THE IMPACT OF [INDEPENDENT VARIABLE]ON [DEPENDENT VARIABLE]

CONTROLLING FOR [CONTROL VARIABLE]

[Your Name]PLS 401, Senior Seminar

Department of Public & International AffairsUNC Wilmington

04/18/23 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.

04/18/23 2

Table 1

[insert the SETUPS frequency tablefor your dependent variable]

04/18/23 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 %].

04/18/23 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]

04/18/23 5

Table 2:

[insert the bivariate SETUPS table andinclude the tau-b & chi-squared probability statistics]

04/18/23 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]).

04/18/23 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.

04/18/23 8

Table 3a

[insert the first SETUPS partial table andinclude the tau-b & chi-squared probability statistics]

04/18/23 9

Table 3b

[insert the second SETUPS partial table andinclude the tau-b & chi-squared probability statistics]

04/18/23 10

Table 3c [if necessary, otherwise delete this slide]

[if necessary, insert the third SETUPS partial table andinclude the tau-b & chi-squared probability statistics]

04/18/23 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]

04/18/23 12

Substantive Implications

• Suggest several implications of these findings for political decision makers and government officials.

• X

• X

04/18/23 13

Methodological Implications

• Suggest several implications of these findings for other researchers interested in this topic.

• X

• X

04/18/23 14

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References• x

• Shively, W. Phillips. 2008. Power & Choice: An Introduction to Political Science. 11e. Boston: McGraw-Hill.

• x