# Do you think the variables are appropriately used Why or why not peer response help

Respond to one of your colleaguesâ€™ posts [below] 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?

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Categorical Data Analysis

There is evidence to suggest whether there is a significant association between gender and how a person earns a living (if a person is an entrepreneur or works for someone else). The categorical variables were selected from the General Social Survey (GSS) database to conduct a Chi-Square test for independence (Frankfort-Nachmias & Leon-Guerrero, 2015). In this example, we want to test if men and women are significantly different working for themselves or someone else. So the independent variable is gender (represented by gender in the dataset) and the dependent variable is earning a living (represented by râ€™s self-employed or works for someone else in the dataset).

output.docx The research question I have constructed is does gender have a significant impact on how a person earns a living?

Ho: Gender and earning a living are independent.

Ha: Gender and earning a living are not independent.

The alternative hypothesis will help determine if the level of one variable can help predict the level of the other variable.

The independent variable in this analysis is gender a nominal variable and will be measured as â€œMaleâ€ or â€œFemaleâ€. The dependent variable in this analysis is respondents and how they earn a living and also a nominal variable and will be measured as Self-employed, works for someone else, donâ€™t know (DK), inapplicable (IAP) or not applicable (NA).

The Chi-Square test of Independence is used to determine if there is a significant relationship between two nominal (categorical) variables. The Chi-Square Test of Independence is arguably the simplest test to examine a causal relationship between two nominal level variables. The frequency of one nominal variable is compared with different values of the second nominal variable to decide if they are independent of each other. The data can be displayed in an R*C contingency table, where R is the row and C is the column. This case sample size N = 2440 with N = 98 missing.

Contingency Table

 Male Female Self-Employed 163 132 Works for Someone Else 949 1196

The Crosstabs results of the 2,342 individuals, 163 or 14.7% of males and 132 or 9.9% of the females were entrepreneur while 949 or 85.3% of the males and 1196 or 90.1% of the females worked for someone else. The Chi-Square results indicated that a measurable significant interaction was found (X2 (1) = 12.678, p < .05). Men were more likely to be self-employed (14.7%) than females (9.9%). Cramers V value (.072) indicates the strength of the relationship and in this case the relationship between the variables are not weak or strong.

Although the strength of the variables is not definitively strong there is still evidence to suggest a predictable reliable relationship connecting the two variables, how an individual earns a living (DV) and gender (IV).

References

Frankfort-Nachmias, C., & Leon-Guerrero, A. (2015). Social statistics for a diverse society (7th ed.). Thousand Oaks, CA: Sage Publications.

Laureate Education (Producer). (2016a). Bivariate categorical tests [Video file]. Baltimore, MD: Author.

Smith, T.W., Marsden, P., Hout, M. & Kim, J. (2012). General Social Surveys, [machine-readable data file] /Principal Investigator, Tom W. Smith; Co-Principal Investigator, Peter V. Marsden; Co-Principal Investigator, Michael Hout; Sponsored by National Science Foundation. –NORC ed.- Chicago: National Opinion Research Center [producer]; Storrs, CT: The Roper Center for Public Opinion Research, University of Connecticut [distributor], 2013.

Wagner, W. E. (2016). Using IBMÂ® SPSSÂ® statistics for research methods and social science statistics (6th ed.). Thousand Oaks, CA: Sage Publications.