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Cross-Tabulation Analysis; Making Comparisons; Controlled Comparisons June 2, 2008

Cross-Tabulation Analysis; Making Comparisons; Controlled Comparisons June 2, 2008. Ivan Katchanovski , Ph.D. POL 242Y-Y. Cross-Tabulation. Cross-tabulation: A method of hypotheses testing Very common Very simple Bivariate analysis

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Cross-Tabulation Analysis; Making Comparisons; Controlled Comparisons June 2, 2008

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  1. Cross-Tabulation Analysis; Making Comparisons; Controlled Comparisons June 2, 2008 Ivan Katchanovski, Ph.D. POL 242Y-Y

  2. Cross-Tabulation • Cross-tabulation: A method of hypotheses testing • Very common • Very simple • Bivariate analysis • Appropriate for nominal, ordinal, and interval-ratio variables • Bivariate table of percentages • The dependent variable is in rows • The independent variable is in columns • Percentage totals are column totals

  3. Example: Cross-tabulation • Research hypothesis: Canadians are more supportive of equality than Americans are • The dependent variable: Preference for equality • in rows • The independent variable: Country • in columns

  4. Example: Cross-tabulation Table 1. Preference for freedom and equality in the US and Canada, percent Source: 1996 Lipset/Meltz survey

  5. Example: Cross-tabulation • Comparison: • compare percentages across columns at the same value of the dependent variable • Look for significant differences: • A rule of thumb for survey data: 4% or more in expected direction • Example from Table 1: • 44% of Canadians, compared to 33% of Americans, prefer equality over freedom • Interpretation of results: • The cross-tabulation analysis supports the research hypothesis.

  6. Graphical Illustration Figure 1. Preference for freedom and equality in the US and Canada, percent Source: 1996 Lipset/Meltz survey

  7. Controlled Comparisons • Analysis of the relationship between and independent variable and a dependent variable controlling for another variable • Types of relationships • Additive: Control variable adds to explanation of an dependent variable by an independent variable • Spurious: Relationship between an independent variable and a dependent variable disappears when a control variable is introduced • Interactive: Relationship between an independent variable and a dependent variable depends on the value of control variable

  8. Example: Additive Relationship Table 2. Preference for freedom and equality in the US and Canada controlling for gender, % (fictional data)

  9. Additive Relationship: Line Graph Figure 2. Preference for equality in the US and Canada controlling for gender, % (fictional data)

  10. Example: Spurious Relationship Table 3. Preference for freedom and equality in the US and Canada controlling for religiosity, % (fictional data)

  11. Spurious Relationship: Line Graph Figure 3. Preference for equality in the US and Canada controlling for religiosity, % (fictional data)

  12. Example: Interactive Relationship Table 4. Preference for freedom and equality in the US and Canada controlling for race, % (fictional data)

  13. Interactive Relationship: Line Graph Figure 4. Preference for equality in the US and Canada controlling for race, % (fictional data)

  14. Exercise Political party preference, 2006 Canadian Election Study Survey, %

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