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


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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

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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

  • 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

  • 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


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Example: Cross-tabulation

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

  • 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.


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Graphical Illustration

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

Source: 1996 Lipset/Meltz survey


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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 l.jpg

Example: Additive Relationship

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


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Additive Relationship: Line Graph

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


Example spurious relationship l.jpg

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 l.jpg

Spurious Relationship: Line Graph

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


Example interactive relationship l.jpg

Example: Interactive Relationship

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


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Interactive Relationship: Line Graph

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


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Exercise

Political party preference, 2006 Canadian Election Study Survey, %


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