Kim Nimon

Tools to Support Interpreting Multiple Regression in the Face of Multicollinearity

While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses.

Beyond Engagement:Toward a Framework and Operational Definition for Employee Work Passion

The purpose of this article is to respond to the lack of consistency in the academic and practitioner literature regarding the construct of employee engagement and to offer a platform for the research and use of a refined construct called employee work passion. This article analyzes the differences between the concepts of engagement of the two groups of writers and proposes a new definition and framework based on social cognitive theory. Three recommendations are made for human resource development researchers and practitioners who seek to improve both the data and the strategies used in constructing engagement or work passion surveys. Engagement or passion surveys should (a) specifically and convincingly assess the affective components of the appraisal process, (b) differentiate descriptive cognitions and intentions, and (c) separate and corroborate intentions from behaviors.

Using commonality analysis in multiple regressions: a tool to decompose regression effects in the face of multicollinearity

tool to decompose regression effects in the face of multicollinearity Jayanti Ray-Mukherjee*, KimNimon, ShomenMukherjee, DouglasW.Morris, RobSlotow andMichelle Hamer TheSchool of Life Sciences, University of KwaZulu-Natal,Westville Campus, Private Bag: X54001, Durban 4000, South Africa; Learning Technologies andCollege of Information, University of North Texas, 3940N. Elm, RmG150Denton, TX, 76207, USA; Department of Biology, LakeheadUniversity, Thunder Bay, ON, P7B 5E1, Canada; and South AfricanNational Biodiversity Institute, Private BagX101, Pretoria 0001, South Africa

Understanding the Results of Multiple Linear Regression: Beyond Standardized Regression Coefficients

Multiple linear regression (MLR) remains a mainstay analysis in organizational research, yet intercorrelations between predictors (multicollinearity) undermine the interpretation of MLR weights in terms of predictor contributions to the criterion. Alternative indices include validity coefficients, structure coefficients, product measures, relative weights, all-possible-subsets regression, dominance weights, and commonality coefficients. This article reviews these indices, and uniquely, it offers freely available software that (a) computes and compares all of these indices with one another, (b) computes associated bootstrapped confidence intervals, and (c) does so for any number of predictors so long as the correlation matrix is positive definite. Other available software is limited in all of these respects. We invite researchers to use this software to increase their insights when applying MLR to a data set. Avenues for future research and application are discussed.

The Jingle Jangle of Employee Engagement: Further Exploration of the Emerging Construct and Implications for Workplace Learning and Performance

While research is emerging around the employee engagement construct, evolution is in early stages of development. Presently, some questions remain about how employee engagement differs from other well-researched and documented constructs such as job satisfaction, job involvement, and job commitment. Although such inquiry is seemingly academic in nature, the use of engagement in practice is gaining momentum, and debate remains healthy as to the utility and statistical validity of the engagement construct. To respond, developing clear lines of interpretation and coordination across varied disciplines seems prudent, but an essential first step is a context-specific, conceptual exploration of the construct of employee engagement in relation to other well-researched job attitude and organizational constructs in the literature. This article explores literature on employee engagement, job satisfaction, commitment, and involvement. Implications for organizational learning and workplace performance are examined in a human resource development (HRD) specific context.

Revisiting Interpretation of Canonical Correlation Analysis: A Tutorial and Demonstration of Canonical Commonality Analysis

In the face of multicollinearity, researchers face challenges interpreting canonical correlation analysis (CCA) results. Although standardized function and structure coefficients provide insight into the canonical variates produced, they fall short when researchers want to fully report canonical effects. This article revisits the interpretation of CCA results, providing a tutorial and demonstrating canonical commonalty analysis. Commonality analysis fully explains the canonical effects produced by using the variables in a given canonical set to partition the variance of canonical variates produced from the other canonical set. Conducting canonical commonality analysis without the aid of software is laborious and may be untenable, depending on the number of noteworthy canonical functions and variables in either canonical set. Commonality analysis software is identified for the canonical correlation case and we demonstrate its use in facilitating model interpretation. Data from Holzinger and Swineford (1939) are employed to test a hypothetical theory that problem-solving skills are predicted by fundamental math ability.

Effectiveness of a Supplemental Early Reading Intervention Scaled up in Multiple Schools

This effectiveness study examined a supplemental reading intervention that may be appropriate as one component of a response-to-intervention (RTI) system. First-grade students in 31 schools who were at risk for reading difficulties were randomly assigned to receive Responsive Reading Instruction (RRI; Denton, 2001; Denton & Hocker, 2006; n = 182) or typical school practice (TSP; n = 40). About 43% of the TSP students received an alternate school-provided supplemental reading intervention. Results indicated that the RRI group had significantly higher outcomes than the TSP group on multiple measures of reading. About 91% of RRI students and 79% of TSP students met word reading criteria for adequate intervention response, but considerably fewer met a fluency benchmark.

Regression Commonality Analysis: A Technique for Quantitative Theory Building

When it comes to multiple linear regression analysis (MLR), it is common for social and behavioral science researchers to rely predominately on beta weights when evaluating how predictors contribute to a regression model. Presenting an underutilized statistical technique, this article describes how organizational researchers can use commonality analysis to more completely interpret their regression effects and thereby inform theory. Using an empirical example from published literature, readers will see how regression commonality analysis can uncover important theoretical relationships that might be left undetected by only examining beta weights.

Measurement Invariance: A Foundational Principle for Quantitative Theory Building

This article describes why measurement invariance is a critical issue to quantitative theory building within the field of human resource development. Readers will learn what measurement invariance is and how to test for its presence using techniques that are accessible to applied researchers. Using data from a LibQUAL+TM study of user perceptions of service quality, we demonstrate how measurement invariance can threaten the validity of group comparison tests and ensuing theoretical conclusions. Moreover, we identify how measurement invariance can uncover important relationships in data that would otherwise go left unnoticed.

A Cognitive Approach to Work Intention The Stuff That Employee Work Passion Is Made of

The Problem. The measurement of intention has not been used enough in employee engagement and work passion research The Solution. Using a social cognitive process model for employee work passion, the authors (a) define and describe the term work intention, (b) present three reasons why work intention should be measured, (c) summarize relevant research, and (d) suggest three recommendations for human resource development (HRD) professionals to consider. The Stakeholders. Stakeholders include human resource development scholars, practitioners, and scholar–practitioners concerned with the consequences of employee engagement.