PSYC 355
SPSS HOMEWORK: BIVARIATE LINEAR REGRESSION ASSIGNMENT
TOEFL® Score College GPA
94
80
112
72
93
101
76
90
83
68
113
74
100
52
3.0
2.7
1.5
2.9
3.2
3.6
2.0
3.7
2.3
3.4
3.6
2.7
3.4
2.7
1. Paste SPSS output. (10 pts)
Descriptive Statistics
Mean Std. Deviation N
GPA_JT 2.9071 .64862 14
TOEFL_Score
JT
86.2857 17.36233 14
Correlations
GPA_JT
TOEFL_Score
JT
Problem Set 1: Linear Regression Analysis
Research Scenario: An admissions counselor wants to find out if TOEFL® scores are
predictive of college GPA for international students at a local university. The TOEFL is the
Test of English as a Foreign Language, which the school requires for admission, and students
can score from 0-120, with 120 being a perfect score. The data from the latest freshmen class
are in the table below. Can TOEFL scores be used as a predictor of college GPA?
Using this table, enter the data into a new SPSS data file and run a linear regression
analysis to test whether TOEFL scores predict college GPA. Create a scatterplot with a
regression line to display the relationship between the variables. Remember to put your
initials within any and all variable names. Follow the directions below the table to
complete the homework.
PSYC 355
Pearson
Correlation
GPA_JT 1.000 .125
TOEFL_Score
JT
.125 1.000
Sig. (1-tailed) GPA_JT . .335
TOEFL_Score
JT
.335 .
N GPA_JT 14 14
TOEFL_Score
JT
14 14
Variables Entered/Removeda
Model
Variables
Entered
Variables
Removed Method
1TOEFL_Score
JTb
. Enter
a. Dependent Variable: GPA_JT
b. All requested variables entered.
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
1.125a.016 -.066 .66983
a. Predictors: (Constant), TOEFL_ScoreJT
ANOVAa
Model
Sum of
Squares df
Mean
Square F Sig.
1 Regression .085 1 .085 .190 .671b
Residual 5.384 12 .449
Total 5.469 13
a. Dependent Variable: GPA_JT
b. Predictors: (Constant), TOEFL_ScoreJT
Coefficientsa
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Model
Unstandardized
Coefficients
Standardiz
ed
Coefficient
s
t Sig.
95.0% Confidence
Interval for B
B Std. Error Beta
Lower
Bound
Upper
Bound
1 (Constant) 2.505 .940 2.663 .021 .456 4.554
TOEFL_Sc
oreJT
.005 .011 .125 .436 .671 -.019 .028
a. Dependent Variable: GPA_JT
2. Write an APA-style Results section based on your analysis. Include your scatterplot as an
APA-style Figure, with figure number and title, as demonstrated in the Results Section
presentation. (Results = 12 pts; Figure = 8 pts)
Results
A multiple linear regression analysis was conducted to examine the relationship between
TOEFL scores and Grade Point Average (GPA) among a sample of students (N = 14). The mean
GPA of the sample was 2.91 (SD = 0.65), while the mean TOEFL score was 86.29 (SD = 17.36).
The correlation analysis revealed a weak positive correlation between TOEFL scores and GPA (r
= 0.125, p = 0.335), indicating that higher TOEFL scores were associated with slightly higher
GPAs. The null hypothesis (H0) tested in the regression analysis was that TOEFL scores do not
significantly predict GPA. The results indicated that TOEFL scores did not significantly predict
GPA, F(1, 12) = 0.190, p = 0.671. The model only counted for 1.6% of the entire variance in
GPA (R² = 0.016). The standardized coefficient (Beta) for TOEFL scores was 0.125 (p = 0.436),
suggesting a minimal effect size.
Figure 1. Scatterplot depicting the relationship between TOEFL scores and GPA among
the sample of students.
PSYC 355
40.00 50.00 60.00 70.00 80.00 90.00 100.00 110.00 120.00
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
TOEFL_ScoreJT
GPA_JT
Overall, the findings suggest that while there is a positive association between TOEFL scores
and GPA, TOEFL scores alone may not be a strong predictor of GPA among this sample of
students. The null hypothesis that TOEFL scores do not significantly predict GPA cannot be
rejected based on the results of this analysis.
Days Spent in
Refugee Camp
HTQ Part 4
Score
22 0.4
84 1.1
50 0.9
96 2.3
106 1.7
72 0.3
40 0.7
Problem Set 2: Linear Regression Analysis
Research Scenario: A social psychologist is interested in whether the number of days spent
in a refugee camp predicts trauma levels in recently resettled refugees. He interviews 17
refugees to determine how many days they spent in a refugee camp before being resettled,
then administers the Harvard Trauma Questionnaire Part IV (HTQ Part 4), where a higher
score indicates higher levels of trauma (Mollica et al., 1992). He compiles the information in
the table below.
Using this table, enter the data into a new SPSS data file and run a linear regression
analysis to test whether number of days in a refugee camp predicts HTQ trauma scores.
Create a scatterplot with a regression line to display the relationship between the
variables. Remember to put your initials within any and all variable names. Follow the
directions below the table to complete the homework.
PSYC 355
172 2.6
199 3.1
215 3.0
138 1.9
290 2.5
70 0.7
67 1.2
63 1.8
184 2.9
53 0.6
1. Paste SPSS output. (10 pts)
Descriptive Statistics
Mean Std. Deviation N
Days_SpentJT 113.0000 74.48993 17
HTQ_Part4_Scores
JT
1.6294 .96745 17
Correlations
Days_SpentJ
T
HTQ_Part4_S
coresJT
Pearson
Correlation
Days_SpentJT 1.000 .842
HTQ_Part4_Scores
JT
.842 1.000
Sig. (1-tailed) Days_SpentJT . <.001
HTQ_Part4_Scores
JT
.000 .
N Days_SpentJT 17 17
HTQ_Part4_Scores
JT
17 17
Variables Entered/Removeda
Model
Variables
Entered
Variables
Removed Method
1HTQ_Part4_S
coresJTb
. Enter
PSYC 355
a. Dependent Variable: Days_SpentJT
b. All requested variables entered.
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
1.842a.709 .690 41.46830
a. Predictors: (Constant), HTQ_Part4_ScoresJT
ANOVAa
Model
Sum of
Squares df
Mean
Square F Sig.
1 Regression 62985.704 1 62985.704 36.628 <.001b
Residual 25794.296 15 1719.620
Total 88780.000 16
a. Dependent Variable: Days_SpentJT
b. Predictors: (Constant), HTQ_Part4_ScoresJT
Coefficientsa
Model
Unstandardized
Coefficients
Standardi
zed
Coefficien
ts
t Sig.
95.0% Confidence
Interval for B
B
Std.
Error Beta
Lower
Bound
Upper
Bound
1 (Constant) 7.327 20.150 .364 .721 -35.622 50.276
HTQ_Part4_S
coresJT
64.853 10.716 .842 6.052 <.001 42.013 87.694
a. Dependent Variable: Days_SpentJT
2. Write an APA-style Results section based on your analysis. Include your scatterplot as an
APA-style Figure, with figure number and title, as demonstrated in the Results Section
presentation. (Results = 12 pts; Figure = 8 pts)
Results
A simple linear regression analysis was conducted to investigate the relationship between
scores on the Harvard Trauma Questionnaire Part 4 (HTQ_Part4) and the number of days spent
in treatment among a sample of participants (N = 17). The mean number of days spent in
PSYC 355
treatment was 113.00 (SD = 74.49), while the mean HTQ_Part4 score was 1.63 (SD = 0.97). The
correlation analysis revealed a strong positive correlation between HTQ_Part4 scores and the
number of days spent in treatment (r = 0.842, p < .001), indicating that higher HTQ_Part4 scores
were associated with a greater number of days spent in treatment. The null hypothesis (H0)
tested in the regression analysis was that HTQ_Part4 scores do not significantly predict the
number of days spent in treatment. However, the regression analysis results demonstrated that
HTQ_Part4 scores significantly predicted the number of days spent in treatment, F(1, 15) =
36.628, p < .001. The model accounted for 70.9% of the variance in the number of days spent in
treatment (R² = 0.709). The standardized coefficient (Beta) for HTQ_Part4 scores was 0.842 (p <
.001), indicating a large effect size.
Figure 2. Scatterplot depicting the relationship between HTQ_Part4 scores and the number
of days spent in treatment among the sample of participants.
0.00 50.00 100.00 150.00 200.00 250.00 300.00 350.00
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
Days_SpentJT
HTQ_Part4_ScoresJT
Overall, the findings suggest that higher scores on the HTQ_Part4 are associated with a
greater number of days spent in treatment. This indicates that trauma severity, as measured by
the HTQ_Part4, may influence treatment duration among individuals in this sample. The null
hypothesis that HTQ_Part4 scores do not significantly predict the number of days spent in
treatment is rejected based on the results of this analysis.
PSYC 355
SNAQ-12 Anxiety Interview
Scores
10 7
8 10
11 6
7 2
7 13
9 3
10 8
10 5
8 6
9 4
11 2
9 2
10 6
7 10
8 17
1. Paste SPSS output. (10 pts)
Correlations
SNAQ_J
T
Anxiety_Intervi
ew_ScoresJT
SNAQ_JT Pearson
Correlation
1 -.409
Sig. (2-tailed) .130
N15 15
Anxiety_Interview_Score
sJT
Pearson
Correlation
-.409 1
Sig. (2-tailed) .130
Problem Set 3: Cumulative Knowledge Question
Research Scenario: During intake sessions, a clinical psychologist specializing in treating
snake phobia administers a measure of snake phobia called the Snake Questionnaire (SNAQ-
12) (Zsido et al., 2018), as well as a standardized interview that measures generalized anxiety.
She wants to determine whether there is a relationship between SNAQ-12 scores and the
results of the anxiety interview.
Using this table, enter the data into a new SPSS data file. Choose the correct analysis (it
will be one learned in a previous module) to determine whether there is a relationship
between SNAQ-12 and anxiety interview scores. Choose and create the correct graph
that corresponds with this analysis. Remember to put your initials within any and all
variable names. Follow the directions below the table to complete this part.
PSYC 355
N15 15
2. Write an APA-style Results section based on your analysis. Include the appropriate graph
as an APA-style Figure, with figure number and title, as demonstrated in the Results
Section presentation. (Results = 12 pts; Figure = 8 pts)
Results
A Pearson correlation analysis was conducted to examine the relationship between scores
on the Stanford Nutritional Assessment Questionnaire (SNAQ) and Anxiety Interview scores
among a sample of participants (N = 15). The null hypothesis (H0) tested in this correlation
analysis was that there is no significant correlation between SNAQ scores and Anxiety Interview
scores. The correlation coefficient between SNAQ scores and Anxiety Interview scores was
found to be -0.409, with a two-tailed p-value of 0.130, indicating a moderate negative
correlation. However, this correlation did not reach statistical significance at the conventional
alpha level of 0.05.
Figure 3. Scatterplot depicting the relationship between scores on the Stanford Nutritional
Assessment Questionnaire (SNAQ) and Anxiety Interview scores among the sample of
participants.
0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00
0.00
2.00
4.00
6.00
8.00
10.00
12.00
Anxiety_Interview_ScoresJT
SNAQ_JT
These findings suggest a moderate negative relationship between nutritional assessment
scores and anxiety interview scores, although this relationship did not reach statistical
significance in this small sample. The null hypothesis that there is no significant correlation
between SNAQ scores, and Anxiety Interview scores cannot be rejected based on the results of
this analysis. Further research with a larger sample size would suffice to explore this relationship
in more depth.
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