Conduct a hypothesis test to examine whether there is a relationship between parent education and child alcohol and tobacco use.
Data for
Exercise
Data Set Description
Monitoring
the future
2013 grade
10.sav
This data set contains variables from the 2013 Monitoring the
Future (MTF) study. These data cover a national sample of 10th
graders with a focus on monitoring substance use and abuse.
Variables for
Exercise
Variable
Name
Description
V1070 A student’s race where 1 = Black, 2 = White, and 3 = Hispanic.
SchoolSkip A count of the number of days a student has skipped school or
classes.
V7253 An ordinal measure asking whether the student has any friends who
have dropped out of school, where 1 = none, 2 = a few, 3 = some,
and 4 = most if not all.
LowParentEdu
c
A three-category summary of both parents’ education where 0 =
both parents have high school diplomas, 1 = one parent has less
than a high school education, 2 = both parents have less than a high
school education.
V7214 An ordinal measure asking students the number of hours they are
home alone after school on a typical day. Responses range from 1 =
0 hours to 6 = 5 or more hours.
SmokeDrink A continuous index measuring the quantity and frequency of
smoking and drinking self- reported by respondents.
- Are students of different races more likely to skip out on school? Criminologists are interested in this question because school acts as a form of social control; if you’re at school, you won’t have as many chances to engage in crime or interact with the justice system! We can test this question using a one-way ANOVA in SPSS with the variables “SchoolSkip” and “V1070”:
a. First, state the null and research hypotheses for this ANOVA.
b. Second, determine the critical value of F you will need for your results to be significant at p < .01. You will need to calculate your within-group and between-groups degrees of freedom in this case; in this case, n = 10,216 and k = 3.
c. Estimating an ANOVA model in SPSS: Doing this by hand would take weeks, but SPSS does it in seconds. To do this, select analyze->compare means->one-way ANOVA. Place the variable “V1070” in the “factor” slot and “SchoolSkip” in the dependent list:
i. Output provided gives the sum of squares within and between groups, your degrees of freedom, and F statistics. It also provides the p value for your specific F statistic.
d. What is your F statistic? What do you conclude about the null hypothesis in this case?
- You may have noticed that an ANOVA test on its own isn’t a very precise assessment of our research question. Let’s follow this up by estimating the difference between pairs of means and the strength of the association:
a. Post Hoc Tests in ANOVA: In the one-way ANOVA menu, you will notice an option to “post- hoc” tests. Click that button and select “Tukey,” which will give you an estimation of Tukey’s honest significant difference:
i. The output provided compares one racial category with all the others, providing the statistical significance of each difference. So, for instance, the first row compares Blacks against Whites and shows that the differ- ence between the means for Blacks and Whites is .449 days skipped.
ii. Interpret the remaining differences between racial groups. Are all groups significantly different from one another? Which group means are closest/furthest from one another?
iii. What new information does this test add to our ANOVA interpretation?
b. By using the ANOVA sum of squares output, calculate η2 by hand:
i. What new information does this statistic tell us?
ii. Consider the difference between statistical and substantive significance. Do we have both in this
case?
- Do students who hangout with individuals who dropout of school also tend to skip out on school themselves? Run a one- way ANOVA test with pairwise comparisons and estimate
η2 (by hand) usingthevariables “V7253” and “SchoolSkip”:
a. Conduct the hypothesis test fully, stating your null hypotheses and identifying test statistics (n = 12,449 and k = 4 in this case). What do you conclude about the null hypothesis?
b. What do the pairwise comparisons in this case tell you? What groups are most prone to
skipping school?
c. What does η2 tell us in this case? What proportion of the variability in school skipping can be explained by one’s peer network?
d. By thinking substantively, what does this tell us about the role of peer networks? Do you think it might relate to other areas of deviant behavior as well?
- Are kids who have lots of unsupervised time more likely to use alcohol or drugs than other kids? Use the variables “SmokeDrink” and “V7214” to test this hypothesis. Also estimate
pairwise comparisons and η2: a. Conduct the hypothesis test fully, stating your null hypotheses and identifying test statistics
(n = 12,169 and k = 6 in this case). What do you conclude about the relationship?
b. What do the pairwise comparisons in this case tell you? Use an alpha of .001 for this test. Are any groups not statistically different? It may help to visually rank each group against one another in a diagram.
c. What does η2 tell us in this case?
d. Given your results, what are your policy recommendations?
- Often researchers talk about the importance of socioeconomic status in producing heightened risk for criminal and delinquent activity. The education of a student’s parents is one way to look at this difference. Let’s see whether the variable “LowParentEduc” is associated with higher levels of student smoking and drinking:
a. Conduct a hypothesis test to examine whether there is a relationship between parent education and child alcohol and tobacco use. Fully state your null hypotheses and
identifying test statistics (n = 10,726 and k = 3 in this case). What do you conclude about the relationship?
b. What do the pairwise comparisons in this case tell you? Use an alpha of .001 for this test. Are any groups not statistically different from one another?
c. What does η2 tell us in this case?
d. Take a moment to consider the “why” part of this result; what mechanisms might explain why the results you have found might lead to delinquent behaviors?
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