Analysis of Categorical Data I: Contingency Tables and the Chi-Square Test

2001 ◽  
pp. 273-298
Keyword(s):  
Categorical Data ◽  
Contingency Tables ◽  
Chi Square ◽  
Chi Square Test

scholarly journals A deterministic approach to contingency tables

2019 ◽  
Author(s):  
Giorgio Gambirasio
Keyword(s):  
Set Theory ◽  
Categorical Data ◽  
Contingency Tables ◽  
Random Variable ◽  
Deterministic Approach ◽  
Chi Square ◽  
Positive Correlation ◽  
Chi Square Test

A tool has been developed to evaluate correlation between variables in 2x2 contingency tables of categorical data. The work is based on elementary Set Theory and does not make use of probabilistic and random variable concepts. This evaluator distinguishes between a negative and a positive correlation and may be an useful complement to chi-square test.

scholarly journals Teaching Biostatistics to Medical Students of the Altai State Medical University

Politehnika ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 7-11
Author(s):  
Nina Trukhacheva ◽  
Nikolay Pupyrev ◽  
Ylia Alyabieva ◽  
Svetlana Tschernysheva
Keyword(s):  
Medical Students ◽  
Categorical Data ◽  
Learning System ◽  
P Value ◽  
Educational Goals ◽  
Chi Square ◽  
New Approaches ◽  
Chi Square Test ◽  
Medical University ◽  
Russian Medical

The current research presents some principles and settings in teaching biostatistics. The purpose of study is to enhance the teaching of biostatistics in Russian medical schools and overcome some problems by applying new approaches and innovation techniques. The research included the questionnaire of students, teachers and postgraduates of the Altai State Medical University. There were applied different approaches and studying by means of learning system MOODLЕ with differentiated courses in biostatistics. Categorical data were analyzed using the chi-square test and a P-value less than 0.05 was considered statistically significant. The findings showed some new approaches and methods in education to improve the biostatistical competence of medical students. The changing in content of biostatistical course would increase students’ motivation if it were maximum approximated to the real medical cases. The analysis of the results proved that some approaches are more effective for teaching biostatistics. They allow students to study according to their personal educational goals and paths.

Contingency Tables and the Chi-Square Test

2009 ◽  
pp. 309-322
Author(s):  
Richard M. Heiberger ◽  
Erich Neuwirth
Keyword(s):  
Contingency Tables ◽  
Chi Square ◽  
Chi Square Test

scholarly journals The Bayes Factor for the Misclassified Categorical Data

2018 ◽  
Vol 7 (4) ◽  
pp. 91
Author(s):  
Tze-San Lee
Keyword(s):  
Categorical Data ◽  
Bayes Factor ◽  
Alternative Hypothesis ◽  
Multinomial Distribution ◽  
P Value ◽  
Categorical Variable ◽  
Chi Square ◽  
Alternative Hypotheses ◽  
Chi Square Test ◽  
Cell Proportion

This article addresses the issue of misclassification in a single categorical variable, that is, how to test whether the collected categorical data are misclassified.  To tackle this issue, a pair of null and alternative hypotheses is proposed. A mixed Bayesian approach is taken to test these hypotheses. Specifically, a bias-adjusted cell proportion estimator is presented that accounts for the bias caused by classification errors in the observed categorical data. The chi-square test is then adjusted accordingly. To test the null hypothesis that the data are not misclassified under a specified multinomial distribution against the alternative hypothesis they are misclassified, the Bayes factor is calculated for the observed data and a comparison is made with the classical p-value. 

scholarly journals Non-Compliance with COVID-19 Screening in Pakistan

2020 ◽  
Vol 24 (Supp-1) ◽  
pp. 50-55
Author(s):  
Furqan Ali Taj ◽  
Muhammad Raheel Raza ◽  
Saima Naz ◽  
Muhammad Umar ◽  
Aqsa Hameed
Keyword(s):  
Categorical Data ◽  
General Public ◽  
Statistical Significance ◽  
Education Level ◽  
Cross Sectional Survey ◽  
Chi Square ◽  
Cross Sectional ◽  
Local Study ◽  
Gender And Education ◽  
Chi Square Test

Objectives: To quantify the non-complaint portion of the general public – not wanting to be screened for COVID-19 and find the reason for this non-compliance, in the general public of Rawalpindi Pakistan. Study Design: Cross-sectional survey. Place and Duration of Study: General public of Rawalpindi, Pakistan. From June 19, 2020, to June 21, 2020. Methodology: A questionnaire was constructed based on a local study, it was injected to the accessible online population through Google Forms. Surveyors collected data from the illiterate population on printed proforma. A sample of 1108 was collected. IBM® SPSS® was used for data analysis. For categorical data, frequencies and percentages were calculated. A Chi-square test was applied for statistical significance. Results: 45.3% of participants were females, 54.7% were males. 37.9% of participants were married and 62.1% were unmarried. 3.8% were illiterate, 40.4% were matriculated and 47.1% had education higher than intermediate. 38.3% was non-compliant population – didn’t want to get screened for COVID-19. 30.7% were non-compliant because of ‘fear of isolation/ quarantine with other COVID-19 patients, leading to worsening of disease’ followed by 26.9% who ‘don’t trust the reliability of the test’. Gender and Education level variables were statistically significant in determining non-compliance. Marital status was found non-significant. Conclusion: A significant portion of the population i.e. 38.3% showed non-compliance with COVID-19 screening, which was statistically associated with gender and education level.

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