5-2 Chi Square - Lecture Outline Using Statistics The Bivariate Table The Logic of Chi Square The Computation of Chi Square The Chi Square Test for

# 5-2 Chi Square - Lecture Outline Using Statistics The...

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Lecture Outline Using Statistics The Bivariate Table The Logic of Chi Square The Computation of Chi Square The Chi Square Test for Independence The Chi Square Test: An Additional Example The Limitations of the Chi Square Test
Using Statistics Chi square is used to test the statistical significance of the relationship between variables that have been arrayed in tabular format.
Using Statistics With chi square you can: Test if unmarried people are more involved in voluntary associations. Determine if a stop smoking works equally well for all genders or people of all ages. Examine if various demographic factors (gender, age, religion, education) are associated with support for medical marijuana.
Using Statistics The chi square test is the most frequently used hypothesis test in the social sciences. The reasons for its popularity include: Assumptions that are easy to satisfy Being applicable to variables measured at the nominal and ordinal levels or variables with many scores No assumption on the shape of the sampling distribution or population is necessary As a nonparametric test, chi square does not require the population or sampling distribution to be normally distributed.
The Bivariate Table Bivariate tables display scores of cases on two variables at the same time. The are also known as Bivariate frequency distributions Cross-tabulations or, more commonly Crosstabs
The Bivariate Table Categories of one variable are arrayed in the rows of the table. By convention, the dependent variable is usually arrayed in the rows. Categories of the other variable are arrayed in the columns of the table. By convention, the independent variable is usually arrayed in the columns. At the intersection of a row and column, a cell , displays the number of cases on the combined variables.
The Bivariate Table Marginals appear at the edges of the table. The column marginal displays the univariate distribution of the dependent variable. The row marginal displays the univariate distribution of the independent variable. The grand total of cases in the entire table is displayed in the bottom-right cell.
The Bivariate Table Example: Support for same-sex marriage is the dependent variable, and its categories (no, yes) are arrayed in the rows. The totals for these categories appear in the marginal (508 said no, 586 said yes). Marital status is the independent variable, and its categories (married, not married) are arrayed in the columns. The totals for these categories appear in the marginal (512 were married, 582 were not.) Each cell displays the number of cases for that combination of variables. For example, 274 married respondents do not support same- sex marriage.

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• Summer '17
• Wimpy
• Same-sex marriage, Statistical hypothesis testing, Chi-square distribution, Pearson's chi-square test, CHI SQUARE

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