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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 5/25/2017 1 Univariate Hypothesis • Theory: –X –X • H1: 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. 5/25/2017 2 Table 1 [insert the SETUPS frequency table for your dependent variable] 5/25/2017 3 Univariate Findings • H1 ([restate hypothesis]) is [supported/ not supported / contradicted] by the sample data in Table 1 because: 1. The pattern predicted by H1 [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 %]. 5/25/2017 4 Bivariate Hypothesis • Theory: –X –X • H2: [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] 5/25/2017 5 Table 2: [insert the bivariate SETUPS table and include the tau-b & chi-squared probability statistics] 5/25/2017 6 Bivariate Findings • H2 ([restate the bivariate hypothesis]) is [supported/ not supported/is contradicted] by the sample data in Table 2 because: 1. The pattern predicted by H2 [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]). 5/25/2017 7 Multivariate Hypothesis • Theory: – X – X • H3: 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 3rd partial-table subgroup, if necessary. 5/25/2017 8 Table 3a [insert the first SETUPS partial table and include the tau-b & chi-squared probability statistics] 5/25/2017 9 Table 3b [insert the second SETUPS partial table and include the tau-b & chi-squared probability statistics] 5/25/2017 10 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] 5/25/2017 11 Multivariate Findings • H3 ([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] 5/25/2017 12 Substantive Implications • Suggest several implications of these findings for political decision makers and government officials. • X • X 5/25/2017 13 Methodological Implications • Suggest several implications of these findings for other researchers interested in this topic. • X • X 5/25/2017 14 References • x • Shively, W. Phillips. 2008. Power & Choice: An Introduction to Political Science. 11e. Boston: McGraw-Hill. • x 5/25/2017 15