Campus Units

Supply Chain and Information Systems

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

4-1-2020

Journal or Book Title

Decision Sciences

Volume

51

Issue

2

First Page

423

Last Page

447

DOI

10.1111/deci.12431

Abstract

The last decade has seen an increase in empirical supply chain management research with dyadic data. Such data structures can further complicate the assessment of nonresponse bias, which plays a key role in establishing the credibility of research results. A survey of 75 research articles with dyadic data, published in five empirically focused supply chain management academic journals, over the last decade, reveals a lack of agreement on methods used in the assessment for potential unit nonresponse bias. Of the various statistical tests found, only the Multivariate Analysis of Variance (MANOVA) approach allows for a single statistical test to be utilized in assessing for potential unit nonresponse bias via incorporation of the design structure of the dyadic data. We investigate the use of an effect size confidence interval coverage, of a MANOVA, to detect a meaningful difference between respondents and nonrespondents correctly. Our results show that with dyadic data, such meaningful differences can be detected with significantly smaller sample size requirements than traditional approaches such as t‐tests or ANOVA. Recommendations are provided for setting up and executing a MANOVA to assess for potential unit nonresponse bias with dyadic data.

Comments

This accepted article is published as Clottey, T. and Benton, W.C., Jr. (2020), Technical Note: Recommendations for Assessing Unit Nonresponse Bias in Dyadic Focused Empirical Supply Chain Management Research. Decision Sciences, 51: 423-447. Doi: 10.1111/deci.12431. Posted with permission.

Copyright Owner

Decision Sciences Institute

Language

en

File Format

application/pdf

Available for download on Friday, April 01, 2022

Published Version

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