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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

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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

• 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

• 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

Source: 1996 Lipset/Meltz survey

### 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

Source: 1996 Lipset/Meltz survey

### 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

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

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

### Example: Spurious Relationship

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

### Spurious Relationship: Line Graph

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

### Example: Interactive Relationship

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

### Interactive Relationship: Line Graph

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

### Exercise

Political party preference, 2006 Canadian Election Study Survey, %