FINAL EXAM
For each type of data analysis, 1) formulate a plausible question, 2) obtain the SPSS/PASW
output needed to answer it, and 3) write an interpretation. Use the rubrics below as a checklist for
your work. It is strongly suggested that, for each problem, you use an example that yielded
significant results for the test. Attach the relevant output, and be sure it is clearly matched
(labelled) with the corresponding interpretations. Each problem will be graded with the rubric
indicated below.
Problem 1: Crosstabs and Chi-Square Test (Use GSS or CANSIM data)
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Plausible well-stated question
Variables selected measured at appropriate level(s) of measurement
Table layout correct in terms of title and placement of the variables (matches your
question)
Correct percentaging
Chi-square test result read correctly
Conclusion stated correctly in terms of
o Chi-square test result and null hypothesis (reject, fail to reject)
o Percentage distributions discussed
o Result stated accurately and clearly in “everyday language”
o Result used to answer your initial question
Two new questions suggested:
o A question that introduces a third variable from the GSS or CANSIM data set that
could be used as the “layer” variable to elaborate the data analysis you just
completed
o A broader question that is raised in a more general way by this result
Note: For the last question you are being asked to relate this specific finding to a broader or
more theoretical issue in the social and behavioural sciences. Your question should not be a
technical or statistical one but one that reflects your intellectual curiosity about how and why
we find the statistical patterning. For example, if we find that men are more likely than
women to say they like action movies, what forces shape this difference? Are boys socialized
differently than girls? Do men feel peer pressure to say they like action movies? Does their
preference reflect biological factors? Your question should place the finding in a broader and
more conceptual context.
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Problem 2: One-Way ANOVA (Use either GSS or CANSIM data or the country data set. If
you use the country data set, be especially careful to choose variables that are at the right
level of measurement for ANOVA)
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Plausible and well-stated question
Variables selected measured at the appropriate level(s) of measurement
ANOVA output produced correctly, with descriptives and (if more than two groups) posthoc measures—e.g., Bonferroni
ANOVA output interpreted correctly:
o Is the result significant, and how do you know?
o Which groups have significantly different means from which other groups?
Result stated clearly in “everyday” language
Broader or more theoretical question formulated, one that is stimulated by this finding
Note: For the last question you are being asked to relate this specific finding to a broader or
more theoretical issue in the social and behavioural sciences. Your question should not be a
technical or statistical one; rather, it should reflect your intellectual curiosity about how and
why one finds the statistical patterning you discuss in the “result” section of your answer to
Problem Two.
Problem 3: Bivariate Linear Regression (Use the country data set)
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Plausible and well-stated question
Variables at the appropriate level(s) of measurement
Scatterplot with total fit (OLS) line that is “roughly” linear
Regression analysis, with brief comments on
o The correlation coefficient (strength and direction)
o R-squared
o The unstandardized coefficients; write the equation of the line with the
unstandardized coefficients
o The standardized coefficient (beta)
Overall result discussed, answering your initial question and commenting in “everyday
language” in a way that shows that you understand the units of analysis (cases) are
countries
Problem 4: Multiple (Linear) Regression—Two Predictor Variables
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Plausible, well-stated question (Start with your Problem Three result.)
Variables at the appropriate level(s) of measurement
Correlation matrix showing correlation coefficients for each pair of variables
Regression analysis output, with brief comments on
o Adjusted R-squared
o The unstandardized coefficients; write the equation of the line with the
unstandardized coefficients
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o The standardized coefficients (betas)
The overall result, answering your initial question and commenting in “everyday
language” showing that you understand the units of analysis (cases) are countries
o What did you learn by adding a second predictor variable to your model?
o Were both predictor variables significantly related to the outcome variable?
A reflection on the following question:
o Does this model provide any insights that could be used to improve well-being (or
quality of life) for people?
Problem 5: Fix This Data Analysis!
Look carefully at the SPSS/PASW output on the next page and the interpretation (below) that
was written for it. It is based on the GSS data set.
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The data analysis technique was chosen correctly, but it is incorrectly identified in the
interpretation
The variable names and values are correct
The observed cell counts are correct, and the marginal counts are correct but not
necessarily in the right locations
The numbers that appear in the test of significance are correct
But there are many problems in both the output and the interpretation! Your task is to
repair the problems. You do not need SPSS/PASW or the GSS data set to do this; you can do it
“manually.”
1. Fix the title and the layout (location of the variables in rows and columns)
2. Percentage correctly
(Hint: To carry out steps 1 and 2 you will need to make a new table)
3. Rewrite each bullet point of interpretation, correcting all errors
Interpretation
The question for this data analysis is as follows: Does happiness cause sex?
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I selected ANOVA as my data analysis technique because both variables are measured at
the interval-ratio level of measurement
From the counts in the table, we can easily see that happy people are more likely to be
men
From the very high value of “Sig.” in the t-test result (.895), we can see that the results
are highly significant
We can reject the null hypothesis
Conclusion: Happiness has a major effect on the sex of respondents, and happy people
are more likely to be men
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