PSYC 354
SPSS H : F T , H , BOMEWORK REQUENCY ABLES ISTOGRAMS AND AR
CHARTS SSIGNMENT A
Problem Set 1: The BDI (Beck et al., 1961) is an instrument widely used to assess levels of
depression in individuals in a variety of settings. The scores range from 0–63 (whole numbers
only). A researcher administers the BDI to a sample of college students. The results appear in the
table below. They are entered as 2 columns here to save space (i.e., you will not need 2 columns
in the SPSS file).
1. Using the data in the table above, set up a data file in SPSS and create a frequency table
to show the frequency of each score on the BDI. Paste the frequency table here: (8 pts)
Scores
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 3.00 1 5.0 5.0 5.0
4.00 1 5.0 5.0 10.0
9.00 3 15.0 15.0 25.0
10.00 3 15.0 15.0 40.0
11.00 5 25.0 25.0 65.0
15.00 2 10.0 10.0 75.0
20.00 2 10.0 10.0 85.0
61.00 2 10.0 10.0 95.0
62.00 1 5.0 5.0 100.0
Total 20 100.0 100.0
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BDI Scores
9
10
9
9
11
3
11
20
11
62
15
11
10
4
61
61
20
15
10
11
PSYC 354
2. How many students have a BDI score of 20? (5 pts)
- Two students have a BDI score of 20.
3. What percent of students have a BDI score of 9? (5 pts)
-The percentage of students with a BDI score of 9 is 15%.
4. Which score has the highest frequency in the table? (5 pts)
-The score that has the highest frequency in the table is 11.
5. Using the same data, create a histogram in SPSS to show the distribution of the BDI data.
Paste the histogram here: (7 pts)
Problem Set 2: The overall livability scores of 12 US cities appear in the columns to the left.
The data are based on data taken from the livability calculator at (http://www.areavibes.com/).
Enter the data in a new SPSS file. When creating the City variable, be sure that the type is set to
“String” in the Variable View so that the names will show up on the graph.
(Continued on next page)
1. Using the data in the table above, set up a data file in SPSS and create a bar chart that
shows the livability rating for each city. (8 pts)
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City Livability Score
Boston
Austin
Chicago
Pittsburgh
San Diego
Jackson,
MS
Detroit
Miami
New York
Charlotte
Atlanta
Seattle
73
83
73
75
80
72
67
71
73
80
78
77
PSYC 354
2. Which three cities have the highest livability scores according to the graph? (5 pts)
-Austin, San Diego, and Charlotte have the highest livability scores.
3. What level of measurement is the variable “City”? Support your answer with information
from the course materials (textbook or presentations). (5 pts)
-The variable city is nominal. According to the book, this measurement involves
classifying things into categories with different names rather than using numbers.
Measurements do not make quantitative distinctions between thing that are
observed.
4. What level of measurement is the variable “Livability Score”? Support your answer with
information from the course materials (textbook or presentations). (5 pts)
-Livability is going to be a ratio level variable. According to the creators of the score,
livability is calculated on a scale of "0-100" with equal intervals between values and
"0" being a meaningful number. Ordinal variables are ranked, however do not have
equal intervals between values. For instance, we would not know how equal the
distance is between 1st and 2nd place and 2nd and 3rd place.
Integration: Answer the following question based on Problems 1 and 2 above and what you
have learned from readings and presentations this week.
1. Why is it appropriate to create a histogram for the BDI scores, but a bar chart for the
livability ratings? (7 pts)
-It is appropriate to create a histogram for the BDI scores because they involve
numerical scores. A histogram is needed to show the continuous variables, which are
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PSYC 354
an “infinite number of possible values that fall between any two observed values,
divisible into an infinite number of fractional parts.
-It is appropriate to create a bar chart for the livability ratings because they involve
using an nominal measurement. A bar chart is needed to show the specific categories
and their different sizes. These can be defined as discrete variables which “consist of
separate indivisible categories, no values can exist between two neighboring
categories.”
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