1. SPSS OUTPUT & INTERPRETATION
13/11/2018
SPSS/SITINORYAACOB/PJJSEM2_2015/2016
SPSS OUTPUT & INTERPRETATION
Dr. Siti Nor Binti Yaacob

2. Quantitative Research
13/11/2018
SPSS/SITINORYAACOB/PJJSEM2_2015/2016
Quantitative Research
Type of research objectives
Objective
Data analysis
Descriptive (univariate)
To describe demographic background (e.g., age, gender), and the levels of independent variables (e.g., loneliness) and dependent variable (e.g., internet addiction.
Descriptive analysis
Difference between two groups (0,1) (bivariate)
To examine the difference on tested variables (e.g., loneliness) among two groups (males and females).
Independent t-test
Relationship (bivariate)
To examine the relationship between independent and dependent variables.
Pearson correlation

3. SPSS/SITINORYAACOB/PJJSEM2_2015/2016
13/11/2018
SPSS/SITINORYAACOB/PJJSEM2_2015/2016
Title: Relationship between loneliness and internet addiction among adolescents in Penang.
Steps in analysis:
Descriptive analysis
Bivariate analysis
T-test (Ho: There is no significant difference in internet addiction between male and female adolescents.)
Pearson correlation (Ho: There is no significant relationship between loneliness and internet addiction.)

4. SPSS/SITINORYAACOB/PJJSEM2_2015/2016
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SPSS/SITINORYAACOB/PJJSEM2_2015/2016
Descriptive Analysis
Step 1: Analyze  Descriptive Statistics  Frequencies…
Step 2: select the variables and click in the box

5. SPSS/SITINORYAACOB/PJJSEM2_2015/2016
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SPSS/SITINORYAACOB/PJJSEM2_2015/2016
Step 3: Click “statistics” and choose “ mean, median, std. deviation, variance, range, minimum and maximum”.  click “continue”

6. SPSS/SITINORYAACOB/PJJSEM2_2015/2016
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SPSS/SITINORYAACOB/PJJSEM2_2015/2016
Step 4: Click “OK”.
Step 5: Interpret output

7. Example of Descriptive Table:
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SPSS/SITINORYAACOB/PJJSEM2_2015/2016
Example of Descriptive Table:

8. Example of Interpretation:
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SPSS/SITINORYAACOB/PJJSEM2_2015/2016
Example of Interpretation:
Respondents’ age
The respondents aged between 15 to 18 years old (mean=16.09, SD.=0.670). Majority (55.1%) of the respondents were 16 years old.

9. SPSS/SITINORYAACOB/PJJSEM2_2015/2016
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SPSS/SITINORYAACOB/PJJSEM2_2015/2016
Independent t-test
Step 1: Analyze  Compare Means  Independent Sample t-test

10. SPSS/SITINORYAACOB/PJJSEM2_2015/2016
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SPSS/SITINORYAACOB/PJJSEM2_2015/2016
Step 2: Choose tested variable and put “groups” into “Grouping Variable”.
Step 3: State “Define Groups” (e.g., male= 0; female=1)
 continue

11. SPSS/SITINORYAACOB/PJJSEM2_2015/2016
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SPSS/SITINORYAACOB/PJJSEM2_2015/2016
Step 4: Click “OK”
Step 5: Output

12. SPSS/SITINORYAACOB/PJJSEM2_2015/2016
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SPSS/SITINORYAACOB/PJJSEM2_2015/2016
Step 6: Interpret output
Based on F significant value of Levene’s test (Circle with red color),
If F Value is not significant (p≥.05), report t-value and p value [sig. (2-tailed)] from “equal variance assumed”.
if F Value is significant (p<.05), report t-value and p value [sig. (2-tailed)] from “equal variance not assumed”.
Mean scores

13. Example of t-test table:
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SPSS/SITINORYAACOB/PJJSEM2_2015/2016
Example of t-test table:
Example of Interpretation:
There was a significant difference (t=3.489, p<.01) in internet addiction between male and female adolescents. Male adolescents (mean= 55.42) were found to have higher level of internet addiction than female adolescents (mean= 47.29). Therefore, null hypothesis was rejected.
Note: Can add some related past studies to support the results.

14. SPSS/SITINORYAACOB/PJJSEM2_2015/2016
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SPSS/SITINORYAACOB/PJJSEM2_2015/2016
Pearson Correlation
Step 1: Analyze  Correlate  Bivariate..

15. SPSS/SITINORYAACOB/PJJSEM2_2015/2016
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SPSS/SITINORYAACOB/PJJSEM2_2015/2016
Step 2: Select variables and click into “Variables” box
then click “OK”

16. SPSS/SITINORYAACOB/PJJSEM2_2015/2016
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SPSS/SITINORYAACOB/PJJSEM2_2015/2016
Step 3: Interpret output

17. Example of Pearson correlation table:
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SPSS/SITINORYAACOB/PJJSEM2_2015/2016
Example of Pearson correlation table:
Example of interpretation:
There was a significant correlation (r= .213, p<.001) between loneliness and internet addiction. The positive correlation between loneliness and internet addiction indicated that the higher the loneliness, the higher the internet addiction among adolescents. Therefore, null Hypothesis was rejected.
Note: Can add some related past studies to support the results.