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<p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年3月24日

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  • Adam C. Merkle

摘要

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.