8210 wk11 discussion

  

As with the previous week’s Discussion, this Discussion assists in solidifying your understanding of statistical testing by engaging in some data analysis. This week you will once again work with a real, secondary dataset to construct a research question, perform categorical data analysis that answers the question, and interpret the results.

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Discussion: Categorical Data Analysis

As with the previous week’s Discussion, this Discussion assists in solidifying your understanding of statistical testing by engaging in some data analysis. This week you will once again work with a real, secondary dataset to construct a research question, perform categorical data analysis that answers the question, and interpret the results.

Whether in a scholarly or practitioner setting, good research and data analysis should have the benefit of peer feedback. For this Discussion, you will post your response to the hypothesis test, along with the results. Be sure and remember that the goal is to obtain constructive feedback to improve the research and its interpretation, so please view this as an opportunity to learn from one another.

To prepare for this Discussion:

· 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.

· Create a research question using the General Social Survey dataset that can be answered using categorical analysis.

By Day 3

Use SPSS to answer the research question. Post your response to the following:

1. Include the General Social Survey Dataset’s mean of Age to verify the dataset you used.

2. What is your research question?

3. What is the null hypothesis for your question?

4. What research design would align with this question?

5. What dependent variable was used and how is it measured?

6. What independent variable is used and how is it measured?

7. If you found significance, what is the strength of the effect?

8. Explain your results for a lay audience and further explain what the answer is to your research question.

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.

By Day 5

Respond to at least one of your colleagues’ posts and comment on the following:

1. Do you think the variables are appropriately used? Why or why not?

2. Does the analysis answer the research question? Be sure and provide constructive and helpful comments for possible improvement.

3. As a lay reader, were you able to understand the results and their implications? Why or why not?

Reference

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)

Walden University, LLC. (Producer). (2016a). Bivariate categorical tests [Video file]. Baltimore, MD: Author.

 

Tes

t

ing for Multiple

R

egression

Name

Institution Name

Course Name

Professor’s Name

Date

Part

1

It is possible to determine the connection between two variables using regression analysis. Using this method, you may determine how much of an effect the independent variable has on the dependent one (Wagner,

2

020). The greatest applications for regression analysis are thus those that include a large number of variables that can be evaluated independently. The major goal of the regression model below was to investigate the link between job status and internet and mobile phone use. According to the data in table 1, this model can account for just 4.2% of the total variance observed. A statistically significant model can be found despite the model’s low significance score. ANOVA table 2 is also statistically significant, meaning that there is a statistically significant difference in the mean values of the variables.

Research Question

Is there any impact of cell phone usage and internet usage on employment status?

.042

.042

Table 1:

Model

Summary

Model R

R Square

Adjusted R Square

Std. Error

of the Estimate

Change Statistics

R Square Change

F

Change

df

1

df2

Sig.

F Change

1

.205a

.042

.46251

219.552

2

10012

.000

a. Predictors:

(Constant)

,

Internet Usage

,

Cell Phone Usage

Model

1

2

219.552

10012

Table 2: ANOVAa

Sum of Squares

df

Mean Square

F Sig.

Regression

93.932

46.966

.000b

Residual

2141.751

.214

Total

2235.684

10014

a. Dependent Variable: Employment Status

b. Predictors: (Constant), Internet Usage, Cell Phone Usage

These crucial coefficients may be found in the following table (see row three). The beta readings indicate that the constant is 0

.057

. Cell phone usage is represented by a score of 0

.134

, while internet usage is represented by a score of 0

.130

. Statistically, each of these beta values is significant.

The model is given as:

Employment Status = 0.057 + 0.134 cell phone + 0.13 Internet usage.

Model

Sig.

1

.000

.000

.000

a. Dependent Variable: Employment Status

Table 3: Coefficientsa

Unstandardized Coefficients

Standardized Coefficients

t

B

Std. Error

Beta

(Constant) .057

.014

3.966

Cell Phone Usage .134

.013

.102

10.283

Internet Usage .130

.008

.160

16.109

Part 2

Research Question

Does the sampling unit has an impact on the employment status?

There are now three dummy variables created from the sample unit variable, one for each kind of location: urban, rural and semi-urban.

Phase 1

The major goal of this model was to examine the link between the location of the sample units (urban vs. rural) and the participants’ job status. This model accounts for 1.1 percent of the variance in the data, according to R square findings. Despite the fact that the result is statistically significant, the value is quite low.

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Change Statistics

R Square Change

F Change

df1

df2

Sig. F Change

1

.011

.011

2

.000

Table 4: Model Summary

.106a

.011

.46890

58.229

10271

a. Predictors: (Constant),

Urban

,

Rural

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

2

58.229

.000b

Residual

10271

Total

a. Dependent Variable: Employment Status

Table 5: ANOVAa

25.606

12.803

2258.283

.220

2283.888

10273

b. Predictors: (Constant), Urban, Rural

The table of regression coefficients may be found in the preceding section, in table 6. In this instance, the Beta values derived from the study are shown. These findings lead us to believe that the beta is always going to be 0

.227

. A positive beta value was found in the rural and urban regions. However, only the urban factor was shown to be statistically significant. Hence, the model can be given as below:

Employment status = 0.227 + 0

.035

rural + 0.17 Urban

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

.000

.042

.000

a. Dependent Variable: Employment Status

Table 6: Coefficientsa

.227

.041

5.467

Rural .035

.021

.072

1.660

.097

Urban

.170

.175

4.026

Phase 2

Using regression analysis, a new set of dummy variables was tested in the second phase. Detailed results are shown in Tables 8,9, and 10. In the second model, the urban and semi-urban sample units were the primary focus of attention. Only 1.1 percent of the model’s volatility can be explained by this data, according to the model summary. The model’s significance is also backed up by the data.

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Change Statistics

R Square Change

F Change

df1

df2

Sig. F Change

1

.106a

.011

.011

.46890

.011

58.229

2

10271

.000

Table 8: Model Summary

a. Predictors: (Constant),

SemiUrban

, Urban

There is a breakdown of the regression model’s coefficients in the following table 9. The constant beta was determined to be 0

.296

in the table. To put it another way: The Beta values for the urban variables were 0.01 and for the semi-urban variables 0.023. Semi-urban was not statistically significant in the model used in this study. The regression equation is given below:

Employment status = 0.296 + 0.1 urban – 0.023 semi-urban.

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

.000

Urban

.000

.014

.097

a. Dependent Variable: Employment Status

Table 9: Coefficientsa

.296

.006

49.734

.100

.010

.103

10.474

SemiUrban

-.023

-.016

-1.660

Implications for Social Change

In order to affect societal change, the data presented above is crucially important. It goes into great depth on how the dependent and independent variables are connected. Part two involves assessing the impact of different sample units on an individual’s employment status. Regression analysis aids in finding the link between variables that are crucial to creating social changes in the environment.

References

Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.

Response 1

Carey-Ann Thurlow

 

RE: Discussion – Week 11 

COLLAPSE

Top of Form

Categorical Data Analysis

When researchers want to detect and describe the relationship between two categorical variables, a cross-tabulation is used (Frankfort-Nachmias et al., 2020). Researchers can then explore the relationship between the two variables by examining the intersections of categories of each variable involved (Wagner, 2020). Bivariate analysis is the analysis of two variables and is the simplest type of cross-tabulation (Wagner, 2020). The following post utilizes the GSS dataset to perform a bivariate analysis between the independent variable ‘gender’, and the dependent variable, ‘discrimination at work because of gender’.

The General Social Survey Dataset

The General Social Survey (GSS) is a dataset that monitors growth and social change in America (Social Capital Gateway, 2022). The GSS surveys participants on demographic, behavioral and attitudinal questions, along with topics of interest such as civil liberties, crime and violence, mortality, psychological well-being and more (GSS, 2022). The mean age of the GSS dataset is 48.71, which is important to consider because this test is performed on the working class, so it can be assumed that the average age of participants includes a pool that have already been working for a number of years.

 

Research Question

Using the General Social Survey (GSS) dataset, a crosstabulation bivariate analysis was performed to answer the research question:

‘Do females report a higher level of discrimination at work over males in the same role?’

The null hypothesis is: ‘There is no relationship between the level of discrimination at work and gender’.

The alternate hypothesis is: ‘There is a relationship between the level of discrimination at work and gender’.

Interpretation of the Crosstabulation

Out of 507 respondents of the survey, only 259 answered this portion of the survey. 248 participants either didn’t answer, refused to answer or left the question blank. Table 1 displays the crosstabulation for male and female participants and whether or not they feel discriminated against at work because of their gender. Out of 136 male respondents for this question, only 1 (0.7%) answered yes, while 135 (99.3%) answered no. Out of 123 female respondents for this question, 12 (9.8%) answered yes, while 111 (90.2%) answered no. If there was no relationship between the two variables, there would be nearly equal percentages.

 

Table 1

Crosstabulation for Whether or Not Respondents Feel Discriminated Against at Work

 

Interpretation of the Chi-Square Tests

The chi-square test is designed to test for significant relationships between two variables organized in a bivariate table (Frankfort-Nachmias et al., 2020). In Table 2, the Pearson chi-square is 11.024, with a significance of <.001. This model displays statistical significance at the p<.001 level, therefore the null hypothesis can be rejected suggesting that there is no relationship between the level of discrimination at work and gender, assuming that there is some relationship between gender and discrimination at work.

 

Table 2

Chi-Square Tests to Determine the Relationship Between the Two Variables

 

Interpretation of the Cramer’s V Correlation

Cramer’s V is another measure of association based on Pearson’s chi-squared and is used for two nominal variables (Frankfort-Nachmias et al., 2020). Table 3 displays the Cramer’s V correlation which describes the strength of the relationship between the variables. A value of 0 indicated no relationship whatsoever and a value of 1 indicates a very strong relationship. Table 3 displays Cramer’s V as .206. In this case the relationship between the variables, which is statistically significant at the p<.001 level is relatively weak.

Table 3

Symmetric Measures for Cramer’s V Correlation

 

Analysis and Summary of the Crosstabulation

The crosstabulation analysis affirmed the alternate hypothesis that there is a relationship between gender and discrimination at work. Although the respondent outcome for ‘yes’ was low (9.8%) in comparison to the total number of participants, the model displayed statistical significance and strength in the relationship between the variables. Implications for social change include awareness around gender differentiation and discrimination at work, which may include introducing seminars and team building events, while upper management may also reflect on wage discrimination and work toward building a more consistent and stable work environment for all employees.

 

References

Billings, J., (n.d.). Primary guide to research statistics: A monograph for use with ED: 8900 courses. 

https://content.waldenu.edu/d1c00f22444bfb7cf79c9487accceada.html

     

Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G., (2020). Social statistics for a diverse society (9th ed.). Thousand Oaks, CA: Sage Publications.

GSS. (2022). The General Social Survey.  A landmark NORC study since 1972. 

https://gss.norc.org/

Social Capital Gateway. (2022). General Social Survey. 

https://www.socialcapitalgateway.org/content/data/general-social-survey-gss

   

Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.

Bottom of Form

Response 2

Kristin Domville 

RE: Discussion – Week 11 

COLLAPSE

Top of Form

Categorical data analysis is used to analyze data where the response to variables are grouped into a mutually exclusive group or unordered category (Frankfort-Nachmias et al., 2020).  The use of bivariate analysis aims to find statistical significance between two variables one independent and one dependent (Frankfort-Nachmias et al., 2020; Walden University, LLC. (Producer), 2016; Warner, 2012).  The following discussion will utilize the General Social Survey Dataset to determine the mean age, develop research questions, and find statistical significance between two variables.

Mean Age

In the General Social Survey Dataset, data entered for the multiple variables provides the social scientist with a wide range of data on participants’ characteristics, attitudes, and behaviors.  The mean age of the participants is 48.27.  Identifying the mean age within this sample signifies the average respondent was middle-aged.  The mean age of 48.17 may indicate that data results are specific to the behaviors and characteristics of a middle-aged individual and may not be generalizable across all age groups.

Research Questions

Public policy about gun control is a controversial subject. Finding the balance between second amendment rights and gun restrictions is a subject United States citizens are passionate about.  Analyzing attitudes about gun policy in relation to other factors such as gender, geographical location and race can guide policymakers to develop gun safety laws.   Developing positive policies on gun control can be a part of positive social change for the country.  The research question developed is:

· Research Question: To what extent is there a relationship between the respondent’s political party affiliation and having a gun in the home.

· Null Hypothesis (HO):  There is no relationship between the respondent’s political party affiliation and having a gun in the home.

· Alternative Hypothesis (HA): There is a relationship between the respondent’s political party affiliation and having a gun in the home

Categorical Analysis

The research design is a quantitative research design.  The data was collected using categorical variables.  The analysis will include using the chi-square test for independence, and then measure the effect of Cramer’s V. The dependent variable is political party. We will look at the relationship between political party and gun ownership.  It is measured as a categorical variable. The independent variable is if the respondent had a gun in their home.  In table 1, Have A Gun In Home and Political Party Affiliation, a cross tabulation model analyzed if the respondents have a gun in their home, does their political party change.  For respondents who have a gun at home (n=8), 28.6% identify as independent near republican while 59 responents who do not have a gun at home identify as a strong democrate.  Out of 46 respondents, the majority of the resondents indicate that they do not own a gun and are affiliated with the democratic party.

Table 1

Have A Gun In Home and Political Party Affiliation

 

In table 2, Chi-Square Tests, the chi square test had a value of 21.851 and an associated p value of .082.  Since this p value is larger than .05 we would accept the null hypothesis and assume there is not a statistically significant relationship between political party affiliation and having a gun in the home (Frankfort-Nachmias et al., 2020; Walden University, LLC. (Producer), 2016; Warner, 2012).

Table 2

Chi-Square Tests

 

The lack of a relationship is also confirmed by the phi of .255 and the Cramers V at .18 (Table 3)

Table 3

Cramer’s V

 

In the General Social Survey, data was collected and anlized on two variables to learn if there was a relationship between gun ownership and political party.  The anlazed data indicated that the effect size is not relevant due to no relationship found between a respondent having a gun at home and their political party affiliation.  Therefore since we learned that there is not a statistically significant relationship between gun ownership and political party, it would be important to find common ground to increase gun safety, and without the need to focus on political party. 

References

Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Sage Publications.

Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Sage Publications.

Walden University, LLC. (Producer). (2016). Bivariate categorical tests [Video file]. Author.

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