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Include the General Social Survey Dataset’s mean Age to verify your used dataset. Research Question: How does being a US citizen affect your income level? Null hypothesis: Being a US citizen does not affect the level of income. Research design: Categorical analysis correlational The dependent variable was, Measured by row. US citizen (Row) Columns measured the independent variable. level of income (column) For this categorical data analysis, the row variable (dependent) was Respondent Feels Discriminated Against Because of Gender, and the column variable (independent) was Respondent Sex (Frankfort-Nachmias et al., 2020). The possible response for the dependent variable, Respondent Feels Discriminated Against Because of Gender, was “yes” or “no.” The likely reaction for the independent variable Respondent Sex was “male” or “female.” This model will create a simple 2 x 2 crosstabulation (Frankfort-Nachmias et al., 2020). This study would be considered a correlational quantitative study design because none of the inputs are being altered in any way for the purposes of the study (Laureate Education, n.d.). Model Analysis Table 1 reports that 1,244 responses, or 49%, are valid cases in the categorical data analysis. The complete detailed assignment of responses can be found in the crosstabulation displayed in Table 2. From the crosstabulation, we can state that 98% of males and 92.9% of females responded that they did not feel discriminated against because of gender. The Chi-Square output in Table 3 confirms that there is a statistically significant relationship (p<.05) between the categorical variables, and the null hypothesis can be rejected (Laureate Education, 2016). However, the Chi- Square output does not tell us about the relationship’s strength, so we need to review Cramer’s V output (Laureate Education, 2016). The Cramer’s V value (.122) in Table 4 indicates a weak positive relationship between the variables. Therefore, we can conclude that while there is a statistically significant positive relationship between the respondent’s sex and whether they felt discriminated against because of gender, the association is weak.

2. As a bivariate table indicates, both of these variables are categorical, nominal variables (Frankfort-Nachmias, Leon-Guerrero, & Davis, 2020). For this correlational research design, the independent variable is the respondent’s race (white, black, other), and the dependent variable has a gun in the home (yes, no, refused). As the researcher, I am not manipulating or controlling the relationship between variables, simply reflecting on the correlation’s strength and/or direction. The Chi-Square Test for Independence, Table 3, tests for a relationship between these variables (Laureate Education, 2016a). The Chi-Square statistic is 83.699 with an associated p-value of . 000. Thus, we can assume that there is a relationship between the variables race of the respondent and having a gun in the home (Frankfort-Nachmias, Leon-Guerrero, & Davis, 2020; Laureate Education, 2016a). The Cramer’s V correlation, Table 4, indicates the strength of the relationship ranging from 0 (no relationship) to 1.0 (perfect relationship) (Laureate Education, 2016a). While statistically significant, the relationship between these two variables is relatively weak at .156. Explanation for layperson: In the first analysis, the researchers examined whether people felt discriminated against because of their gender. They compared this feeling with the respondents' gender (male or female). They found that 98% of males and 92.9% of females said they did not feel discriminated against due to their gender. The statistical test they used shows that there is indeed a meaningful link between these two factors, and they can reject the idea that the two factors are unrelated. However, the association could be more vital. This means that although there's a connection, it could be more powerful. In the second analysis, I explored whether there's a connection between a person's race (white, black, or other) and whether there's a gun in their home. I found that there is a relationship between these two factors. My statistical test shows this relationship is significant, which means it's unlikely to be a coincidence. However, the strength of this connection could be more muscular, indicating that while it's there, it could be a more robust and influential relationship. For the first study, I found a connection between how people feel discriminated against because of their gender and their gender itself, but it's not a very strong connection. In the second study, I discovered a link between a person's race and whether there's a gun in their home, but it's not a strong link. I examined how different factors are connected in both cases without actively changing or manipulating anything. They used statistical tests to see if these connections were real and not just by chance.

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