This article discusses the use of exploratory factor analysis (EFA) in psychological research, highlighting the critical decisions researchers must make when conducting EFA. The authors argue that despite its widespread use, researchers often make questionable decisions that can lead to misleading results. They review major methodological decisions in EFA, including study design, sample selection, model specification, factor extraction, and rotation. The authors emphasize that each decision has important consequences for the results obtained. They also discuss the implications of these practices for psychological research and the reasons for current practices. EFA is a statistical method used to identify underlying factors that explain the correlations among measured variables. It is based on the common factor model, which assumes that each measured variable is a linear function of common factors and unique factors. The authors compare EFA with principal components analysis (PCA), noting that while PCA is computationally simpler, EFA is more appropriate when the goal is to identify latent constructs. They also discuss the importance of selecting an appropriate number of factors, the choice of model-fitting procedures, and the use of factor rotation to achieve a simple structure. The authors highlight the importance of making sound methodological decisions in EFA, including selecting appropriate measured variables, determining sample size, and choosing the right model-fitting procedure. They also emphasize the need for researchers to consider the theoretical and empirical basis for their decisions and to avoid overly homogeneous samples. The article concludes with recommendations for conducting EFA, including the use of descriptive fit indexes such as RMSEA and ECVI to determine the number of factors. The authors argue that EFA is a powerful tool for psychological research, but its effectiveness depends on the soundness of the methodological decisions made.This article discusses the use of exploratory factor analysis (EFA) in psychological research, highlighting the critical decisions researchers must make when conducting EFA. The authors argue that despite its widespread use, researchers often make questionable decisions that can lead to misleading results. They review major methodological decisions in EFA, including study design, sample selection, model specification, factor extraction, and rotation. The authors emphasize that each decision has important consequences for the results obtained. They also discuss the implications of these practices for psychological research and the reasons for current practices. EFA is a statistical method used to identify underlying factors that explain the correlations among measured variables. It is based on the common factor model, which assumes that each measured variable is a linear function of common factors and unique factors. The authors compare EFA with principal components analysis (PCA), noting that while PCA is computationally simpler, EFA is more appropriate when the goal is to identify latent constructs. They also discuss the importance of selecting an appropriate number of factors, the choice of model-fitting procedures, and the use of factor rotation to achieve a simple structure. The authors highlight the importance of making sound methodological decisions in EFA, including selecting appropriate measured variables, determining sample size, and choosing the right model-fitting procedure. They also emphasize the need for researchers to consider the theoretical and empirical basis for their decisions and to avoid overly homogeneous samples. The article concludes with recommendations for conducting EFA, including the use of descriptive fit indexes such as RMSEA and ECVI to determine the number of factors. The authors argue that EFA is a powerful tool for psychological research, but its effectiveness depends on the soundness of the methodological decisions made.