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Understanding the exploratory/confirmatory data analysis continuum: Moving beyond the "replication crisis".

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The replication crisis highlights the need for clear data analysis strategies. This article clarifies exploratory data analysis (EDA), confirmatory data analysis (CDA), and rough CDA, offering guidance for psychological research.

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Area of Science:

  • Psychology
  • Research Methodology
  • Data Analysis

Background:

  • The "replication crisis" necessitates stricter research standards and transparency.
  • Confirmatory Data Analysis (CDA) is proposed, but may stifle creativity.
  • Exploratory Data Analysis (EDA) is suggested as an alternative for discovery.

Purpose of the Study:

  • To clarify the meaning, tools, philosophy, and ethics of EDA, CDA, and "rough CDA."
  • To distinguish these approaches from problematic practices like p-hacking and HARKing.
  • To provide a framework for understanding ethical boundaries in data analysis.

Main Methods:

  • Conceptual clarification of data analysis typologies.
  • Development of a graphical framework to illustrate relationships and boundaries.
  • Analysis of the role of researcher intentions and transparency.

Main Results:

  • Many researchers misunderstand or are unaware of EDA and its proper application.
  • Most psychological research is not yet mature enough for strict CDA.
  • Existing tools are often mismatched with research agendas.

Conclusions:

  • The appropriate data analytic approach hinges on intentions and transparency.
  • Recommendations for integrating EDA, CDA, and rough CDA into graduate training.
  • Suggestions for fostering cumulative research to address the replication crisis.