Hierarchical Linear Modeling: Testing Multilevel Theories

In the previous post I provided information relating to a recent book chapter. This book chapter describes different techniques in developing and disseminating multilevel theories. In this article I present a new publication that identifies, briefly, how to test multilevel theories. This main statistical analysis methodology is commonly referred to Hierarchical Linear Modeling (HLM; Raudenbush & Bryk, 2002), but is also referred to multilevel regression analysis or random coefficient regression modeling (Cohen, Cohen, West, & Aiken, 2003), multilevel models (Hox, 2010), mixed models and random effects models (McCoach, 2010).

The online reference for the new article is provided below. This article will be available in print at the beginning of 2015.

Turner, J. R. (2014). Hierarchical linear modeling: Testing multilevel theories. Advances in Developing Human Resources [Published Online]. doi:10.1177/1523422314559808

Part of the structured abstract is provided below:

The Problem: While nested structures occur naturally in organizational and educational settings, past research has failed to recognize these nested structures. Ordinary least squares (OLS) methods assume independence of observation, fixing the intercepts and slopes across all groups. By not accounting for nested structures, errors of inference can occur with the risk of compromising the validity of the results.
The Solution: As new theories become more complex multilevel representations of phenomena, testing these complex theories require hierarchical linear modeling (HLM). HLM provides human resource development (HRD) practitioners with a better method to test multilevel theories while taking into account nested structures, providing a more accurate representation across the different levels.


References:

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). New York, NY: Routledge.

Hox, J. (2010). Multilevel analysis: Techniques and applications (2nd ed.). New York, NY: Routledge.

McCoach, B. D. (2010). Hierarchical linear modeling. In G. R. Hancock & R. O, Mueller (Eds.), The reviewer's guide to quantitative methods in the social sciences: revise, accept, reject (pp. 123-140). New York, NY: Routledge.

Raudenbush, S. W., & Byrk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.