PSYC B5
L. Larkin
FINAL EXAM
Study Guide
Now that we are at the end of this class, you should be able to accomplish each
one of the “student learning outcomes” listed below and on your syllabus
that was presented at the beginning of the semester:
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Identify and describe the four levels of data measurement
Describe the difference between descriptive and
inferential statistics
Identify and apply the appropriate descriptive and
inferential statistics for each level of data
Effectively employ and interpret parametric and
nonparametric statistics
You final exam will focus on these four points with 45 MC questions (to be
completed on scantron #882-E), 2 short answer questions (worth 5pts ea), 8
word problems (worth 1pt ea) in which you select the appropriate statistical
test to analyze the data (no calculations will be required), and 3 SPSS output
boxes (worth 2 pts ea) that you will be required to discuss (70 points total
possible).
An average of 6 questions will be included from each of your chapter exams for
the MC portion. Specifically review the following:
1. Give an example of each of the four scales of measurement
2. Clarify the difference between a qualitative and a quantitative variable,
and a continuous and a discrete variable
3. Give an example of both a descriptive and inferential statement as
discussed in a lecture handout
4. Explain how you decide which of the three measures of central tendency is
most preferred to describe your data.
5. Explain how you decide which type of graph is most appropriate to
describe your data.
6. What do z-scores communicate about our data? Why is a z-score a useful
statistical tool?
7. What do we know about our data when the standard deviation is large?
8. As with measures of central tendency, discuss on what should you base
the selection of the measure of variability.
9. Describe the difference between an experimental and a correlational
design.
10. Discuss what a researcher must take into consideration when choosing a
sample and why.
11. Explain how you would determine which correlational coefficient to
compute for your data. How do you decide what is considered a “weak” or
“strong” relationship?
12. Discuss why statisticians use linear regression.
13. Clarify the assumptions common to all parametric statistics
14. What is the basis of all inferential statistics?
15. Describe each of the steps in hypothesis testing.
16. Explain when a z-test is used in statistical hypothesis testing.
17. What does it mean when a statistician reports that the results from an
experiment are significant?
18. Discuss when a t-test is used instead of a z-test.
19. What does a significant t-value indicate?
20. Describe the two ways in which samples can be considered related.
21. Explain how you determine which two-sample t-test to employ.
22. What does it mean when Fobt is significant?
23. Discuss when non-parametric procedures are used instead of parametric
procedures.
24. List each of the nonparametric tests discussed in lecture and the
corresponding parametric test.
25. How does a researcher determine if their SPSS output shows significant
results? How can they determine if their standard deviation is large? How
do they decide which test was selected? When do they consider a
confidence interval?