Research Topic · Peer-Reviewed

Chi Square Tests

The chi-square test is a family of statistical procedures for analysing categorical data by comparing observed frequencies with those expected under a specified null hypothesis. The chi-square test of independence assesses whether two categorical variables are associated within a contingency table, while the goodnes…

Curated from this journal's research 📚 12 peer-reviewed articles cited Cited 6× across the literature 🔖 ISSN 2643-2811

Overview

The chi-square test is a family of statistical procedures for analysing categorical data by comparing observed frequencies with those expected under a specified null hypothesis. The chi-square test of independence assesses whether two categorical variables are associated within a contingency table, while the goodness-of-fit test evaluates whether an observed distribution conforms to an expected one. The test statistic sums the squared differences between observed and expected counts relative to the expected counts and is referred to a chi-square distribution with degrees of freedom determined by the table dimensions. Valid application requires independent observations, mutually exclusive categories, and sufficiently large expected cell counts, with exact tests substituted when frequencies are small. The research in this area uses chi-square and related categorical methods to examine associations such as vaccine uptake among healthcare workers, racial and ethnic differences in cardiovascular risk classification, adolescent communication on reproductive health, and the relationship between demographic factors and clinical or behavioural outcomes; structural-equation and survival approaches appear where multivariable modelling is required. The chi-square test matters because much research data are categorical, and it provides a straightforward, widely used means of detecting and quantifying associations between nominal variables. The journal publishes peer-reviewed research employing chi-square and categorical-data analysis across epidemiological and clinical studies.

Research published in this journal

12 peer-reviewed articles, ranked by relevance. Each links to its DOI.

How this research is being cited

The 12 articles above have been cited 6 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Nov 2026.

A sample of recent works citing this journal's research on Chi Square Tests, linking to each citing work.

Editorial oversight

Curated from peer-reviewed research published in Journal of Model Based Research (ISSN 2643-2811).

Journal editorial board
Ghanshyam Govindbhai Tejani · India Yang Chen · United States Yin-Quan Tang · Malaysia

This page summarises published research for orientation; it is not medical or professional advice.