Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf ·...
Transcript of Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf ·...
![Page 1: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/1.jpg)
Introduction to Data Analysis
![Page 2: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/2.jpg)
Basics
Levels of Measurement Nominal Ordinal Interval Ratio
Variables Independent Dependent Moderating Mediating Control
Key Terms Concepts Construct Variable Definition
Dictionary Operational
![Page 3: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/3.jpg)
Data Analysis Process
![Page 4: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/4.jpg)
DATA ANALYSIS
DATA ENTRY
STAGES OF DATA ANALYSIS
ERROR
CHECKING
AND
VERIFICATION
CODING
EDITING
![Page 5: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/5.jpg)
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.
![Page 6: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/6.jpg)
Editing
The Process Of Checking And Adjusting The Data For Omissions For Legibility For Consistency
And Readying Them For Coding And Storage
![Page 7: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/7.jpg)
Editing
IN-HOUSEEDITING
FIELD EDITING
![Page 8: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/8.jpg)
Reasons for Editing
Criteria
Consistent
Uniformly entered
Arranged forsimplification
Complete
Accurate
![Page 9: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/9.jpg)
Birth Year Recorded By Interviewer
1873? 1973 MORE LIKELY
![Page 10: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/10.jpg)
Coding
Involves assigning numbers or other symbols to answers so the responses can be grouped into a limited number of classes or categories.
Example: “M” for Male and “F” for Female “1” for Male and “2” for Female Numeric vs Alphanumeric
Numeric versus Alphanumeric Open ended questions Check accuracy by using 10% of responses
![Page 11: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/11.jpg)
Coding Rules
Categories should be
Appropriate to the research problemExhaustive
Mutually exclusive Derived from one classification principle
![Page 12: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/12.jpg)
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
![Page 13: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/13.jpg)
Exhaustiveness
What is your race?•Malay
•Chinese
•Indians
•Others
![Page 14: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/14.jpg)
Mutual Exclusivity
What is your occupation type?• Professional •Crafts
•Managerial •Operatives
•Sales •Unemployed
•Clerical •Housewife
•Others
![Page 15: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/15.jpg)
Single Dimension
What is your occupation type?• Professional•Crafts
•Managerial •Operatives
•Sales •Unemployed
•Clerical •Housewife
•Others
![Page 16: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/16.jpg)
Coding Open-ended Responses
![Page 17: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/17.jpg)
Coding Open Ended Questions
![Page 18: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/18.jpg)
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
![Page 19: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/19.jpg)
Code Book
Identifies each variable Provides a variable’s description Identifies each code name and position
on storage medium
![Page 20: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/20.jpg)
Sample SPSS Codebook
![Page 21: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/21.jpg)
Data Entry
Database Programs
Optical Recognition
Digital/Barcodes
Voicerecognition
Keyboarding
![Page 22: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/22.jpg)
Data Transformation
WeightsAssigning numbers to responses on a
pre-determined rule Respecification of the Variable
Transforming existing data to form new variables or items
Recode Compute
![Page 23: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/23.jpg)
Scale Transformation
Reason for Transformationto improve interpretation and
compatibility with other data setsto enhance symmetry and stabilize
spread improve linear relationship between
the variables (Standardized score)
s
XXz i
-
![Page 24: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/24.jpg)
Characteristics of Distributions
![Page 25: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/25.jpg)
Summarizing Distributions with Shape
![Page 26: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/26.jpg)
Parameter & Statistics
Variable Population Sample Mean
µ
X
Proportion
p
Variance
2
s2 Standard deviation
s
Size
N
n
Standard error of the mean
x
Sx
![Page 27: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/27.jpg)
Statistical Testing Procedures
Obtain critical test value
Interpret the test
Stages
Choose statistical test
State null hypothesis
Select level of significance
Compute difference
value
![Page 28: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/28.jpg)
Hypotheses
Null H0: = 50 mpg H0: < 50 mpg H0: > 50 mpg
Alternate HA: 50 mpg HA: > 50 mpg HA: < 50 mpg
![Page 29: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/29.jpg)
Accept/Reject
![Page 30: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/30.jpg)
Accept/Reject
![Page 31: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/31.jpg)
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 UKolmogorov-SmirnovWald-Wolfowitz
Friedman two-way ANOVA
Median extensionKruskal-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
![Page 32: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/32.jpg)
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
![Page 33: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/33.jpg)
Reliability - Command
![Page 34: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/34.jpg)
Reliability
Reliabil ity Statistics
.977 5
Cronbach'sAlpha N of Items
Item-Total Statistics
15.25 6.681 .973 .96515.26 6.560 .925 .97215.24 6.906 .929 .97215.21 6.825 .900 .97515.25 6.555 .935 .970
Att1Att2Att3Att4Att5
Scale Mean ifItem Deleted
ScaleVariance if
Item Deleted
CorrectedItem-TotalCorrelation
Cronbach'sAlpha if Item
Deleted
Question:
How reliable are our instruments?
![Page 35: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/35.jpg)
Reliability
Reliabil ity Statistics
.912 4
Cronbach'sAlpha N of Items
Item-Total Statistics
11.20 4.243 .761 .90011.03 4.135 .855 .86811.00 4.021 .856 .86711.21 4.250 .736 .909
Sn1Sn2Sn3Sn4
Scale Mean ifItem Deleted
ScaleVariance if
Item Deleted
CorrectedItem-TotalCorrelation
Cronbach'sAlpha if Item
Deleted
![Page 36: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/36.jpg)
Reliability
Reliabil ity Statistics
.919 4
Cronbach'sAlpha N of Items
Item-Total Statistics
10.48 4.984 .814 .89510.45 4.793 .826 .89210.43 5.042 .809 .89710.40 5.246 .814 .897
Pbc1Pbc2Pbc3Pbc4
Scale Mean ifItem Deleted
ScaleVariance if
Item Deleted
CorrectedItem-TotalCorrelation
Cronbach'sAlpha if Item
Deleted
![Page 37: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/37.jpg)
Reliability
Reliabil ity Statistics
.966 5
Cronbach'sAlpha N of Items
Item-Total Statistics
15.28 6.591 .951 .95115.28 6.612 .888 .96115.29 6.553 .901 .95915.28 6.716 .877 .96215.24 6.445 .904 .958
Intent1Intent2Intent3Intent4Intent5
Scale Mean ifItem Deleted
ScaleVariance if
Item Deleted
CorrectedItem-TotalCorrelation
Cronbach'sAlpha if Item
Deleted
![Page 38: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/38.jpg)
Table in Report
Variable N of Item ItemDeleted
Alpha
Attitude 5 - 0.977
SN 4 - 0.912
Pbcontrol 4 - 0.919
Intention 5 - 0.966
Actual 3 - 0.933
![Page 39: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/39.jpg)
Example - Recoding
Perceived EnjoymentPE1 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
![Page 40: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/40.jpg)
Recoding
![Page 41: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/41.jpg)
Recoding
![Page 42: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/42.jpg)
Data before Transformation
![Page 43: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/43.jpg)
Data after Transformation
![Page 44: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/44.jpg)
Frequencies - Command
![Page 45: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/45.jpg)
Frequencies
Gender
144 75.0 75.0 75.048 25.0 25.0 100.0
192 100.0 100.0
MaleFemaleTotal
ValidFrequency Percent Valid Percent
Cumulat iv ePercent
Current Position
34 17.7 17.7 17.766 34.4 34.4 52.154 28.1 28.1 80.232 16.7 16.7 96.96 3.1 3.1 100.0
192 100.0 100.0
TechnicianEngineerSr EngineerManagerAbov e managerTotal
ValidFrequency Percent Valid Percent
Cumulat iv ePercent
Question:
1. Is our sample representative?
2. Data entry error
![Page 46: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/46.jpg)
Table in Report
Frequency PercentageGender
MaleFemale
PositionTechnicianEngineerSr EngineerManagerAbove manager
14448
346654326
75.025.0
17.734.428.116.73.1
![Page 47: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/47.jpg)
Descriptives - Command
![Page 48: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/48.jpg)
DescriptivesDescriptive 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 .349192 2.00 5.00 3.7031 .67034 -.101 .175 .755 .349192 2.00 5.00 3.4792 .73672 .015 .175 -.028 .349192 2.00 5.00 3.8188 .63877 -.528 .175 .687 .349192 2.33 5.00 4.0625 .58349 -.361 .175 -.328 .349192
AgeYears working in theorganizationTotal years ofworking experienceAtt itudesubject iv ePbcontrolIntentionActualValid N (listwise)
Stat istic Stat istic Stat istic Stat istic Stat istic Stat istic Std. Error Stat istic Std. ErrorN Minimum Maximum Mean Std.
Dev iationSkewness Kurtosis
Question:
1. Is there variation in our data?
2. What is the level of the phenomenon we are measuring?
![Page 49: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/49.jpg)
Table in Report
MeanStd.
DeviationAttitude 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
![Page 50: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/50.jpg)
Chi Square Test - Command
![Page 51: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/51.jpg)
CrosstabulationGender * Intention Level Crosstabulation
110 34 14476.4% 23.6% 100.0%70.5% 94.4% 75.0%57.3% 17.7% 75.0%
46 2 4895.8% 4.2% 100.0%29.5% 5.6% 25.0%24.0% 1.0% 25.0%
156 36 19281.3% 18.8% 100.0%
100.0% 100.0% 100.0%81.3% 18.8% 100.0%
Count% within Gender% within Intention Lev el% of TotalCount% within Gender% within Intention Lev el% of TotalCount% within Gender% within Intention Lev el% of Total
Male
Female
Gender
Total
Low HighIntention Level
Total
Chi-Square Tests
8.934b 1 .0037.704 1 .006
11.274 1 .001.002 .001
8.888 1 .003
192
Pearson Chi-SquareContinuity Correctiona
Likelihood RatioFisher's Exact TestLinear-by-LinearAssociationN of Valid Cases
Value dfAsy mp. Sig.
(2-sided)Exact Sig.(2-sided)
Exact Sig.(1-sided)
Computed only for a 2x2 tablea.
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?
![Page 52: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/52.jpg)
T-test - Command
![Page 53: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/53.jpg)
t-test (2 Independent)
Group Statistics
144 3.9000 .60302 .0502548 3.5750 .68619 .09904
GenderMaleFemale
IntentionN Mean
Std.Deviation
Std. ErrorMean
Independent Samples Test
3.591 .060 3.122 190 .002 .32500 .10410 .11965 .53035
2.926 72.729 .005 .32500 .11106 .10364 .54636
Equal variancesassumedEqual variancesnot assumed
IntentionF 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?
![Page 54: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/54.jpg)
Paired t-test - Command
![Page 55: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/55.jpg)
t-test (2 Dependent)
Paired Samples Statistics
3.8188 192 .63877 .046104.0625 192 .58349 .04211
IntentionActual
Pair1
Mean NStd.
DeviationStd. Error
Mean
Paired Samples Correlations
192 .817 .000Intention & ActualPair 1N Correlation Sig.
Paired Samples Test
-.24375 .37326 .02694 -.29688 -.19062 -9.049 191 .000Intention - ActualPair 1Mean
Std.Deviation
Std. ErrorMean 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?
![Page 56: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/56.jpg)
One Way ANOVA - Command
![Page 57: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/57.jpg)
One way ANOVA (k independent)
ANOVA
Intention
7.864 4 1.966 5.247 .00170.068 187 .37577.933 191
Between GroupsWithin GroupsTotal
Sum ofSquares df Mean Square F Sig.
Intention
Duncana,b
66 3.642432 3.662534 3.894154 4.00006 4.5333
.101 1.000
Current PositionEngineerManagerTechnicianSr EngineerAbov e managerSig.
N 1 2Subset 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?
![Page 58: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/58.jpg)
Correlation - Command
![Page 59: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/59.jpg)
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 .000192 192 192 192 192
.212** -.052 1 .281** .031
.003 .471 .000 .665192 192 192 192 192
.808** .653** .281** 1 .817**
.000 .000 .000 .000192 192 192 192 192
.606** .552** .031 .817** 1
.000 .000 .665 .000192 192 192 192 192
Pearson CorrelationSig. (2-tailed)NPearson CorrelationSig. (2-tailed)NPearson CorrelationSig. (2-tailed)NPearson CorrelationSig. (2-tailed)NPearson CorrelationSig. (2-tailed)N
Att itude
subject iv e
Pbcontrol
Intention
Actual
Att itude subject iv e Pbcontrol Intention Actual
Correlation is signif icant at the 0.01 lev el (2-tailed).**.
![Page 60: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/60.jpg)
Table Presentation
Attitude subjective Pbcontrol Intention ActualAttitude 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
![Page 61: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/61.jpg)
Command
![Page 62: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/62.jpg)
Multiple Regression
Question:
Which variables can explain the intention to share?
Variables Entered/Removedb
Pbcontrol,subject iv e,Att itude
a . Enter
Model1
VariablesEntered
VariablesRemoved Method
All requested variables entered.a.
Dependent Variable: Intentionb.
Model Summaryb
.832a .693 .688 .35703 1.501Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Durbin-Watson
Predictors: (Constant), Pbcontrol, subjective, Attitudea.
Dependent Variable: Intentionb.
![Page 63: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/63.jpg)
Multiple Regression
ANOVAb
53.968 3 17.989 141.127 .000a
23.964 188 .12777.933 191
RegressionResidualTotal
Model1
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)Att itudesubject iv ePbcontrol
Model1
B Std. Error
UnstandardizedCoeff icients
Beta
StandardizedCoeff icients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Intentiona.
![Page 64: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/64.jpg)
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
Dimension1234
Model1
EigenvalueCondit ion
Index (Constant) At titude subjectiv e PbcontrolVariance Proportions
Dependent Variable: Intentiona.
![Page 65: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/65.jpg)
Assumptions (Outliers)
Casewise Diagnosticsa
3.152 5.00 3.8748 1.125204.042 5.00 3.5570 1.442953.071 4.20 3.1037 1.096313.152 5.00 3.8748 1.125204.042 5.00 3.5570 1.442953.071 4.20 3.1037 1.09631
Case Number708283166178179
Std. Residual IntentionPredicted
Value Residual
Dependent Variable: Intentiona.
![Page 66: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/66.jpg)
![Page 67: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/67.jpg)
After Removing OutliersModel Summaryb
.900a .810 .807 .27373 1.725Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Durbin-Watson
Predictors: (Constant), Pbcontrol, subjective, Attitudea.
Dependent Variable: Intentionb.
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)Att itudesubject iv ePbcontrol
Model1
B Std. Error
UnstandardizedCoeff icients
Beta
StandardizedCoeff icients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Intentiona.
ANOVAb
58.261 3 19.420 259.182 .000a
13.637 182 .07571.898 185
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), Pbcontrol, subjective, Attitudea.
Dependent Variable: Intentionb.
![Page 68: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/68.jpg)
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 ValueStd. Predicted ValueStandard Error ofPredicted ValueAdjusted Predicted ValueResidualStd. ResidualStud. ResidualDeleted ResidualStud. Deleted ResidualMahal. DistanceCook's DistanceCentered LeverageValue
Minimum Maximum MeanStd.
Deviation N
Dependent Variable: Intentiona.
![Page 69: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/69.jpg)
Assumptions (Normality)
6420-2-4
Regression Standardized Residual
70
60
50
40
30
20
10
0
Freq
uenc
y
Mean = -1.99E-17Std. Dev. = 0.992N = 192
Dependent Variable: Intention
Histogram
![Page 70: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/70.jpg)
Assumptions (Normality of the Error term)
1.00.80.60.40.20.0
Observed Cum Prob
1.0
0.8
0.6
0.4
0.2
0.0
Expe
cted C
um Pr
obDependent Variable: Intention
Normal P-P Plot of Regression Standardized Residual
![Page 71: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/71.jpg)
Assumptions (Constant Variance)
5.004.504.003.503.002.502.00
Intention
4
2
0
-2Regr
essio
n Stu
dent
ized
Resid
ual
Dependent Variable: Intention
Scatterplot
![Page 72: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/72.jpg)
Assumptions (Linearity)
10-1-2
Attitude
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
Inte
ntio
n
Dependent Variable: Intention
Partial Regression Plot
![Page 73: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/73.jpg)
Assumptions (Linearity)
210-1-2
subjective
2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
Inte
ntio
n
Dependent Variable: Intention
Partial Regression Plot
![Page 74: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/74.jpg)
Assumptions (Linearity)
10-1-2
Pbcontrol
2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
Inten
tion
Dependent Variable: Intention
Partial Regression Plot
![Page 75: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/75.jpg)
Table PresentationVariable Dependent = Intention
Standardized BetaAttitudeSubjective NormPerceived Control
0.607**0.238**0.105**
R2
Adjusted R2
F ValueD-W
0.6930.688
141.131.501
*p< 0.05, **p< 0.01
![Page 76: Introduction to Data Analysis - Ramayahramayah.com/wp-content/uploads/2010/07/L11.pdf · Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization](https://reader033.fdocuments.in/reader033/viewer/2022043003/5f8246c4de372c36bf48dfe5/html5/thumbnails/76.jpg)