Robustness Conditions for MIIV-2SLS When the Latent Variable or Measurement Model is Structurally Misspecified

Bollen, Kenneth A.; Gates, Kathleen M.; Fisher, Zachary

doi:10.1080/10705511.2018.1456341

Cited by 24 publications

(29 citation statements)

References 24 publications

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“…The estimation approach, called model implied instrumental variable estimation (Bollen, 1996) is well established and has been shown by K.M.G. and colleagues to provide robust estimates even in the presence of model misspecifications that are likely to occur in fMRI data analysis (Bollen et al, 2018). Figure 6 depicts the dynamic factor model approach with relations among brain networks as well as relations capturing the extent to which specific brain regions (or voxels) relate to others within a given network.…”

Section: Group Iterative Multiple Model Estimation: a Tool For Multismentioning

confidence: 99%

BRAIN Initiative: Cutting-Edge Tools and Resources for the Community

et al. 2019

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The overarching goal of the NIH BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative is to advance the understanding of healthy and diseased brain circuit function through technological innovation. Core principles for this goal include the validation and dissemination of the myriad innovative technologies, tools, methods, and resources emerging from BRAIN-funded research. Innovators, BRAIN funding agencies, and non-Federal partners are working together to develop strategies for making these products usable, available, and accessible to the scientific community. Here, we describe several early strategies for supporting the dissemination of BRAIN technologies. We aim to invigorate a dialogue with the neuroscience research and funding community, interdisciplinary collaborators, and trainees about the existing and future opportunities for cultivating groundbreaking research products into mature, integrated, and adaptable research systems. Along with the accompanying Society for Neuroscience 2019 Mini-Symposium, "BRAIN Initiative: Cutting-Edge Tools and Resources for the Community," we spotlight the work of several BRAIN investigator teams who are making progress toward providing tools, technologies, and services for the neuroscience community. These tools access neural circuits at multiple levels of analysis, from subcellular composition to brain-wide network connectivity, including the following: integrated systems for EM-and florescence-based connectomics, advances in immunolabeling capabilities, and resources for recording and analyzing functional connectivity. Investigators describe how the resources they provide to the community will contribute to achieving the goals of the NIH BRAIN Initiative. Finally, in addition to celebrating the contributions of these BRAIN-funded investigators, the Mini-Symposium will illustrate the broader diversity of BRAIN Initiative investments in cutting-edge technologies and resources.

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Section: Group Iterative Multiple Model Estimation: a Tool For Multismentioning

confidence: 99%

BRAIN Initiative: Cutting-Edge Tools and Resources for the Community

et al. 2019

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“…In this section I have discussed the conditions of robustness when using the MIIV-2SLS estimator. Bollen (2001) and Bollen, Fisher, and Gates (2018) present general conditions for robustness that I illustrated. MIIV-2SLS is robust when the MIIVs and the equation from a structurally misspecified model are the same as the MIIVs for the structurally correct model even if other equations are misspecified.…”

Section: Robustnessmentioning

confidence: 99%

“…The conditions in Bollen (2001) are fairly general and it would be useful to have more specific ones. Bollen et al (2018) clarifies when structural misspecifications in the latent variable model affect the measurement model and vice versa, but even more could be done to clarify the robustness conditions within the measurement model or within the latent variable model. There also is an issue of the optimal selection of MIIVs for an equation when there are a large number of MIIVs to choose from.…”

Section: Open Questionsmentioning

confidence: 99%

Model Implied Instrumental Variables (MIIVs): An Alternative Orientation to Structural Equation Modeling

Bollen

2018

Multivariate Behavioral Research

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Few dispute that our models are approximations to reality. Yet when it comes to structural equation models (SEMs), we use estimators that assume true models (e.g. maximum likelihood) and that can create biased estimates when the model is inexact. This article presents an overview of the Model Implied Instrumental Variable (MIIV) approach to SEMs from Bollen (1996). The MIIV estimator using Two Stage Least Squares (2SLS), MIIV-2SLS, has greater robustness to structural misspecifications than system wide estimators. In addition, the MIIV-2SLS estimator is asymptotically distribution free. Furthermore, MIIV-2SLS has equation-based overidentification tests that can help pinpoint misspecifications. Beyond these features, the MIIV approach has other desirable qualities. MIIV methods apply to higher order factor analyses, categorical measures, growth curve models, dynamic factor analysis, and nonlinear latent variables. Finally, MIIV-2SLS permits researchers to estimate and test only the latent variable model or any other subset of equations. In addition, other MIIV estimators beyond 2SLS are available. Despite these promising features, research is needed to better understand its performance under a variety of conditions that represent empirical applications. Empirical and simulation examples in the article illustrate the MIIV orientation to SEMs and highlight an R package MIIVsem that implements MIIV-2SLS.

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“…A second and related benefit is that the latent variable model parameter estimation is conducted separately from measurement model parameter estimation. As noted above, much work has shown that the strict assumption of homogeneity in processes across individuals is often not met in neuroimaging (Finn et al, 2015;Gates, Molenaar, Iyer, Nigg, & Fair, 2014;Laumann et al, 2015;Miller et al, 2002), has been shown previously that relations (even misspecifications) occurring for the latent variable model do not influence the measurement model parameter estimates (Bollen et al, 2018) and that MIIV-2SLS estimates of dynamic factor model parameters can be more robust to model misspecification when compared to traditional system-wide estimators (pseudo-ML, Kalman filter) (Fisher, Bollen, & Gates, 2019). Hence in this framework, estimating the entire final model using MIIV-2SLS will provide the same measurement model parameter estimates as those one would obtain by estimating the model with no latent variable paths.…”

Section: The Miiv-2sls Estimatormentioning

confidence: 99%

Latent variable GIMME using model implied instrumental variables (MIIVs).

Gates¹,

Fisher²,

Bollen³

2020

Psychological Methods

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Researchers across many domains of psychology increasingly wish to arrive at personalized and generalizable dynamic models of individuals' processes. This is seen in psychophysiological, behavioral, and emotional research paradigms, across a range of data types. Errors of measurement are inherent in most data. For this reason, researchers typically gather multiple indicators of the same latent construct and use methods, such as factor analysis, to arrive at scores from these indices. In addition to accurately measuring individuals, researchers also need to find the model that best describes the relations among the latent constructs. Most currently available data-driven searches do not include latent variables. We present an approach that builds from the strong foundations of Group Iterative Multiple Model Estimation (GIMME), the idiographic filter, and model implied instrumental variables with two-stage least squares estimation (MIIV-2SLS) to provide researchers with the option to include latent variables in their data-driven model searches. The resulting approach is called Latent Variable GIMME (LV-GIMME). GIMME is utilized for the data-driven search for relations that exist among latent variables. Unlike other approaches such as the idiographic filter, LV-GIMME does not require that the latent variable model to be constant across individuals. This requirement is loosened by utilizing MIIV-2SLS for estimation. Simulated data studies demonstrate that the method can reliably detect relations among latent constructs, and that latent constructs provide more power to detect effects than using observed variables directly. We use empirical data examples drawn from functional MRI and daily self-report data.

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Robustness Conditions for MIIV-2SLS When the Latent Variable or Measurement Model is Structurally Misspecified

Cited by 24 publications

References 24 publications

BRAIN Initiative: Cutting-Edge Tools and Resources for the Community

BRAIN Initiative: Cutting-Edge Tools and Resources for the Community

Model Implied Instrumental Variables (MIIVs): An Alternative Orientation to Structural Equation Modeling

Latent variable GIMME using model implied instrumental variables (MIIVs).

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