# Sociology

1. Make a hypothesis for the relationship  between TAXRICH1 and RACE. Make TAXRICH1 the dependent variable and RACE the independent variable.
2. Is the relationship depicted in the crosstabs a positive or negative relationship? Explain and support your answer. If you think the direction of the relationship is not meaningful for your variables say so and explain why.
3. Is the relationship observed in your crosstabulation results a weak, moderate or strong one? Report the LAMBDA  (look in the value cell of the directional  measures table – the attitudes about taxes for the rich dependent) and CRAMER’S V  (look in the value cell of the symmetric measures table for cramer’s V) produced by SPSS  to defend your answer. .Remember that values of Lambda and Cramer’s V between 0-0.3 is weak, 0.3 to 0.5 is moderate and 0.5+ is strong.
4. Is the  relationship depicted in the crosstabs statistically significant or not? Look in the chi square test table for the relevant information. Report the  value of the PEARSON CHI SQUARE, the DEGREES OF FREEDOM(DF) and the probability associated with the chi square(look in THE ASYMPTOTIC SIGNIFICANCE CELL OF THE PEARSON CHI SQUARE for this probability). Remember that when this probability is less than or equal to .05, the relationship observed in the crosstab is statistically significant. You must reject the null hypothesis of no relationship between your dependent and independent variables. You will therefore be left with the alternative hypothesis that there is a  relationship between the two variables in the population. However, if the probability associated with the chi square statistic  is larger than 0.05, then the relationship is statistically nonsignificant, meaning that the relationship is likely due to random chance and you cannot reject the null hypothesis of no relationship in the population.
5. Is the relationship observed in the crosstabulation table consistent or not consistent with your hypothesis?  If you cannot reject the null hypothesis of no relationship then you have to conclude that the relationship observed in the crosstabulation table is not consistent with your hypothesis, unless you made a null hypothesis, which is not usually done. However, if you can reject the null hypothesis, you may still conclude that the relationship is not consistent with your hypothesis. You have to compare the relationship observed in the crosstabulation table with the hypothesis you made to decide whether the former is consistent or not consistent with the latter. Please explain your answer.

Pat b:

1. Make a hypothesis involving CAPPUN and RACE. Treat CAPPUN as the dependent variable and RACE as the independent variable.
2. What are the levels of measurement for the dependent and the independent variables?
3. What measures of association are appropriate for assessing the strength of the relationship?
4. Discuss the relationship depicted in the crosstabs table, using selected percentage data to support your claims.
5. Is the relationship observed in your crosstabulation results a weak, moderate or strong one? Inspect the LAMBDA and CRAMER’S V produced by SPSS and use them to answer. Be sure to report numbers to support your statements.
6. Use the chi-square statistic (PEARSON CHI-SQUARE) and its degrees of freedom (DF) to examine whether the relationship is statistically significant or not. Is the  relationship depicted in the crosstabs statistically significant or not? Look in the chi square test table for the relevant information. Report the  value of the PEARSON CHI SQUARE, the DEGREES OF FREEDOM(DF) and the probability associated with the chi square(look in THE ASYMPTOTIC SIGNIFICANCE CELL OF THE PEARSON CHI SQUARE for this probability). Remember that when this probability is less than or equal to .05, the relationship observed in the crosstab is statistically significant. You must reject the null hypothesis of no relationship between your dependent and independent variables. You will therefore be left with the alternative hypothesis that there is a  relationship between the two variables in the population. However, if the probability associated with the chi square statistic  is larger than 0.05, then the relationship is statistically nonsignificant, meaning that the relationship is likely due to random chance and you cannot reject the null hypothesis of no relationship in the population.
7. Is the relationship observed in the crosstabulation table consistent or not consistent with your hypothesis?  If you cannot reject the null hypothesis of no relationship then you have to conclude that the relationship observed in the crosstabulation table is not consistent with your hypothesis, unless you made a null hypothesis, which is not usually done. However, if you can reject the null hypothesis, you may still conclude that the relationship is not consistent with your hypothesis. You have to compare the relationship observed in the crosstabulation table with the hypothesis you made to decide whether the former is consistent or not consistent with the latter. Explain whatever answer you give.
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