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Statistics for clinicians. 2. Nominal data (I).

A S Nanivadekar¹, A R Kannappan

¹Pfizer Limited, Bombay.

The Journal of the Association of Physicians of India

|December 1, 1990

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This study explains how to analyze nominal data using percentages and statistical tests. It details methods like the z-test, chi-square test, Fisher

Area of Science:

Statistics
Data Analysis

Background:

Nominal data involve categorizing items into distinct classes.
Data are typically presented as proportions or percentages.
Statistical methods are crucial for interpreting nominal data variations.

Purpose of the Study:

To outline statistical approaches for analyzing nominal data.
To guide the selection of appropriate tests for comparing percentages and assessing associations.
To provide a framework for understanding chance variation in sample percentages.

Main Methods:

Estimation of population percentages from sample data using standard error.
Comparison of percentage differences between groups using z-test or chi-square test.
Application of Fisher's test for small sample sizes (<40 observations).

Related Experiment Videos

Use of Yates' correction for 2x2 tables in chi-square analysis.

McNemar's modification of the chi-square test for paired data.

Chi-square test for assessing significance of differences and attribute associations.

Main Results:

Standard error quantifies chance variation in percentage estimates.
Z-test and chi-square test are primary tools for comparing group percentages.
Fisher's test offers a more accurate alternative to chi-square for small samples.
Yates' correction refines chi-square for 2x2 tables, but is not used for larger tables.
McNemar's test is specifically designed for paired nominal data.
The chi-square test effectively determines both differences and associations between attributes.

Conclusions:

Appropriate statistical tests are essential for valid analysis of nominal data.
Sample size and data structure (paired vs. independent) dictate test selection.
Understanding these statistical methods enhances the interpretation of categorical data.
The chi-square test is versatile for analyzing differences and associations in nominal data.