This document provides information on analyzing data using SPSS 13.0. It discusses objectives of data analysis, an overview of SPSS, data management, entry, analysis techniques including descriptive analysis, reliability analysis, factor analysis, tests of differences and relationships. Descriptive analysis is used to describe distributions. Reliability analysis and factor analysis are used to assess validity and reliability. Appropriate techniques for comparing groups and analyzing relationships depend on measurement levels and number of variables. Examples of outputs from SPSS are also included.

1. DATA ANALYSIS
For Management and
Marketing Research Project Report
SPSS 13.0
By:
Assoc Prof Dr Amran Awang
Faculty of Business Management
UiTM Perlis
Jan-May 2007

2. Objective:
• To know SPSS
• To manage data
• To enter and analyze data
• To interpret the findings
• To report the result

3. SPSS version 13.0
Statistical Package for Social Science
• User friendly
• Window orientation
• Use original copy
• Familiarity

4. Data Management
• Ensure response rate
• Examine
• Verify
• Explore
• Organize

5. Data entry
• Go to variable view
• Create variable name, label and value
• Give a number to each question
• Enter data

6. Data analysis
Descriptive/Frequency
(Measures of location, variability, and shape)
- Demographics (Number and/or percentage)
- Cross-tabulation (Number and/or percentage)
Goodness of Measures
(Factor analysis, summated scale and reliability)
Inferential/Hypothesis testing
- t-test or ANOVA
- Correlation if non directional
- Regression if directional

7. The Right Technique in Data Analysis?
What is the purpose of the analysis?
- Descriptive, compare group, relationship
What is the level of measurement?
- Parametric and Non-parametric
How many variables are involved?
- Univariate, bivariate, multivariate
What kind of tests?
Descriptive or Inferential.
If inferential set the significance level

8. Descriptive Analysis
Purpose: To describe the distribution of the demographic
variable
Frequencies distribution – if 1 ordinal or nominal
Cross-tabulation – if 2 ordinal or nominal
Means – if 1 interval or ratio
Means of subgroup – if 1 interval or ratio by subgroup

9. GOODNESS OF MEASURE
Validity (criterion)
- Factor analysis
Reliability
- Cronbach alpha

10. FACTOR ANALYSIS
• Go to analyze – data reduction- factor
- Enter items of IV or DV into dialogue box
- Tick descriptive – initial solution – coefficient- sig. level-
determinant-KMO & Bartlett test-inverse-reproduced-
-anti-image
- Tick extraction – principal component
- Tick rotation – varimax – rotated solution -loading plot
- Tick score – display factor coefficient matrix
- Tick option – sorted by size
- Tick ok

11. • Verify the output – look in anti-image table (on the diagonal
you will see the MSA with symbol of a. Remark the item
MSA should be more than .50, if not, drop the item and
run FA again.
- When all item MSA > .50, verify Total Variance Explained
and the total cumulative % should be > .50.
- Verify rotated factor matrix table, all factor coefficient or
loadings should be > .50 for sample of 100 or less,
and >.40 for sample of 101- 200.
- Verify cross-loadings so that the same item does not
cross load on same factor for more than .30. Drop the
cross-loading item(s) and run FA again.
- Verify the factor, if they fix the TF do summated score.
If not rename or redefine the factors or dimensions and
then repeat as above.
FACTOR ANALYSIS … CONT.

12. RELIABILITY
• Go to analyze – scale – reliability analysis
- Enter items to be analyzed
- Tick statistics – descriptive for –
item – scale – scale if item deleted.
• Verify the output
- If the scale (Cronbach alpha) > .70, the reliability
of the variable is achieved (Nunnally, 1978)
- If not verify the table and check the alpha
scale if item deleted to detect for improvement.
- Drop item stated in scale if item deleted and run
reliability again.
- Do summated scale to formulate a variable.

13. FACTOR ANALYSIS VS. RELIABILITY
If factor analysis does not in harmony with
reliability then go back to LR to confirm
the relationship found in previous studies
and how it was done.

14. KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .619
Bartlett's Test of Sphericity Approx. Chi-Square 327.667
df 91
Sig. .000
Total Variance Explained
Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings
Component Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %
1 2.672 19.087 19.087 2.672 19.087 19.087 1.941 13.864 13.864
2 2.116 15.111 34.198 2.116 15.111 34.198 1.911 13.648 27.512
3 1.314 9.385 43.583 1.314 9.385 43.583 1.521 10.866 38.378
4 1.129 8.065 51.648 1.129 8.065 51.648 1.489 10.635 49.012
5 1.024 7.316 58.964 1.024 7.316 58.964 1.393 9.952 58.964
6 .915 6.538 65.502
7 .908 6.485 71.987
8 .820 5.860 77.848
9 .729 5.209 83.056
10 .628 4.484 87.540
11 .541 3.865 91.405
12 .471 3.365 94.771
13 .403 2.876 97.647
14 .329 2.353 100.000
Extraction Method: Principal Component Analysis.
Output of Factor Analysis

15. Rotated Component Matrix
Component
1 2 3 4 5
BO12 .742 6.966E-02 8.411E-02 .291 1.308E-02
BO7 .724 -5.468E-02 -.214 -8.570E-02 .146
BO6 .722 -1.121E-02 .243 .136 .129
BO8 6.309E-02 .801 .149 -4.440E-02 -4.116E-02
BO14 -.303 .772 .183 2.543E-02 .170
BO13 -.197 -.627 .137 .127 .333
BO11 -6.129E-02 8.341E-02 -.802 6.165E-02 .153
BO4 3.467E-02 .318 .599 9.943E-02 -7.414E-02
BO9 -1.251E-02 -1.622E-02 -1.564E-02 .820 5.378E-02
BO1 -.236 .230 .359 -.556 .143
BO2 .303 2.235E-02 .190 .539 .223
BO3 .196 .114 -3.516E-02 -9.854E-02 .797
BO10 -.127 .169 .212 -.214 -.577
BO5 4.868E-02 .260 .349 -.136 -.379
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with
Kaiser Normalization. a Rotation converged in 6 iterations.
Output of Factor Analysis … cont.

16. R E L I A B I L I T Y A N A L Y S I S - S C A L E (A L P H A)
Mean Std Dev Cases
1. BO12 3.9580 1.0269 143.0
2. BO6 3.4825 .9704 143.0
3. BO7 2.9650 .8914 143.0
N of
Statistics for Mean Variance Std Dev Variables
SCALE 10.4056 4.9048 2.2147 3
Item-total Statistics
Scale Scale Corrected
Mean Variance Item- Alpha
if Item if Item Total if Item
Deleted Deleted Correlation Deleted
BO12 6.4476 2.3194 .4894 .5030
BO6 6.9231 2.4518 .4974 .4916
BO7 7.4406 2.9243 .3890 .6348
Reliability Coefficients
N of Cases = 143.0 N of Items = 3
Alpha = .6465

17. Test of Differences
Purpose: To evaluate the differences between 2 or
more groups with respect to a variable of interest
Techniques depended on:
• Levels of measurement of the variable
- Types of data (parametric or non-parametric)
• Number of groups
- One or more than two groups
• Independence of the groups
- If more than two groups
- Independence or related groups
INFERENTIAL

18. Test of Differences
One or
more than
2 groups
Independent
or related
One More than two groups
Independent
or related
Independent Related
Nominal: Frequency
Χ2 test
K-S
Runs
Binomial
Ordinal: Mann-Whitney
Continuous:
t-test
z-test
One-way ANOVA
Nominal:
Χ2 test
McNemar
Ordinal:
Wilcoxon
Sign
Rank
Continuous:
Paired t-test
Nominal:
Χ2 test
Ordinal:
Mann-Whitney
Median
K-S
Kruskal-Wallis
ANOVA
Continuous:
1-way ANOVA
Independent Related
Nominal:
Χ2 test
Ordinal:
Sign
Wilcoxon
McNemar
Friedman
2-way ANOVA
Continuous:
Paired t-test
Factorial
2-way ANOVA

19. Relationship
Purpose: To establish relationship between variables
Technique depended on:
• Whether or not exist dependent variable(s)
• Number of dependent and independent variables
• Types of data (parametric or non-parametric)
• Levels of measurement of the variable

20. Relationship Tests
One
Scale of
DV
Parametric
Scale of
IV
Parametric
Multiple
Regression
Analysis
(MRA)
Non-parametric
MRA w/
dummy
Loglinear
Scale
of IV
Non-parametric
Conjoint
analysis
Non-parametric
Scale
of IV
Parametric
Discriminant
Analysis
Logit & probit
Scale of
DV
More than 1
Scale
of IV
Parametric
Parametric Non-parametric
Canonical
Correlation
LISREL
Multivariate
ANOVA
Any DV?
Yes
No
Variable
Cluster
analysis
Factor
Analysis
MDS
Factor
Analysis
Latent
Structure
MDS
Cluster
analysis
Parametric Non-parametric
How many
DV?

21. Chi-square Test
• Non-parametric measurement
• Test of differences
• Two or more nominal variables
Steps: go to descriptive
go to analyze
go to crosstab
enter variables into row and column
go to statistics
tick chi-square

22. Compare means
(Only two variables analyze each time)
• go to analyze
• go to compare means
• go to independent-sample t-test
• enter variable
• enter grouping

23. Analysis of Variance
(ANOVA)
• go to analyze
• go to compare means
• go to one-way-ANOVA
• enter variable
• enter factor

24. Correlation Analysis
• go to analyze
• go to correlate
• go to bivariate
• enter variable

25. Multiple Regression Analysis
(One Level)
• go to analyze
• go to regression
• go to linear
• enter DV
• enter IV
• Method: enter
• Statistic: tick: model fit
R square change
Descriptive
Collinearity diagnostic
Durbin Watson
Plot Scatterplot

26. Chi-square result
H1: There are significant different in distribution of
business forms in the three states in northern Malaysia
STATE * FORM Crosstabulation
Count
FORM Total
Proprietor Partnership Pte Ltd Ltd
STATE Perlis 12 5 28 1 46
Kedah 51 16 31 1 99
Penang 20 16 24 5 65
Total 83 37 83 7 210
Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 22.675 6 .001
Likelihood Ratio 21.745 6 .001
Linear-by-Linear Association .275 1 .600
N of Valid Cases 210
a 3 cells (25.0%) have expected count less than 5. The minimum expected count is 1.53.
Interpretation: (2 = 22.67, p < .01), there is a significant different
in distribution of business forms between the three states in
northern Malaysia. Therefore, H1 is accepted.

27. Group Statistics
STATE N Mean SD S. E. of
Mean
AUTO Perlis 46 1.3696 .48802 .07195
Kedah 99 1.5051 .50252 .05051
INNO Perlis 46 1.5435 .50361 .07425
Kedah 99 1.7677 .42446 .04266
Independent Samples Test
t-test for Equality of Means
t df Sig. (2-tailed) Mean Diff. S. E. Diff.
AUTO Equal variances assumed -1.525 143 .130 -.1355 .08886
Equal variances not assumed -1.541 90.208 .127 -.1355 .08791
INNO Equal variances assumed -2.787 143 .006 -.2242 .08045
Equal variances not assumed -2.618 75.817 .011 -.2242 .08564
t – test
H1: Bumiputera SMEs in Perlis and Kedah show significant
different in autonomy and innovative orientation
Interpretation: If significant level set at p < .05, then there is statistical
significant different between autonomy and innovative orientation among
Bumi SMEs in Perlis and Kedah. Therefore, H1 partially accepted.

28. ANOVA
H1: There is significant different in autonomy and innovative
orientation among Bumiputera SMEs in the three
states in northern Malaysia
Sum of Squares df Mean Square F Sig.
AUTO Between Groups .749 2 .375 1.519 .221
Within Groups 51.065 207 .247
Total 51.814 209
INNO Between Groups 4.393 2 2.196 10.074 .000
Within Groups 45.131 207 .218
Total 49.524 209
Interpretation:
(F = 10.074, p < .01). If significant level is set at p < .05, then
there is statistical significant different in innovative but not
statistical significant in autonomy orientation among Bumi SMEs
in the three states in Northern Malaysia.
Therefore, H1 is partially accepted.

29. Correlation
H1: Autonomy and innovative orientation among Bumiputera
SMEs in northern Malaysia are related significantly
Correlations
Autonomy Innovative
Autonomy Pearson Correlation 1 .072
Sig. (2-tailed) . .297
N 210 210
Innovative Pearson Correlation .072 1
Sig. (2-tailed) .297 .
N 210 210
Interpretation:
(r = .072, p < .297) if significant level is set at p < .05, then
there is no statistical significant correlation between autonomy
and innovativeness. Therefore, H1 rejected.

30. Interpretation of Findings
Statistics
Autonomy Innovative
N Valid 210 210
Missing 0 0
Mean 3.0444 5.6603
Median 3.0000 6.0000
Mode 2.00 6.00
Std. Deviation 1.21533 .97658
Skewness .458 -.908
Std. Error of Skewness .168 .168
Kurtosis -.225 1.057
Std. Error of Kurtosis .334 .334
Minimum 1.00 2.00
Maximum 7.00 7.00
a Multiple modes exist. The smallest value is shown
STATE
Frequency % Valid % Cumulative %
Valid Perlis 46 21.9 21.9 21.9
Kedah 99 47.1 47.1 69.0
Penang 65 31.0 31.0 100.0
Total 210 100.0 100.0

31. Multiple Regression Analysis
H1: Autonomy and innovative orientation are positively related to
performance among Bumiputera SMEs in northern Malaysia
Model Summary
R R Square Adj R Square S. E. E. Change Statistics
Model R Sq Chg F Change df1 df2 Sig. F Chg
1 .171 .29 .10 1.52865 .026 2.730 2 205 .008
a Predictors: (Constant), Autonomy Dimension, Innovativeness Dimension
Coefficients
Ustd Coef. Std Coef. t Sig. Coll Stat
Model B S. E. Beta Tolerance VIF
1 (Constant) 4.053 .752 5.390 .000
Innovative .112 .113 .071 1.989 .024 .911 1.098
Autonomy .190 .090 .151 2.126 .005 .944 1.059
a Dependent Variable: Performance

32. MULTIPLE REGRESSION ANALYSIS…CONT.
Consider Some Multiple Regression Assumptions:
1. Normality – Verify Skewness < 2.0 or histogram
2. Linearity – Verify p-p plot of std. regress residuals
3. Homocedasticity – Verify scatterplot of residuals
4. Free from error term – Durbin Watson between 1.5 – 2.5
5. Free from multicollinearity – Correlation < .70,
- Verify VIF < 10, Tol < .10
6. Get rid of all outliers

33. Normal P-P Plot of Reg Std Resid
Dependent Variable: ROS
Observed Cum Prob
1.00
.75
.50
.25
0.00
Expected
Cum
Prob
1.00
.75
.50
.25
0.00
Scatterplot
Dependent Variable: ROS
Regression Standardized Predicted Value
3
2
1
0
-1
-2
-3
Regression
Studentized
Residual
3
2
1
0
-1
-2
Regression Standardized Residual
1.50
1.25
1.00
.75
.50
.25
0.00
-.25
-.50
-.75
-1.00
-1.25
-1.50
-1.75
Histogram
Dependent Variable: ROS
Frequency
14
12
10
8
6
4
2
0
Std. Dev = .83
Mean = 0.00
N = 107.00

34. REGRESSION ANALYSIS…CONT.
Interpretation: A 3-stage analysis
1. Coefficient of determination showed by R2 and adjusted R2.
(R2 = .29, adj R2 = .10, p < .01). This means that 29% variation in
performance can explained by variation in overall IV (autonomy and
innovative as one IV). 10% of the variation in performance is explained
by variation in multiple IV (autonomy and innovative).
p < .01 means the relationship between IV and DV is represented
by the sample more than 99% and only less than 1% can be explained
by chance. Then go to coefficient table.
2. Beta coefficient showed in coefficient table. Unstandardized Coeff.beta
is the actual beta (slope value of regression curve) can be more than 1.
Standardized Beta is beta value between 0 to 1). Innovative B = .112,
p < .05, and autonomy B = .190, p < .01.
3. Autonomy and innovative is positively related to
performance. Therefore, H1 is accepted.

35. THANK YOU ALL
Now analyze your own data