The document provides a comprehensive overview of data analysis, covering fundamental concepts such as levels of measurement, various types of variables, and key terms. It details the data analysis process, including stages like data entry, error checking, and transformation, alongside statistical methods for hypothesis testing. Additionally, it addresses reliability and validity of measurement instruments through factor analysis and reliability statistics.

2. Introduction to Data Analysis

3. Basics
 Levels of
Measurement
 Nominal
 Ordinal
 Interval
 Ratio
 Variables
 Independent
 Dependent
 Moderating
 Mediating
 Control
 Key Terms
 Concepts
 Construct
 Variable
 Definition
 Dictionary
 Operational

4. Data Analysis Process

5. DATA ANALYSIS
DATA ENTRY
STAGES OF DATA ANALYSIS
ERROR
CHECKING
AND
VERIFICATION
CODING
EDITING

6. Introduction
Preparation of Data
 Editing, Handling Blank responses, Coding,
Categorization and Data Entry
 These activities ensure accuracy of the data and
its conversion from raw form to reduced data
Exploring, Displaying and Examining
data
 Breaking down, inspecting and rearranging data
to start the search for meaningful descriptions,
patterns and relationship.

7. Coding Rules
Categories
should be
Appropriate to the
research problem
Exhaustive
Mutually exclusive
Derived from one
classification principle

8. Appropriateness
 Let’s say your population is
students at institutions of higher
learning
 What is you age group?
 15 – 25 years
 26 – 35 years
 36 – 45 years
 Above 45 years

9. Exhaustiveness
 What is your race?
 Malay
 Chinese
 Indians
 Others

10. Mutual Exclusivity
 What is your occupation type?
 Professional  Crafts
 Managerial  Operatives
 Sales  Unemployed
 Clerical  Housewife
 Others

11. Single Dimension
What is your occupation type?
 Professional  Crafts
 Managerial  Operatives
 Sales  Unemployed
 Clerical  Housewife
 Others

12. Coding Open-ended Responses

13. Coding Open Ended Questions

14. Handling Blank Responses
How do we take care of missing
responses?
 If > 25% missing, throw out the questionnaire
 Other ways of handling
• Use the midpoint of the scale
• Ignore (system missing)
• Mean of those responding
• Mean of the respondent
• Random number

15. How to Select a Test
Two-Sample Tests
____________________________________________
k-Sample Tests
____________________________________________
Measurement
Scale One-Sample Case Related Samples
Independent
Samples Related Samples
Independent
Samples
Nominal  Binomial
 x2
one-sample test
 McNemar  Fisher exact
test
 x2
two-samples
test
 Cochran Q  x2
for k samples
Ordinal  Kolmogorov-
Smirnov one-sample
test
 Runs test
 Sign test
Wilcoxon
matched-pairs
test
 Median test
Mann-Whitney
U
Kolmogorov-
Smirnov
Wald-Wolfowitz
 Friedman two-
way ANOVA
 Median
extension
Kruskal-Wallis
one-way ANOVA
Interval and
Ratio
 t-test
 Z test
 t-test for paired
samples
 t-test
 Z test
 Repeated-
measures ANOVA
 One-way
ANOVA
 n-way ANOVA

16. Data Transformation
Weights
Assigning numbers to responses on a
pre-determined rule
Respecification of the Variable
Transforming existing data to form new
variables or items
 Recode
 Compute

17. Scale Transformation
Reason for Transformation
to improve interpretation and
compatibility with other data sets
to enhance symmetry and stabilize
spread
improve linear relationship between
the variables (Standardized score)
s
X
X
z i
-


18. Data Transformation
Section 1 - Computer Anxiety
Computers make me feel
uncomfortable
1 2 3 4 5 6 7
I get a sinking feeling when I
think of trying to use a computer
1 2 3 4 5 6 7
Computers scare me 1 2 3 4 5 6 7
I feel comfortable using a
computer
1 2 3 4 5 6 7
Working with a computer makes
me nervous
1 2 3 4 5 6 7

19. Sample SPSS Codebook

20. Research Model
Attitude
Intention to
Share
Information
Subjective
norm
5 items
4 items
3 items
Perceived
Behavioral
Control
4 items
Actual
Sharing of
Information
5 items

21. Factor Analysis - Command

22. Assumptions in FA
KMO and Bartlett's Test
.882
2878.230
78
.000
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy.
Approx. Chi-Square
df
Sig.
Bartlett's Test of
Sphericity
KMO should be > 0.5
Bartlett’s Test should
be significant ie; p <
0.05
Question:
How valid is our instrument?

23. Measure of Sampling Adequacy
MSA Comment
0.80 and above Meritorious
0.70 – 0.80 Middling
0.60 – 0.70 Mediocre
0.50 – 0.60 Miserable
Below 0.50 Unacceptable

24. Assumptions in FA
Anti-image Matrices
.045 -.021 -.032 -.036 -.029 .014 -.013 -.027 .027 -.003 -.007 .011 -.011
-.021 .123 -.016 -.018 -.015 -.020 .000 .016 -.038 -.013 -.009 .004 -.010
-.032 -.016 .107 .012 -.018 -.015 .021 -.010 -.014 -.005 .009 -.020 .014
-.036 -.018 .012 .161 -.014 .000 -.013 .033 -.020 .001 .022 -.007 -.005
-.029 -.015 -.018 -.014 .106 -.019 .005 .024 -.012 .017 -.006 -.007 .022
.014 -.020 -.015 .000 -.019 .317 -.121 -.063 .033 -.003 -.003 .070 -.052
-.013 .000 .021 -.013 .005 -.121 .233 -.066 -.072 .014 -.003 -.041 .029
-.027 .016 -.010 .033 .024 -.063 -.066 .223 -.127 -.021 .035 .002 .004
.027 -.038 -.014 -.020 -.012 .033 -.072 -.127 .375 .004 -.008 -.001 .022
-.003 -.013 -.005 .001 .017 -.003 .014 -.021 .004 .253 -.161 .013 -.069
-.007 -.009 .009 .022 -.006 -.003 -.003 .035 -.008 -.161 .243 -.076 .011
.011 .004 -.020 -.007 -.007 .070 -.041 .002 -.001 .013 -.076 .247 -.164
-.011 -.010 .014 -.005 .022 -.052 .029 .004 .022 -.069 .011 -.164 .250
.863a -.286 -.458 -.421 -.420 .118 -.130 -.272 .210 -.031 -.067 .100 -.108
-.286 .961a -.142 -.131 -.131 -.100 -.002 .094 -.179 -.073 -.050 .025 -.054
-.458 -.142 .936a .095 -.165 -.084 .131 -.064 -.071 -.030 .055 -.125 .087
-.421 -.131 .095 .942a -.105 .001 -.066 .175 -.081 .003 .111 -.036 -.027
-.420 -.131 -.165 -.105 .939a -.102 .031 .154 -.058 .105 -.039 -.044 .134
.118 -.100 -.084 .001 -.102 .891a -.447 -.236 .095 -.012 -.010 .252 -.185
-.130 -.002 .131 -.066 .031 -.447 .899a -.290 -.243 .060 -.015 -.171 .119
-.272 .094 -.064 .175 .154 -.236 -.290 .879a -.438 -.088 .153 .007 .019
.210 -.179 -.071 -.081 -.058 .095 -.243 -.438 .886a .014 -.025 -.005 .073
-.031 -.073 -.030 .003 .105 -.012 .060 -.088 .014 .785a -.650 .052 -.273
-.067 -.050 .055 .111 -.039 -.010 -.015 .153 -.025 -.650 .767a -.311 .046
.100 .025 -.125 -.036 -.044 .252 -.171 .007 -.005 .052 -.311 .732a -.662
-.108 -.054 .087 -.027 .134 -.185 .119 .019 .073 -.273 .046 -.662 .749a
Att1
Att2
Att3
Att4
Att5
Sn1
Sn2
Sn3
Sn4
Pbc1
Pbc2
Pbc3
Pbc4
Att1
Att2
Att3
Att4
Att5
Sn1
Sn2
Sn3
Sn4
Pbc1
Pbc2
Pbc3
Pbc4
Anti-image Covariance
Anti-image Correlation
Att1 Att2 Att3 Att4 Att5 Sn1 Sn2 Sn3 Sn4 Pbc1 Pbc2 Pbc3 Pbc4
Measures of Sampling Adequacy(MSA)
a.

25. How many Factors?
Total Variance Explained
6.730 51.772 51.772 6.730 51.772 51.772 4.476 34.427 34.427
3.328 25.596 77.368 3.328 25.596 77.368 3.287 25.288 59.715
.962 7.400 84.769 .962 7.400 84.769 3.257 25.054 84.769
.439 3.376 88.145
.432 3.325 91.470
.232 1.786 93.256
.215 1.651 94.907
.183 1.407 96.313
.131 1.010 97.324
.124 .951 98.275
.102 .785 99.059
.088 .673 99.733
.035 .267 100.000
Component
1
2
3
4
5
6
7
8
9
10
11
12
13
Total
% of
Variance Cumulative % Total
% of
Variance Cumulative % Total
% of
Variance Cumulative %
Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings
Extraction Method: Principal Component Analysis.

26. How many Factors? - Scree Plot
13
12
11
10
9
8
7
6
5
4
3
2
1
Component Number
7
6
5
4
3
2
1
0
E
ig
e
n
v
a
lu
e
Scree Plot

27. Rotation – Orthogonal or Non
Orthogonal Factor Rotation
Orthogonal Factor Rotation
Unrotated
Unrotated
Factor II
Factor II
Unrotated
Unrotated
Factor I
Factor I
Rotated
Rotated
Factor I
Factor I
Rotated Factor II
Rotated Factor II
-1.0
-.50
0
+.50
+1.0
-.50
-1.0
+1.0
+.50
V1
V2
V3
V4
V5
Unrotated
Unrotated
Factor II
Factor II
Unrotated
Unrotated
Factor I
Factor I
Oblique
Oblique
Rotation:
Rotation:
Factor I
Factor I
Orthogonal Rotation:
Orthogonal Rotation:
Factor II
Factor II
-1.0
-.50
0
+.50
+1.0
-.50
-1.0
+1.0
+.50
V1
V2
V3
V4
V5
Orthogonal
Orthogonal
Rotation: Factor I
Rotation: Factor I
Oblique Rotation:
Oblique Rotation:
Factor II
Factor II
Oblique Factor Rotation
Oblique Factor Rotation

28. Assigning Questions
Amount of shared, or common
variance, among the variables
General guidelines all communnalities
should be above 0.5
Communalities
1.000 .967
1.000 .910
1.000 .910
1.000 .880
1.000 .928
1.000 .738
1.000 .847
1.000 .857
1.000 .749
1.000 .806
1.000 .815
1.000 .805
1.000 .808
Att1
Att2
Att3
Att4
Att5
Sn1
Sn2
Sn3
Sn4
Pbc1
Pbc2
Pbc3
Pbc4
Initial Extraction
Extraction Method: Principal Component Analysis.
Rotated Component Matrix a
.897 .146 .377
.855 .197 .375
.871 .128 .369
.885 .098 .296
.907 .053 .319
.401 -.046 .758
.409 -.013 .825
.376 -.070 .843
.264 -.081 .820
.129 .888 .025
.106 .894 -.062
.067 .892 -.064
.076 .894 -.043
Att1
Att2
Att3
Att4
Att5
Sn1
Sn2
Sn3
Sn4
Pbc1
Pbc2
Pbc3
Pbc4
1 2 3
Component
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 5 iterations.
a.

29. Significant Loadings
Factor Loading Sample Size Needed
0.30 350
0.35 250
0.40 200
0.45 150
0.50 120
0.55 100
0.60 85
0.65 70
0.70 60
0.75 50

30. Table in Report
Component
1 2 3
Att1 .897 .146 .377
Att2 .855 .197 .375
Att3 .871 .128 .369
Att4 .885 .098 .296
Att5 .907 .053 .319
Sn1 .401 -.046 .758
Sn2 .409 -.013 .825
Sn3 .376 -.070 .843
Sn4 .264 -.081 .820
Pbc1 .129 .888 .025
Pbc2 .106 .894 -.062
Pbc3 .067 .892 -.064
Pbc4 .076 .894 -.043
Eigenvalue 4.476 3.287 3.257
% Variance
(84.77)
34.43 25.29 25.05

31. Reliability - Command

32. Reliability
Reliability Statistics
.977 5
Cronbach's
Alpha N of Items
Item-Total Statistics
15.25 6.681 .973 .965
15.26 6.560 .925 .972
15.24 6.906 .929 .972
15.21 6.825 .900 .975
15.25 6.555 .935 .970
Att1
Att2
Att3
Att4
Att5
Scale Mean if
Item Deleted
Scale
Variance if
Item Deleted
Corrected
Item-Total
Correlation
Cronbach's
Alpha if Item
Deleted
Question:
How reliable are our instruments?
Should be
preferably > 0.3

33. Reliability
Reliability Statistics
.912 4
Cronbach's
Alpha N of Items
Item-Total Statistics
11.20 4.243 .761 .900
11.03 4.135 .855 .868
11.00 4.021 .856 .867
11.21 4.250 .736 .909
Sn1
Sn2
Sn3
Sn4
Scale Mean if
Item Deleted
Scale
Variance if
Item Deleted
Corrected
Item-Total
Correlation
Cronbach's
Alpha if Item
Deleted

34. Reliability
Reliability Statistics
.919 4
Cronbach's
Alpha N of Items
Item-Total Statistics
10.48 4.984 .814 .895
10.45 4.793 .826 .892
10.43 5.042 .809 .897
10.40 5.246 .814 .897
Pbc1
Pbc2
Pbc3
Pbc4
Scale Mean if
Item Deleted
Scale
Variance if
Item Deleted
Corrected
Item-Total
Correlation
Cronbach's
Alpha if Item
Deleted

35. Reliability
Reliability Statistics
.966 5
Cronbach's
Alpha N of Items
Item-Total Statistics
15.28 6.591 .951 .951
15.28 6.612 .888 .961
15.29 6.553 .901 .959
15.28 6.716 .877 .962
15.24 6.445 .904 .958
Intent1
Intent2
Intent3
Intent4
Intent5
Scale Mean if
Item Deleted
Scale
Variance if
Item Deleted
Corrected
Item-Total
Correlation
Cronbach's
Alpha if Item
Deleted

36. Table in Report
Variable N of Item Item
Deleted
Alpha
Attitude 5 - 0.977
SN 4 - 0.912
Pbcontrol 4 - 0.919
Intention 5 - 0.966
Actual 3 - 0.771

37. Example - Recoding
Perceived Enjoyment
PE1 The actual process of
using Instant Messenger is
pleasant
1 2 3 4 5 6 7
PE2 I have fun using Instant
Messenger
1 2 3 4 5 6 7
PE3 Using Instant Messenger
bores me
1 2 3 4 5 6 7
PE4 Using Instant Messenger
provides me with a lot of
enjoyment
1 2 3 4 5 6 7
PE5 I enjoy using Instant
Messenger
1 2 3 4 5 6 7

38. Recoding - Command

39. Data before Transformation

40. Computing New Variable - Command

41. Data after Transformation

42. Frequencies - Command

43. Frequencies
Gender
144 75.0 75.0 75.0
48 25.0 25.0 100.0
192 100.0 100.0
Male
Female
Total
Valid
Frequency Percent Valid Percent
Cumulative
Percent
Current Position
34 17.7 17.7 17.7
66 34.4 34.4 52.1
54 28.1 28.1 80.2
32 16.7 16.7 96.9
6 3.1 3.1 100.0
192 100.0 100.0
Technician
Engineer
Sr Engineer
Manager
Above manager
Total
Valid
Frequency Percent Valid Percent
Cumulative
Percent
Question:
1. Is our sample representative?
2. Data entry error

44. Table in Report
Frequency Percentage
Gender
Male
Female
Position
Technician
Engineer
Sr Engineer
Manager
Above manager
144
48
34
66
54
32
6
75.0
25.0
17.7
34.4
28.1
16.7
3.1

45. Descriptives - Command

46. Descriptives
Descriptive Statistics
192 19 53 33.39 8.823 .667 .175 -.557 .349
192 1 18 5.36 4.435 1.448 .175 1.333 .349
192 1 28 9.04 7.276 1.051 .175 -.025 .349
192 2.00 5.00 3.8104 .64548 -.480 .175 .242 .349
192 2.00 5.00 3.7031 .67034 -.101 .175 .755 .349
192 2.00 5.00 3.4792 .73672 .015 .175 -.028 .349
192 2.00 5.00 3.8188 .63877 -.528 .175 .687 .349
192 2.33 5.00 4.0625 .58349 -.361 .175 -.328 .349
192
Age
Years working in the
organization
Total years of
working experience
Attitude
subjective
Pbcontrol
Intention
Actual
Valid N (listwise)
Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error
N Minimum Maximum Mean Std.
Deviation
Skewness Kurtosis
Question:
1. Is there variation in our data?
2. What is the level of the phenomenon we are measuring?

47. Table in Report
Mean Std. Deviation
Attitude 3.81 0.65
Subjective Norm 3.70 0.67
Behavioral Control 3.48 0.74
Intention 3.82 0.64
Actual 4.06 0.58

48. Chi Square Test - Command

49. Crosstabulation
Gender * Intention Level Crosstabulation
110 34 144
76.4% 23.6% 100.0%
70.5% 94.4% 75.0%
57.3% 17.7% 75.0%
46 2 48
95.8% 4.2% 100.0%
29.5% 5.6% 25.0%
24.0% 1.0% 25.0%
156 36 192
81.3% 18.8% 100.0%
100.0% 100.0% 100.0%
81.3% 18.8% 100.0%
Count
% within Gender
% within Intention Level
% of Total
Count
% within Gender
% within Intention Level
% of Total
Count
% within Gender
% within Intention Level
% of Total
Male
Female
Gender
Total
Low High
Intention Level
Total
Chi-Square Tests
8.934b 1 .003
7.704 1 .006
11.274 1 .001
.002 .001
8.888 1 .003
192
Pearson Chi-Square
Continuity Correctiona
Likelihood Ratio
Fisher's Exact Test
Linear-by-Linear
Association
N of Valid Cases
Value df
Asymp. Sig.
(2-sided)
Exact Sig.
(2-sided)
Exact Sig.
(1-sided)
Computed only for a 2x2 table
a.
0 cells (.0%) have expected count less than 5. The minimum expected count is 9.
00.
b.
Question:
Is level of sharing dependent on gender?

50. T-test - Command

51. t-test
(2 Independent)
Group Statistics
144 3.9000 .60302 .05025
48 3.5750 .68619 .09904
Gender
Male
Female
Intention
N Mean
Std.
Deviation
Std. Error
Mean
Independent Samples Test
3.591 .060 3.122 190 .002 .32500 .10410 .11965 .53035
2.926 72.729 .005 .32500 .11106 .10364 .54636
Equal variances
assumed
Equal variances
not assumed
Intention
F Sig.
Levene's Test for
Equality of Variances
t df Sig. (2-tailed)
Mean
Difference
Std. Error
Difference Lower Upper
95% Confidence
Interval of the
Difference
t-test for Equality of Means
Question:
Does intention to share vary by gender?

52. Paired t-test - Command

53. t-test
(2 Dependent)
Paired Samples Statistics
3.8188 192 .63877 .04610
4.0625 192 .58349 .04211
Intention
Actual
Pair
1
Mean N
Std.
Deviation
Std. Error
Mean
Paired Samples Correlations
192 .817 .000
Intention & Actual
Pair 1
N Correlation Sig.
Paired Samples Test
-.24375 .37326 .02694 -.29688 -.19062 -9.049 191 .000
Intention - Actual
Pair 1
Mean
Std.
Deviation
Std. Error
Mean Lower Upper
95% Confidence
Interval of the
Difference
Paired Differences
t df Sig. (2-tailed)
Question:
Are there differences between intention to
share and actual sharing behavior?

54. One Way ANOVA - Command

55. One way ANOVA
(k independent)
ANOVA
Intention
7.864 4 1.966 5.247 .001
70.068 187 .375
77.933 191
Between Groups
Within Groups
Total
Sum of
Squares df Mean Square F Sig.
Intention
Duncana,b
66 3.6424
32 3.6625
34 3.8941
54 4.0000
6 4.5333
.101 1.000
Current Position
Engineer
Manager
T
echnician
Sr Engineer
Above manager
Sig.
N 1 2
Subset for alpha = .05
Means for groups in homogeneous subsets are displayed.
Uses Harmonic Mean Sample Size = 19.157.
a.
The group sizes are unequal. The harmonic mean
of the group sizes is used. Type I error levels are
not guaranteed.
b.
Question:
Does intention vary by position?

56. Mann-Whitney - Command

57. Mann-Whitney
(2 independent)
Question:
Does the variables vary by gender?
Ranks
144 103.64 14924.00
48 75.08 3604.00
192
Gender
Male
Female
Total
Intention
N Mean Rank Sum of Ranks
Test Statisticsa
2428.000
3604.000
-3.266
.001
Mann-Whitney U
Wilcoxon W
Z
Asymp. Sig. (2-tailed)
Intention
Grouping Variable: Gender
a.

58. Kruskal-Wallis - Command

59. Kruskal-Wallis
(k independent)
Question:
Does the variables vary by position?
Ranks
34 101.32
66 79.68
54 114.54
32 81.63
6 171.17
192
Position
Technician
Engineer
Sr Engineer
Manager
Above manager
Total
Intention
N Mean Rank
Test Statisticsa,b
28.179
4
.000
Chi-Square
df
Asymp. Sig.
Intention
Kruskal Wallis Test
a.
Grouping Variable: Position
b.

60. Correlation - Command

61. Correlation
(Interval/ratio)
Question:
Are the variables related?
Correlations
1 .697** .212** .808** .606**
.000 .003 .000 .000
192 192 192 192 192
.697** 1 -.052 .653** .552**
.000 .471 .000 .000
192 192 192 192 192
.212** -.052 1 .281** .031
.003 .471 .000 .665
192 192 192 192 192
.808** .653** .281** 1 .817**
.000 .000 .000 .000
192 192 192 192 192
.606** .552** .031 .817** 1
.000 .000 .665 .000
192 192 192 192 192
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Attitude
subjective
Pbcontrol
Intention
Actual
Attitude subjective Pbcontrol Intention Actual
Correlation is significant at the 0.01 level (2-tailed).
**.

62. Table Presentation
Attitude subjective Pbcontrol Intention Actual
Attitude 1
subjective
.740** 1
Pbcontrol .201** -.047 1
Intention .885** .662** .326** 1
Actual .660** .553** .059 .805** 1
*p< 0.05, **p< 0.01

63. Correlation
(Ordinal)
Correlations
1.000 .043 -.415** -.019 -.090
. .314 .000 .417 .154
130 130 130 130 130
.043 1.000 -.476** -.263** -.259**
.314 . .000 .001 .001
130 130 130 130 130
-.415** -.476** 1.000 .117 .160*
.000 .000 . .092 .035
130 130 130 130 130
-.019 -.263** .117 1.000 .029
.417 .001 .092 . .372
130 130 130 130 130
-.090 -.259** .160* .029 1.000
.154 .001 .035 .372 .
130 130 130 130 130
Correlation Coefficient
Sig. (1-tailed)
N
Correlation Coefficient
Sig. (1-tailed)
N
Correlation Coefficient
Sig. (1-tailed)
N
Correlation Coefficient
Sig. (1-tailed)
N
Correlation Coefficient
Sig. (1-tailed)
N
Pay
Promotion
Work
Supervision
Coworkers
Spearman's rho
Pay Promotion Work Supervision Coworkers
Correlation is significant at the 0.01 level (1-tailed).
**.
Correlation is significant at the 0.05 level (1-tailed).
*.
Question:
Are the variables related?

64. Friedman Test - Command

65. Friedman
(k related samples)
Question:
Is the rating similar?
Ranks
2.34
2.67
2.70
2.29
Sn1
Sn2
Sn3
Sn4
Mean Rank
Test Statisticsa
192
43.149
3
.000
N
Chi-Square
df
Asymp. Sig.
Friedman Test
a.

66. Regression - Command

67. Multiple
Regression
Question:
Which variables can explain the intention to
share?
Variables Entered/Removed b
Pbcontrol,
subjective,
Attitude
a
. Enter
Model
1
Variables
Entered
Variables
Removed Method
All requested variables entered.
a.
Dependent Variable: Intention
b.
Model Summaryb
.832a .693 .688 .35703 1.501
Model
1
R R Square
Adjusted
R Square
Std. Error of
the Estimate
Durbin-
Watson
Predictors: (Constant), Pbcontrol, subjective, Attitude
a.
Dependent Variable: Intention
b.

68. Multiple Regression
ANOVAb
53.968 3 17.989 141.127 .000a
23.964 188 .127
77.933 191
Regression
Residual
Total
Model
1
Sum of
Squares df Mean Square F Sig.
Predictors: (Constant), Pbcontrol, subjective, Attitude
a.
Dependent Variable: Intention
b.
Coefficientsa
.191 .197 .971 .333
.601 .059 .607 10.103 .000 .453 2.210
.227 .056 .238 4.043 .000 .472 2.116
.143 .037 .165 3.821 .000 .877 1.140
(Constant)
Attitude
subjective
Pbcontrol
Model
1
B Std. Error
Unstandardized
Coefficients
Beta
Standardized
Coefficients
t Sig. Tolerance VIF
Collinearity Statistics
Dependent Variable: Intention
a.

69. Assumptions (Multicollinearity)
Collinearity Diagnosticsa
3.936 1.000 .00 .00 .00 .00
.043 9.581 .00 .02 .10 .55
.013 17.195 .91 .19 .02 .21
.008 22.890 .09 .79 .88 .24
Dimension
1
2
3
4
Model
1
Eigenvalue
Condition
Index (Constant) Attitude subjective Pbcontrol
Variance Proportions
Dependent Variable: Intention
a.

70. Assumptions (Outliers)
Casewise Diagnostics a
3.152 5.00 3.8748 1.12520
4.042 5.00 3.5570 1.44295
3.071 4.20 3.1037 1.09631
3.152 5.00 3.8748 1.12520
4.042 5.00 3.5570 1.44295
3.071 4.20 3.1037 1.09631
Case Number
70
82
83
166
178
179
Std. Residual Intention
Predicted
Value Residual
Dependent Variable: Intention
a.

72. After Removing Outliers
Model Summaryb
.900a .810 .807 .27373 1.725
Model
1
R R Square
Adjusted
R Square
Std. Error of
the Estimate
Durbin-
Watson
Predictors: (Constant), Pbcontrol, subjective, Attitude
a.
Dependent Variable: Intention
b.
Coefficientsa
.067 .153 .441 .659
.758 .050 .784 15.281 .000 .396 2.523
.085 .047 .091 1.801 .073 .412 2.426
.145 .029 .173 5.015 .000 .875 1.143
(Constant)
Attitude
subjective
Pbcontrol
Model
1
B Std. Error
Unstandardized
Coefficients
Beta
Standardized
Coefficients
t Sig. Tolerance VIF
Collinearity Statistics
Dependent Variable: Intention
a.
ANOVAb
58.261 3 19.420 259.182 .000a
13.637 182 .075
71.898 185
Regression
Residual
Total
Model
1
Sum of
Squares df Mean Square F Sig.
Predictors: (Constant), Pbcontrol, subjective, Attitude
a.
Dependent Variable: Intention
b.

73. Assumptions – Advanced Diagnostics
(Hair et al., 2006)
Residuals Statisticsa
2.1329 4.9380 3.8188 .53156 192
-3.172 2.106 .000 1.000 192
.027 .111 .048 .020 192
2.1423 4.9493 3.8179 .53167 192
-.96087 1.44295 .00000 .35421 192
-2.691 4.042 .000 .992 192
-2.731 4.253 .001 1.012 192
-.98909 1.59761 .00086 .36911 192
-2.779 4.461 .004 1.031 192
.130 17.495 2.984 3.453 192
.000 .485 .011 .051 192
.001 .092 .016 .018 192
Predicted Value
Std. Predicted Value
Standard Error of
Predicted Value
Adjusted Predicted Value
Residual
Std. Residual
Stud. Residual
Deleted Residual
Stud. Deleted Residual
Mahal. Distance
Cook's Distance
Centered Leverage
Value
Minimum Maximum Mean
Std.
Deviation N
Dependent Variable: Intention
a.

74. Assumptions (Normality)
6
4
2
0
-2
-4
Regression Standardized Residual
70
60
50
40
30
20
10
0
F
r
e
q
u
e
n
c
y
Mean = -1.99E-17
Std. Dev. = 0.992
N = 192
Dependent Variable: Intention
Histogram

75. Assumptions
(Normality of the Error term)
1.0
0.8
0.6
0.4
0.2
0.0
Observed Cum Prob
1.0
0.8
0.6
0.4
0.2
0.0
E
x
p
e
c
te
d
C
u
m
P
r
o
b
Dependent Variable: Intention
Normal P-P Plot of Regression Standardized Residual

76. Assumptions (Constant Variance)
5.00
4.50
4.00
3.50
3.00
2.50
2.00
Intention
4
2
0
-2
R
e
g
re
s
s
io
n
S
tu
d
e
n
tiz
e
d
R
e
s
id
u
a
l
Dependent Variable: Intention
Scatterplot

77. Assumptions (Linearity)
1
0
-1
-2
Attitude
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
In
ten
tio
n
Dependent Variable: Intention
Partial Regression Plot

78. Assumptions (Linearity)
2
1
0
-1
-2
subjective
2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
In
te
n
tio
n
Dependent Variable: Intention
Partial Regression Plot

79. Assumptions (Linearity)
1
0
-1
-2
Pbcontrol
2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
In
te
n
tio
n
Dependent Variable: Intention
Partial Regression Plot

80. Table Presentation
Variable Dependent = Intention
Standardized Beta
Attitude
Subjective Norm
Perceived Control
0.607**
0.238**
0.105**
R2
Adjusted R2
F Value
D-W
0.693
0.688
141.13
1.501
*p< 0.05, **p< 0.01

81. Discriminant - Command

82. Discriminant
Analysis
Analysis Case Processing Summary
127 66.1
0 .0
0 .0
0 .0
65 33.9
65 33.9
192 100.0
Unweighted Cases
Valid
Missing or out-of-range
group codes
At least one missing
discriminating variable
Both missing or
out-of-range group codes
and at least one missing
discriminating variable
Unselected
Total
Excluded
Total
N Percent
Dividing the Sample into Estimation and
Split/Holdout Sample: Random Selection
Command:
TRANSFORM  RANDOM
NUMBER SEED
TRANSFORM  COMPUTE
Randz = UNIFORM(1) > 0.65 
will give  65% of respondent for
estimation and the remainder for
holdout sample
Question:
Which variables can discriminate high
and low intention to share?
Group Statistics
3.6481 .62349 104 104.000
3.5409 .62813 104 104.000
3.4038 .76981 104 104.000
4.3130 .57470 23 23.000
4.2609 .60995 23 23.000
3.6522 .78240 23 23.000
3.7685 .66448 127 127.000
3.6713 .68190 127 127.000
3.4488 .77494 127 127.000
Attitude
Norm
pbc
Attitude
Norm
pbc
Attitude
Norm
pbc
Level
Low
High
Total
Mean
Std.
Deviation Unweighted Weighted
Valid N (listwise)

83. Test for Model
Test Results
5.942
.939
6
9055.846
.465
Box's M
Approx.
df1
df2
Sig.
F
Tests null hypothesis of equal population covariance matrices.
Wilks' Lambda
.796 28.214 3 .000
Test of Function(s)
1
Wilks'
Lambda Chi-square df Sig.

84. Goodness of Model
Tests of Equality of Group Means
.850 22.007 1 125 .000
.833 24.998 1 125 .000
.985 1.949 1 125 .165
Attitude
Norm
pbc
Wilks'
Lambda F df1 df2 Sig.
Eigenvalues
.257a 100.0 100.0 .452
Function
1
Eigenvalue
% of
Variance Cumulative %
Canonical
Correlation
First 1 canonical discriminant functions were used in the
analysis.
a.

85. Coefficients
Standardized Canonical
Discriminant Function Coefficients
.322
.741
.321
Attitude
Norm
pbc
1
Function
Canonical Discriminant Function Coefficients
.524
1.185
.415
-7.759
Attitude
Norm
pbc
(Constant)
1
Function
Unstandardized coefficients
Structure Matrix
.883
.828
.246
Norm
Attitude
pbc
1
Function
Pooled within-groups correlations between discriminating
variables and standardized canonical discriminant functions
Variables ordered by absolute size of correlation within
function.

86. Classification
N
N
Z
N
Z
N
B
A
A
B
B
A
CU
Z



ZA = centroid Group A
ZB = centroid Group B
NA & NB = Number in each group
Functions at Group Centroids
-.236
1.069
Level
Low
High
1
Function
Unstandardized canonical discriminant
functions evaluated at group means
Classification Function Coefficients
2.848 3.532
8.746 10.293
6.553 7.095
-32.031 -44.209
Attitude
Norm
pbc
(Constant)
Low High
Level
Fisher's linear discriminant functions

87. Predictive Validity
Classification Resultsb,c,d
100 4 104
16 7 23
96.2 3.8 100.0
69.6 30.4 100.0
100 4 104
16 7 23
96.2 3.8 100.0
69.6 30.4 100.0
52 0 52
6 7 13
100.0 .0 100.0
46.2 53.8 100.0
Level
Low
High
Low
High
Low
High
Low
High
Low
High
Low
High
Count
%
Count
%
Count
%
Original
Cross-validateda
Original
Cases Selected
Cases Not Selected
Low High
Predicted Group
Membership
Total
Cross validation is done only for those cases in the analysis. In cross validation, each
case is classified by the functions derived from all cases other than that case.
a.
84.3% of selected original grouped cases correctly classified.
b.
90.8% of unselected original grouped cases correctly classified.
c.
84.3% of selected cross-validated grouped cases correctly classified.
d.

88. Benchmark for Comparison
 How good is the Hit Ratio? Compute Hit Ratio for
split sample and compare it against
 Maximum Chance Criterion: This is just the size of the largest
group. Minimum criterion to be met by the Hit Ratio
 Proportional Chance Criterion: Should be used when group sizes
are unequal. If two groups this is given as follows:
 Cpro = p2 + (1 - p)2 p = proportion in group
 Press’s Q: Compares No. of correct classification (n) against Total
Sample (N) and Number of Groups (k)
 Press Q  2
with 1 degree of freedom. (3.84, 6.64)
1)
-
N(k
k)]
*
(n
-
[N
Q
Press
2


89. Logistic Regression- Command

90. Logistic Regression- Command

91. Case Processing Summary
192 100.0
0 .0
192 100.0
0 .0
192 100.0
Unweighted Cases
a
Included in Analysis
Missing Cases
Total
Selected Cases
Unselected Cases
Total
N Percent
If weight is in effect, see classification table for the total
number of cases.
a.
Dependent Variable Encoding
0
1
Original Value
Low
High
Internal Value
Classification Tablea,b
0 84 .0
0 108 100.0
56.3
Observed
Low
High
Sharing Level
Overall Percentage
Step 0
Low High
Sharing Level Percentage
Correct
Predicted
Constant is included in the model.
a.
The cut value is .500
b.
Initial Output
This is the proportion of
respondents in the high
Sharing category
Correctly classifies all those
With high values but misses
All those with low values.
No missing cases

92. Variables in the Equation
.251 .145 2.984 1 .084 1.286
Constant
Step 0
B S.E. Wald df Sig. Exp(B)
Variables not in the Equation
30.588 1 .000
38.624 1 .000
.120 1 .729
41.833 3 .000
Attitude
SN
PBC
Variables
Overall Statistics
Step
0
Score df Sig.
Output
The Wald statistics is like the
t-value. The constant by
itself does not significantly
Improve prediction
The constant is entered
first, the other variables
are not included

93. Block 1: Method = Enter
Omnibus Tests of Model Coefficients
48.073 3 .000
48.073 3 .000
48.073 3 .000
Step
Block
Model
Step 1
Chi-square df Sig.
Model Summary
215.088a .221 .297
Step
1
-2 Log
likelihood
Cox & Snell
R Square
Nagelkerke
R Square
Estimation terminated at iteration number 5 because
parameter estimates changed by less than .001.
a.
Hosmer and Lemeshow Test
71.722 8 .000
Step
1
Chi-square df Sig.
Output
A significant chi square indicates
That the predicted probabilities do
Not match the observed probabilities.
This is not what we usually want.
The Model accounts
for 29.7% of the variance

94. Contingency Table for Hosmer and Lemeshow Test
16 15.496 2 2.504 18
18 15.112 2 4.888 20
14 12.942 6 7.058 20
12 8.200 8 11.800 20
4 1.512 0 2.488 4
2 12.814 32 21.186 34
10 6.312 8 11.688 18
2 6.777 20 15.223 22
0 3.959 20 16.041 20
6 .875 10 15.125 16
1
2
3
4
5
6
7
8
9
10
Step
1
Observed Expected
Sharing Level = Low
Observed Expected
Sharing Level = High
Total
Contingency Table – Hosmer Lemeshow
This is a more detailed assessment
of the Hosmer Lemeshow Test.
need to look at how close or
how different are the observed
and expected values for each group

95. Classification Tablea
48 36 57.1
8 100 92.6
77.1
Observed
Low
High
Sharing Level
Overall Percentage
Step 1
Low High
Sharing Level Percentage
Correct
Predicted
The cut value is .500
a.
Variables in the Equation
.717 .415 2.993 1 .084 2.049 .909 4.617
1.351 .443 9.302 1 .002 3.860 1.620 9.193
-.197 .243 .657 1 .418 .821 .510 1.323
-6.741 1.501 20.171 1 .000 .001
Attitude
SN
PBC
Constant
Step
1
a
B S.E. Wald df Sig. Exp(B) Lower Upper
95.0% C.I.for EXP(B)
Variable(s) entered on step 1: Attitude, SN, PBC.
a.
Classification
The overall Predictive
Accuracy = 77.1%
An increase of 1 unit on the Attitude
measure increases the odds of
Sharing at a higher level by 2.049
times, controlling for SN and PBC
An increase of 1 unit on the SN
measure increases the odds of
Sharing at a higher level by 3.860
times, controlling for SN and PBC