1. Dr. Siti Nor Binti Yaacob
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SITINORYAACOB/FEM4999B/2014/2015
APPENDIX 2
CONTOH PENYEDIAAN JADUAL DAN INTERPRETASI DATA
SPSS
Dr. Siti Nor Binti Yaacob

2. Quantitative Research
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Quantitative Research
Type of research objectives
Objective
Data analysis
Descriptive
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 groups (bivariate)
Difference between two groups (0,1)
To examine the difference on tested variables (e.g., loneliness) among two groups (males and females).
Independent t-test
Difference between more than two groups (0,1,2)
To examine the difference on tested variables (e.g., internet addiction) among ethnics groups (Malay, Chinese and Indian).
One way ANOVA

3. Quantitative Research
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SITINORYAACOB/FEM4999B/2014/2015
Quantitative Research
Type of research objectives
Objective
Data analysis
Association between 2 categorical variables
To examine the association between 2 categorical variables
Chi-Square
Relationship
(bivariate)
To examine the relationship between independent and dependent variables.
Pearson correlation
Relationship / Predicting Effect
(multivariate)
To determine the unique predictor of dependent variable.
Multiple regression analysis

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Data Entry
Varaible View

5. Data Transformation(recode data)
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Data Transformation(recode data)
Transform  Recode Into Different Variable  “choose your item”  input variable --> renew the name in output variable (name and label)  Change.
Old and New Values  eg. 1 change to 5  add  continue  OK

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Reliability test
Analyze  Scale  Reliability Analysis  choose all of the tested items
Scale label, type the name of variable .
Statistics  Choose “Item, Scale, and Scale if Item Deleted”.
Continue OK.

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Descriptive Analysis
Step 1: Analyze  Descriptive Statistics  Frequencies…
Step 2: select the variables and click in the box

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Step 3: Click “statistics” and choose “ mean, median, std. deviation, variance, range, minimum and maximum”.  click “continue”

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Step 4: Click “OK”.
Step 5: Interpret output

10. Example of Descriptive Table:
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Example of Descriptive Table:

11. Example of Interpretation:
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Example of Interpretation:
Respondents’ age
The respondents aged between 15 and 18 years old (Mean=16.09, SD.=0.670). Majority of the respondents (55.1%) were 16 years old.

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Independent t-test
Step 1: Analyze  Compare Means  Independent Sample t-test

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

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Step 4: Click “OK”
Step 5: Output

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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

16. Example of t-test table:
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Example of t-test table:
Example of Interpretation:
There was difference in internet addiction between male and female adolescents (t=3.489, p<.01). Male adolescents (mean= 55.42) were found to have higher level of internet addiction than female adolescents (mean= 47.29).
Null hypothesis was rejected.
Note: You can add some related past studies to support the result.

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One way ANOVA
Step 1: Analyze  Compare Means  One-Way Anova
Step 2: Choose “tested continuous variables into the “Dependent list”.
Step 3: Choose categorical variable into the “Factor”.
Step 4: Post Hoc  Tukey
Step 5: Options  Choose “Descriptive”.
Step 6: Continue  OK

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Output

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20. One way ANOVA- Interpretation
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One way ANOVA- Interpretation

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Chi-Square
Step 1: Analyze  Descriptive statistics  Crosstabs
Step 2: Row: “races”; Column: “internet access”
Step 3: Statistics  check “chi-square” & “contingency coefficient”  Continue  OK

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Output of Chi-square

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Output of Chi-square
A valid Chi-square will present the expected count of less than 5 in the table should not be more than 15.0% of the total number in the same row (Sirkin, 2005). X²
Not a valid chi-square model.

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Interpretation
If there is a valid chi-square model:
28.8

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Pearson Correlation
Step 1: Analyze  Correlate  Bivariate..

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Step 2: Select variables and click into “Variables” box
then click “OK”

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Step 3: Interpret output

28. Example of Pearson correlation table:
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Example of Pearson correlation table:
Example of interpretation:
The result showed that there was a significant positive correlation between loneliness and internet addiction (r= .213, p<.001). The direction of relationship indicated that respondents with higher loneliness score, tend to report higher internet addiction. Therefore, null hypothesis that there is no significant relationship between loneliness and internet addiction was rejected.
Note: You can add some related past studies to support the results.

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Multiple regression
Step 1: Analyze  Regression  Linear.
Step 2: Choose dependent variable into “Dependent”.
Step 3: Choose independent variables into “Independent variables”.
Step 4: Statistics  Estimates, Confidence intervals, model fit, descriptives”.
Step 5: Continue OK.

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