introduction to data analysis - ramayahramayah.com/wp-content/uploads/2010/07/l11.pdf ·...

Introduction to Data Analysis

Basics

Levels of Measurement Nominal Ordinal Interval Ratio

Variables Independent Dependent Moderating Mediating Control

Key Terms Concepts Construct Variable Definition

Dictionary Operational

Data Analysis Process

DATA ANALYSIS

DATA ENTRY

STAGES OF DATA ANALYSIS

ERROR

CHECKING

AND

VERIFICATION

CODING

EDITING

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.

Editing

The Process Of Checking And Adjusting The Data For Omissions For Legibility For Consistency

And Readying Them For Coding And Storage

Editing

IN-HOUSEEDITING

FIELD EDITING

Reasons for Editing

Criteria

Consistent

Uniformly entered

Arranged forsimplification

Complete

Accurate

Birth Year Recorded By Interviewer

1873? 1973 MORE LIKELY

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

Coding Rules

Categories should be

Appropriate to the research problemExhaustive

Mutually exclusive Derived from one classification principle

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

Exhaustiveness

What is your race?•Malay

•Chinese

•Indians

•Others

Mutual Exclusivity

What is your occupation type?• Professional •Crafts

•Managerial •Operatives

•Sales •Unemployed

•Clerical •Housewife

•Others

Single Dimension

What is your occupation type?• Professional•Crafts

•Managerial •Operatives

•Sales •Unemployed

•Clerical •Housewife

•Others

Coding Open-ended Responses

Coding Open Ended Questions

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

Code Book

Identifies each variable Provides a variable’s description Identifies each code name and position

on storage medium

Sample SPSS Codebook

Data Entry

Database Programs

Optical Recognition

Digital/Barcodes

Voicerecognition

Keyboarding

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

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

-

Characteristics of Distributions

Summarizing Distributions with Shape

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

Statistical Testing Procedures

Obtain critical test value

Interpret the test

Stages

Choose statistical test

State null hypothesis

Select level of significance

Compute difference

value

Hypotheses

Null H0: = 50 mpg H0: < 50 mpg H0: > 50 mpg

Alternate HA: 50 mpg HA: > 50 mpg HA: < 50 mpg

Accept/Reject

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

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

Reliability - Command

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?

Reliability


.912 4



11.20 4.243 .761 .90011.03 4.135 .855 .86811.00 4.021 .856 .86711.21 4.250 .736 .909

Sn1Sn2Sn3Sn4


ScaleVariance if

Item Deleted



Deleted

Reliability


.919 4



10.48 4.984 .814 .89510.45 4.793 .826 .89210.43 5.042 .809 .89710.40 5.246 .814 .897

Pbc1Pbc2Pbc3Pbc4


ScaleVariance if

Item Deleted



Deleted

Reliability


.966 5



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


ScaleVariance if

Item Deleted



Deleted

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

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

Recoding

Data before Transformation

Data after Transformation

Frequencies - Command

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

Table in Report

Frequency PercentageGender

MaleFemale

PositionTechnicianEngineerSr EngineerManagerAbove manager

14448

346654326

75.025.0

17.734.428.116.73.1

Descriptives - Command

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?

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

Chi Square Test - Command

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?

T-test - Command

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?

Paired t-test - Command

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?

One Way ANOVA - Command

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?

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 .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).**.

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

Command

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.


Multiple Regression

ANOVAb

53.968 3 17.989 141.127 .000a

23.964 188 .12777.933 191

RegressionResidualTotal

Model1




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.

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


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


After Removing OutliersModel Summaryb

.900a .810 .807 .27373 1.725Model1

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Durbin-Watson



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


ANOVAb

58.261 3 19.420 259.182 .000a

13.637 182 .07571.898 185

RegressionResidualTotal

Model1




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


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

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

Assumptions (Constant Variance)

5.004.504.003.503.002.502.00

Intention

4

2

0

-2Regr

essio

n Stu

dent

ized

Resid

ual


Scatterplot

Assumptions (Linearity)

10-1-2

Attitude

1.5

1.0

0.5

0.0

-0.5

-1.0

-1.5

Inte

ntio

n


Partial Regression Plot


210-1-2

subjective

2.0

1.5

1.0

0.5

0.0

-0.5

-1.0

Inte

ntio

n




10-1-2

Pbcontrol

2.0

1.5

1.0

0.5

0.0

-0.5

-1.0

Inten

tion



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

introduction to data analysis - ramayahramayah.com/wp-content/uploads/2010/07/l11.pdf ·...

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