Multitrait–Multimethod Analysis: A Comment on Castro-Schilo, Widaman, and Grimm (2013)

Geiser, Christian; Koch, Tobias; Eid, Michael

doi:10.1080/10705511.2014.919816

Cited by 21 publications

(13 citation statements)

References 23 publications

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“…However, results of previous studies have shown that the CT-CM modeling approach is associated with various theoretical and empirical problems (e.g., Marsh, 1989; Kenny and Kashy, 1992; Marsh and Grayson, 1994; Steyer, 1995; Eid, 2000; Geiser et al, in press). In addition, the CFA-MTMM model by Grimm et al (2009) is limited to single-indicator measurement designs and does not allow specifying trait-specific method factors.…”

Section: Longitudinal Cfa-mtmm Modelsmentioning

confidence: 99%

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

et al. 2014

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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.

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Section: Longitudinal Cfa-mtmm Modelsmentioning

confidence: 99%

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

et al. 2014

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“…With structurally different methods, each type of structurally different method represents a unique perspective or “fixed effect.” The underlying sampling procedure implies that structurally different methods are “at the same level” with the targets and not nested within the targets (as is the case with interchangeable methods). A meaningful way to compare structurally different methods is thus to contrast them against a reference method, as is done in the CT − C( M − 1) approach (Geiser et al, 2008 , 2014b ). This parallels what is commonly done in regression analysis with fixed categorical predictors, where researchers may use K − 1 dummy code variables to represent the K levels of a fixed factor.…”

Section: Introductionmentioning

confidence: 99%

Collapsing factors in multitrait-multimethod models: examining consequences of a mismatch between measurement design and model

2015

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Models of confirmatory factor analysis (CFA) are frequently applied to examine the convergent validity of scores obtained from multiple raters or methods in so-called multitrait-multimethod (MTMM) investigations. Many applications of CFA-MTMM and similarly structured models result in solutions in which at least one method (or specific) factor shows non-significant loading or variance estimates. Eid et al. (2008) distinguished between MTMM measurement designs with interchangeable (randomly selected) vs. structurally different (fixed) methods and showed that each type of measurement design implies specific CFA-MTMM measurement models. In the current study, we hypothesized that some of the problems that are commonly seen in applications of CFA-MTMM models may be due to a mismatch between the underlying measurement design and fitted models. Using simulations, we found that models with M method factors (where M is the total number of methods) and unconstrained loadings led to a higher proportion of solutions in which at least one method factor became empirically unstable when these models were fit to data generated from structurally different methods. The simulations also revealed that commonly used model goodness-of-fit criteria frequently failed to identify incorrectly specified CFA-MTMM models. We discuss implications of these findings for other complex CFA models in which similar issues occur, including nested (bifactor) and latent state-trait models.

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“…We chose to use the CT-C( M – 1) approach for our extension to mediation analysis, because this approach has been shown to overcome a number of limitations of previous CFA-MTMM approaches. First, in contrast to most other approaches, the CT-C( M – 1) model uses latent variables that have been explicitly and clearly defined as conditional expectations or functions of conditional expectations of observed variables; as a result, all latent variables in the model have a clear meaning and interpretation ( Eid, 2000 ; Eid et al, 2003 ; Geiser et al, 2008 , 2014 ). Second, the CT-C( M – 1) model solves identification problems present in other models ( Eid, 2000 ).…”

Section: A Novel Approach To MI Mediation Analysismentioning

confidence: 99%

“…For example, father’s underestimation of impulsivity relative to mother reports is not necessarily perfectly correlated with father’s underestimation of externalizing problems relative to mother reports. Additional advantages of the CT-C( M – 1) model in relation to other CFA-MTMM models are discussed in Geiser et al (2008) as well as Geiser et al (2014) .…”

Section: A Novel Approach To MI Mediation Analysismentioning

confidence: 99%

“…We now present a way to combine the variable-specific trait factors that is appropriate when indicators are essentially homogenous. For this purpose, we use a so-called latent means modeling approach in which common factors are defined as averages of the variable-specific trait factors ( Pohl and Steyer, 2010 ; Geiser et al, 2014 ). The basic idea of the latent means approach is to define or “construct” a common factor as the average of variable-specific trait factors.…”

Section: Ct-c( M – 1) Mi Mediation Modelmentioning

confidence: 99%

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Analyzing Statistical Mediation with Multiple Informants: A New Approach with an Application in Clinical Psychology

et al. 2015

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Testing mediation models is critical for identifying potential variables that need to be targeted to effectively change one or more outcome variables. In addition, it is now common practice for clinicians to use multiple informant (MI) data in studies of statistical mediation. By coupling the use of MI data with statistical mediation analysis, clinical researchers can combine the benefits of both techniques. Integrating the information from MIs into a statistical mediation model creates various methodological and practical challenges. The authors review prior methodological approaches to MI mediation analysis in clinical research and propose a new latent variable approach that overcomes some limitations of prior approaches. An application of the new approach to mother, father, and child reports of impulsivity, frustration tolerance, and externalizing problems (N = 454) is presented. The results showed that frustration tolerance mediated the relationship between impulsivity and externalizing problems. The new approach allows for a more comprehensive and effective use of MI data when testing mediation models.

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Data-Generating Mechanisms Versus Constructively Defined Latent Variables in Multitrait–Multimethod Analysis: A Comment on Castro-Schilo, Widaman, and Grimm (2013)

Cited by 21 publications

References 23 publications

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

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

Collapsing factors in multitrait-multimethod models: examining consequences of a mismatch between measurement design and model

Analyzing Statistical Mediation with Multiple Informants: A New Approach with an Application in Clinical Psychology

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