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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 l.jpg

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

Ivan Katchanovski, Ph.D.

POL 242Y-Y


Cross tabulation l.jpg
Cross-Tabulation Comparisons

  • 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 l.jpg
Example: Cross-tabulation Comparisons

  • 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 l.jpg
Example: Cross-tabulation Comparisons

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

Source: 1996 Lipset/Meltz survey


Example cross tabulation5 l.jpg
Example: Cross-tabulation Comparisons

  • 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 l.jpg
Graphical Illustration Comparisons

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

Source: 1996 Lipset/Meltz survey


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Controlled Comparisons 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 l.jpg
Example: Additive Relationship Comparisons

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


Additive relationship line graph l.jpg
Additive Relationship: Line Graph Comparisons

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


Example spurious relationship l.jpg
Example: Spurious Relationship Comparisons

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


Spurious relationship line grap h l.jpg
Spurious Relationship: Line Grap Comparisons h

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


Example interactive relationship l.jpg
Example: Interactive Relationship Comparisons

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


Interactive relationship line graph l.jpg
Interactive Relationship: Line Graph Comparisons

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


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Exercise Comparisons

Political party preference, 2006 Canadian Election Study Survey, %


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