Bayesian Probability and Statistics in Management Research

Zyphur, Michael J.; Oswald, Frederick L.

doi:10.1177/0149206312463183

Cited by 46 publications

(37 citation statements)

References 63 publications

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“…Moreover, it allows researchers to test models that would otherwise be under-identified using Frequentist estimation methods (e.g., estimating all the cross-loadings and residual covariances). Given that a full exposition is beyond the scope of this paper, interested readers are encouraged to read the work of Asparouhov, Muth en, and Morin (2015), Muth en and Asparouhov (2012), van de Schoot et al (2014), and Zyphur and Oswald (2013).…”

Section: Frequentist Vs Bayesianmentioning

confidence: 99%

“…BCFA focuses not only on estimating the model, but generating model changes to produce a better model fit and better representation of the data (Asparouhov et al, 2015). Consequently, the BCFA approach tends to identify cross-loadings, residual covariances, and other parameter estimates that might otherwise be missed with traditional CFA models (see Asparouhov et al, 2015;Muth en & Asparouhov, 2012;van de Schoot et al, 2014;Zyphur & Oswald, 2013).…”

Section: Benefits Of Bcfamentioning

confidence: 99%

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An evaluation of the Behavior and Instructional Management Scale's psychometric properties using Portuguese teachers

Sass

Lopes

Oliveira

et al. 2016

Teaching and Teacher Education

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Section: Frequentist Vs Bayesianmentioning

confidence: 99%

Section: Benefits Of Bcfamentioning

confidence: 99%

An evaluation of the Behavior and Instructional Management Scale's psychometric properties using Portuguese teachers

Sass

Lopes

Oliveira

et al. 2016

Teaching and Teacher Education

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“…J. Lee & Anderson, 2001). In addition to the benefits to statistical inference (see Wagenmakers, 2007;Wetzels et al, 2011;Zyphur & Oswald, 2013), the present study illustrates the flexibility of the Bayesian approach, particularly in performing multilevel modeling of non-linear functions with non-normal data. The Bayesian approach also provided a more objective metric for judging the degree to which strategy changes were abrupt or gradual, and provided a means for integrating strategy use into models of the learning curve.…”

Section: Modeling Strategy Changementioning

confidence: 80%

Abrupt strategy change underlies gradual performance change: Bayesian hierarchical models of component and aggregate strategy use.

Wynton

Anglim

2017

Journal of Experimental Psychology: Learning, Memory, and Cogni

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Abstract. While researchers have often sought to understand the learning curve in terms of multiple component processes, few studies have measured and mathematically modeled these processes on a complex task. In particular, there remains a need to reconcile how abrupt changes in strategy use can co-occur with gradual changes in task completion time. Thus, the current study aimed to assess the degree to which strategy change was abrupt or gradual, and whether strategy aggregation could partially explain gradual performance change. It also aimed to show how Bayesian methods could be used to model the effect of practice on strategy use. To achieve these aims, 162 participants completed 15 blocks of practice on a complex computer-based task-the Wynton Anglim Booking (WAB) Task. The task allowed for multiple component strategies (i.e., memory retrieval, information reduction, and insight) that could also be aggregated to a global measure of strategy use. Bayesian hierarchical models were used to compare abrupt and gradual functions of component and aggregate strategy use. Task completion time was well-modeled by a power function, and global strategy use explained substantial variance in performance. Change in component strategy use tended to be abrupt, whereas change in global strategy use was gradual and well-modeled by a power function. Thus, differential timing of component strategy shifts leads to gradual changes in overall strategy efficiency, and this provides one reason for why smooth learning curves can co-occur with abrupt changes in strategy use.

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“…Fifth, recognizing variance relative to a prior distribution can facilitate detection of publication bias in a manner that is more statistically grounded than the calculation of the fail-safe total sample size. Sixth, because Bayesian effect size estimation requires an accurate prior distribution (Zyphur & Oswald, 2013), errant estimates of variance will have a cascading effect, adversely influencing all the subsequent studies that rely on the prior distribution.…”

Section: Discussionmentioning

confidence: 99%

Improving the Meta-Analytic Assessment of Effect Size Variance With an Informed Bayesian Prior

Steel

Kammeyer‐Mueller

Paterson

2014

Journal of Management

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Meta-analytic estimation of effect size variance is critical for determining the degree to which a relationship or finding generalizes across contexts. In most meta-analyses, population effect size variability is estimated by subtracting expected sampling error variance from observed variance, using only information from a limited set of available studies. We propose an improved Bayesian variance estimation technique that incorporates findings from previous meta-analytic research through an informed prior distribution of likely levels of effect size variance. The logic of exchangeability as a conceptual foundation for using an informed prior is explicated. On the basis of Monte Carlo simulations, we find the traditional method of meta-analytic variance estimation the most biased and least accurate technique across all sizes of meta-analyses considered. The Bayesian methodology incorporating an informed prior proved to be the most accurate and overall least biased of all estimation methods. Conceptual advantages and limitations that must be taken into account when incorporating an informed prior to estimate variability of effect sizes in a meta-analysis are also discussed.

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Bayesian Probability and Statistics in Management Research

Cited by 46 publications

References 63 publications

An evaluation of the Behavior and Instructional Management Scale's psychometric properties using Portuguese teachers

An evaluation of the Behavior and Instructional Management Scale's psychometric properties using Portuguese teachers

Abrupt strategy change underlies gradual performance change: Bayesian hierarchical models of component and aggregate strategy use.

Improving the Meta-Analytic Assessment of Effect Size Variance With an Informed Bayesian Prior

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