Survey Experiment Clause Samples

Survey Experiment. ‌ To test how selection institutions affect the interpretation of information and blame attribution, I use evidence from a survey experiment. The survey was fielded in the summer of 2017, was developed and hosted on Qualtrics, and was completed by respondents recruited from Amazon’s MTurk. Table 4.1 presents summary char- acteristics of the sample. Respondents for the analysis presented were required to reside in the U.S.A at the time they completed the survey and must have accurately answered a simple attention check question. Respondents were shown information about a hypothetical court with seven mem- bers. They were told that judges were drawn from large and deep candidate pool that included many women. The names of seven judges were listed along with an arbi- Table 4.1: Survey Respondent Characteristics Male Female Gender .43 (422) .57 (556) <▇▇ ▇▇-▇▇ ▇▇-▇▇ 50+ Age .08 (84) .35 (346) .34 (340) .21 (208) Very Cons. Smwht Cons. Moderate Smwht Lib. Very Lib. Ideol. .06(60) .21 (213) .23 (223) .31 (301) .18 (175) High School Some Cllge BA/▇▇ ▇▇▇▇▇▇▇ Doctorate Edu. .07(70) .32 (313) .40(393) .15 (155) .04 (40) trary bar association score. There were two treatment variables. First, the number of women’s names listed varied from zero to three. The names were drawn from a list of past and current state supreme court judges in which gender-ambiguous names were removed.3 Second, half the respondents were told that judges were selected by the governor. The other half of respondents were told that judges were selected through a merit procedure in which a commission generated a short list of three names from which the governor chose one judge to fill a vacancy. Respondents were not given any information about the composition of the short-lists generated by the commission. Based on the information given, respondents were asked if the selection of judges appeared fair or not using a five-point scale: Definitely Unfair, Probably Unfair, Neither Fair nor Unfair, Probably Fair, and Definitely Fair. Figure 4.1 shows the survey instrument for the merit selection and two women justices treatment group. 3I removed gender-ambiguous names from a list of all state supreme court judges selected between 1960 and 2010 from the State High Court Justice Database (https : //▇▇▇.▇▇▇.▇▇▇/▇▇▇▇▇▇▇/▇▇▇▇▇▇▇/research.htm) and then randomly drew male and female names from the list. Figure 4.1: Example Survey Instrument
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Survey Experiment. ‌ To test the assumption that people’s prior beliefs in the fairness of institutions depend on the institutions themselves, I conducted a survey experiment fielded in December 2017. The survey was designed in Qualtrics and fielded through Amazon’s Mechanical Turk (MTurk). Respondents were shown information about two selection procedures to a hypo- thetical court. In one selection procedure, the governor is tasked with selecting judges to vacancies (gubernatorial selection). In the other procedure, a commission generates a short list of candidates from which the governor selects a judge to fill the vacancy (merit selection). After reading brief summaries of the selection procedures, respondents were asked “Which selection procedure do you think will be more fair?” If selection institutions do not affect respondents’ prior beliefs in the fairness of institutions, respondents should select each type equally or indicate that they two systems are equally fair (a third option). In contrast, if prior beliefs about fairness systematically vary across institution, respondents should favor one institution over the other. Given the debate surrounding merit selection and the argument that the commission will decrease the role of politics in selection, I expect respondents to perceive merit selection as more fair than gubernatorial selection. Figure 3.2 shows the survey instrument. Hypothesis 1: If respondents believe that merit selection procedures are more fair than gubernatorial selection procedures, more respondents will indicate that merit selection is more fair than gubernatorial selection. Hypothesis 2: If respondents’ beliefs in the fairness of a two-stage selection process stems from the process itself rather than the name, respondents will indicate that the two-stage process is more fair than a gubernato- rial selection process at similar rates regardless of whether the process is labeled “merit” selection or “commission assisted” selection. 3.2.1 Results, Hypothesis 1: Merit v. Gubernatorial Selec- tion‌ To test whether respondents view merit selection as more fair than gubernatorial selection prior to information about outcomes, I compare the proportion of respon- dents in the merit group who selected merit selection as more fair to those who selected gubernatorial selection as more fair. Figure 3.3 shows the proportion of respondents in the merit group who indicated each answer. Just over 80% of respondents indicated that Merit Selection is more fair; 10% o...
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Survey Experiment. The survey experiment, which tested respondents’ opinions on solitary confinement in the prison system, is the empirical center of this thesis. This chapter begins with the data and methods used to analyze the survey experiment. The survey experiment used original data collected from a survey distributed through Amazon Mechanical Turk. To analyze the survey data, I employed an ordinal logistic regression analysis. The chapter also reports the results of the survey experiment. I first assess the topline results from the survey questions, which are presented in tables. I then observe the cross tabulations of the dependent variable with the treatment variables and independent variables such as racial resentment. I then analyze the results from the ordinal logistic regression, which inform the discussion and conclusion of this study. I conducted a survey experiment to observe what factors influence public opinion on the use of solitary confinement in the United States prison system. Survey experiments are particularly useful for their high internal validity and ability to determine causality (▇▇▇▇▇▇ and ▇▇▇ ▇▇▇▇▇ 2015). Randomized experiments are the gold standard for demonstrating causation (Remler and ▇▇▇ ▇▇▇▇▇ 2015). Specifically, experiments have the “well known advantage of greater precision in estimating causal effects” (▇▇▇▇▇▇▇ and ▇▇▇▇▇▇▇ 2000, 563). Randomized experimental research design involves the application of a treatment to the experimental groups, which can then be compared against a control group to observe a significant difference. In a randomized experiment, the treatment and control are randomly assigned. As a result, the treatment is exogenous. Furthermore, the treatment and control groups are statistically equivalent, allowing for conclusions to be drawn based solely on the application of the treatment. Randomized experimental design was necessary for this study, as the racial and offense treatments were applied to statistically equivalent groups in order to infer causation. The control group, which received no treatment, served as the counterfactual, as it was statistically equivalent with the treatment groups. The individuals were surveyed through an online platform called Amazon Mechanical Turk [Mturk]. The total number of respondents was 904. Table 1 depicts the breakdown of respondents by treatment, control, and image. The sample was checked for demographic distribution across the eight treatment groups. The survey included a test q...
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