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CHYS 3P15 (11)
Lecture 9

Lecture 9, Mar 12.docx

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Department
Child and Youth Studies
Course
CHYS 3P15
Professor
Patricia Kirkpatrick
Semester
Winter

Description
3P15, Mar 12, Lecture 9 Chapter 12 Bivariate Statistics for Nominal Data Introduction • Social scientists are interested in uncovering the associations or relationships between several variables. • To simultaneously analyze two variables that cannot be ranked or ordered, you need to use bivariate analysis for nominal variables. Some Sample Research Questions • Are members of particular visible minority groups more susceptible to certain diseases? • Are men more likely than women to gamble? • Are Torontonians more likely than Edmontonians to be homeowners? • Does this differ for men and women? What You Need to Begin Answering these Questions • A representative sample • A knowledge of the level of measurement of each variable • A knowledge of bivariate statistics and measures of association Analysis with Two Nominal Variables • The first step for performing bivariate analysis is organizing the data so that patterns can be easily discerned. • It is useful to create a frequency table, with the information organized into two columns (e.g., Table 12.1). • The variable that we think is modifying the outcome is an independent variable. • The outcome of interest is the dependent variable. • *Note that these will not always be clear* Effectiveness of a Pizza Commercial on Television Response of Respondent to Television Commercial f Order a Pizza 40 Grab Food from the 100 Refrigerator Change Channels 110 No Reaction 750 Total 1000 Bivariate Statistics: Visualizing a Relationship • Need to make data intelligible to identify the existence of relationships • Often convenient to use a cross-tabulation or contingency table • Put independent variable (IV) in columns, dependent variable (DV) in rows (if known) A Bivariate Contingency Table Sex of Response of Respondent to Respondent Television Commercial Female Male Order a Pizza 10 30 Grab Food from the Refrigerator 60 40 Change Channels 60 50 No Reaction 490 260 Total 620 380 Measures of Association • It is always useful to able to see relationships in bivariate tables. • It is also useful, however, to have a single number to indicate the significance of the relationship. • Is there a significant relationship between sex of respondent and reaction to pizza commercial? The Chi-Square Test of Significance • Introduced by Karl Pearson in 1900 • Useful for determining whether a rel
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