Seven reasons why you should not categorize continuous data.

Area of Science:

• Measurement theory
• Statistical analysis
• Research methodology

Background:

• Continuous attributes are often imprecisely measured.
• Existing statistical methods may not adequately preserve data continuity.
• Inaccurate measurement and analysis can lead to flawed research conclusions.

Purpose of the Study:

• To highlight the importance of measuring continuous attributes with continuous measures.
• To advocate for statistical analyses that retain data continuity.
• To encourage critical evaluation of research conclusions based on data integrity.

Main Methods:

• Review of logical, philosophical, measurement, and statistical principles.
• Analysis of existing research practices in data measurement and analysis.
• Examination of the relationship between data continuity and conclusion validity.

Main Results:

• Numerous logical, philosophical, measurement, and statistical reasons support using continuous measures for continuous attributes.
• Statistical analyses should be chosen to preserve the continuity of the data.
• Failure to maintain continuity can invalidate research findings.

Conclusions:

• Researchers must prioritize using continuous measurement techniques for continuous variables.
• Statistical methods must be employed that respect and maintain data continuity.
• The validity of research conclusions is directly dependent on the appropriate handling of continuous data throughout the analysis process.