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Many of us share the challenge to amalgamate previous research with multiple variables in literature reviews or conducting synthesis analysis, etc. Steven Rogelberg shared the below link to a "great new online relative weights resource Scott and James put together for the field. It is terrific."

The article explaining the tool:

http://link.springer.com/article/10.1007/s10869-014-9351-z

Tonidandel, S., & LeBreton, J. M. (2015). RWA web: A free, comprehensive, web-based, and user-friendly tool for relative weight analyses. Journal of Business and Psychology, volume 30, 2,  207-215

The link to the website for the free tool:

http://relativeimportance.davidson.edu

Scott Tonidandel and James LeBreton also wrote an excellent earlier article in 2011 in the Methods Corner on relative importance of analyses, at the request of the editor of the Journal of Business and Psychology.

Chair: James M. LeBreton; Pennsylvania State U.;
Discussant: Ronald S. Landis; Illinois Institute of Technology;
Discussant: Jose M. Cortina; George Mason U.;

The amount of information (data) that is generated by individuals (both within and outside of their employing organizations) may seem overwhelming at times. For example, in 2010 Eric Schmidt was serving as the Chief Executive Officer at Google and has been quoted as saying, "There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every 2 days." Whether we like it or not, the Big Data revolution is upon us. Unfortunately, Big Data are often difficult to conceptualize, measure, and analyze. For example, how do we conceptualize the variables in a data set containing information about pixels extracted from a streaming video? How can we take large volumes of text data measured via organizational emails and extract meaningful information about the culture of the organization? What statistical analyses may be used to simplify a vast and complicated data set, especially one where traditional statistics such as regression or factor analysis may encounter problems? Collectively this session a) reminds us that ours is a learning profession (and thus those who are willing to sow the seeds of learning new methods will reap the benefits of generating more sophisticated and inclusive theory), b) introduces some of the challenges and opportunities associated with Big Data (especially data that are emerging via digitization of our public and private worlds), and c) illustrates a few of the more promising analytic techniques applicable to Big Data projects

Dealing With Lots of Measurements in a Big Data World: An Introduction to Random Projections   
Presenter: Scott Tonidandel; Davidson College;

The Big Data (R)evolution in Organizational Research: An Introduction and Definition   
Presenter: Ramon Wenzel; U. of Western Australia;
Presenter: Niels Van Quaquebeke; Kuehne Logistics U.;

Augmenting Organizational Research with the Text Mining Toolkit: All Aboard!   
Presenter: Vladimer Kobayashi; U. of Amsterdam;
Presenter: Hannah Ariane Berkers; U. of Amsterdam;
Presenter: Stefan T. Mol; U. of Amsterdam;
Presenter: Gábor Kismihók; U. of Amsterdam;
Presenter: Deanne N. DenHartog; U. of Amsterdam;

Bayesian Inference and Big Data Streams: Potential Use Cases in Organizations and Management   
Presenter: Deniz Tuncalp; Istanbul Technical U.;

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