Chapter 11 Advanced Data Analysis

8/2/2019 Chapter 11 Advanced Data Analysis

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1- 1Malhotra Hall Shaw Oppenheim Essentials of Marketing Research Copyright 2004 Pearson Education Australia

Essentials of

Marketing Research

MALHOTRA

HALLSHAWOPPENHEIM

ANAPPLIEDORIENTATION

PowerPoint to accompany

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1- 2Malhotra Hall Shaw Oppenheim Essentials of Marketing Research Copyright 2004 Pearson Education Australia

PART FOUR

Chapter 11

Advanced Data Analysing


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11-3Malhotra Hall Shaw Oppenheim Essentials of Marketing Research Copyright 2004 Pearson Education Australia

Chapter Objectives

After reading this chapter, you should be able to: Discuss the scope of the ANOVA technique. Describe one-way ANOVA. Describe n-way ANOVA and the testing of

significance.

Describe ANCOVA. Discuss MANOVA. Discuss the concepts of the correlation coefficient

and the partial correlation. Explain the nature and methods of multiple regression

analysis.


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ANOVA

Analysis of variance (ANOVA) examines thedifferences in the mean values of the dependentvariable (interval scale)associated with the effect ofthe controlled independent variable (nominal scale),

after taking into account the influence of theuncontrolled independent variables.

e.g. Do the brand evaluation of groups exposed to different

commercials vary?How do consumers intentions to buy the brand vary with

different price levels?


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One-way ANOVA

We are testing to determine the effect of in-store

promotion (X) on sales (Y).

H0: 1 = 2 = 3H1: 12 3

Descriptives

Sales

10 8.3000 1.33749 .42295 7.3432 9.2568 6.00 10.00

10 6.2000 1.75119 .55377 4.9473 7.4527 4.00 9.00

10 3.7000 2.00278 .63333 2.2673 5.1327 1.00 7.00

30 6.0667 2.53164 .46221 5.1213 7.0120 1.00 10.00

high

medium

low

Total

N Mean Std. Deviation Std. Error Lower Bound Upper Bound

95% Confidence Interval for

MeanMinimum Maximum

See Table 11.1 page 317 for the data set


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One-way ANOVA cont.

ANOVA

Sales

106.067 2 53.033 17.944 .000

79.800 27 2.956

185.867 29

Between Groups

Within Groups

Total

Sum ofSquares df Mean Square F Sig.

Multiple Comparisons

Dependent Variable: Sales

Tukey HSD

2.1000* .76884 .029 .1937 4.0063

4.6000* .76884 .000 2.6937 6.5063

-2.1000* .76884 .029 -4.0063 -.1937

2.5000* .76884 .008 .5937 4.4063

-4.6000* .76884 .000 -6.5063 -2.6937

-2.5000* .76884 .008 -4.4063 -.5937

(J) In-store promotion

medium

low

high

low

high

medium

(I) In-store promotion

high

medium

low

Mean

Difference

(I-J) Std. Error Sig. Lower Bound Upper Bound

95% Confidence Interval

The mean difference is significant at the .05 l evel.*.

Main effect (in-store promotion)

Residuals

RejectH0, themeansare not

equal


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One-way ANOVA cont.

Interpretation

57.1% (ie. 2 = 106.067/185.856) of the variation insales is accounted for by in-store promotion,indicating a modest effect.

The mean sales figures are different, that is atleast one pair of means is statistically different.

All combination of means are statisticallydifferent, therefore the different levels of in-store

promotion will impact sales.



N-way ANOVA

N-way analysis of variance examines thedifferences in the mean values of the dependentvariable (interval scale)associated with the effectof more than one independent variable (nominal

scale). Enables the examination of interactions between

the factors.



N-way ANOVA

We are testing to determine the effect of in-storepromotion and couponing (X) on sales (Y).

H0: 1 = 2H1: 12



N-way ANOVA cont.

Descriptive Statistics


9.2000 .83666 5

7.6000 1.14018 5

5.4000 1.14018 5

7.4000 1.88225 157.4000 1.14018 5

4.8000 .83666 5

2.0000 .70711 5

4.7333 2.43389 15

8.3000 1.33749 10

6.2000 1.75119 10

3.7000 2.00278 10

6.0667 2.53164 30

In-store promotion

high

medium

low

Totalhigh

medium

low

Total

high

medium

low

Total

Coupon level

$20 store-wide coupon

No coupon

Total

Mean Std. Deviation N



N-way ANOVA cont.

Tests of Between-Subjects Effects


162.667a 5 32.533 33.655 .000

1104.133 1 1104.133 1142.207 .000

53.333 1 53.333 55.172 .000

106.067 2 53.033 54.862 .000

3.267 2 1.633 1.690 .206

23.200 24 .967

1290.000 30

185.867 29

Source

Corrected Model

Intercept

COUPON

INSTORE

COUPON * INSTORE

Error

Total

Corrected Total

Type III Sum

of Squares df Mean Square F Sig.

R Squared = .875 (Adjusted R Squared = .849)a.

Overall

Main effect

Main effectInteraction

Interaction isnot significantMain effect of promotion is significant

Main effect of coupon is significant



N-way ANOVA cont.

Interpretation

Higher level of in-store promotion results inhigher sales

The distribution of a storewide coupon results inhigher sales

The effect of each is independent of the other



Analysis of Covariance

Examine differences in the mean values of thedependent variable related to the effect of thecontrolled independent variables.

Dependent variable [metric]

Independent variable [one categorical and one metric]

Example

To determine the effect of in-store promotion andcouponing on sales while controlling for the affluenceof clientele.



Analysis of Covariance cont.

Tests of Between-Subjects Effects


163.505a 6 27.251 28.028 .000

103.346 1 103.346 106.294 .000

.838 1 .838 .862 .363

53.333 1 53.333 54.855 .000

106.067 2 53.033 54.546 .000

3.267 2 1.633 1.680 .208

22.362 23 .972

1290.000 30

185.867 29

Source

Corrected Model

Intercept

CLIENTEL

COUPON

INSTORE

COUPON * INSTORE

Error

Total

Corrected Total

Type III Sum

of Squares df Mean Square F Sig.

R Squared = .880 (Adjusted R Squared = .848)a.

Not significant

We can conclude that the affluence of the clientele does not have an effect

on the sales of the department store



Multivariate Analysis of Variance(MANOVA)

Examine group differences across multipledependent variables simultaneously

Appropriate when 2 or more dependent variablesare correlated


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Correlation coefficient

The correlation coefficient, r, is a statisticsummarising the strength of associationbetween two metric (interval or ratio) variables.

-1 0 +1

Strongnegative

relationship

Strongpositive

relationship

Norelationship

Examples

How strongly are sales related to advertising expenditure?

Are consumers perceptions of quality related to their perceptions of price?


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Correlation coefficient cont.

Correlations

1 .179**

. .000

446 424

.179** 1

.000 .

424 425

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Trust the website

Satisfaction with Website

Trust the

website

Satisfaction

with Website

Correlation is significant at the 0.01 level (2-tai led).**.

Positive relationshipbetween trust and

satisfaction


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Regression Analysis

ANOVAb

16.598 1 16.598 13.953 .000a

501.986 422 1.190

518.584 423

Regression

Residual

Total

Model

1

Sum ofSquares df Mean Square F Sig.

Predictors: (Constant), Trust the websitea.

Dependent Variable : Satisfaction with Websiteb.

Model Summary

.179a .032 .030 1.09066

Model

1

R R Square

Adjusted

R Square

Std. Error of

the Estimate

Predictors: (Constant), Trust the websitea.

Model is significant


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Regression Analysis cont.

Coefficientsa

4.102 .199 20.656 .000.143 .038 .179 3.735 .000

(Constant)Trust the website

Model

1

B Std. Error

Unstandardized

Coefficients

Beta

Standardized

Coefficients

t Sig.

Dependent Variable: Satisfaction with Websitea.

Sales = 4.1 + 0.14 Trust

Significant linear relationshipbetween satisfaction with the

website and trust in thewebsite


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Partial correlation coefficientCorrelations

1 .222** .277** .373** .237** .235** .179** .286**

. .000 .000 .000 .000 .000 .000 .000

425 424 399 421 421 421 424 425

.222** 1 .162** .119* -.007 .250** .192** .268**

.000 . .001 .012 .878 .000 .000 .000

424 447 414 442 443 442 444 447

.277** .162** 1 .359** .078 .147** .106* .300**

.000 .001 . .000 .112 .003 .030 .000

399 414 417 414 415 413 415 417.373** .119* .359** 1 .087 .077 .050 .334**

.000 .012 .000 . .069 .107 .293 .000

421 442 414 444 440 440 442 444

.237** -.007 .078 .087 1 .139** .191** .136**

.000 .878 .112 .069 . .003 .000 .004

421 443 415 440 446 441 442 446

.235** .250** .147** .077 .139** 1 .327** .131**

.000 .000 .003 .107 .003 . .000 .006

421 442 413 440 441 445 441 445

.179** .192** .106* .050 .191** .327** 1 .128**

.000 .000 .030 .293 .000 .000 . .007

424 444 415 442 442 441 446 446

.286** .268** .300** .334** .136** .131** .128** 1

.000 .000 .000 .000 .004 .006 .007 .

425 447 417 444 446 445 446 450

Pearson Correla tion

Sig. (2-tailed)

N


Sig. (2-tailed)

N


Sig. (2-tailed)

NPearson Correla tion

Sig. (2-tailed)

N


Sig. (2-tailed)

N


Sig. (2-tailed)

N


Sig. (2-tailed)

N


Sig. (2-tailed)

N

Satisfaction with Website

Ease of use

Sociable

innovative

Speed of download

Customer control

Trust the website

Appearance

Satisfaction

with Website Ease of use Sociable innovative

Speed of

download

Customer

control

Trust the

website Appearance

Correlation is significant at the 0.01 level (2-tailed).**.

Correlation i s signifi cant at the 0.05 l evel (2-tailed).*.


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Multiple Regression

Model Summary

.494a .245 .231 .95995

Model

1

R R Square

Adjusted

R Square

Std. Error of

the Estimate

Predictors: (Constant), Trust the website, innovative,

Ease of use, Speed of download, Sociable, Customer

control, Appearance

a.

ANOVAb

113.922 7 16.275 17.661 .000a

352.013 382 .922

465.936 389

Regression

Residual

Total

Model

1

Sum of

Squares df Mean Square F Sig.

Predictors: (Constant), Trust the website, innovative, Ease of use, Speed of

download, Sociable, Customer control, Appearance

a.

Dependent Variable: Satisfaction with Websiteb.

1


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Multiple Regression cont.

Coefficientsa

.283 .450 .629 .5309.897E-02 .051 .097 1.951 .052

5.793E-02 .024 .115 2.425 .016

6.352E-02 .034 .093 1.891 .059

.220 .046 .237 4.757 .000

.181 .054 .156 3.359 .001

.117 .044 .129 2.658 .008

5.230E-02 .038 .067 1.373 .171

(Constant)Appearance

Ease of use

Sociable

innovative

Speed of download

Customer controlTrust the website

Model

1

B Std. Error

Unstandardized

Coefficients

Beta

Standardized

Coefficients

t Sig.

Dependent Variable : Satisfaction with Websitea.

2


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Interpretation 1 The overall model model is significant at = 0.05

H0: 1 = 2 = 3 = 4 = 5 = 6 = 7 =0H1: 1234567 0

2Testing which of the independent variables have a significantimpact on satisfaction with the website

H0: 1 = 0H1: 1 0

Ease of use, innovative website,speed of download, and

respondents perception of control are significant variables ininfluencing satisfaction rating of the website.


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ANOVAb

111.267 4 27.817 30.119 .000a

376.813 408 .924

488.080 412

Regression

Residual

Total

Model

1

Sum of

Squares df Mean Square F Sig.

Predictors: (Constant), Customer control, innovative, Speed of download, Ease of

use

a.

Dependent Variable: Satisfaction with Websiteb.

Coefficientsa

.673 .422 1.595 .112

8.088E-02 .023 .159 3.522 .000

.293 .040 .319 7.238 .000

.205 .051 .178 4.040 .000

.134 .041 .148 3.266 .001

(Constant)

Ease of use

innovative

Speed of download

Customer control

Model1

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig.

Dependent Variable: Satisfaction with Websitea.


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In predicting satisfaction ratings of a website wecan use the following equation

Satisfaction = .673 + 0.08ease + 0.29innov. + 0.21speed + 0.13control

For every 1 unit increase in ease the satisfactionrating will increase by 0.08 units

Substituting values for each of the variables will

produce the overall satisfaction rating of thewebsite.

Chapter 11 Advanced Data Analysis

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