The Time Has Come

Abstract

The use of Bayesian methods for data analysis is creating a revolution in fields ranging from genetics to marketing. Yet, results of our literature review, including more than 10,000 articles published in 15 journals from January 2001 and December 2010, indicate that Bayesian approaches are essentially absent from the organizational sciences. Our article introduces organizational science researchers to Bayesian methods and describes why and how they should be used. We use multiple linear regression as the framework to offer a step-by-step demonstration, including the use of software, regarding how to implement Bayesian methods. We explain and illustrate how to determine the prior distribution, compute the posterior distribution, possibly accept the null value, and produce a write-up describing the entire Bayesian process, including graphs, results, and their interpretation. We also offer a summary of the advantages of using Bayesian analysis and examples of how specific published research based on frequentist analysis-based approaches failed to benefit from the advantages offered by a Bayesian approach and how using Bayesian analyses would have led to richer and, in some cases, different substantive conclusions. We hope that our article will serve as a catalyst for the adoption of Bayesian methods in organizational science research.

Balancing Internal and External R&D Strategies to Improve Innovation and Financial Performance

“…Bayesian Estimation was chosen to run the CFA and SEM as this estimation type has several advantages over others, such as maximum likelihood estimation. For instance, it: (a) is not based on the normality of the variables; (b) works better with smaller samples; (c) does not suppose a linear relationship; and (d) considers previous knowledge by demanding the imputation of a prior distribution, which may be a result of previous studies (Kruschke, Aguinis, & Joo, 2012). We used a convergence statistic below 1.1 as acceptable (Gelman, Carlin, Stern, & Rubin, 2014) and a confidence interval of 95% for the regression weights.…”

Everything that you have ever been told about assessment center ratings is confounded.

“…The main contribution of the study to practice is to provide an uncluttered perspective on the possible bases for reliability in AC ratings -a perspective that may offer guidance to applied psychologists in terms of the sources of measurement reliability in ACs and the design elements that may maximize AC reliability. In addition, recognizing that Bayesian approaches are well suited to complex models such as AC measurement models, and responding to calls in the organizational literature for Bayesian methods (Kruschke, Aguinis, & Joo, 2012;Zyphur, Oswald, & Rupp, 2015) and for applications of Bayesian generalizability theory (LoPilato, Carter, & Wang, 2015), we seek to contribute to the methodology used for the analysis of AC data and data from multifaceted measures generally.…”

Per capita income and public health expenditure: what makes good child health outcomes in Tanzania? a comparison of Frequentist and Bayesian approach (1995-2013)

“…Various authors have pointed out problems with sample size, especially those that applied frequentist approach to study the effect of public health expenditure on health outcomes. Among them (for example, Dieleman, 2013) argued about ill-powered sample size used and simultaneity problem encountered between GDP per capita and public health expenditure ( Akinci et al, 2014, andBarenberg et al, 2015) thus requiring instrumental variables or using two stages least-squares regression model to overcome endogeneity. Dieleman, (2013) claimed that studies linking between public health expenditure and health outcomes might be due to small sample size such as that by Anand & Ravalion,(1993), Filmer & Pritchett, (1999) used less than 100 observations.…”

“…Among them (for example, Dieleman, 2013) argued about ill-powered sample size used and simultaneity problem encountered between GDP per capita and public health expenditure ( Akinci et al, 2014, andBarenberg et al, 2015) thus requiring instrumental variables or using two stages least-squares regression model to overcome endogeneity. Dieleman, (2013) claimed that studies linking between public health expenditure and health outcomes might be due to small sample size such as that by Anand & Ravalion,(1993), Filmer & Pritchett, (1999) used less than 100 observations. This paper contributes to this literature by comparing both Bayesian Approach and Frequentist Approach (that is, the time-series analysis) in studying the impact of public health expenditure on health outcomes in Tanzania.…”