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BIVARIATE/MULTIVARIATE DESCRIPTIVE STATISTICS Displaying and analyzing the relationship between categorical variables.

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Presentation on theme: "BIVARIATE/MULTIVARIATE DESCRIPTIVE STATISTICS Displaying and analyzing the relationship between categorical variables."— Presentation transcript:

1 BIVARIATE/MULTIVARIATE DESCRIPTIVE STATISTICS Displaying and analyzing the relationship between categorical variables

2 Using the “crosstabulation” technique Open file Position stress gender.sav or.xls Variables –Position on the police force Values: Patrol officer, Sergeant –Stress Values: Low, High What is the hypothesis? (Guess)

3 Hypothesis Two-tailed (do not predict the direction of the relationship) –Changes in Position cause changes in Stress One-tailed (predict the direction of the relationship) –Higher ranking officers have more stress

4 Using cross-tabulation (“crosstabs”) Analyze|Descriptive statistics|Crosstabs Place independent variable in columns Place dependent variable in rows Select “Cells” & check “column percentages” Optional: For the dependent variable, to have the “L” row appear above the “H” row, select “Format” & check “descending”

5 Job Stress Position on police force Low High Total Sergeant 86 30 116 Patrol Officer 24 60 84 Total 110 90 N= 200 Two-tailed hypothesis: as the level of the independent variable changes (from Patrol Officer to Sergeant), does the distribution of the dependent variable change? One-tailed hypothesis: Is this change in the hypothesized direction?

6 Introducing a “control” variable Could the apparent effect of position on stress actually be caused by another independent variable? Perhaps it’s “Gender” (M/F) –To challenge the opinion that Position affects Stress, must choose a variable that is probably related to Position –Gender is probably related to Position In SPSS, add “Gender” to “Layer”

7 Job Stress Position on police force Low High Sergeant 70%53% Patrol Officer 30%47% Total 100% 100% Job Stress Position on police force Low High Sergeant 83%23% Patrol Officer 17%77% Total 100% 100% Females Males “First-order” Partial Tables Does the original, “zero-order” relationship between variables still hold true for the Male value of the control variable? For the Female value?

8 Job Stress Position on police force: Males Low High Sergeant 83%23% Patrol Officer 17%77% Total 100% 100% Job Stress Position on police force: Females Low High Sergeant 70%53% Patrol Officer 30%47% Total 100% 100%

9 Insert percentages… Ethnicity  support for police

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11 Has Reported a Crime Never Reported a Crime First-order partial tables Control variable: prior report as a crime victim Insert percentages…

12 Has Reported a Crime Never Reported a Crime First-order partial tables Control variable: prior report as a crime victim

13 First-order partial tables Reported a crime Never Reported a Crime Zero-order table Control variable: prior report as a crime victim Hypothesis: Ethnicity determines support for police


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