Chi Square test of Independence in R - calculation and interpretation
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I'll show how to conduct the chi-square test of independence and interpret the results using R statistics.
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General information:
=================
The chi-square test of independence is used when you have two categorical variables and want to test them for independence. Ordinal variables work as well, but keep in mind that the contingency table grows larger with more possibilities of combinations which also results in a larger required sample size to achieve a viable amount of power and reduce the beta error.
For example:
===========
I have a categorical variable depicting the place of living: urban, suburban and rural county and another categorical variable depiciting the posessions of a driver's license: yes or no.
The goal is to test, whether the place of living and the posession of a driver's license are "correlated". For example that people from rural county are more likely to have a driver's license than people living in urban areas. Reason being the presence of a public transportation system as a surrogate.
The chisq.test() function in R can be used to reach that goal. I'll however use the CrossTable()-function of the gmodels-package because it enables to easily display expected cell frequencies as well as column, row and total proportions. Another bonus is the availability of the Fisher's exact test, which is recommended when you have expected cell counts below 5. Use fisher = TRUE in that case.
Calculating the required sample size:
==============================
🎥 Sample size for Chi-Square test of In...
⏰ Timestamps:
==============
0:00 Introduction
0:0 Example
0:19 Create contingeny table for observed frequencies with xtabs()
0:50 Expected frequencies with CrossTable() from gmodels
1:49 Conduction chi-square test of independence
1:10 Interpreting the results of the chi-square-test of independence
2:19 Fisher's exact test
1:47 Conclusion and outlook
If you have any questions or suggestions regarding Chi Square test of independence in R - calculation and interpretation, please use the comment function. Thumbs up or down to decide if you found the video helpful. #statisticampc
#useR #statorials
Support channel? 🙌🏼
===================
Paypal donation: https://www.paypal.com/paypalme/Bjoer...
Amazon affiliate link: https://amzn.to/49BqP5H
➡️ Watch next: How to calculate the effect size for ...
General information:
=================
The chi-square test of independence is used when you have two categorical variables and want to test them for independence. Ordinal variables work as well, but keep in mind that the contingency table grows larger with more possibilities of combinations which also results in a larger required sample size to achieve a viable amount of power and reduce the beta error.
For example:
===========
I have a categorical variable depicting the place of living: urban, suburban and rural county and another categorical variable depiciting the posessions of a driver's license: yes or no.
The goal is to test, whether the place of living and the posession of a driver's license are "correlated". For example that people from rural county are more likely to have a driver's license than people living in urban areas. Reason being the presence of a public transportation system as a surrogate.
The chisq.test() function in R can be used to reach that goal. I'll however use the CrossTable()-function of the gmodels-package because it enables to easily display expected cell frequencies as well as column, row and total proportions. Another bonus is the availability of the Fisher's exact test, which is recommended when you have expected cell counts below 5. Use fisher = TRUE in that case.
Calculating the required sample size:
==============================
🎥 Sample size for Chi-Square test of In...
⏰ Timestamps:
==============
0:00 Introduction
0:0 Example
0:19 Create contingeny table for observed frequencies with xtabs()
0:50 Expected frequencies with CrossTable() from gmodels
1:49 Conduction chi-square test of independence
1:10 Interpreting the results of the chi-square-test of independence
2:19 Fisher's exact test
1:47 Conclusion and outlook
If you have any questions or suggestions regarding Chi Square test of independence in R - calculation and interpretation, please use the comment function. Thumbs up or down to decide if you found the video helpful. #statisticampc
#useR #statorials
Support channel? 🙌🏼
===================
Paypal donation: https://www.paypal.com/paypalme/Bjoer...
Amazon affiliate link: https://amzn.to/49BqP5H
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