A Metadata-Only Feature-Augmented Method Factor for Ex-Post Correction and Attribution of Common Method Variance

TL;DR

This paper proposes a metadata-only, feature-augmented method factor (FAMF-SEM) for ex-post correction of common method variance in surveys, leveraging questionnaire design features without extra data or marker variables.

Contribution

It introduces a novel, fixed-weight method factor based on survey design features, avoiding reliance on additional data or marker variables for CMV correction.

Findings

Effective CMV adjustment without extra data

Clear links between survey design features and CMV

Accessible Excel-based implementation

Abstract

Common Method Variance (CMV) is a recurring problem that reduces survey accuracy. Popular fixes such as the Harman single-factor test, correlated uniquenesses, common latent factor models, and marker variable approaches have well known flaws. These approaches either poorly identify issues, rely too heavily on researchers' choices, omit real information, or require special marker items that many datasets lack. This paper introduces a metadata-only Feature-Augmented Method Factor (FAMF-SEM): a single extra method factor with fixed, item-specific weights based on questionnaire details like reverse coding, page and item order, scale width, wording direction, and item length. These weights are set using ridge regression, based on residual correlations in a basic CFA, and remain fixed in the model. The method avoids the need for additional data or marker variables and provides CMV-adjusted results with clear links to survey design features. An AMOS/LISREL-friendly, no-code Excel workflow demonstrates the method. The paper explains the rationale, provides model details, outlines setup, presents step-by-step instructions, describes checks and reliability tests, and notes limitations.