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The replication crisis in social and behavioral sciences has raised concernsabout the reliability and validity of empirical studies. While research in theliterature has explored contributing factors to this crisis, the issues relatedto analytical tools have received less attention. This study focuses on awidely used analytical tool - confirmatory factor analysis (CFA) - andinvestigates one issue that is typically overlooked in practice: accuratelyestimating factor-loading signs. Incorrect loading signs can distort therelationship between observed variables and latent factors, leading tounreliable or invalid results in subsequent analyses. Our study aims toinvestigate and address the estimation problem of factor-loading signs in CFAmodels. Based on an empirical demonstration and Monte Carlo simulation studies,we found current methods have drawbacks in estimating loading signs. To addressthis problem, three solutions are proposed and proven to work effectively. Theapplications of these solutions are discussed and elaborated.

Are the Signs of Factor Loadings Arbitrary in Confirmatory Factor Analysis? Problems and Solutions