Correlation and Regression

Answer all three of the questions below. All work must be shown. Assignment in a Word document with SPSS/PSPP output attached where appropriate.

Problem 1: Operationalizing variables can be a challenge. One measure of a country’s human rights record is a measure known as the Political Terror Scale. This variable was developed by Mark Gibney at UNC-Asheville, and is coded for individual states according to the following criteria:

Countries receive a score of 1 if people are not imprisoned for their views, and torture and political murder are extremely rare.

Countries receive a score of 2 if there is some political imprisonment for nonviolent political activity, and torture and political murder are rare.

Countries receive a score of 3 if there is extensive political imprisonment, and torture and political murder are common.

Countries receive a score of 4 if Level 3 practices are extended to larger numbers of the population.

Countries receive a score of 5 if Level 3 practices are extended to the whole population.

To obtain these data, scholars used both Amnesty International Country Reports as well as Annual Reports from the US State Department. An astute observer might suggest that the use of State Department data may not be very reliable, because the US underreports abuses in countries it likes and overreports them in countries it does not. Is this the case? Using the human rights data for Assignment 4 (global_pts_comparison.sav), generate a correlation coefficient between the two measures (pts05am and pts05us). Using these findings, what can we preliminarily say about whether the State Department data is biased?

Problem 2: Whether and to what extent IMF lending programs have negative effects on the economies of borrowing states is a contentious issue. In the early days of research in this topic, scholars compared economic outcomes from countries under IMF programs to those from countries not under IMF programs. For our purposes, let us consider the effects of Fund lending programs on foreign direct investment. Using the Latin America capital flows data for Assignment 4 (lat_am_cap_flows_example_MR.sav), compute a t-test on foreign direct investment as a percentage of GNP using the dichotomous independent variable selected, which indicates whether or not countries are under an IMF program in that year. Using these findings, what can we say here? Is it the case that IMF programs attract foreign direct investment or deter it?

Problem 3: An ongoing puzzle for scholars who study gender rights is why some countries have more female representation than others. Your dependent variable is the percentage of women in the lower house of parliament. Using the cross-national dataset for Assignment 4 (crossnational.sav), compute a simple bivariate regression of GDP per capita (measured in $10K USD) on women’s parliamentary representation and interpret the results. What do these findings tell us about this relationship? How do we know we’re right? If Liechtenstein had an income level of 4.15, what would be its predicted level of women’s representation? How does this compare to the actual level of women’s representation?

Price: $18.41

Solution: The downloadable solution consists of 9 pages, 941 words and 4 charts.
Deliverable: Word Document