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.