1. THE IMPACT OF [INDEPENDENT VARIABLE] ON [DEPENDENT VARIABLE] CONTROLLING FOR [CONTROL VARIABLE] [Your Name] PLS 401, Senior Seminar Department of Public & International Affairs UNC Wilmington 12/5/20151

2. Univariate Hypothesis Theory: – X H 1 : predict the distribution of values across the categories of your dependent variable. If relevant, predict whether you expect to find a conflict or consensus distribution. 12/5/20152

3. Table 1 [insert the SETUPS frequency table for your dependent variable] 12/5/20153

4. Univariate Findings H 1 ([restate hypothesis]) is [supported/ not supported / contradicted] by the sample data in Table 1 because: 1.The pattern predicted by H 1 [is/is not observed in/is contradicted by] the sample data. 2.The pattern observed in the sample [is/is not] statistically significant. The random-sampling error margin for this size sample is [± x %]. 12/5/20154

5. Bivariate Hypothesis Theory: – X H 2 : [one category of the independent variable] is more likely than [another category of the independent variable] to [exhibit a particular value of the dependent variable]. [for example: males are more likely than females to support the death penalty – where gender is the independent variable and attitude toward the death penalty is the dependent variable] 12/5/20155

6. Table 2: [insert the bivariate SETUPS table and include the tau-b & chi-squared probability statistics] 12/5/20156

7. Bivariate Findings H 2 ([restate the bivariate hypothesis]) is [supported/ not supported/is contradicted] by the sample data in Table 2 because: 1.The pattern predicted by H 2 [is/is not] observed in the sample data. The tau-b is [x.xx] which indicates that the relationship is [weak/moderate/strong]. 2.This sample finding [is/is not] statistically significant. The chi-squared probability of random-sampling error [is/is not] less than 0.05 (it is [x.xx]). 12/5/20157

8. Multivariate Hypothesis Theory: – X H 3 : controlling for [the control variable] [does / does not] change the impact of [the independent variable] on [the dependent variable] across the partial tables. – In the [first partial-table subgroup], the bivariate relationship will be [weaker / the same / stronger] than in the total population. – In the [second partial-table subgroup], the bivariate relationship will be [weaker / the same / stronger] than in the total population. – Add a prediction for the 3 rd partial-table subgroup, if necessary. 12/5/20158

9. Table 3a [insert the first SETUPS partial table and include the tau-b & chi-squared probability statistics] 12/5/20159

10. Table 3b [insert the second SETUPS partial table and include the tau-b & chi-squared probability statistics] 12/5/201510

11. Table 3c [if necessary, otherwise delete this slide] [if necessary, insert the third SETUPS partial table and include the tau-b & chi-squared probability statistics] 12/5/201511

12. Multivariate Findings H 3 ([restate the multivariate hypothesis)] is [supported / not supported / contradicted] by the sample data. 1.The strength of the bivariate relationship [did / did not] change as predicted in the partial-table subgroups. [Report and interpret the tau-b statistics] 2.The statistical significance of the bivariate relationship [did / did not] change in the partial-table subgroups. [Report and interpret the chi-squared probability statistics] 12/5/201512

13. Substantive Implications Suggest several implications of these findings for political decision makers and government officials. X 12/5/201513

14. Methodological Implications Suggest several implications of these findings for other researchers interested in this topic. X 12/5/201514

15. 12/5/201515 References x Shively, W. Phillips. 2008. Power & Choice: An Introduction to Political Science. 11e. Boston: McGraw-Hill. x