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Statistical Analysis and Interpretation of GSS18 Dataset: Exploring Relationships between Variable Sets

June 15, 2023
Jennifer Flores
Jennifer Flores
🇺🇸 United States
Statistical Analysis
Jennifer Flores, Ph.D. in statistics from Monmouth University, with 5+ years' experience, specializes in aiding students with statistical assignments for academic success.
Key Topics
  • Problem Description:
    • A) Mother's Religion and Religious Preference
    • B) Spending Time at a Bar and Age
    • C) Poor Mental Health and Extra Work Hours
    • D) Weeks Worked Last Year and Depression
    • E) Height and Weight
    • F) Mandatory Overtime and Real Income

Problem Description:

The data analysis assignment involves conducting statistical analyses using the GSS18 dataset to explore relationships between different sets of variables. Students are required to determine the appropriate hypothesis test (Chi-Square, t-test, ANOVA, or Correlation) for each set of variables and provide detailed interpretations of the statistical outputs. Below are the solutions for the assignment:

A) Mother's Religion and Religious Preference

Variables:

  • #478 – MARELKID - Nominal
  • #768 – RELIG (R’s religion preference) - Nominal

Chi-Square Tests

ValuedfAsymp.Sig. (2-sided)
Pearson Chi-Square2706.759a1100.000
Likelihood Ratio932.54 71100.000
Linear-by-Linear Association150.30610.000
N of Valid Cases1131
a. 115 cells (87.1%) have expected count less than 5. The minimum expected c ount is .00.Solution: For these variables, a chi-square test was performed.

The p-value is < 0.05, indicating that the alternative hypothesis (H1) is true. This implies a statistical relationship between someone's mother's religion when they were a child and their religious preference. The association is moderate, as indicated by Cramer's V (0.489) and lambda (0.429). Further analysis of crosstabulations is required to understand the precise relationship.

B) Spending Time at a Bar and Age

Variables:

  • #869 – SOCBAR (spend evening at bar) - Ordinal (7 groups)
  • #28 – AGE (respondent's age) - Ratio

Solution: For these variables, an ANOVA test was conducted. ANOVAAge of respondent

Sum of SquaresdfMean SquareFSig.
Between Groups34702.74565783.79119.3190.000
Within Groups461939.9621543299.378
Total496642 .7071549

The p-value is < 0.05, confirming that the alternative hypothesis (H1) is true, suggesting that at least one group's mean is different. Specifically, respondents who never spend evenings at a bar are 5 to 17 years younger than those who spend evenings at a bar several times a week.

C) Poor Mental Health and Extra Work Hours

Variables:

  • #524 – MNTLHLTH (days of poor mental health in the past 30 days) - Ratio
  • #527 – MOREDAYS (days per month R worked extra hours) - Ratio

Solution: A correlation analysis was performed for these variables.

Correlation
Days of poor mental health past 30 daysDays per month R work extra hours
Days of poor mentalPearson Correlation10.026
health past 30 daysSig. (2-tailed)0.327
N14081391
Days per month R workPearson Correlation0.0261
extra hoursSig. (2-tailed)0.327
N13911401

The p-value is > 0.05, supporting the null hypothesis (H0), indicating no significant connection between the number of days of poor mental health and the number of days worked with extra hours in a month.

D) Weeks Worked Last Year and Depression

Variables:

  • #983 – WEEKSWRK (weeks R worked last year) - Ratio
  • #177 –told have depression - Nominal Independent Sample Test
Levene's Test Variancesfor Equality oft-test for Equalityof Means
MeanStd. Error95% Confidence DifferenceInterval of the
FSig.tdfSig. (2-tailed)DifferenceDifferenceLowerUpper
Weeks R worked last Equal variances year assumed18.718.000-2.6831389.007-2.106785-3.645-566
Equal variances not assumed-2.362353.816.019-2.106.891-3.859-353

Solution:

A significant difference was found through a t-test, with a p-value < 0.05, confirming that the average number of weeks worked in the past year differs between individuals diagnosed with depression and those who are not. The analysis shows a 95% confidence level for the average number of weeks worked, with those without depression working 0.4 to 4 weeks more than those diagnosed with depression.

E) Height and Weight

Variables:

  • #305 – HEIGHT - Ratio
  • #984 – WEIGHT - Ratio

Solution: Correlation analysis was used for these ratio variables.Correlation

R weighs how muchR is how ta.l.l
R weighs how muchPearson Correlation10.457
Sig.(2-tailed)0.000
N138.0.1374
R is howtallPearson Correlation0.4571
Sig.(2-tailed)0.000
N13741402

The p-value is < 0.05, indicating that there is a moderate positive association between a person's height and weight (R = 0.457). About 20.88% of the change in weight can be attributed to changes in height, while 79.12% can be attributed to other factors.

F) Mandatory Overtime and Real Income

Variables:

  • #532 – MUSTWORK (mandatory to work extra hours) - Nominal
  • #722 – REALRINC (R’s income in constant dollars) - Ratio

Solution: For these variables, a t-test was conducted. Independent Sample Test

Levene's Test Variancesfor Equality oft-test for Equalityof Means
MeanStd. Error95% Confidence DifferenceInterval of the
FSig.tdfSig. (2-tailed)DifferenceDifferenceLowerUpper
R's income in constant $ Equal variances assumed.038.8441.1741192.2412127.7931812.996-1429.2255684.811
Equal variances not assumed1.180621.3392392127.7931803.898-1414.6825670.268

The p-value is > 0.05, supporting the null hypothesis (H0), suggesting that the mean income in constant dollars is equal for those required to work overtime and those who are not. Further analysis is not required as we have accepted the null hypothesis.

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