8210 wk11 discussion

Response 1

Carey-Ann Thurlow 

RE: Discussion - Week 11 

COLLAPSE

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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.

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Response 2

Kristin Domville 

RE: Discussion - Week 11 

COLLAPSE

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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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