A Review of Exploratory Factor Analysis Decisions and Overview of Current Practices: What We Are Doing and How Can We Improve?

Authors within the fields of cyberpsychology and human−computer interaction have demonstrated a particular interest in measurement and scale creation, and exploratory factor analysis (EFA) is an extremely important statistical method for these areas of research. Unfortunately, EFA requires several statistical and methodological decisions to which the best choices are often unclear. The current article reviews five primary decisions and provides direct suggestions for best practices. These decisions are (a) the data inspection techniques, (b) the factor analytic method, (c) the factor retention method, (d) the factor rotation method, and (e) the factor loading cutoff. Then the article reviews authors' choices…