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

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

Ivan Katchanovski, Ph.D.

POL 242Y-Y

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 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-tabulation Comparisons

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

Source: 1996 Lipset/Meltz survey

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 Comparisons

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

Source: 1996 Lipset/Meltz survey

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 Comparisons

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

Additive Relationship: Line Graph Comparisons

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

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 Comparisons h

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

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 Comparisons

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

Exercise Comparisons

Political party preference, 2006 Canadian Election Study Survey, %