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WEEK 9: BIVARIATE ANALYSIS II

Office Hours: Thursday 1.30-2.30pm FW102. WEEK 9: BIVARIATE ANALYSIS II. Null Hypothesis?. Null Hypothesis is a hypothesis which the researcher tries to disprove, reject or falsify Example: Does gender influence the salary of a British worker? What is the Null Hypothesis?

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WEEK 9: BIVARIATE ANALYSIS II

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  1. Office Hours: Thursday 1.30-2.30pm FW102 WEEK 9: BIVARIATE ANALYSIS II

  2. Null Hypothesis? • Null Hypothesis is a hypothesis which the researcher tries to disprove, reject or falsify • Example: Does gender influence the salary of a British worker? • What is the Null Hypothesis? • There is no association between gender and salary in UK.

  3. Can we reject the Null Hypothesis?

  4. Chi square test • To find out if there is a significant association between the variables • Calculates Expected frequencies (when Null Hypothesis is true) and compares them with the data we have (Observed Frequencies)

  5. Locate the p-value and Chi square

  6. Locate the p-value and Chi square

  7. t-test (is there a relationship?) • Same logic with Chi square test: • Find the expected values when there is no association between the variables and compare them with the actual data we have. • But use t-test when we have continuousvariables (real numbers)

  8. t-ratio (is the relationship significant?) Shows difference between expected values when null hypothesis is true & the observed values • If the t-value is close to or greater than +/-2, then the relationship is usually significant at what is called the .05 level.

  9. R-squared (strenght of relationship) • R2 ranges from 0 to 1 • 0explains nothing • 1perfect association • So if R2 is 0.50, we can say that 50% of the variance in Y can be explained by the variance in X • Or, you make 50% less errors when guessing Y by knowing X, as compared to guessing Y when not knowing X

  10. The form of the relationship (Simple regression equation) Y= a + b X

  11. Simple regression graph • Y = a + bX Intercept or a, the point where the line cuts the Y axis Slope of the line or b, the amount of change in Y that you get if X increases by one unit

  12. GROUP EXERCISE • Possible reasons for voting for Far-right parties (BNP) • racist sentiments • political alienation (public dissatisfaction with the working of democracy) • social alienation • What do you think? Which one is the main reason?

  13. Measuring Political concepts • How much do you trust the Parliament at Westminster? • political alienation • On balance, would you say that most people can't be trusted or that most people can be trusted? • Social alienation • how do you feel about black and white people? • Racist sentiment

  14. Null Hypotheses • political alienation • There is no association between voting for BNP and the level of trust in the parliament • Social alienation • There is no association between voting for BNP and trusting people • Racist sentiment • There is no association between voting for BNP and feelings about black and white people

  15. Descriptive statistics

  16. Table 1: Support for the BNP and racial attitudes, OLS (Ordinary Least Squared) regression a Dependent Variable: Feelings_BNP

  17. Guidelines for Analysis  THREE STEPS 1-SIGNIFICANCE OF THE ASSOCIATION • The relationship between X (feelings for blacks) and Y (support for BNP) is significant at the 0.05 level (we can reject the null hypothesis of no association). 2-DIRECTION OF THE ASSOCIATION • The b coefficient of -0.219 is negative, indicating that the people who have positive feelings towards black people tend to have lower levels of support for BNP. 3-MAGNITUDE

  18. Guidelines for Analysis 3-MAGNITUDE OF THE “X” EFFECT • For every percentage point increase in X (positive feelings towards black people), the level of support for BNP (Y) will decrease by 0.219 points on the 0-10 scale.

  19. Table 2 Support for the BNP and political trust, OLS regression • Dependent variable: Feeling BNP

  20. Table 3 Support for the BNP and social trust, OLS regression • Dependent variable: Feeling BNP

  21. Dependent Variable: Feelings_BNP Dependent Variable: Feelings_BNP Table 3 Support for the BNP and attitudes towards white people, OLS regression Dependent Variable: Feelings_BNP

  22. Next Week Multivariate Analysis: Testing Hypotheses Testing hypotheses with OLS regression Modelling in political science, multiple relationships between variables, interpreting OLS regression analysis Check out the moodle for readings 

  23. Have a Nice Week! 

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