Assignment: Testing for Bivariate Categorical Analysis
You have had plenty of practice with data analysis in the Discussions and hopefully you have received helpful and encouraging feedback from your colleagues. Now, for the last time in the course, it is time once again to put all of that good practice to use and answer a social research question using categorical statistical tools. As you begin the Assignment, be sure and pay close attention to the assumptions of the test. Specifically, make sure the variables are categorical level variables.
For this Assignment, you will consider three different scenarios. Each of these scenarios include a research question. You will examine each scenario, choose a categorical data analysis and run a sample test.
To prepare for this Assignment:
- Review Chapters 10 and 11 of the Frankfort-Nachmias & Leon-Guerrero course text and the media program found in this week’s Learning Resources related to bivariate categorical tests.
- Using the SPSS software, open the Afrobarometer dataset found in this week’s Learning Resources.
- Next, review the Chi Square Scenarios found in this week’s Learning Resources and consider each research scenario for this Assignment.
- Based on the dataset you chose and for each research scenario provided, using the SPSS software, choose a categorical data analysis and run a sample test.
- Once you perform your categorical data analysis, review Chapter 11 of the Wagner text to understand how to copy and paste your output into your Word document.
For this Assignment:
Write a 1- to 2-paragraph analysis of your categorical data results for each research scenario. If you are using the Afrobarometer Dataset, report the mean of Q1 (Age). In your analysis, display the data for the output. Based on your results, provide an explanation of what the implications of social change might be.
Use proper APA format, citations, and referencing for your analysis, research question, and display of output.
By Day 7
Submit your Assignment: Testing for Bivariate Categorical Analysis.
Submission and Grading Information
To submit your completed Assignment for review and grading, do the following:
- Please save your Assignment using the naming convention “WK11Assgn+last name+first initial.(extension)” as the name.
- Click the Week 11 Assignment Rubric to review the Grading Criteria for the Assignment.
- Click the Week 11 Assignment link. You will also be able to “View Rubric” for grading criteria from this area.
- Next, from the Attach File area, click on the Browse My Computer button. Find the document you saved as “WK11Assgn+last name+first initial.(extension)” and click Open.
- If applicable: From the Plagiarism Tools area, click the checkbox for I agree to submit my paper(s) to the Global Reference Database.
- Click on the Submit button to complete your submission.
Week Eleven: Final Assignment
Posted on: Saturday, August 6, 2022 7:44:43 AM EDT
Please consider the following as a general guide of what is expected within all assignments:
– Title Page [see Walden University Template for formatting].
– Introduction [required]: When drafting a formal, scholarly or academic paper alway start with an introduction. The introduction immediately orients any audience to the paper’s purpose. IE: The following is a selection of articles on fair hiring practices. Each source will be annotated to inform an audience of the general focus and scope of the sources…….
– Articles [if a bibliography] each article or research source is listed by formal reference. Immediately thereafter, the author of the bibliography gives a concise overview of the article and/or reference’s content and purpose [one paragraph]. The bibliography continues with what the writer has gleamed from the source as relevant to the paper’s purpose [see introduction above].
OR
-Content [assignments other than annotated bibliographies] using APA formatted headings, hold the hand of your audience assisting through topical transitions allowing them to follow and anticipate.
– Summary [required]: having considered a topic, looking at varied sources to learn about the topic synthesize the sources into a few summary statements. Continuing with the example: The selection and review of articles on fair hiring practices makes evident some of the most common errors to avoid are…..OR…a couple of statements threading together what has been learned by your scholastic engagement of the content.
PRONOUNS: Note in the above instructions not a single demonstrative or personal pronouns appears.Overuse of pronouns is considered a potential “…affront to clarity an can exclude a passive audience (APA 7.0).”
APA Tutorial: Do not lose valuable points in grading by excluding core elements or avoiding headings necessary to facilitate audience access or by including pronouns limiting and/or prohibiting audience access. Please focus upon the misuse/overuse of pronouns considered an “affront” upon clarity with academic/scientific/formal writing. Pronouns potentially exclude your audience and unnecessarily conceal critical content. Take a look at an example;
WRONG: This information was prepared to make clear that those critical polices of the agency you must follow when hiring somebody..
RIGHT: The brief, informational brochure presents the mandatory policies of the Federal Office of Discrimination when engaging hiring processes.
Posted by: John Billings
Posted to: RSCH-8210D-2/RSCH-8210C-2-Quantitative Reasoning-2022-Summer-QTR-Term-wks-1-thru-11-(05/30/2022-08/14/2022)-PT27
Week Eleven: Assignment Guidance
Posted on: Saturday, August 6, 2022 7:42:38 AM EDT
Almost there!
During the upcoming week the final assignment reads:
Use SPSS to answer the research question. Post your response to the following:
What is your research question?
What is the null hypothesis for your question?
What research design would align with this question?
What dependent variable was used and how is it measured?
What independent variable is used and how is it measured?
If you found significance, what is the strength of the effect?
Explain your results for a lay audience and further explain what the answer is to your research question.
Use the list of questions to self-audit the final product before submitting to make sure all elements are evident.
Be sure to support your Main Post and Response Post with reference to the week’s Learning Resources and other scholarly evidence in APA Style.
After engaging several, isolated exercises within quantitative statistics, the week’s assignment asks you to bring all learned content home! The responses to the questions above, formatted into sound paragraphs, will look like what would one expect to read in a formal proposal under methodology. After identifying a statement of the problem and a proposed purpose, the answers would introduce a proposed action plan and a defense of the statistical method selected to a universal audience.
NOTE: while adhering to solid statistical method, the questions compel you to write a clear, concise and comprehensive response accessible to any universal audience. There should be, in the end, no questions. You are the expert communicating effectively to those without substantive experience in the social sciences.
CAUTIONS [issues discussed both in discussion threads and within personal assignment feedback]:
– no visual data output displays should be listed one after another.
– dedicated explanatory texts should be sufficient as to allow any passive audience to anticipate, access and understand the data output display that follows.
– the product of a “model Summary” should not appear within the final document. Instead, if necessary to engage, the nominal values of the model summery should be offered narratively not visually.
– no topical sentence should begin with a conjunction or demonstrative pronoun.
– avoid phrases such as: “It is important that…”. “It is critical that…” or “It is imperative that…”
Posted by: John Billings
Posted to: RSCH-8210D-2/RSCH-8210C-2-Quantitative Reasoning-2022-Summer-QTR-Term-wks-1-thru-11-(05/30/2022-08/14/2022)-PT27
Week Ten: Dummy Variables
Posted on: Friday, July 29, 2022 9:50:18 AM EDT
Sometimes, by creative constructs [drafting and using responsible assumptions] a researcher can manipulate data sets to provide more insights [dummy variables].
In social science, many of the predictor variables a researcher may want to use are inherently quantitative and measured categorically (i.e., race, gender, political party affiliation, etc.). During week 10, you will learn how to use categorical variables within multiple regression models.
Having now discussed the benefits of multiple regression, we have been reticent about what can go wrong in our models. For models to provide accurate estimates, we must adhere to a set of assumptions. You have had plenty of opportunity to interpret coefficients for metric variables in regression models. Using and interpreting categorical variables takes just a little bit of extra practice. In this Discussion, you will have the opportunity to practice how to recode categorical variables [dummy] so they can be used in a regression model and how to properly interpret the coefficients.
A dummy variable is a numerical variable used within regression analyses to represent subgroups of the sample within a study. In research design, a dummy variable is often used to distinguish different treatment groups. In the simplest case, we would use a (0,1) dummy variable where a person is given a value of 0 if in the control group or a 1 if in the treated group. Dummy variables are useful because they enable a single regression equation to represent multiple groups: meaning no need to write out separate equation models for each subgroup.
Further, social scientists often need to work with categorical variables in which the different values have no real numerical relationship with each other. Examples include variables for race, political affiliation, or marital status. If you have a variable for political affiliation with possible responses including Democrat, Independent, and Republican, it obviously doesn’t make sense to assign values of (1 – 3) and interpret, by error, that a Republican is somehow three times more politically affiliated then a Democrat. The solution is to use a dummy variable(s) with only two values, zero and one. By creating a variable called “Republican” and assign the group a 1 indicating, simply, members are “Republican” and all others within the study are not.
The decision to code a level is often arbitrary but must be responsible [makes sense]. The level which is not coded is the category to which all other categories will be compared. As such, often the biggest group will be the not-coded category. For example, often “Caucasian” will be the not-coded group if the race of most participants in the sample. Following, if you have a variable called “Asian”, the coefficient on the “Asian” variable in your regression will show the effect being Asian rather than Caucasian has on your dependent variable.
References
Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.
- Chapter 7, “Cross-Tabulation and Measures of Association for Nominal and Ordinal Variables”
- Chapter 11, “Editing Output” (previously read in Week 2, 3, 4, 5. 6, 7, and 8)
Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Thousand Oaks, CA: Sage Publications.
- Chapter 9, “Bivariate Tables” (pp. 281-325)
- Chapter 10, “The Chi-Square Test and Measures of Association” (pp. 327-373)
media
Walden University, LLC. (Producer). (2016a). Bivariate categorical tests [Video file]. Baltimore, MD: Author.
Note: The approximate length of this media piece is 5 minutes.
In this media program, Dr. Matt Jones demonstrates bivariate categorical tests using the SPSS software.
