However, it cannot tell you whether the categories you constructed are meaningful. IMPORTANT: Be very careful when constructing your categories! A Chi-square test can tell you information based on how you divide up the data. Thus, by dividing a class of 54 into groups according to whether they attended class and whether they passed the exam, you might construct a data set like this: Additionally, the data in a Chi-square grid should not be in the form of percentages, or anything other than frequency (count) data. However, arranging students into the categories "Pass" and "Fail" would. For example, if you want to test whether attending class influences how students perform on an exam, using test scores (from 0-100) as data would not be appropriate for a Chi-square test. It will not work with parametric or continuous data (such as height in inches). That means that the data has been counted and divided into categories. It is also called a "goodness of fit" statistic, because it measures how well the observed distribution of data fits with the distribution that is expected if the variables are independent.Ī Chi-square test is designed to analyze categorical data. The Chi-square test is intended to test how likely it is that an observed distribution is due to chance. Tutorial: Pearson's Chi-square Test for Independence Ling 300, Fall 2008
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |