Abstract
The Chi-square test is perhaps the most frequently used (or overused) nonparamteric statistical test. The Chi-square test, named for the Greek letter χ (i.e., Chi or the Greek letter for x), is typically used to test for differences in proportions between two or more groups. The Chi-square test is also called a goodness of fit test. That is to say, the Chi-square test is used to see if grouped data actually fit into declared groups, or if the data instead do not fit into the group. For this lesson, Chi-square will be demonstrated using data in two formats: (1) Chi-square using R will first be demonstrated where the data are presented as an external file imported into R, with data organized at the level of individual subjects, (i.e., each row represents the data for an individual subject) and (2) Chi-square using R will also be demonstrated where data are not at the level of individual subjects but data are instead presented in summary format, as a collapsed contingency table.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Although it is beyond the immediate purpose of this text on R, it is still useful to have some background with Boolean terms used for selection. As time permits, become acquainted with the following terms: EQ (equals), NE (not equals), LT (less than), LTE or LE (less than or equal), GT (greater than), and GTE or GE (greater than or equal).
Author information
Authors and Affiliations
3.1 Electronic Supplementary Material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
MacFarland, T.W., Yates, J.M. (2016). Chi-Square. In: Introduction to Nonparametric Statistics for the Biological Sciences Using R. Springer, Cham. https://doi.org/10.1007/978-3-319-30634-6_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-30634-6_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-30633-9
Online ISBN: 978-3-319-30634-6
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)