One of the most common uses for this test is to assess whether two categorical variables are significantly related or not. The formula for a Chi-Square statistic is The Chi-Square test of independence is right-tailed The Chi-Square distribution is one of the most important distributions in statistics, together with the normal distribution and the F-distribution The distribution of the test statistic is the Chi-Square distribution, with \((r-1)\times(c-1)\) degrees of freedom, where r is the number of rows and c is the number of columns The main properties of a Chi-Square test of independence are: The idea of the test is to compare the sample information (the observed data), with the values that would be expected if the two variables were indeed independent. This means of the total population of data scientist’s majority 53 are male. This video explains about the analysis steps in SPSS for Likert Scale. Since Chi-square value (140) is greater than critical value of 3.841, we reject the null hypothesis meaning there is a dependency between gender and data science preference. Analyze Likert scale questionnaire into numeric SPSS. ![]() The chi square test is one option to compare respondent response and analyze results against the hypothesis. Sometimes, a Chi-Square test of independence is referred as a Chi-Square test for homogeneity of variances, but they are mathematically equivalent. There are few steps in analyzing your ordinal scale data from Likert Scale. ![]() ![]() ![]() Chi-Square of independence is a test used for categorical variables in order to assess the degree of association between two variables.
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