Ch 1_MDA_6e_PH

8/7/2019 Ch 1_MDA_6e_PH

http://slidepdf.com/reader/full/ch-1mda6eph 1/32

8/7/2019 Ch 1_MDA_6e_PH


2

LEARN I NG OBJE CTIV ES:LEARN I NG OBJE CTIV ES:

U pon completing this chapter, you should be able to do theU pon completing this chapter, you should be able to do thefollowing:following:

1.1. Ex plain what multivariate analysis is and when its application Ex plain what multivariate analysis is and when its application is appropriate.is appropriate.

2.2. Define and discuss the specific techniques included in Define and discuss the specific techniques included in multivariate analysis.multivariate analysis.

3.3. Determine which multivariate technique is appropriate for aDetermine which multivariate technique is appropriate for aspecific research problem.specific research problem.

4.4. Discuss the nature of measurement scales and their Discuss the nature of measurement scales and their relationship to multivariate techniques.relationship to multivariate techniques.

5.5. Describe the conceptual and statistical issues inherent in Describe the conceptual and statistical issues inherent in multivariate analyses.multivariate analyses.

Chapter 1: Introduction Chapter 1: Introduction

8/7/2019 Ch 1_MDA_6e_PH


3

W hat is it? Multivariate Data Analysis = all W hat is it? Multivariate Data Analysis = all statistical methods that simultaneously analyzestatistical methods that simultaneously analyzemultiple measurements on each individual or multiple measurements on each individual or object under investigation.object under investigation.

W hy use it? W hy use it?

Measurement Measurement

Ex planation & Prediction Ex planation & Prediction

Hypothesis TestingHypothesis Testing

W hat is Multivariate Analysis? W hat is Multivariate Analysis?

8/7/2019 Ch 1_MDA_6e_PH


4

The VariateThe Variate

Measurement S cales Measurement S cales N onmetric N onmetric

Metric Metric

Multivariate Measurement Multivariate Measurement

Measurement E rror Measurement E rror

Types of Techniques Types of Techniques

Basic Concepts of Multivariate AnalysisBasic Concepts of Multivariate Analysis

8/7/2019 Ch 1_MDA_6e_PH


5

The variate is a linear combination of variables withThe variate is a linear combination of variables withempirically determined weights.empirically determined weights.

W eights are determined to best achieve theW eights are determined to best achieve the

objective of the specific multivariate technique.objective of the specific multivariate technique. Variate equation : (Y) =Variate equation : (Y) = W 1W 1 X X 11 ++ W 2 W 2 X X 2 2 + . . . ++ . . . + W n W n X X n n

E ach respondent has a variate value (Y).E ach respondent has a variate value (Y).

The Y The Y valuevalue is ais a linear combination linear combination of the entireof the entireset of variables. It is the dependent variable.set of variables. It is the dependent variable.

Potential Independent Variables Potential Independent Variables ::X1 = incomeX1 = income

X2 = education X2 = education

X3 = family sizeX3 = family size

X4 = ?? X4 = ??

The VariateThe Variate

8/7/2019 Ch 1_MDA_6e_PH


6

Types of Data and Measurement S cales Types of Data and Measurement S cales

DataData

Metric Metric or or

QuantitativeQuantitative

N onmetric N onmetric or or

QualitativeQualitative

N ominal N ominal S caleS cale

Ordinal Ordinal S caleS cale

Interval Interval S caleS cale

RatioRatioS caleS cale

8/7/2019 Ch 1_MDA_6e_PH


7

N onmetric N onmetric

oo N ominal N ominal size of number is not related to the amount of thesize of number is not related to the amount of thecharacteristic being measured characteristic being measured

oo Ordinal Ordinal larger numbers indicate more (or less) of thelarger numbers indicate more (or less) of thecharacteristic measured, but not how much more (or less).characteristic measured, but not how much more (or less).

Metric Metric

oo Interval Interval contains ordinal properties, and in addition, therecontains ordinal properties, and in addition, thereare equal differences between scale points.are equal differences between scale points.

ooR

atioR

atio contains interval scale properties, and in addition,contains interval scale properties, and in addition,there is a natural zero point.there is a natural zero point.

N OT E: The level of measurement is critical in determining theN OT E: The level of measurement is critical in determining theappropriate multivariate technique to use! appropriate multivariate technique to use!

Measurement S cales Measurement S cales

8/7/2019 Ch 1_MDA_6e_PH


8

All variables have some error. W hat are theAll variables have some error. W hat are the

sources of error? sources of error? Measurement error = distorts observed Measurement error = distorts observed

relationships and makes multivariaterelationships and makes multivariatetechniques less powerful.techniques less powerful.

Researchers use summated scales, for whichResearchers use summated scales, for which

several variables are summed or averaged several variables are summed or averaged together to form a composite representation together to form a composite representation of a concept.of a concept.


8/7/2019 Ch 1_MDA_6e_PH


9

In addressing measurement error, researchers In addressing measurement error, researchers

evaluate two important characteristics of evaluate two important characteristics of measurement :measurement :

Validity = the degree to which a measureValidity = the degree to which a measureaccurately represents what it is supposed to.accurately represents what it is supposed to.

Reliability = the degree to which the observed Reliability = the degree to which the observed

variable measures the true value and is thus variable measures the true value and is thus error free.error free.


8/7/2019 Ch 1_MDA_6e_PH


10

S tatistical S ignificance and Power S tatistical S ignificance and Power

Type I error Type I error , or , or EE, is the probability of rejecting the null , is the probability of rejecting the null hypothesis when it is true.hypothesis when it is true.

Type II error Type II error , or , or FF, is the probability of failing to reject the null , is the probability of failing to reject the null hypothesis when it is false.hypothesis when it is false.

Power Power , or , or 11- - FF, is the probability of rejecting the null hypothesis , is the probability of rejecting the null hypothesis when it is false.when it is false.

H H 0 0 t rue t rue H H 0 0 fals e fals e

F ail to Rejec t H F ail to Rejec t H 0 0 11--EE FF

Type II error Type II error

Rejec t H Rejec t H 0 0 EE

Type I error Type I error

11--FF

Power Power

8/7/2019 Ch 1_MDA_6e_PH


11

Power is Determined by Three Factors :Power is Determined by Three Factors :

E ffect size:E ffect size: the actual magnitude of the effect of the actual magnitude of the effect of interest (e.g., the difference between means or theinterest (e.g., the difference between means or the

correlation between variables).correlation between variables). Alpha ( Alpha ( EE):): as as EE is set at smaller levels, power is set at smaller levels, power

decreases. Typically,decreases. Typically, EE = .05.= .05.

S ample size:S ample size: as sample size increases, power as sample size increases, power increases. W ith very large sample sizes, even very increases. W ith very large sample sizes, even very

small effects can be statistically significant, raisingsmall effects can be statistically significant, raisingthe issue of practical significance vs. statistical the issue of practical significance vs. statistical significance.significance.

8/7/2019 Ch 1_MDA_6e_PH


12

Figure 1Figure 1--1 Impact of S ample S ize on Power 1 Impact of S ample S ize on Power

8/7/2019 Ch 1_MDA_6e_PH


13

Rules of Thumb 1Rules of Thumb 111

Statistical Power Analysis

� Researchers should always design the study to achieve

a power level of .80 at the desired significance level.� More stringent significance levels (e.g., .01 instead of

.05) require larger samples to achieve the desired

power level.

� Conversely, power can be increased by choosing a less

stringent alpha level (e.g., .10 instead of .05).� Smaller effect sizes always require larger sample sizes

to achieve the desired power.

� Any increase in power is most likely achieved by

increased sample size.

8/7/2019 Ch 1_MDA_6e_PH


14

Types of Multivariate Techniques Types of Multivariate Techniques

Dependence techniques :Dependence techniques : a variable or set of variables a variable or set of variables is identified as the dependent variable to be predicted is identified as the dependent variable to be predicted or ex plained by other variables known as independent or ex plained by other variables known as independent

variables.variables.

oo Multiple Regression Multiple Regression

oo Multiple Discriminant Analysis Multiple Discriminant Analysis

oo Logit/Logistic Regression Logit/Logistic Regression

oo Multivariate Analysis of Variance (MAN OV A) and Multivariate Analysis of Variance (MAN OV A) and CovarianceCovariance

oo Conjoint Analysis Conjoint Analysis

oo Canonical Correlation Canonical Correlation

oo S tructural E quations Modeling ( SE M)S tructural E quations Modeling ( SE M)

8/7/2019 Ch 1_MDA_6e_PH


15

Interdependence techniques :Interdependence techniques : involve theinvolve thesimultaneous analysis of all variables in the set,simultaneous analysis of all variables in the set,

without distinction between dependent variables without distinction between dependent variables and independent variables.and independent variables.

oo Principal Components and Common Factor Analysis Principal Components and Common Factor Analysis

oo Cluster Analysis Cluster Analysis

oo Multidimensional S

caling (perceptual mapping)Multidimensional S

caling (perceptual mapping)oo Correspondence Analysis Correspondence Analysis

Types of Multivariate Techniques Types of Multivariate Techniques

8/7/2019 Ch 1_MDA_6e_PH


16

S electing a Multivariate TechniqueS electing a Multivariate Technique

1.1. W hat type of relationship is being ex amined W hat type of relationship is being ex amined dependence or interdependence? dependence or interdependence?

2.2. Dependence relationship: How many variables areDependence relationship: How many variables arebeing predicted? being predicted? W hat is the measurement scale of the dependent W hat is the measurement scale of the dependent

variable? variable? W hat is the measurement scale of the predictor W hat is the measurement scale of the predictor

variable? variable? 3.3. Interdependence relationship: Are you ex aminingInterdependence relationship: Are you ex aminingrelationships between variables, respondents, or relationships between variables, respondents, or objects? objects?

8/7/2019 Ch 1_MDA_6e_PH


17

Multiple Regression Multiple Regression

Asingle metric dependent

Asingle metric dependent

variable is predicted by variable is predicted by

several metric independent several metric independent

variables.variables.

8/7/2019 Ch 1_MDA_6e_PH


18

A non A non--metric (categorical)metric (categorical)

dependent variable is predicted by dependent variable is predicted by

several metric independent variables.several metric independent variables.

Discriminant Analysis Discriminant Analysis

8/7/2019 Ch 1_MDA_6e_PH


19

Logistic Regression Logistic Regression

A single nonmetric dependent variable is A single nonmetric dependent variable is

predicted by several metric independent predicted by several metric independent

variables. This technique is similar tovariables. This technique is similar to

discriminant analysis, but relies on calculations discriminant analysis, but relies on calculations

more like regression.more like regression.

8/7/2019 Ch 1_MDA_6e_PH


20

MAN OV AMAN OV A

S everal metric dependent variables S everal metric dependent variables

are predicted by a set are predicted by a set of nonmetric of nonmetric

(categorical) independent variables.(categorical) independent variables.

8/7/2019 Ch 1_MDA_6e_PH


21

C AN O N IC AL ANALY S I S C AN O N IC AL ANALY S I S

S everal metric dependent variables S everal metric dependent variables are predicted by several metric are predicted by several metric independent variables.independent variables.

8/7/2019 Ch 1_MDA_6e_PH


22

. . . is used to understand . . . is used to understand respondents preferences for respondents preferences for products and services.products and services.

I n doing this, it determines theI n doing this, it determines the

importance of importance of bothboth::

attributesattributes and and

levels of attributeslevels of attributes

. . . based on a smaller subset of . . . based on a smaller subset of

combinations of attributes and combinations of attributes and

levels.levels.

CO N JOI N T ANALY S I S

8/7/2019 Ch 1_MDA_6e_PH


8/7/2019 Ch 1_MDA_6e_PH


24

S tructural E quations Modeling ( SE M)S tructural E quations Modeling ( SE M)

E stimates multiple, interrelated dependence

E stimates multiple, interrelated dependencerelationships based on two components :relationships based on two components :

1.1. S tructural Model S tructural Model

2.2. Measurement Model Measurement Model

8/7/2019 Ch 1_MDA_6e_PH


25

. . . .. . . . analyzes the structure of theanalyzes the structure of theinterrelationships among a large number of interrelationships among a large number of variables to determine a set of common variables to determine a set of common

underlying dimensions (factors).underlying dimensions (factors).

Factor Analysis

8/7/2019 Ch 1_MDA_6e_PH


26

. . . .. . . . groups objects (respondents, products,groups objects (respondents, products,firms, variables, etc.) so that each object is firms, variables, etc.) so that each object is similar to the other objects in the cluster and similar to the other objects in the cluster and different from objects in all the other clusters.different from objects in all the other clusters.

Cluster Analysis Cluster Analysis

8/7/2019 Ch 1_MDA_6e_PH


27

Multidimensional S calingMultidimensional S caling

. . .. . . identifies unrecognized dimensions that identifies unrecognized dimensions that affect purchase behavior based on customer affect purchase behavior based on customer judgments of :judgments of :

similarities similarities or or

preferences preferences

and transforms these into distances and transforms these into distances represented as perceptual maps.represented as perceptual maps.

8/7/2019 Ch 1_MDA_6e_PH


28

Correspondence Analysis Correspondence Analysis

. . .. . . uses non uses non--metric data and evaluates metric data and evaluates

either linear or non either linear or non--linear relationships in an linear relationships in an

effort to develop a perceptual mapeffort to develop a perceptual map

representing the association between objects representing the association between objects

(firms, products, etc.) and a set of descriptive(firms, products, etc.) and a set of descriptive

characteristics of the objects.characteristics of the objects.

8/7/2019 Ch 1_MDA_6e_PH


29

G uidelines for Multivariate Analysis G uidelines for Multivariate Analysis

E stablish Practical S ignificance as W ell as E stablish Practical S ignificance as W ell as

S tatistical S ignificance.S tatistical S ignificance. S ample S ize Affects All Results.S ample S ize Affects All Results.

Know Your Data.Know Your Data.

S trive for Model Parsimony.S trive for Model Parsimony.

Look at Your E rrors.Look at Your E rrors.

Validate Your Results.Validate Your Results.

8/7/2019 Ch 1_MDA_6e_PH


30

S tage 1:S tage 1: Define the Research Problem, Objectives, and Define the Research Problem, Objectives, and

Multivariate Technique(s) to be U sed Multivariate Technique(s) to be U sed

S tage 2 :S tage 2 : Develop the Analysis Plan Develop the Analysis Plan S tage 3 :S tage 3 : E valuate the Assumptions U nderlying theE valuate the Assumptions U nderlying the

Multivariate Technique(s)Multivariate Technique(s)

S tage 4:S tage 4: E stimate the Multivariate Model and Assess E stimate the Multivariate Model and Assess

Overall Model Fit Overall Model Fit S tage 5 :S tage 5 : Interpret the Variate(s)Interpret the Variate(s)

S tage 6:S tage 6: Validate the Multivariate Model Validate the Multivariate Model

A S tructured Approach toA S tructured Approach toMultivariate Model Building:Multivariate Model Building:

8/7/2019 Ch 1_MDA_6e_PH


31

V ariable DescriptionV ariable Description V ariable TypeV ariable Type

Data Warehouse Classification VariablesData Warehouse Classification Variables

X1X1 Customer TypeCustomer Type nonmetric nonmetric X2 X2 I ndustry TypeI ndustry Type nonmetric nonmetric

X3X3 F irm SizeF irm Size nonmetric nonmetric

X4X4 R egionR egion nonmetric nonmetric

X5 X5 Distribution SystemDistribution System nonmetric nonmetric

Performance Perceptions VariablesPerformance Perceptions Variables

X6 X6 P roduct Quality P roduct Quality metric metric

X7 X7 E E- - Commerce Activities/WebsiteCommerce Activities/Website metric metric

X8 X8 Technical Support Technical Support metric metric X9X9 Complaint R esolutionComplaint R esolution metric metric

X10 X10 Advertising Advertising metric metric

X11X11 P roduct LineP roduct Line metric metric

X12 X12 Salesforce I mageSalesforce I mage metric metric

X13X13 Competitive P ricing Competitive P ricing metric metric

X14X14 Warranty & ClaimsWarranty & Claims metric metric

X15 X15 N ew P roductsN ew P roducts metric metric

X16 X16 O rdering & Billing O rdering & Billing metric metric X17 X17 P rice F lexibility P rice F lexibility metric metric

X18 X18 Delivery Speed Delivery Speed metric metric

Outcome/Relationship MeasuresOutcome/Relationship Measures

X19X19 SatisfactionSatisfaction metric metric

X20 X20 Likelihood of R ecommendationLikelihood of R ecommendation metric metric

X21X21 Likelihood of F uture P urchaseLikelihood of F uture P urchase metric metric

X22 X22 Current P urchase/Usage Level Current P urchase/Usage Level metric metric

X23X23 Consider Strategic Alliance/ P artnership in F utureConsider Strategic Alliance/ P artnership in F uture nonmetric nonmetric

Description of HB AT Primary Database Variables Description of HB AT Primary Database Variables

8/7/2019 Ch 1_MDA_6e_PH


32

Multivariate Analysis Multivariate Analysis Learning Checkpoint :Learning Checkpoint :

1.1. W hat is multivariate analysis? W hat is multivariate analysis?

2.2. W hy use multivariate analysis? W hy use multivariate analysis? 3.3. W hy is knowledge of measurement scales W hy is knowledge of measurement scales

important in using multivariate analysis? important in using multivariate analysis?

4.4. W hat basic issues need to be ex amined W hat basic issues need to be ex amined

when using multivariate analysis? when using multivariate analysis?

5.5. Describe the process for applyingDescribe the process for applying

multivariate analysis.multivariate analysis.

Ch 1_MDA_6e_PH

Documents

Transcript of Ch 1_MDA_6e_PH