Nota Dr. Norila, Kajian Tinjauan

1. How to analyze survey research
data?

2. SPSS Interface
Data Editor
• Variable view
– Name – eg id, gen, schcat, a1, … (space bar, ?, ! *, !, …
considered as illegal characters)
– Type
– Width
– Decimals
– Label – ID, Gender, School Category, Item 1a, etc
– Values – eg 1 – Strongly Disagree, 2 – Disagree, 3 – Neither
agree nor disagree, 4 – Agree, 5 – Strongly Agree
– Missing
– Columns
– Align
– Measure
– Role

3. SPSS Interface
Data Editor
• Data view
– Entering data
– Save file

4. Reliability Analysis
• Analyze > Scale > Reliability Analysis
• Overall
–Corrected Item –Total Correlation. Omit
item with r < .3
• Construct a
• Construct b
• Construct c

5. Descriptive
Persepsi Pelajar Terhadap Sekolah
• Jantina & Tingkatan
– Analyze > Descriptive Statistics > Frequencies >
Jantina & Tingkatan
• Jantina Mengikut Tingkatan
– Analyze > Descriptive Statistics > Crosstabs > Rows
(Jantina) & Column (Tingkatan)

6. Descriptive
Persepsi Pelajar Terhadap Sekolah
• Score & percentage for each item
– Analyze > Descriptive Statistics > Frequencies >
(All items)

7. Calculate Means
(Persepsi Pelajar Terhadap Sekolah)
• Recode negative items
– Transform > Recode
– 1  5
– 2  4
– 3  3
– 4  2
– 5  1

8. Calculate Means
(Persepsi Pelajar Terhadap Sekolah)
• Means
– Overall
– Transform > Compute variable > Target variable
(MEAN) > Numeric Expression

9. Calculate Means
(Persepsi Pelajar Terhadap Sekolah)
• Means
– Prasarana Sekolah
– Tenaga Pengajar
– Kepimpinan Sekolah

10. Compare Means
(Persepsi Pelajar Terhadap Sekolah)
• Gender (Male, Female)
– Overall
– Prasarana Sekolah
– Tenaga Pengajar
– Kepimpinan Sekolah

11. T Test
• One-Sample T Test
– To compare sample mean and population mean
• Independent-Samples T Test
– To compare means of 2 different groups
• Paired-Samples T Test
– To compare means of 2 sets of data of the same
independent variable

14. Jika Ujian Levene
signifikan (p>.05),
gunakan baris pertama.
Jika Ujian Levene tidak
signifikan (p<.05,
gunakan baris kedua.
Ujian menunjukkan t(34) = .537,
p=.595 adalah tidak signifikan.
Keputusan ujian menunjukkan tidak
terdapat perbezaan skor min
persepsi terhadap sekolah yang
signifikan antara pelajar lelaki dan
pelajar perempuan

15. Assumptions
• Normality
– The assumption of normality is a prerequisite for
many inferential statistic.
– To explore normality graphically
• Histogram
• Box-plot
• Stem-and-leaf plot
– To explore normality statistically
• Shapiro-Wilk statistic
• Skewness
• Kurtosis

16. Assumptions
• Normality (graphically)
– Analyze > Descriptive Statistics > Frequencies
– Move variable (eg Min Keseluruhan) into the
Variable box.
– Click Charts >`Histogram’, `Show normal curve on
histogram’

18. Assumptions
• Normality (statistically) – Shapiro-Wilk
– Analyze > Descriptive Statistics > Explore
– Move variable (eg Min Keseluruhan) into the
Dependent List box.
– Click Plots > Normality plots with test
– If the significance level is >.05, then normality is
assumed.

20. Assumptions
• Homogeneity of variance
– The groups should come from populations with
equal variances
– Levene test
• If the test is significant (p < .05), it shows that the
variances are unequal.
• If the test is not significant (p > .05), it shows that there
is no significant differences between the variances of
the groups.

21. Compare Means
(Persepsi Pelajar Terhadap Sekolah)
• Tingkatan (Ting 1, Ting 2, Ting 3)
– Overall
– Prasarana Sekolah
– Tenaga Pengajar
– Kepimpinan Sekolah

22. Analysis of Variance
• One Way ANOVA
– To compare means of more than 2 groups of an
independent variable.
– Analyze > Compare Means > One-Way ANOVA
– Options > Descriptive > Homogeneity of variance
test

23. Analysis of Variance
• One Way ANOVA
– To compare means of more than 2 groups of an
independent variable.
– Analyze > Compare Means > One-Way ANOVA
– Options > Descriptive > Homogeneity of variance
test

25. The analysis showed that there is no
statistically significant difference at
the level of p < 0.05, F(2, 35) = 1.461,
p = .247.

26. Correlation
• Relationship between 2 variables.
– Value of correlation coefficient, r: -1 < r < 1.
– r < 0 – negative correlation
– r > 0 – positive correlation
– r = 0 – no correlation
– r + 1 – perfect correlation
– R > 0.8 – strong correlation
– R < 0.5 – weak correlation

27. Correlation
• Relationship between 2 variables.
– Pearson product-moment coefficient
• The relationship between 2 continuous variables
– Phi coefficient
• The relationship between 2 categorical variables
– Point-biserial correlation
• The relationship between a continuous and a
categorical variable
– Spearman’s rank-order correlation
• Assumption underlying correlation cannot be met
adequately

28. Correlation
• Assumptions:
– Related pairs: data must be collected from related
pairs
– Scale of measurement: data should be interval or
ratio in nature
– Normality
– Linearity: the relationship between the 2 variables
must be linear
– Homoscedasticity: the variability in scores for one
variable is roughly the same at all values of the
other variable

29. Correlation
– Normality
• Normality – Shapiro-Wilk
–Analyze > Descriptive Statistics > Explore
–Move variable into the Dependent List box.
–Click Plots > Normality plots with test
–If the significance level is >.05, then
normality is assumed.

30. Correlation
– Linearity: the relationship between the 2 variables
must be linear
– Homoscedasticity: the variability in scores for one
variable is roughly the same at all values of the
other variable
– Scatterplot: Graphs  Legacy Dialog 
Scatter/Dot  Simple Scatter

32. There is a linear
relationship
between pre-test
and post-test
The scores cluster uniformly
around the regression line, the
assumption of
homoscedasticity has not been
violated

33. Correlation
Analyze  Correlate  Bivariate  Pearson

34. Non-Parametric techniques
• Assumptions:
–Random sampling
–Variability across distribution
–Independence i.e. subjects appear in anly
one group and the groups are not related in
anyway

35. Non-Parametric techniques
• Mann-Whitney test
• Kruskal-Wallis test
• Spearman’s rank order correlation
• Chi-square

36. Non-Parametric techniques
• Chi-square
– To discover if there is relationship between 2
categorical variables
• Mann-Whitney test
- To test that 2 independent samples come from
populations having the same distribution.
• Kruskal-Wallis test
– To examine differences between two or more groups.
• Spearman’s rank order correlation (Spearman rho)
– A non-parametric alternative to the parametric
bivariate correlation

37. Chi-square
• To discover if there is relationship between 2
categorical variables
–Variable 1: Gender (Male, Female)
–Variable 2: Hometown (Kuching, Kota
Kinabalu, Others Urban, Rural)

38. Chi-square
• Hypotheses:
–Ho: Hometown and gender are independent
–Ha: Hometown and gender are related
• Analyze  Descriptive  Crosstabs 
Statistics  Chi square

40. Conclusion: Hometown and gender are related.