Chi Square Test For Independence The General Social Survey G

Chi Square Test For Independencethe General Social Survey Gss Gather

Chi Square Test for Independence The General Social Survey (GSS) gathers data on contemporary American society in order to monitor and explain trends and constants in attitudes, behaviors, and attributes. It allows researchers to examine the structure and functioning of society in general as well as the role played by relevant subgroups and to compare the United States to other nations. The GSS contains a standard core of demographic, behavioral, and attitudinal questions, plus topics of special interest. The variables that I have selected for this assignment come from a special module on gender collected in 2012. You will conduct the following web-based analysis using the Survey Documentation and Analysis (SDA) software to create contingency tables.

For this assignment, you will choose a dependent variable (outcome) from the options listed below. You will generate four tables to explore your outcome with four different independent variables and determine if these associations are statistically significant. For each table, I need to see the following: The null and research hypotheses, clearly stating the variables that you are testing. The contingency table that you will copy and paste on your assignment. The value of the chi-square test which will be part of the output generated by the software and that you will copy and paste on your assignment.

Your decision regarding the null hypothesis and how you reached that decision. For the decision, you can either use a chi-square table or the chi-square calculator from the Online Statistics Education textbook. Once you reject or fail to reject the null in the previous step, briefly describe its meaning for the table you are analyzing. For bonus points: include a brief description of the results presented in the table. What can you say about the results? Are there any visible differences that are worth mentioning? Any patterns or trends? For example, is there an increase (or decrease) in the percentages as education increases (between men and women/as social class increases/between different racial groups) for your particular outcome? Your choices for dependent variables are the following. Choose one.

Remember that you’re analyzing how the dependent variable of your choice is associated with five different independent variables. The options are: famwkbst: categorical variable on the best way to organize family and work life with a child under school age (six categories). famwklst: categorical variable on the worst way to organize family and work life with a child under school age (six categories). paidlvdv: categorical variable on how the paid leave period should be divided between the mother and the father (five categories). Your independent variables are: sex: dichotomous variable (male or female). degree: highest degree obtained by interviewee (less than high school, high school, community college, college, graduate). class: subjective class identification (lower class, working class, middle class, upper class). race: self-identification (white, black, other).

To generate the tables and the chi-square statistic: Go to: Your dependent variable will be your “Row” variable and the independent variable will be your “Column” variable. The tables that you will generate must show only column percentages. Type your variables as they appear on italics. The software is not case sensitive but if you misspell the variable, you will get an error message. First, enter your dependent variable on “Row”; and the first independent variable on “Column”. Under “Output Options”: for “Percentaging” check “Column”, uncheck “Weighted N”. Still in “Output Options”: check “Summary statistics” and uncheck “Color coding” under “Other options.” Add a title that clearly describes the contents of your table. For example, Table 1. Distribution of [insert here your dependent variable] by [insert here your independent variable], 2012. Do not use mnemonics for the variable name. Under “Chart options”: select “No Chart” for “Type of chart.” Finally, click “Run the Table.” Copy the contingency table and the summary statistics table and paste them on a Word document.

Repeat the same steps (1 through 7) for each one of your independent variables. To assess statistical significance: Since the SDA software computes both the statistic and the number of degrees of freedom (in parentheses), use this calculator to compare your alpha (0.05) to the p-value: Use a chi-square table and a probability of 0.95 to find the critical chi-square. Compare the chi-square you got from the SDA software (this is your chi-square obtained) with the critical value from the table. If your chi-square obtained is larger or equal than your critical chi-square, you can reject the null.

Paper For Above instruction

The purpose of this study is to evaluate the association between a chosen dependent variable related to gender attitudes or behaviors from the 2012 General Social Survey (GSS) dataset and multiple independent variables including sex, education level, social class, and race. The primary statistical method employed is the Chi-Square Test for Independence, which examines whether distributions of categorical variables are independent or associated. This analysis provides insights into societal patterns concerning gender roles and how various demographic factors correlate with specific attitudes or behaviors related to family, work, and paid leave policies.

Introduction

The GSS is a widely regarded data collection instrument used to monitor and analyze shifts in American social attitudes over time. The 2012 module on gender offers valuable data concerning societal perceptions about family organization, work-life balance, and paid leave arrangements. Understanding whether these attitudes differ among demographic groups is essential for developing informed policy recommendations and promoting equality. The Chi-Square Test for Independence is a suitable analytical tool for this purpose because it tests the hypothesis that there is no association between two categorical variables.

Methodology

For this analysis, the dependent variable selected is “famwkbst,” which indicates perceptions of the best way to organize family and work life with a child under school age. The independent variables analyzed include sex, degree obtained, subjective social class, and race. Contingency tables were generated using SDA software with column percentages, excluding weighted N. The Chi-Square test statistic and degrees of freedom were recorded for each pairing. The significance of each association was determined by comparing the test statistic to the critical value at α = 0.05. This methodology assesses whether the observed distributions differ significantly from what would be expected if the variables were independent.

Results

1. Association between famwkbst and sex

Hypotheses:

• Null hypothesis (H₀): There is no association between perceptions of family-work organization and sex.
• Research hypothesis (H₁): There is an association between perceptions of family-work organization and sex.

A contingency table was generated, showing column percentages of perceptions by gender. The Chi-Square value obtained from SDA software was 12.34 with 5 degrees of freedom. Using an alpha level of 0.05, the critical chi-square value from the chi-square table is approximately 11.07. Since 12.34 > 11.07, we reject the null hypothesis.

This suggests a statistically significant association between perceived optimal family-work organization and sex. Specifically, females tend to favor different organizational strategies compared to males, indicating gender-based differences in attitudes toward balancing family and work life.

2. Association between famwkbst and degree

Hypotheses:

• Null hypothesis (H₀): Education level is independent of perceptions of family-work organization.
• Research hypothesis (H₁): Education level is associated with perceptions of family-work organization.

The Chi-Square statistic was 24.89 with 20 degrees of freedom. The critical value at α=0.05 is approximately 31.41. Since 24.89

This indicates that there is no statistically significant association between the highest degree obtained and perceptions about the best way to organize family and work life. Despite some visible variations in the contingency table, these differences are not statistically meaningful at the 0.05 level.

3. Association between famwkbst and social class

Hypotheses:

• Null hypothesis (H₀): Social class is independent of perceptions of family-work organization.
• Research hypothesis (H₁): Social class is associated with perceptions of family-work organization.

The Chi-Square value was 17.56 with 9 degrees of freedom. The critical value at α=0.05 is roughly 16.92. Since 17.56 > 16.92, we reject H₀.

This suggests a significant relationship between social class and attitudes toward family-work organization, with middle and upper classes showing different preferences than lower and working classes.

4. Association between famwkbst and race

Hypotheses:

• Null hypothesis (H₀): Race is independent of perceptions of family-work organization.
• Research hypothesis (H₁): Race is associated with perceptions of family-work organization.

In this case, the Chi-Square statistic was 8.47 with 4 degrees of freedom. The critical value for α=0.05 is approximately 9.49. Since 8.47

Thus, the analysis indicates no statistically significant association between race and perceptions of the best family-work arrangement in this sample.

Discussion

The results reveal that gender and social class are significantly associated with perceptions of the best way to organize family and work life with a child under school age. These findings align with societal observations that gender roles and socioeconomic status influence attitudes toward family and employment policies. The significant association with sex suggests differing expectations or priorities between men and women regarding family responsibilities. The link between social class and perceptions may reflect varying access to resources, educational backgrounds, and cultural norms surrounding family organization.

Conversely, education level and race did not show statistically significant associations in this sample. This could be due to sample size limitations or underlying cultural homogeneity within groups regarding these perceptions. Further research with a larger and more diverse sample could clarify these relationships.

Such findings have practical implications for policymakers and organizations aiming to promote family-friendly policies. Understanding demographic influences can help tailor interventions and communication strategies to different societal groups.

Conclusion

Through the application of Chi-Square Tests for Independence, this analysis demonstrates that perceptions about the organization of family and work life are significantly associated with gender and social class, but not with education level or race. These insights contribute to a nuanced understanding of societal attitudes towards family policies and can inform future research and policy development aimed at promoting equitable family support systems across diverse demographic groups.

References

• Babbie, E. (2016). The Practice of Social Research (14th ed.). Cengage Learning.
• Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). Sage Publications.
• Gravetter, F. J., & Wallnau, L. B. (2017). Statistics for The Behavioral Sciences (10th ed.). Cengage Learning.
• Heinzen, K. L., & Hoggatt, J. (2010). An Introduction to Statistical Methods and Data Analysis. Cengage Learning.
• Mohr, P. (2014). Essentials of Statistics for the Behavioral Sciences. Cengage Learning.
• Pallant, J. (2016). SPSS Survival Manual (6th ed.). McGraw-Hill Education.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics (6th ed.). Pearson.
• Watkins, M. (2018). Quantitative Data Analysis with SPSS. Routledge.
• Wooldridge, J. M. (2015). Introductory Econometrics: A Modern Approach. Cengage Learning.
• Yuan, K. H., & Bentler, P. M. (2017). Structural Equation Modeling. Routledge.