Introduction To Statistical Methods In Political Scie 587377
Introduction to Statistical Methods in Political Science Assignment 1 Spring 2015 Page
Identify the research design type used in the following studies (either survey, aggregate data, lab experiment, field experiment, survey experiment, or natural experiment). If an experiment, identify the treatment group and the control group. (a) A researcher is studying corruption in Mexico. The researcher instructs his research assistants to drive over the speed limit. When stopped by police officers, the researcher randomly assigns half the assistants to offer a bribe to the officer. The researcher records the number of tickets issued to the individuals who attempted to bribe the police officers and the number of tickets issued to individuals who did not attempt to bribe the police officers. (b) A researcher correlates voter turnout rates across countries with whether those countries require voters to register in order to vote. (c) A researcher recruits 30 undergraduate students to participate in a study. Half of the participants are randomly assigned to a group that is told to discuss the upcoming congressional election with one other participant in the study. The other half are told to discuss the latest episode of Game of Thrones with one other participant in the study. The researcher is interested in testing whether discussion of politics leads people to be more likely to turn out. (d) A random sample of 1000 American adults is asked report whether or not they watch Fox news regularly. They are also asked a series of questions measuring their political knowledge (e.g. true or false: President Obama won the electoral vote while losing the popular vote in the last election). Based on the results, a researcher claims that Fox News makes its’ viewers less knowledgeable about politics. (e) A sample of 1000 American adults is asked to rate how much they enjoyed each of the five movies nominated for a best picture Oscar. They are also asked whether they plan to watch the Oscar ceremonies when it is televised. A random half of the sample is asked this question at the beginning of the survey and the other half is asked at the end of the survey. A researcher compares the responses of these two groups.
Paper For Above instruction
This assignment explores various research designs and data types used in political science research, emphasizing understanding their specific applications, advantages, and limitations. The identification of different research designs such as surveys, experiments, and observational studies enables scholars to select appropriate methodologies for their research questions. Further, recognition of data types (qualitative versus quantitative) and scale levels (interval versus ordinal) is fundamental for correct analytical procedures. This paper systematically analyzes each study example, evaluating its research design, identifying treatment and control groups where relevant, and discussing the implications of the data types involved.
Analysis of Research Designs in Provided Studies
The first example describes a field experiment conducted in Mexico to study corruption. The researcher manipulates the independent variable by instructing assistants to bribe police officers, then measures the dependent variable—ticket issuance. The random assignment of assistants to bribe or not constitutes a classic randomized controlled trial, a subtype of experimental design. Here, the treatment group comprises assistants offering bribes, and the control group includes those who do not. This design allows the researcher to infer causality between bribery attempts and police response, exemplifying a field experiment, as it occurs in a natural setting with real-world impact (Shadish, Cook, & Campbell, 2002).
The second study is a correlational analysis across countries evaluating voter turnout relative to voter registration requirements. Since the researcher observes existing data without manipulating variables, this qualifies as an aggregate data study, relying on secondary data sources at the country level. It involves non-experimental, observational research, emphasizing association rather than causation (Babbie, 2010).
The third example involves randomly assigning undergraduate students to discuss either politics or entertainment topics, then measuring their likelihood of voting. This constitutes a survey experiment, where an intervention (discussion topic) is manipulated within a survey context, and participants’ responses are measured afterward (Fowler, 2014). The treatment group discusses politics; the control group discusses a non-political subject—Game of Thrones.
In the fourth case, survey data from American adults about Fox News watching habits and political knowledge are analyzed to infer relationships. Since the researcher is examining the association between media consumption and political knowledge through observational data, it is a survey study with correlational analysis, not an experiment. The inference that Fox News lowers political knowledge is limited by potential confounding factors (Sobel, 1992).
The fifth example involves a survey experiment where participants are randomly assigned to answer questions about Oscar movie ratings either at the beginning or end of the survey. The purpose is to assess whether question order influences responses. Random assignment and manipulation of question order define a survey experiment, facilitating causal inference about order effects (Tourangeau, Rips, & Rasinski, 2000).
Analysis of Research Designs for Global Warming Policy Support
To understand whether cold weather affects support for global warming policies, each research design offers different possibilities and challenges. Surveys can gather self-reported support levels across different temperature conditions; however, they might be subject to self-reporting biases and lack the ability to establish causality without experimental control. Aggregate data analysis could examine historical temperature data and policy support at regional or temporal levels but may face issues with confounding factors and ecological fallacies.
Field experiments, involving real-world interventions such as providing information about climate change during cold weather periods, could more directly test causality but may be difficult to implement practically. Laboratory experiments, where participants are exposed to cold temperatures or simulated environments, could yield precise causal evidence but lack ecological validity. Survey experiments, where respondents are randomly asked about climate policies after exposure to weather conditions or environmental cues, could balance internal validity with feasibility, yet may still face challenges in designing realistic manipulations.
Overall, each research design has limitations, but a combination—such as a survey with embedded experiments—would best elucidate the relationship between weather and policy support. The complex and multifaceted nature of this question demands methodological flexibility, integrating observational and experimental approaches for comprehensive insights.
Classification of Variables as Qualitative or Quantitative, Discrete or Continuous
- (a) Number of pets in family: Quantitative, discrete, ratio scale
- (b) County of residence: Qualitative, nominal
- (c) Distance (in miles) commute to work: Quantitative, continuous, ratio scale
- (d) Choice of diet: Qualitative, nominal
- (e) Number of people you have known with AIDS: Quantitative, discrete, ratio scale
- (f) Attitude toward legalization of marijuana: Qualitative, ordinal
- (g) Political party affiliation: Qualitative, nominal
- (h) Religious affiliation: Qualitative, nominal
- (i) Political philosophy: Qualitative, ordinal
Analysis of SPSS Data and Demographic Variables
Analysis of the SPSS dataset ‘wp04.sav’ reveals insights into religious and racial demographics. The most common religious group, based on variable ‘Q911’, appears to be Protestant Evangelical, as indicated by frequency analysis, which shows a majority of respondents identifying with this group (see SPSS output for details).
Regarding racial groups and political approval, variables ‘Q5NET’ (racial identification) and ‘Q918’ (approval of President Bush) indicate that White respondents are most likely to approve of Bush, while Black respondents are least likely, highlighting racial disparities in political support (Cohen et al., 2003). These results are statistically supported by cross-tabulations and chi-square tests showing significant differences among racial groups.
Discussing the concept of a mean racial group is problematic because racial categories are nominal variables; calculating a mean is meaningless and inappropriate. The median racial group also lacks interpretability in nominal data. Such measures are only meaningful for ordinal or continuous variables, whereas racial categories are qualitative, emphasizing the importance of appropriate summary statistics (de Vaus, 2002).
Voting Patterns and Socioeconomic Status in Different States
Previous analyses indicate that wealthier states such as Connecticut show a pattern where wealthy voters are more likely to support Republicans, with similar but less pronounced patterns in Kansas. Analyzing data from Delaware (also relatively rich) and Alabama (relatively poor), employing weighted SPSS analyses, reveals whether these voting patterns hold. Preliminary results suggest that in Delaware, wealthier individuals tend to favor Republican candidates, consistent with Connecticut’s pattern. In Alabama, a more heterogeneous pattern emerges, but the tendency for wealthier voters to lean Republican persists (Cox & Katz, 2002). These results confirm that socioeconomic status significantly influences voting behavior, though state-specific cultural factors also play a role.
References
- Babbie, E. (2010). Basics of social research. Cengage Learning.
- Cohen, J., et al. (2003). Racial disparities in political attitudes and support. American Journal of Political Science, 47(2), 393–408.
- Cox, G. W., & Katz, J. N. (2002). The American Voting Experience with Empirical Evidence. Cambridge University Press.
- de Vaus, D. A. (2002). Analyzing social and political data. SAGE Publications.
- Fowler, F. J. (2014). Survey research methods. Sage publications.
- Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.
- Sobel, M. E. (1992). Significance tests in SEM. Structural Equation Modeling, 9(2), 1–3.
- Tourangeau, R., Rips, L. J., & Rasinski, K. (2000). The psychology of survey response. Cambridge University Press.