Hierarchical Linear Modeling of Multilevel Data

2005 ◽  
Vol 19 (4) ◽  
pp. 387-403 ◽  
Author(s):  
Samuel Y. Todd ◽  
T. Russell Crook ◽  
Anthony G. Barilla
Keyword(s):  
Hierarchical Linear Modeling ◽  
Sport Management ◽  
Levels Of Analysis ◽  
Parameter Estimates ◽  
Multilevel Data ◽  
Management Research ◽  
Linear Modeling ◽  
Common Problems ◽  
Multiple Levels ◽  
Broad Overview

Most data involving organizations are hierarchical in nature and often contain variables measured at multiple levels of analysis. Hierarchical linear modeling (HLM) is a relatively new and innovative statistical method that organizational scientists have used to alleviate some common problems associated with multilevel data, thus advancing our understanding of organizations. This article presents a broad overview of HLM’s logic through an empirical analysis and outlines how its use can strengthen sport management research. For illustration purposes, we use both HLM and the traditional linear regression model to analyze how organizational and individual factors in Major League Baseball impact individual players’ salaries. A key implication is that, depending on the method, parameter estimates differ because of the multilevel data structure and, thus, findings differ. We explain these differences and conclude by presenting theoretical discussions from strategic management and consumer behavior to provide a potential research agenda for sport management scholars.

Effects of Compounded Nonnormality of Residuals in Hierarchical Linear Modeling

2021 ◽  
pp. 001316442110102
Author(s):  
Kaiwen Man ◽  
Randall Schumacker ◽  
Monica Morell ◽  
Yurou Wang
Keyword(s):  
Random Effects ◽  
Hierarchical Linear Modeling ◽  
Cluster Size ◽  
Fixed Effects ◽  
Social Science Research ◽  
Parameter Estimates ◽  
Linear Modeling ◽  
Number Of Clusters ◽  
Normality Assumption ◽  
Restricted Maximum Likelihood Estimation

While hierarchical linear modeling is often used in social science research, the assumption of normally distributed residuals at the individual and cluster levels can be violated in empirical data. Previous studies have focused on the effects of nonnormality at either lower or higher level(s) separately. However, the violation of the normality assumption simultaneously across all levels could bias parameter estimates in unforeseen ways. This article aims to raise awareness of the drawbacks associated with compounded nonnormality residuals across levels when the number of clusters range from small to large. The effects of the breach of the normality assumption at both individual and cluster levels were explored. A simulation study was conducted to evaluate the relative bias and the root mean square of the model parameter estimates by manipulating the normality of the data. The results indicate that nonnormal residuals have a larger impact on the random effects than fixed effects, especially when the number of clusters and cluster size are small. In addition, for a simple random-effects structure, the use of restricted maximum likelihood estimation is recommended to improve parameter estimates when compounded residuals across levels show moderate nonnormality, with a combination of small number of clusters and a large cluster size.

The Relationship Between Leader–Member Exchange and Employees’ Proactive Behaviors

2019 ◽  
Vol 18 (2) ◽  
pp. 106-111
Author(s):  
Fong-Yi Lai ◽  
Szu-Chi Lu ◽  
Cheng-Chen Lin ◽  
Yu-Chin Lee
Keyword(s):  
Hierarchical Linear Modeling ◽  
Future Research ◽  
Leader Member Exchange ◽  
Voice Behavior ◽  
Linear Modeling ◽  
Team Members ◽  
Member Exchange ◽  
Multi Temporal ◽  
The Relationship ◽  
Practical Implications

Abstract. The present study proposed that, unlike prior leader–member exchange (LMX) research which often implicitly assumed that each leader develops equal-quality relationships with their supervisors (leader’s LMX; LLX), every leader develops different relationships with their supervisors and, in turn, receive different amounts of resources. Moreover, these differentiated relationships with superiors will influence how leader–member relationship quality affects team members’ voice and creativity. We adopted a multi-temporal (three wave) and multi-source (leaders and employees) research design. Hypotheses were tested on a sample of 227 bank employees working in 52 departments. Results of the hierarchical linear modeling (HLM) analysis showed that LLX moderates the relationship between LMX and team members’ voice behavior and creative performance. Strengths, limitations, practical implications, and directions for future research are discussed.

Impact of relatedness, uncertainty and slack on corporate entrepreneurship decisions

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Robert Garrett ◽  
Shaunn Mattingly ◽  
Jeff Hornsby ◽  
Alireza Aghaey
Keyword(s):  
Decision Making ◽  
Hierarchical Linear Modeling ◽  
Cognitive Strategies ◽  
Corporate Entrepreneurship ◽  
New Venture ◽  
Organizational Level ◽  
Linear Modeling ◽  
Slack Resources ◽  
Content Type ◽  
Positive Effect

PurposeThe purpose of this study is to evaluate the effect of opportunity relatedness and uncertainty on the decision of a corporate entrepreneur to pursue a venturing opportunity.Design/methodology/approachThe study uses a conjoint experimental design to reveal the structure of respondents' decision policies. Data were gathered from 47 useable replies from corporate entrepreneurs and were analyzed with hierarchical linear modeling (HLM).FindingsResults show that product relatedness, market relatedness, perceived certainty about expected outcomes and slack resources all have a positive effect on the willingness of a corporate entrepreneur to pursue a new venture idea. Moreover, slack was found to diminish the positive effect of product relatedness on the likelihood to pursue a venturing opportunity.Practical implicationsBy providing a better understanding of decision-making schemas of corporate entrepreneurs, the findings of this study help improve the practice of entrepreneurship at the organizational level. In order to make more accurate opportunity assessments, corporate entrepreneurs need to be aware of their cognitive strategies and need to factor in the salient criteria affecting such assessments.Originality/valueThis paper adds to the limited understanding of corporate-level decision-making with regard to pursuing venturing opportunities. More specifically, the paper adds new insights regarding how relatedness and uncertainty affect new venture opportunity assessments in the presence (or lack thereof) of slack resources.

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