8210 11 assgn

  

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.

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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.

 

© 2016 Laureate Education, Inc. Page 1 of 1

Week 11

Scenarios

1. Results of your literature review conclude that trust in the police is an integral

part of any democracy. You wish to test whether a relationship between trust in
the police and presence of democracy (measured with dichotomous variable)
exists in Africa. Using Afrobarometer 2015, please provide: a 1–2 APA style
paragraph statement that furnishes an answer to this question, note the relevant
statistics, comment on meaningfulness, and include your relevant SPSS output.

2. Following up on your previous analysis, you now wish to determine whether a
relationship exists between citizen trust in police and whether respondents reside
in rural, urban or semi-urban settings? Using Afrobarometer 2015, please
provide: a 1–2 APA style paragraph statement that furnishes an answer to this
question, note the relevant statistics, comment on meaningfulness, and include
your relevant SPSS output. In addition, please comment on what could be
influencing the results you obtained.

3. Is there a relationship between perceptions of current economic conditions and
extent of a democracy? Using Afrobarometer 2015, please provide: a 1–2 APA
style paragraph statement that furnishes an answer to this question, note the
relevant statistics, comment on meaningfulness, and include your relevant SPSS
output. In addition, please comment on what could be influencing the results you
obtained.

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Skill Builder 18: Interpreting Regression Coefficients for Dummy-Coded Variables

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Topic 1 – Interpreting Regression Models with Dummy-Coded Variables

EXIT SKILL BUILDER
How to Create Dummy-Coded Variables
by Robin KouvarasRobin Kouvaras
Topic 2 of 5

Learning Objective:
Interpret regression models with dummy-coded variables.

How to Create Dummy-Coded Variables

Dummy-coded variables are created by only using the values of 0 and 1. The general rule used for dummy coding is that you need one (1) fewer dummy-coded variables than you have groups (# total groups – 1). So, for our variable of marital status, we would need two (2) dummy-coded variables because we have chosen to focus on three (3) marital status groups (3 – 2 = 1). The group for which we do not create a dummy-coded variable is typically called the reference category. Often the reference category will be the one that researchers want to compare to other groups. For our research, we might choose “married” as our reference category if we want to compare non-married individuals to married individuals.  
Before we conduct our regression analyses in SPSS, then, we will need to create two (2) dummy-coded variables for marital status:
one variable for the divorced group
one variable for the never-married group
We will use a 1 to indicate membership to that category (e.g., to indicate that someone is divorced for the “divorced” dummy-coded variable) and 0 to indicate non-membership.  
The table below shows how we would dummy-code our marital status variables.

Notice the Following
If the original value for an individual’s marital status is a 1 (indicating married), that individual would have a 0 for the “divorced” variable and a 0 for the “never married” variable. This is because they are not a “member” of either of these groups, they are not divorced, and they are not in the never-married category. This same logic holds for the remaining two (2) values of marital status. If an individual is divorced, they get a 1 for the divorced group, for example, and a 0 for the never-married group.
Also, note that each individual in the data set will have a value (either a 0 or a 1) for each dummy-coded variable that the researcher creates. 

Suppose the researcher decides to add an additional marital status group (separated), so that she now has the following marital status groups: married, divorced, never married, and separated.

Hint: Count the number of groups you have and subtract 1.

 How many dummy-coded variables would the researcher need to create for her regression model?

5

2

3

4

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Topic 3 – Interpreting the Coefficients for Dummy-Coded Variables

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Skill Builder 18: Interpreting Regression Coefficients for Dummy-Coded Variables

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EXIT SKILL BUILDER

Topic 2 – How to Create Dummy-Coded Variables

EXIT SKILL BUILDER
Interpreting the Coefficients for Dummy-Coded Variables
by Robin KouvarasRobin Kouvaras
Topic 3 of 5

Learning Objective:
Interpret regression models with dummy-coded variables.

How to Interpret Regression Results

Now that you are familiar with how to create dummy-coded variables, we will discuss how to interpret your regression results. Below is the SPSS output using the marital status groups to predict the frequency of religious attendance using multiple regression. Below the regression output, there is also the SPSS output that shows the mean for religious attendance for each of the marital status groups.

SPSS output using the marital status groups to predict the frequency of religious attendance using multiple regression.
Coefficientsa

Model Unstandardized Coefficients Standardized Coefficients t
Sig.
B Std. Error Beta
1 (Constant)
4.328 .095 blank 45.627 .000
Divorced -1.239 .206 -.166 -6.009 .000
Never Married -1.190 .174 -.189 -6.825 .000

Legend for Coefficientsa

p-value for the Never Married predictor variable.

p-value for the Divorced predictor variable.

SPSS output that shows the mean for religious attendance for each of the marital status groups.
Descriptives
HOW OFTEN R ATTENDS RELIGIOUS SERVICES

Blank N Mean Std. Deviation Std. Error 95% Confidence Interval for the Mean Minimum
Maximum
Lower Bound Upper Bound
MARRIED 789 4.33 2.731 .097 4.14 4.52 0 8
DIVORCED 212 3.09 2.687 .185 2.73 3.45 0 8
NEVER MARRIED 332 3.14 2.484 .136 2.87 3.41 0 8
Total 1333 3.83 2.728 .075 3.69 3.98 0 8

Let’s focus on the unstandardized regression coefficients in the output. Each coefficient will indicate how that particular group compares to the reference category (e.g., married) on the dependent variable. The coefficient reflects the comparison between the mean value of the dependent variable for the reference category and the mean value for the group represented by that particular coefficient. For example, first, take a look at the unstandardized regression coefficient for “divorced” (-1.239). This value reflects how the divorced group compares to the married group on religious attendance and indicates that the mean religious attendance for the divorced group is 1.239 units lower than that for the married group. 

A few more things about the output:

If you subtract the mean for divorced (3.09) from the mean for married (4.33), you can see that you get the absolute value of the coefficient for the divorced variable: 4.33 – 3.09 = 1.24.  (If you round 1.239, you get 1.24.)  

bullet
If you subtract the mean for divorced (3.09) from the mean for married (4.33), you can see that you get the absolute value of the coefficient for the divorced variable: 4.33 – 3.09 = 1.24.  (If you round 1.239, you get 1.24.)  

If the value had been positive (1.239 instead of -1.239), it would indicate that the divorced group had a higher mean than the married group on the dependent variable.  

bullet
If the value had been positive (1.239 instead of -1.239), it would indicate that the divorced group had a higher mean than the married group on the dependent variable.  

Similar to when you are interpreting the coefficients for continuous predictor variables in a regression model, the difference between the reference category and the indicated group is only considered to be statistically significant if the p-value is less than alpha.  In our results above, if we assume an alpha of .05 (or even .01), each predictor would be statistically significant, indicating that each group (divorced, never married) differs from the reference category of married on the dependent variable.

bullet
Similar to when you are interpreting the coefficients for continuous predictor variables in a regression model, the difference between the reference category and the indicated group is only considered to be statistically significant if the p-value is less than alpha.  In our results above, if we assume an alpha of .05 (or even .01), each predictor would be statistically significant, indicating that each group (divorced, never married) differs from the reference category of married on the dependent variable.

Also similar to when you are interpreting the coefficients for continuous predictor variables in a regression model, you can use the absolute value of the standardized regression coefficients to gauge the effect size for each variable; values closer to 0 indicate weaker effects, and values closer to 1 indicate stronger effects.

bullet
Also similar to when you are interpreting the coefficients for continuous predictor variables in a regression model, you can use the absolute value of the standardized regression coefficients to gauge the effect size for each variable; values closer to 0 indicate weaker effects, and values closer to 1 indicate stronger effects.

Hint: Remember that the unstandardized regression coefficients reflect a comparison to the reference category about the mean value of the outcome variable.

Take a look now at the unstandardized regression coefficient for never married (-1.19). What would be an appropriate interpretation of this value?

The never married group mean for religious attendance is 1.19 units lower than the mean for the divorced group.

The never married group mean for religious attendance is 1.19 units higher than the mean for the divorced group.

The never married mean is 1.19 units lower than the married group mean for the dependent variable.

The never married mean is 1.19 units lower than the married group mean for the independent variable

SUBMIT

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Topic 4 – Module Summary and Quiz

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© 2016 Laureate Education, Inc. Page 1 of 1

Week 11

Scenarios

1. Results of your literature review conclude that trust in the police is an integral

part of any democracy. You wish to test whether a relationship between trust in
the police and presence of democracy (measured with dichotomous variable)
exists in Africa. Using Afrobarometer 2015, please provide: a 1–2 APA style
paragraph statement that furnishes an answer to this question, note the relevant
statistics, comment on meaningfulness, and include your relevant SPSS output.

2. Following up on your previous analysis, you now wish to determine whether a
relationship exists between citizen trust in police and whether respondents reside
in rural, urban or semi-urban settings? Using Afrobarometer 2015, please
provide: a 1–2 APA style paragraph statement that furnishes an answer to this
question, note the relevant statistics, comment on meaningfulness, and include
your relevant SPSS output. In addition, please comment on what could be
influencing the results you obtained.

3. Is there a relationship between perceptions of current economic conditions and
extent of a democracy? Using Afrobarometer 2015, please provide: a 1–2 APA
style paragraph statement that furnishes an answer to this question, note the
relevant statistics, comment on meaningfulness, and include your relevant SPSS
output. In addition, please comment on what could be influencing the results you
obtained.

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