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date: 03 April 2020

Abstract and Keywords

Statistical data analysis routinely expresses results in terms of main effects and interactions at the level of variables. The implicit assumption of this routine is that the observed relationships are valid across the entire range of admissible scores. In contrast, applications of configural frequency analysis (CFA) proceed from the assumption that main effects and interactions reflect local relationships among variables. In other words, effects can be identified for some categories of variables but not for others. In this chapter, an introduction to CFA is provided in three sections. The first section covers sample questions that can be answered with CFA. These questions illustrate the range of CFA application, and they show that CFA focuses on local effects. The second section presents technical elements of CFA, including the CFA base model, the CFA null hypothesis, significance tests used in CFA, and methods of α protection.The third section provides sample base models of CFA and data applications. The sample base models cover Prediction CFA, two-Group CFA, CFA methods for the predictions of endpoints and for the analysis of the relationships between series of measures, and CFA methods for the analysis of mediator hypotheses.

Keywords: configural frequency analysis (CFA), local effects, longitudinal CFA, two-Group CFA, mediatior CFA, moderator CFA

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