Public Affairs Data Analysis and Applied Statistics Your research question should be raised from public affairs, administration, and policy.
The dataset should be ready for use to examine your particular research question.
The dataset should also meet all the requirements indicated in the syllabus (e.g., at least 50 observations and at least 5 variables, at least 2 interval variables and 1 categorical variable). Data analysis is not required for this assignment.
Public Affairs Data Analysis and Applied Statistics
Submit your research question (in a Word file) AND a complete dataset (in an Excel file) that you will use for the applied essay
You are required to write a 5-7 page (double-spaced, NO MORE THAN 7 pages please) essay to demonstrate how you apply the statistics that you learn from this course to real-world inquiries in public affairs and administration.
You should prepare a quantitative dataset using online sources for data analysis. Your dataset should contain AT LEAST 50 observations and AT LEAST 5 variables (at least two variables with interval level measurement and at least one variable with categorical measurement).
You should use basic descriptive statistics (mean, median, standard deviation, minimum, and maximum) to summarize the interval level variables and use frequency distribution to summarize the categorical variables.
You are also required to use three (3) inferential analysis techniques one of them must be multivariate linear regression (no simple linear regression please) to test meaningful hypotheses. The dependent variable in the linear regression model should be measured at the interval level.
Public Affairs Data Analysis and Applied Statistics
I would suggest you organize your essay by answering the following questions successively:
1) What are your research question and hypotheses? Please make sure that (a) your research question is from public affairs/administration field, (b) each of your hypotheses should propose an impact of one independent variable on the dependent variable, and (c) each of your hypotheses sounds reasonable.
2) Describe the data set you are using: What are your observations, and how many valid observations in your dataset? Where do you get the data? (Please provide the online link.)
Public Affairs Data Analysis and Applied Statistics
When and who (or which organization) originally collected the data? How were the data collected (through in-person interview, phone survey, mail survey, or it is census data, or through some other approach)?
3) How is each of the variables (including the dependent variable) in your hypotheses measured? What is the measurement unit if the variable is measured at the interval level?
If you recode any variable or generated any new variable, how do you do that? Please note: measurement tells about what the variable is about and what the values mean. For example, city level educational attainment is measured by the percent of citizens with a BA or BS degree or higher out of the total population of 25 or older. Its measurement unit is percent.
Public Affairs Data Analysis and Applied Statistics
4) Use basic descriptive statistics to summarize at least two interval-level variables, and use frequency distribution to summarize at least one categorical level variable. You may create a nice table or chart to help you report the descriptive statistics.
5) Which inferential techniques (including multivariate linear regression) do you use to test each of your hypotheses? For each technique, why do you think it is appropriate? Please make sure you specify your dependent variable and independent variables in your inferential analyses.
Public Affairs Data Analysis and Applied Statistics
6) Please interpret the analysis results and report whether the hypothesis is supported. In correlation analysis, please (a) interpret the correlation coefficient and then (b) use the t-test result to show the significance of the relationship. If you apply contingency table with chi-square test, please (a) use the column percent to report the specific relationship, and
(b) Use chi-square test to show whether the relationship is significant. If you use group
mean comparison with t-test, please (a) compare the sample means first, and then (b) use t-test result to report whether the difference is significant or not.
7) When you interpret the results of the multivariate linear regression (as a more comprehensive inferential analysis technique), please (a) explain each of the partial coefficients, (b) the significance of the partial coefficients, and (c) also report the model fit statistics.
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