Item level correction: detecting, removing, and reporting common methods variance

Adam C. Merkle ()
Additional contact information
Adam C. Merkle: University of Tampa

Journal of Marketing Analytics, 2025, vol. 13, issue 2, No 8, 405-423

Abstract: Abstract Item level correction (ILC) is a data transformation method to detect, remove, and report common methods variance (CMV) in survey results. This practical guide provides a step-by-step approach to prepare and transform a dataset for analysis by removing CMV. ILC is a process that begins with survey design and ends with data transformation, before any statistical analysis of the research model. This method is a solution-oriented approach which moves past the discussion of when or how much CMV is acceptable. ILC produces a new standardized dataset for analysis. This transformed dataset contains measures that are theoretically free of CMV related to preselected method or marker variables. Some of the benefits of performing item level correction include: (1) using multiple method/marker variables without negatively impacting sample size, (2) item parceling avoidance to address uneven CMV influences across each individual survey item, and (3) eliminating the comparative model approach for assessing the presence of CMV while ensuring accurate reporting of construct reliability, validity, correlation, regression, and SEM results based on CMV-reduced measures. This article is meant to assist you in performing ILC in future research projects and offers guidelines about how to properly and fully report results in your methodology section.

Keywords: Item level correction; Common methods variance; Marker variables; Method variables; Measured latent marker variable (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1057/s41270-025-00399-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:jmarka:v:13:y:2025:i:2:d:10.1057_s41270-025-00399-2

Ordering information: This journal article can be ordered from
http://www.springer. ... gement/journal/41270

DOI: 10.1057/s41270-025-00399-2

Access Statistics for this article

Journal of Marketing Analytics is currently edited by Maria Petrescu and Anjala Krishnen

More articles in Journal of Marketing Analytics from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().