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Data Analysis Using SPSS
By: Akmal Aini Othman
The key to GOOD descriptive research is
knowing exactly what you want to measure and
selecting a survey method in which every
respondent is willing to cooperate and capable of
giving you complete and accurate information
efficiently –Joe Ottaviani-
COMPLETELY
CERTAIN
ABSOLUTE
AMBIGUITY CAUSAL OR
DESCRIPTIVE EXPLORATORY
Uncertainty Influences
The Type Of Research
Source: Zikmund, 2009
Problem
discovery
Problem definition
(statement of
research objectives)
Secondary
(historical)
data
Experience
survey
Pilot
study
Case
study
Selection of
exploratory research
technique
Selection of
basic research
method
Experiment
Survey
Observation Secondary
Data Study Laboratory Field Interv iew Questionnaire
Selection of
research
technique Sampling
Probability Nonprobability
Collection of
data
(fieldwork)
Editing and
coding
data
Data
processing
Interpretation
of
findings
Report
Data
Gathering
Data
Processing
and
Analysis
Conclusions
and Report
Research Design
Problem Discovery
and Definition
Source: Zikmund, 2009
Thesis Contents
Chap 1 - Introduction
Chap 2 - Literature Review
Chap 3 – Methodology
Chap 4 – Findings & Discussion
Chap 5 – Conclusion and
Recommendation
Thesis Contents
Introduction – why & what this research
Literature Review – who have done this research & how, what results, what shortcomings
Research Framework & Data Collection – why this framework, hypotheses; measurements, sample, how data can be collected
Data Collection & Analysis – what methods most appropriate, findings
Conclusion – have u achieved what you set out to do?
Thesis Contents
Chap 1 – Introduction
Background of the study
Problem Statement
Research Question
Research Objective
Hypothesis
Significance of the study
Limitation
Thesis Contents
Chap 4 – Findings and Discussion
Descriptive Analysis
Test of Goodness of Data – e.g Normality &
Multicollinearity
Factor Analysis
Reliability and Validity Test
Inferential Analysis / Hypothesis Testing
Data Preparation Process
Select Data Analysis Strategy
Prepare Preliminary Plan of Data Analysis
Check Questionnaire
Edit
Code
Transcribe
Clean Data
Statistically Adjust the Data
Source: Malhotra, 2012
Questionnaire Checking
A questionnaire returned from the field may be unacceptable for several reasons.
Parts of the questionnaire may be incomplete.
The pattern of responses may indicate that the respondent did not understand or follow the instructions.
One or more pages are missing.
The questionnaire is received after the preestablished cutoff date.
The questionnaire is answered by someone
who does not qualify for participation.
Editing
Treatment of Unsatisfactory Results
Returning to the Field – The questionnaires with
unsatisfactory responses may be returned to the field, where the interviewers recontact the respondents.
Assigning Missing Values – If returning the questionnaires to the field is not feasible, the editor may assign missing values to unsatisfactory responses.
Discarding Unsatisfactory Respondents – In this approach, the respondents with unsatisfactory responses are simply discarded.
Coding
Data coding
Coding the variables
Coding the response/items for each variable
Eg. Variable for gender = sex
Coding item 1=male, 2=female
The numerical scale can be coded by using the actual number circled by the respondents (question 6 to 21)
Random checks should be conducted to ensure data are coded correctly
Table 12.1
Coding of Serakan Co. Questionnaires
___________________________________________________________________________________________________________
1. Age (years) 2. Education 3. Job level 4. Sex
[1] Under 25 [1] High school [1] Manager [1] M
[2] 25-35 [2] Some college [2] Supervisor [2] F
[3] 36-45 [3] Bachelor’s degree [3] Clerk 5. Work shift
[4] 46-55 [4] Master’s degree [4] Secretary [1] First
[5] Over 55 [5] Doctoral degree [5] Technician [2] Second
[6] Other (specify) [6] Other (specify) [3] Third
5a. Employment Status
[1] Part time
[2] Full time
_________________________________________________________________________________________________
Here are some questions that ask you to tell us how you experience your work life in general.
Please circle the appropriate number on the scales below.
To what extent would you agree with the following statements, on a scale of 1 to 7, 1 denoting very low agreement and 7
denoting very high agreement? ___________________________________________________________________________________________________________
6. The major happiness of my life comes from my job. 1 2 3 4 5 6 7 7. Time at work flies by quickly. 1 2 3 4 5
6 7 8. I live, eat and breathe my job. 1 2 3 4 5 6 7
9. My work is fascinating. 1 2 3 4 5 6 7 10. My work gives me a sense of accomplishment. 1 2 3 4 5 6 7 11. My supervisor praises good work. 1 2 3 4 5 6 7
12. The opportunities for advancement are very good here. 1 2 3 4 5 6 7 13. My coworkers are very stimulating. 1 2 3 4 5 6 7
14. People can live comfortably with their pay in this organization. 1 2 3 4 5 6 7 15. I get a lot of cooperation at the workplace. 1 2 3 4 5 6 7 16. My supervisor is not very capable. 1 2 3 4 5 6 7
17. Most things in life are more important than work. 1 2 3 4 5 6 7 18. Working here is a drag. 1 2 3 4 5 6 7
19. The promotion policies here are very unfair. 1 2 3 4 5 6 7 20. My pay is barely adequate to take care of my expenses. 1 2 3 4 5 6 7 21. My work is not the most important part of my life. 1 2 3 4 5 6 7
__________________________________________________________________________________________________________
Data Transcription Fig. 14.4
Transcribed Data
CATI/ CAPI
Keypunching via CRT Terminal
Digital Tech.
Optical Recognition
Bar Code & Other
Technologies
Verification: Correct Keypunching Errors
Disks Other
Storage Computer Memory
Raw Data
Data Cleaning Consistency Checks
Consistency checks identify data that are out
of range, logically inconsistent, or have extreme values.
Computer packages like SPSS, SAS, EXCEL and MINITAB can be programmed to identify
out-of-range values for each variable and print out the respondent code, variable code,
variable name, record number, column number, and out-of-range value.
Extreme values should be closely examined.
Data Cleaning Treatment of Missing Responses
Substitute a Neutral Value – A neutral value, typically the mean response to the variable, is substituted for the missing responses.
Substitute an Imputed Response – The respondents' pattern of responses to other questions are used to impute or calculate a suitable response to the missing questions.
In casewise deletion, cases, or respondents, with any missing responses are discarded from the analysis.
In pairwise deletion, instead of discarding all cases with any missing values, the researcher uses only the cases or respondents with complete responses for each calculation.
Basic Terms
Levels of
Measurement
Nominal
Ordinal
Interval
Ratio
Variables
Independent
Dependent
Moderating
Mediating
Key Terms
Variable
Dimension
Item
Definition
Dictionary
Operational
Research Framework
Job Satisfaction (Mediating)
Management (Independent)
5 items
Productivity (Dependent)
5 items
Advancement (Independent)
Salary (Independent)
Workload (Independent)
Gender (Moderating)
3 items
4 items
4 items
7 3 8
Scale Nominal Numbers
Assigned to Runners
Ordinal Rank Order
of Winners
Interval Performance
Rating on a 0 to 10 Scale
Ratio Time to
Finish, in Seconds
Third place
Second place
First place
Finish
Finish
8.2 9.1 9.6
15.2 14.1 13.4
Source: Malhotra, 2007
What is Statistics – process of making sense
of data
Descriptive Stat – describe the basic features of data using tables, graphs, summary stats
Inferential Stat – generalising from samples to
populations performing estimations, hypothesis tests, determining relationships and making
predictions
Descriptive Statistics
Norminal data – frequencies, %, cross tabulation, mode, pie chart, bar chart
Ordinal data - frequencies, %, cross tabulation,
mode, median, pie chart, bar chart
Interval & Ratio data – mean, variance, std deviation, skewness, kurtosis, index number,
histogram, box plot, stem and leaf plot
Inferential Statistics
Statistical Techniques:
Exploring differences between groups
Exploring relationship
Parametric – Data must be interval and the distribution must be normal
Nonparametric – Data is categorical (norminal/ordinal) or interval but distribution is not normal
Data analysis
Basic objectives:
Getting a feel for the data
Testing the goodness of data
Testing the hypotheses
• Feel for the data
Checking for the central tendency and the dispersion
If there is less variability, the questions could be not properly worded
Check for similar response for every questions
Remember, if there is no variability in the data, then no
variance can be explained
Data analysis
It is always prudent to obtain:
Frequency distributions for the demographic variables
The mean, standard deviation, range and variance on the other dependent and independent variables
An inter-correlation matrix of the variables, regardless whether hypotheses are related to the these analyses. If the correlation between two variables is high, say over .75, we should wonder whether they are really two different concepts or we are measuring the same concepts.
Data analysis
Testing goodness of data
Reliability
Cronbach’s alpha. The closer Cronbach’s alpha is to 1, the higher the internal consistency reliability
Split-half reliability coefficient
Stability measures include:
• Parallel from reliability
• Test-retest reliability
Validity
Criterion-related validity
Convergent validity
Discriminant validity
Choosing the Test Depends on:
Data – Norminal or Interval/Ratio Data
Samples – one/two/k-samples
Purpose – Describing, Comparing two
statistics or Looking at relationship
A Classification of Univariate Techniques
Independent Related Independent Related
* Two- Group test * Z test * One-Way
ANOVA
* Paired t test * Chi-Square
* Mann-Whitney * Median * K-S * K-W ANOVA
* Sign * Wilcoxon * McNemar * Chi-Square
Metric Data Non-numeric Data
Univariate Techniques
One Sample Two or More
Samples One Sample Two or More
Samples
* t test * Z test
* Frequency * Chi-Square * K-S * Runs * Binomial
Source: Malhotra, 2012
Univariate Analysis
Univariate analysis is the simplest form of analyzing data. “Uni” means “one”, so in other words your data has only one variable. It doesn't deal with causes or relationships (unlike regression) and it's major purpose is to describe; it takes data, summarizes that data and finds patterns in the data.
It explores each variable in a data set, separately. It looks at the range of values, as well as the central tendency of the values. It describes the pattern of response to the variable. It describes each variable on its own.
www.csulb.edu/.../696uni.htm
A Classification of Multivariate Techniques
More Than One
Dependent Variable
* Multivariate Analysis of Variance * Canonical Correlation * Multiple Discriminant
Analysis * Structural Equation Modeling and Path Analysis
* Cross-Tabulation * Analysis of Variance
and Covariance * Multiple Regression * 2-Group
Discriminant/Logit * Conjoint Analysis
* Factor Analysis * Confirmatory Factor Analysis
One Dependent
Variable Variable
Interdependence Interobject
Similarity
* Cluster Analysis * Multidimensional
Scaling
Dependence
Technique Interdependence
Technique
Multivariate Techniques
Source: Malhotra, 2012
Multivariate Analysis
Multivariate Data Analysis refers to any statistical technique used to analyze data that arises from more than one variable. This essentially models reality where each situation, product, or decision involves more than a single variable.
Steps Involved in Hypothesis Testing
Draw Research Conclusion
Formulate H0 and H1
Select Appropriate Test
Choose Level of Significance
Determine Probability
Associated with Test Statistic (p value)
Determine Critical Value of
Test Statistic TSCR
Determine if TSCAL falls
into (Non) Rejection Region
Compare with Level of
Significance,
Reject or Do not Reject H0
Collect Data and Calculate Test Statistic
Hypothesis Testing – Hnull & Halternative
A null hypothesis is a statement of the status quo, one of no difference or no effect. If the null
hypothesis is not rejected, no changes will be made.
An alternative hypothesis is one in which some
difference or effect is expected. Accepting the alternative hypothesis will lead to changes in
opinions or actions.
The null hypothesis refers to a specified value of the population parameter (e.g., ), not a
sample statistic (e.g., …).
m , s , p X
Hypothesis Testing – Hnull & Halternative
A null hypothesis may be rejected, but it can never be accepted based on a single
test. In classical hypothesis testing, there is no way to determine whether the null hypothesis is true.
The null hypothesis is formulated in such a
way that its rejection leads to the acceptance of the desired conclusion. The alternative hypothesis represents the conclusion for
which evidence is sought.
H 0 : p 0 . 4 0
H 1 : p > 0 . 40
Hypothesis Testing – Hnull & Halternative
The test of the null hypothesis is a one-tailed test, because the alternative hypothesis is
expressed directionally. If that is not the case, then a two-tailed test would be required, and the hypotheses would be expressed as:
H 0 : p = 0 . 4 0
H 1 : p 0 . 4 0
One-Tailed & Two-Tailed Test
One-Tailed & Two-Tailed Test
Test Statistic
The test statistic measures how close the sample has come to the null hypothesis.
The test statistic often follows a well-known
distribution, such as the normal, t, or chi-square distribution.
In our example, the z statistic,which follows
the standard normal distribution, would be appropriate.
z = p - p
s p
where
s p = p ( 1 - p )
n
Type I and Type II error
Type I Error Type I error occurs when the sample results lead to the
rejection of the null hypothesis when it is in fact true. Type II Error Type II error occurs when, based on the sample results,
the null hypothesis is not rejected when it is in fact false.
Descriptive Analysis
Frequencies - Command
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 PercentCumulat iv e
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
Abov e manager
Total
Valid
Frequency Percent Valid PercentCumulat iv e
Percent
Question:
1. Is our sample representative?
2. Data entry error
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
Descriptives - Command
Descriptives
Question:
1. Is there variation in our data?
2. What is the level of the phenomenon we are measuring?
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation Skewness Kurtosis
Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error
JS 192 2.00 5.00 3.8188 .63877 -.528 .175 .687 .349
Mgt 192 2.00 5.00 3.8104 .64548 -.480 .175 .242 .349
WL 192 2.00 5.00 3.7031 .67034 -.101 .175 .755 .349
Slr 192 2.00 5.00 3.4792 .73672 .015 .175 -.028 .349
Adv 192 2.33 5.00 4.0625 .58349 -.361 .175 -.328 .349
Valid N (listwise) 192
Table in Report
Mean
Std.
Deviation
Job Satisfaction 3.82 0.64
Management 3.81 0.65
Work Load 3.70 0.67
Salary 3.48 0.74
Advancement 4.06 0.58
Research Framework
Job Satisfaction (Dependent)
Management (Independent)
5 items
5 items
Advancement (Independent)
Salary (Independent)
Workload (Independent)
3 items
4 items
4 items
H1
H2
H3
H4
Factor Analysis (FA)- Command
Assumptions in FA
KMO should be > 0.5
Bartlett’s Test should be significant ie; p < 0.05
Question:
How valid is our instrument?
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .890
Bartlett's Test of Sphericity
Approx. Chi-Square 3178.651
df 120
Sig. .000
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
Rotated Component Matrixa
Component
1 2 3 4
Management1 .859 .155 .354 .280
Management2 .829 .204 .358 .228
Management3 .851 .137 .360 .191
Management4 .845 .111 .280 .260
Management5 .884 .061 .299 .230
WorkLoad1 .417 -.060 .721 .154
WorkLoad2 .395 -.019 .791 .232
WorkLoad3 .357 -.077 .808 .250
Workload4 .250 -.075 .836 .110
Salary1 .120 .886 .018 .038
Salary2 .108 .886 -.080 .025
Salary3 .065 .894 -.047 -.042
Salary4 .072 .897 -.026 -.032
Advancement1 .355 -.107 .169 .748
Advancement2 .308 -.096 .109 .785
Advancement4 .132 .226 .385 .726
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
Assigning Questions
Amount of shared, or common
variance, among the variables
General guidelines all communnalities
should be above 0.5
Communalities
Initial Extraction
Management1 1.000 .965
Management2 1.000 .909
Management3 1.000 .910
Management4 1.000 .872
Management5 1.000 .927
WorkLoad1 1.000 .721
WorkLoad2 1.000 .836
WorkLoad3 1.000 .848
Workload4 1.000 .779
Salary1 1.000 .802
Salary2 1.000 .804
Salary3 1.000 .807
Salary4 1.000 .811
Advancement1 1.000 .725
Advancement2 1.000 .732
Advancement4 1.000 .744
Extraction Method: Principal Component
Analysis.
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
Total Variance Explained
Component Initial Eigenvalues Extraction Sums of Squared
Loadings
Rotation Sums of Squared Loadings
Total % of
Variance
Cumulative
%
Total % of
Variance
Cumulative
%
Total % of
Variance
Cumulative
%
1 7.694 48.085 48.085 7.694 48.085 48.085 4.438 27.739 27.739
2 3.394 21.211 69.296 3.394 21.211 69.296 3.361 21.006 48.745
3 1.120 7.000 76.296 1.120 7.000 76.296 3.246 20.287 69.032
4 .984 6.149 82.445 .984 6.149 82.445 2.146 13.414 82.445
5 .531 3.319 85.765
6 .448 2.799 88.563
7 .423 2.646 91.210
8 .338 2.113 93.323
9 .229 1.430 94.753
10 .199 1.245 95.999
11 .176 1.102 97.101
12 .123 .771 97.873
13 .120 .750 98.623
14 .101 .633 99.256
15 .085 .534 99.790
16 .034 .210 100.000
Extraction Method: Principal Component Analysis.
How many Factors?
How many Factors? - Scree Plot
Reliability - Command
Item-Total Statistics
Scale Mean if
Item Deleted
Scale Variance
if Item Deleted
Corrected Item-
Total
Correlation
Cronbach's
Alpha if Item
Deleted
Management1 15.25 6.681 .973 .965
Management2 15.26 6.560 .925 .972
Management3 15.24 6.906 .929 .972
Management4 15.21 6.825 .900 .975
Management5 15.25 6.555 .935 .970
Reliability Statistics
Cronbach's
Alpha
N of Items
.977 5
Question:
How reliable are our instruments?
Should be preferably > 0.3
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 - 0.933
Computing New Variable - Command
Data after Transformation
Inferential Analysis
Chi Square Test - Command
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 Lev el
% of Total
Count
% within Gender
% within Intention Lev el
% of Total
Count
% within Gender
% within Intention Lev el
% 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-LinearAssociation
N of Valid Cases
Value dfAsy mp. Sig.
(2-sided)Exact Sig.(2-sided)
Exact Sig.(1-sided)
Computed only f or a 2x2 tablea.
0 cells (.0%) hav e expected count less than 5. The minimum expected count is 9.00.
b.
Question:
Is level of sharing dependent on gender?
T-test - Command
t-test
(2 Independent)
Group Statistics
144 3.9000 .60302 .05025
48 3.5750 .68619 .09904
Gender
Male
Female
Intention
N MeanStd.
DeviationStd. 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 variancesassumed
Equal variancesnot assumed
Intention
F Sig.
Levene's Test f orEquality of Variances
t df Sig. (2-tailed)Mean
Dif f erenceStd. ErrorDif f erence Lower Upper
95% Conf idenceInterv al of the
Dif f erence
t-test for Equality of Means
Question:
Does intention to share vary by gender?
Paired t-test - Command
t-test
(2 Dependent)
Paired Samples Statistics
3.8188 192 .63877 .04610
4.0625 192 .58349 .04211
Intention
Actual
Pair1
Mean NStd.
DeviationStd. Error
Mean
Paired Samples Correlations
192 .817 .000Intention & ActualPair 1
N Correlation Sig.
Paired Samples Test
-.24375 .37326 .02694 -.29688 -.19062 -9.049 191 .000Intention - ActualPair 1
MeanStd.
DeviationStd. Error
Mean Lower Upper
95% Conf idenceInterv al of the
Dif f erence
Paired Dif f erences
t df Sig. (2-tailed)
Question:
Are there differences between intention to share and actual sharing behavior?
One Way ANOVA - Command
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 ofSquares 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 PositionEngineer
Manager
Technician
Sr Engineer
Abov e manager
Sig.
N 1 2
Subset f or 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 meanof the group sizes is used. Ty pe I error levels arenot guaranteed.
b.
Question:
Does intention vary by position?
Kruskal-Wallis - Command
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
Abov e manager
Total
Intention
N Mean Rank
Test Statisticsa,b
28.179
4
.000
Chi-Square
df
Asy mp. Sig.
Intention
Kruskal Wallis Testa.
Grouping Variable: Posit ionb.
Correlation - Command
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
subjectiv e
Pbcontrol
Intention
Actual
At titude subjectiv e Pbcontrol Intention Actual
Correlation is signif icant at the 0.01 level (2-tailed).**.
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
Regression - Command
Multiple
Regression
Question:
Which variables can explain the intention to share?
Variables Entered/Removedb
Pbcontrol,subjective,Attitude
a. Enter
Model1
VariablesEntered
VariablesRemoved Method
All requested v ariables entered.a.
Dependent Variable: Intent ionb.
Model Summaryb
.832a .693 .688 .35703 1.501
Model
1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Durbin-Watson
Predictors: (Constant), Pbcontrol, subjective, Attitudea.
Dependent Variable: Intentionb.
R square – how much of the variance in the dependent variable is explained by the model
Multiple Regression ANOVAb
53.968 3 17.989 141.127 .000a
23.964 188 .127
77.933 191
Regression
Residual
Total
Model
1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), Pbcontrol, subjective, Attitudea.
Dependent Variable: Intentionb.
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)
At titude
subjectiv e
Pbcontrol
Model
1
B Std. Error
UnstandardizedCoeff icients
Beta
StandardizedCoeff icients
t Sig. Tolerance VIF
Collinearity Statistics
Dependent Variable: Intentiona.
Regression Equation
Thank you [email protected]
