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

Bollen, Kenneth A.

doi:10.1080/00273171.2018.1483224

Cited by 55 publications

(50 citation statements)

References 43 publications

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“…Several emerging methodologies, many from outside the management literature, have been proposed for instrumental variable approaches (e.g., Maydeu-Olivares et al, 2020). One is using instrumental variables in SEM contexts through model-implied instrumental variables (MIIVs; Bollen, 1996; 2019). Typical instrumental variables are external to the model—what Bollen (2012) calls auxiliary variables .…”

Section: Endogeneity: Causes and Associated Solutions In The Literaturementioning

confidence: 99%

Endogeneity: A Review and Agenda for the Methodology-Practice Divide Affecting Micro and Macro Research

Hill

Johnson

Greco

et al. 2020

Journal of Management

327

257

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An expanding number of methodological resources, reviews, and commentaries both highlight endogeneity as a threat to causal claims in management research and note that practices for addressing endogeneity in empirical work frequently diverge from the recommendations of the methodological literature. We aim to bridge this divergence, helping both macro and micro researchers understand fundamental endogeneity concepts by: (1) defining a typology of four distinct causes of endogeneity, (2) summarizing endogeneity causes and methods used in management research, (3) organizing the expansive methodological literature by matching the various methods to address endogeneity to the appropriate resources, and (4) setting an agenda for future scholarship by recommending practices for researchers and gatekeepers about identifying, discussing, and reporting evidence related to endogeneity. The resulting review builds literacy about endogeneity and ways to address it so that scholars and reviewers can better produce and evaluate research. It also facilitates communication about the topic so that both micro- and macro-oriented researchers can understand, evaluate, and implement methods across disciplines.

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Section: Endogeneity: Causes and Associated Solutions In The Literaturementioning

confidence: 99%

Endogeneity: A Review and Agenda for the Methodology-Practice Divide Affecting Micro and Macro Research

Hill

Johnson

Greco

et al. 2020

Journal of Management

327

257

View full text Add to dashboard Cite

show abstract

“…(3) Inferring that one or more parameter estimates are unbiased (true) estimates of causal effects constitutes a stricter assumption. Both omitted confounders and measurement error, along with a host of more technical issues, can bias estimates to various degrees (though remedies to even these challenges exist; Bollen ). For these reasons, appropriate caution is required when drawing conclusions.…”

Section: Interpretations and Considerationsmentioning

confidence: 99%

Scientist’s guide to developing explanatory statistical models using causal analysis principles

Grace

Irvine

2020

Ecology

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Recent discussions of model selection and multimodel inference highlight a general challenge for researchers: how to convey the explanatory content of a hypothesized model or set of competing models clearly. The advice from statisticians for scientists employing multimodel inference is to develop a well‐thought‐out set of candidate models for comparison, though precise instructions for how to do that are typically not given. A coherent body of knowledge, which falls under the general term causal analysis, now exists for examining the explanatory scientific content of candidate models. Much of the literature on causal analysis has been recently developed, and we suspect may not be familiar to many ecologists. This body of knowledge comprises a set of graphical tools and axiomatic principles to support scientists in their endeavors to create “well‐formed hypotheses,” as statisticians are asking them to do. Causal analysis is complementary to methods such as structural equation modeling, which provides the means for evaluation of proposed hypotheses against data. In this paper, we summarize and illustrate a set of principles that can guide scientists in their quest to develop explanatory hypotheses for evaluation. The principles presented in this paper have the capacity to close the communication gap between statisticians, who urge scientists to develop well‐thought‐out coherent models, and scientists, who would like some practical advice for exactly how to do that.

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“…The most important question is how well a researcher can control it? Among the several attempts that have been shown empirically to yield satisfactory performance in controlling for an extraneous variable, such as C in non-experimental research, is the use of instrumental variable approach ( Antonakis et al, 2010 ; Bollen, 2019 ). The basic idea of the instrumental variable approach is to find a fourth variable called an instrument that satisfies some properties (which will be explained in the “Materials and Methods” section) and use it to discern the actual effect of A on B from any confounding effects of C ( Greenland, 2000 ; Bollen, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Self-Efficacy Between Previous and Current Mathematics Performance of Undergraduate Students: An Instrumental Variable Approach to Exposing a Causal Relationship

Zakariya

2021

Front. Psychol.

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PurposeSelf-efficacy has been argued theoretically and shown empirically to be an essential construct for students’ improved learning outcomes. However, there is a dearth of studies on its causal effects on performance in mathematics among university students. Meanwhile, it will be erroneous to assume that results from other fields of studies generalize to mathematics learning due to the task-specificity of the construct. As such, attempts are made in the present study to provide evidence for a causal relationship between self-efficacy and performance with a focus on engineering students following a mathematics course at a Norwegian university.MethodThe adopted research design in the present study is a survey type in which collected data from first-year university students are analyzed using structural equation modeling with weighted least square mean and variance adjusted (WLSMV) estimator. Data were generated using mainly questionnaires, a test of prior mathematics knowledge, and the students’ final examination scores in the course. The causal effect of self-efficacy was discerned from disturbance effects on performance by using an innovative instrumental variable approach to structural equation modeling.ResultsThe findings confirmed a significant direct effect of the prior mathematics knowledge test (β = 0.52, SE = 0.01, p < 0.001) on self-efficacy, a significant direct effect (β = 0.43, SE = 0.19, p = 0.02) of self-efficacy on performance, and a substantial mediating effect (β = 0.22, SE = 0.10, p = 0.03) of self-efficacy between a prior mathematics knowledge test and performance. Prior mathematics knowledge and self-efficacy explained 30% variance of the performance. These findings are interpreted to be substantial evidence for the causal effect of self-efficacy on students’ performance in an introductory mathematics course.ConclusionThe findings of the present study provide empirically supports for designing self-efficacy interventions as proxies to improve students’ performance in university mathematics. Further, the findings of the present study confirm some postulates of Bandura’s agentic social cognitive theory.

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

Cited by 55 publications

References 43 publications

Endogeneity: A Review and Agenda for the Methodology-Practice Divide Affecting Micro and Macro Research

Endogeneity: A Review and Agenda for the Methodology-Practice Divide Affecting Micro and Macro Research

Scientist’s guide to developing explanatory statistical models using causal analysis principles

Self-Efficacy Between Previous and Current Mathematics Performance of Undergraduate Students: An Instrumental Variable Approach to Exposing a Causal Relationship

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