Only do Problem set 3
POL 51: Scientific Study of Politics Professor Jones Fall 2019 Problem sets 2 and 3: Inferential statistics, visualization of data, and regression modeling. Problem set 2 involves questions 1-3 and problem set 3 involves questions 4-7. Problem set 2 is due November 26. Problem set 3 is due December 5. Overview: This assignment will utilize a novel data set I recently collected. The survey itself involves an experiment, and the experimental component will be analyzed in this homework set. You will use R to compute some very straightforward inferential statistics, plots, and a multiple regression model. You will have ample time to do this homework assignment; however, do not wait until the last minute to complete it. The goal of the assignment is to increase your familiarity with R, testing hypotheses, and interpreting political science/political psychology data. Data overview: In late September and early October of 2019, a national sample of white (non- Latino) Americans was undertaken. In all, about 3,100 individuals completed the survey. The survey was designed to be regionally representative of the white population from the nine Census regions, as well as representative of the age distribution of the American white population. The survey imposed quotas such that we oversampled partisan identifiers (Republican and Democrat). About 40 percent of the identifiers are Republican and about 40 percent are Democratic. The remaining 20 identify as independent. Quotas were imposed such that about 50 percent of the sample identified as male and 50 percent identified as female. Details on these quotas will be discussed in class. Experimental component: In studies of communication and mass media, scholars are often interested in “framing.” Framing entails the concept of how an issue is presented—or framed— to the public by politicians or the news media. In this study, respondents were randomly exposed to one of six experimental conditions. These conditions are in the codebook and in class, I will discuss the conditions. Of interest here is whether or not attributions of blame for migrant deaths are sensitive to framing. In other words, is it possible to “push” individuals up or down on the blame attribution scale. Theory: This survey is a component to my research agenda examining attitudes about the immigration issue, writ large. In recent years, I’ve come to be interested in the concept of “blame attribution;” that is, the reasons people think about for why bad things happen. There are two ways to think about blame: dispositional and situational. “Dispositional blamers” are more prone to blame the individual for his/her plight/problems; “situational blamers” are more prone to blame systemic causes for why negative outcomes occur. This concept was explored deeply in homework 1. In this study, I am interested in how beliefs about the legitimacy of the U.S. political system is related to the willingness to endorse or oppose dispositional attributions of blame. This theory emanates from social psychology and the work of Jost and his associates. As a precursor to beginning this assignment, you should all read Jost (2019; posted on Canvas) to better understand the theory. I STRONGLY recommend reading this. In a nutshell, SJT says that individuals who endorse the legitimacy of the political system are more likely to accept or tolerate outcomes that may be non-democratic, unfair, or lead to heightened inequality. Further, high system justifiers seem to be more willing to support public policies that may actually have a negative impact on themselves. In my survey, I ask a battery of questions that measures the concept of system justification. These items are then combined in the form of an additive scale such that higher scores reflect greater endorsement of system justifying beliefs and lower scores means lower endorsement of such beliefs. One question you will consider is the relationship between SJ beliefs and blame attribution for migrant deaths on the U.S.-Mexico border. To measure blame attribution, I relied on a series of pairwise, forced-choice comparison questions where respondents were asked to state which “reason” was most to blame. For each pair, the respondent could choose an item that was “systemic” or “dispositional.” I scaled together these questions to form a measure such that higher scores on the scale implies greater endorsement of dispositional blame and lower scores imply lower endorsement of dispositional blame (i.e. higher endorsement of situational blame). In addition to these two scales, I asked about a respondent’s partisan affiliation, gender, their support for different kinds of immigration policies, and an assessment of the size of the Latina/o population that is undocumented, all variables that were used to some extent in the first problem set. In addition to these factors, I’m also interested in how framing influences assessment of blame attribution. R Tasks For this assignment, you will need to do the following tasks in R. Before you begin, create a dummy variable for party affiliation coded 1 if a respondent scores a 5 or higher on the variable named “pid7” and coded 0 if a respondent scores a 3 or lower on the variable “pid7.” I will explain in class what this coding means (spoiler alert: this is a different coding than used in problem set 1). 1. This set of questions looks at the system justification scale, the blame attribution scale, and the undocumented population estimate variable and asks whether or not there are differences in them due to party affiliation and gender identification. Test the following hypotheses using a t-test: a. Republican identifiers will be more likely to endorse stronger system justifying beliefs than Democratic identifiers. In your write up, formally state the null and alternative hypotheses. What does your test show and, given the results, do you have sufficient evidence to reject the null? b. Males will be more likely to endorse stronger system justifying beliefs than females. In your write up, formally state the null and alternative hypotheses. What does your test show and, given the results, do you have sufficient evidence to reject the null? c. Republican identifiers will be more likely to endorse dispositional attributions of blame than Democratic identifiers. In your write up, formally state the null and alternative hypotheses. What does your test show and, given the results, do you have sufficient evidence to reject the null? d. Males will be more likely to endorse dispositional attributions of blame than females. In your write up, formally state the null and alternative hypotheses. What does your test show and, given the results, do you have sufficient evidence to reject the null? e. Republican identifiers will be more likely to overstate the size of the Hispanic undocumented population than Democratic identifiers. In your write up, formally state the null and alternative hypotheses. What does your test show and, given the results, do you have sufficient evidence to reject the null? f. Males and females will be exhibit significant differences in their estimates of the size of the Hispanic undocumented population. In your write up, formally state the null and alternative hypotheses. What does your test show and, given the results, do you have sufficient evidence to reject the null? g. Create a dot chart of undocumented population estimates for males and females. How does this chart related to 1f? 2. This set of questions asks you to examine the experimental conditions and how they relate to blame attribution. Please do the following tasks. a. Do a side-by-side box plot of the blame attribution scale for the six experimental conditions for: i. all respondents ii. Republicans iii. Democrats What do the plots show? Are there important party differences that you see over experimental conditions. In what ways do Democrats and Republicans look differently? b. Conduct a t-test testing the following hypotheses: i. Republicans exposed to the climate narrative will be significantly less likely to endorse dispositional blame compared to Republicans exposed to the high-crime narrative. In your write up, formally state the null and alternative hypotheses. What does your test show and, given the results, do you have sufficient evidence to reject the null? ii. Democrats exposed to the climate narrative will be significantly less likely to endorse dispositional blame compared to Democrats exposed to the high-crime narrative. In your write up, formally state the null and alternative hypotheses. What does your test show and, given the results, do you have sufficient evidence to reject the null? iii. Republicans exposed to the visa narrative will be significantly less likely to endorse dispositional blame compared to Republicans exposed to the high-crime narrative. In your write up, formally state the null and alternative hypotheses. What does your test show and, given the results, do you have sufficient evidence to reject the null? iv. Democrats exposed to the visa narrative will be significantly less likely to endorse dispositional blame compared to Democrats exposed to the high-crime narrative. In your write up, formally state the null and alternative hypotheses. What does your test show and, given the results, do you have sufficient evidence to reject the null? 3. Estimate a regression model treating blame attribution as a function of the experimental conditions. From this model, provide the predicted value for blame attribution for each condition. Do any of the experimental conditions seem to produce different prediction and if so, in what direction? 4. Estimate a regression model treating blame attribution as a function of a dummy variable