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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

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

Ivan Katchanovski, Ph.D.

POL 242Y-Y

cross tabulation
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
example cross tabulation
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
example cross tabulation4
Example: Cross-tabulation

Table 1. Preference for freedom and equality in the US and Canada, percent

Source: 1996 Lipset/Meltz survey

example cross tabulation5
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.
graphical illustration
Graphical Illustration

Figure 1. Preference for freedom and equality in the US and Canada, percent

Source: 1996 Lipset/Meltz survey

controlled comparisons
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
example additive relationship
Example: Additive Relationship

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

additive relationship line graph
Additive Relationship: Line Graph

Figure 2. Preference for equality in the US and Canada controlling for gender, % (fictional data)

example spurious relationship
Example: Spurious Relationship

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

spurious relationship line grap h
Spurious Relationship: Line Graph

Figure 3. Preference for equality in the US and Canada controlling for religiosity, % (fictional data)

example interactive relationship
Example: Interactive Relationship

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

interactive relationship line graph
Interactive Relationship: Line Graph

Figure 4. Preference for equality in the US and Canada controlling for race, % (fictional data)

exercise
Exercise

Political party preference, 2006 Canadian Election Study Survey, %

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