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November 10, 2025 01:33
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| #LINES 3-22 ARE EXAMPLES OF BIVARIATE ANALYSIS AND INTERPRETATION FOR 2 CATEGORICAL VARIABLES; LINES 25-49 ARE FOR QUANTITATIVE DEPENDENT AND CATEGORICAL INDEPENDENT. | |
| ####CHI SQUARE for categorical independent variable and categorical dependent variable | |
| #only change the variable names | |
| data.chisq1 <- chisq.test(wave5addhealth$H5OD2A, wave5addhealth$H5HR2) | |
| data.chisq1 | |
| ##INTERPRETATION OF CHI SQUARE: The chi square test of independence shows that there is a statistically significant | |
| # association between sex at birth and living arrangements among adults in the U.S. (chi squared=14.715; p<.05). | |
| #when you have a chi square with p-value less than .05, create a crosstab to see the relationship between the variables. | |
| #NOTE THAT YOU NEED TO PUT YOUR DEPENDENT VARIABLE FIRST; dependent ~ independent | |
| #The order that you list variables in a crosstab is critical for ensuring that you're | |
| #correctly interpreting the results. We "percent down, compare across" to see group | |
| #differences in the dependent variable by groups of the independent variable. | |
| lehmansociology::crosstab(H5HR2 ~ H5OD2A, data = wave5addhealth, | |
| title = "Living Arrangements by Sex Assigned at Birth", | |
| format= "column_percent") | |
| #Interpretation: 85% of males live in their own place, compared to 89% of females. | |
| #A higher percent of those who were assigned the male sex at birth (9.1%) live with | |
| #their parents as adults who are in their 30s or early 40s, compared to 6.4% of females. | |
| ####ANOVA for a quantitative dependent variable (DV) and categorical independent variable (IV) | |
| #run an analysis of variance (ANOVA); only change variable names and put in this order DV ~ IV | |
| data.aov1 <- aov(wave5addhealth$H5ID23 ~ wave5addhealth$H5HR2, data=wave5addhealth) | |
| summary(data.aov1) | |
| #remove hashtag on line below to run Tukey ONLY if the F test is statistically significant AND there are more than 2 categories on the IV. | |
| #TukeyHSD(data.aov1) | |
| ##INTERPRETATION OF ANOVA: The ANOVA results show a statistically signficant relationship between living arrangements | |
| # and the amount of time spent watching TV, movies, and videos among adults in the United States (F=8.58; p<.05). | |
| # Tukey's post-hoc test shows more specifically that adults who live in their parents' home or another persons' home | |
| # watch significantly more TV, movies, or videos than those who live in their own place (p<.05). There is a difference | |
| # of about 4 hours per week between those living arrangements. | |
| #when you have an ANOVA with p-value less than .05, create a bivariate bar graph to see the relationship between the variables. | |
| ##BAR GRAPH FOR QUANTITATIVE DEPENDENT VARIABLE AND CATEGORICAL INDEPENDENT VARIABLE | |
| ggplot(data=subset(wave5addhealth, !is.na(H5HR2)))+stat_summary(aes(x=H5HR2,y=H5ID23),fun.y=mean,geom="bar")+ | |
| ylab("Average Hours Per Week")+ | |
| xlab("Current Living Arrangements")+ | |
| ggtitle("Bar Graph of Average Time Spent Watching TV/Movies/Videos by Living Arrangements") | |
| #Interpretation: The graph shows that Add Health respondents who live in their own | |
| #home watch about 13 hours of TV, movies, and videos per week. The highest average | |
| #time spent watching TV is among those living in their parents' home or another | |
| #person's home; these group average about 17.5 hours per week. |
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