Data Types in Clinical Research

Key Points

Categorical data describe qualitative characteristics, while quantitative data capture numerical measurements that can be discrete or continuous.
Identifying the correct data type is essential for selecting appropriate statistical methods and ensuring valid study results.
Misclassification of data can lead to incorrect statistical analysis, reduced study power, and misleading conclusions in clinical research.

Categorical data describe characteristics or groupings rather than numerical values. They can be further divided into two subtypes:
- Nominal data: Categories without intrinsic order (e.g., blood type, gender).^1,2
- Ordinal data: Categories with a meaningful order but without consistent intervals between categories (e.g., cancer stage, pain scale).^1,3
Quantitative data represent numerical measurements and can be divided into two subtypes:
- Discrete data: Countable values, often integers (e.g., number of hospital admissions, number of medications).^1,4,5
- Continuous data: Measured on a continuum and can take any value within a range (e.g., blood pressure, serum creatinine).^1,4,5
Nominal and ordinal data are both categorical, but only ordinal data have a ranked order. Discrete and continuous data are both quantitative, but discrete data are countable, while continuous data are measurable and can be subdivided infinitely, depending on measurement precision.^1,4,5

The selection of statistical tests depends on the type and distribution of the data.

Table 1. Common statistical tests that can be used with each of the major data types

Variable misclassification: Treating ordinal variables as continuous (e.g., averaging pain scores) or continuous variables as categorical (e.g., dichotomizing body mass index) can distort associations, reduce statistical power, and obscure clinically relevant differences. This practice introduces bias and can lead to incorrect inferences.^3,6,7
Loss of information through simplification: Simplifying continuous data into binary or categorical outcomes (such as “high” versus “low”) may enhance interpretability but typically results in substantial loss of information, decreased sensitivity to true effects, and increased risk of type I error.^3,6
Overly detailed classification: Excessive precision or too many subcategories can fragment data, create sparse cell counts, and complicate analysis without improving interpretability. The optimal approach balances simplicity with sufficient granularity to retain clinical meaning.¹

Understanding data types informs study design, hypothesis formulation, and analytical methodology. Proper classification enhances the internal validity (accuracy within study conditions) and external validity (generalizability to broader populations). Misclassification or inappropriate statistical handling can introduce systematic bias, compromise study reproducibility, and hinder meta-analytic synthesis.^1,5
Clinicians and researchers should consider data type early in protocol development to ensure that data collection instruments, statistical plans, and study endpoints align appropriately.^1,5

References

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Xu B, Feng X, Burdine RD. Categorical data analysis in experimental biology. Dev Biol. 2010;348(1):3-11. PubMed
Verhulst B, Neale MC. Best practices for binary and ordinal data analyses. Behav Genet. 2021;51(3):204-14. PubMed
Bensken WP, Pieracci FM, Ho VP. Basic introduction to statistics in Medicine, Part 1: Describing data. Surg Infect (Larchmt). 2021;22(6):590-596. PubMed
Smeltzer MP, Ray MA. Statistical considerations for outcomes in clinical research: A review of common data types and methodology. Exp Biol Med (Maywood). 2022;247(9):734-42. PubMed
Barnwell-Ménard JL, Li Q, Cohen AA. Effects of categorization method, regression type, and variable distribution on the inflation of Type-I error rate when categorizing a confounding variable. Stat Med. 2015;34(6):936-49. PubMed
Naggara O, Raymond J, Guilbert F, Roy D, Weill A, Altman DG. Analysis by categorizing or dichotomizing continuous variables is inadvisable: an example from the natural history of unruptured aneurysms. AJNR Am J Neuroradiol. 2011;32(3):437-40. PubMed