Prof. Dr. René Algesheimer Marketing analytics ii

Chair of Marketing and Market ResearchDepartment of Business AdministrationUniversität Zürich, Switzerland © Zürich, 2016/2017. All rights reserved.

SyllabusEach Fall SemesterLast edit: 01.06.2016

Marketing analytics iiProf. Dr. René Algesheimer

Marketing Analytics II - Syllabus - 1

Preamble

Welcome to my “Marketing Analytics” syllabus!

“I learnt very early on the difference between knowing the name of something and knowing something.”

Richard Feynman

This course aims to deepen student’s knowledge about actual research problems in marketing and consumer research and to support student’s development into a well-informed practitioner of state-of-the-art market research. A practitioner is capable to formulate and structure market research problems, to collect and to analyze quantita-tive market research data, and finally to infer effective marketing decisions based on data analysis. This interactive course enables students to design and conduct market research analyses using a variety of confirmatory multivariate tools like regression, variance analysis, discriminance analysis, structural equations modelling, but also exploratory multivariate tools like factor analysis, cluster analysis, or multi-dimensional scaling. Depending on the time and the interest of the students this course will also cover topics like conjoint analysis, social network analysis or data mining methods. These skills are also very useful for students that plan to go into a consulting or marketing career.

This course will always take place in the fall semesters and it is the follow-up of the course Marketing Analytics I. Nevertheless, students can also start with this course if they have a profound statistical knowledge. You’ll find all the necessary information concerning the course within this syllabus. From time to time, updates will be posted on our website, at the “Marketing” blackboard at Andreasstrasse 15, 4th floor, and on our eLearning platform.

www.market-research.uzh.ch.

I am pleased to welcome you to my course.

Enjoy this introduction.

All the best,

René Algesheimer


Quick Overview:

Instructor:

Prof. Dr. René Algesheimer,

Office: Andreasstrasse 14, CH-8050 Zürich, Switzerland

Phone: +41 44 634 2918

E-mail: [email protected]

Office hours are by appointment.

Web: www.market-research.uzh.ch

Teaching Assistants:

Raluca Gui

Xin-You Zou

Target Audience:

This course is reckonable for MA and is assigned to the „Wahlpflichtbereich” BWL 4.

Frequency:

Each fall semester

AP (ECTS):

6

Work load statement:

Part Workload Total Time ECTS

Course attendance 15 lectures à 90min, 2 weeks 22.5h

Exercise attendance 15 exercises à 90min, 2 weeks 22.5h

L&E preparation 14h per week, 2 weeks 28h

Literature study Preparation before class 71h

Group assignments Two workshops plus preparation 36h

Total 180h 6

Maximum Amount of Students:

limited only by room size

Content:

Practical introduction into understanding, applying, interpreting and documenting quantitative market research methods by using R 3.1.

Language:


English

Basic Literature:

Field, Andy (AF): Discovering Statistics Using R, 1st ed., London et al.: Sage, 2012.

Hair, Joseph F. Jr.; Black, William C.; Babin, Barry J. & Anderson, Rolph E. (HBBA): Multivariate Data Analysis. A Global Perspective, 7th ed., Upper Saddle River et al.: Pearson, 2010.

James, Gareth (GJ) et al.: An Introduction to Statistical Learning with Applications in R, Springer, 2013.

Additional literature will be given in-class.

Prerequisite:

Recommended: Marketing Analytics I, Statistics, Empirical Research Methods.

Access:

Join our courses and make up your mind if you want to participate. In the positive case, register yourself and sign up for the courses, you want to participate, at our chair. Then officially register yourself using the booking tools at the University of Zurich.

Grading:

Participation, exercises, group work & presentations, code-writing..

Dates:

Block course, September 5th -September 16th, 9.30-17.30h each day and homework.

Location:

Lectures: AND-4-03/06

Further information:

º www.market-research.uzh.ch

º blackboard Marketing, Andreasstreasse 15, 4th floor

Note:

This information in the syllabus supports the official information in the electronic university calendar (VVZ – Vorlesungsverzeichnis). In cases of doubt, the official information at the VVZ is valid.


1. INTRODUCTION AND OBJECTIVE

“I checked it very thoroughly,“ said the computer, “and that quite definitely is the answer.

I think the problem, to be quite honest with you, is that you’ve never actually know what the question is.“

D. Adams, The Hitchhiker’s Guide to the Galaxy

Course Purpose & Objectives

At the heart of superior marketing practice there is always a decision. One, for example, has to decide how to price a product, what kind of distribution channels one wants to use, or how to advertise a specific product. In order to reduce complexity and support one alternative from a multitude, quantitative marketing methods are essential in organizations. Thus, gaining a thorough understanding of instruments that can be implemented and applied to a diversity of marketing settings is the purpose of this course and of the market research course trilogy. The objective of this course is to become accustomed with, understand and apply quantitative marketing methods that are typically used in marketing management.

The reasons for this are twofold: First, many students are afraid of maths and statis-tics. Nevertheless, mathematics is the ultimate language (besides music) and I believe it is an essential building block in the development of many career paths. Students can fear these subjects because no one has explained them in a language that may be understood and enjoyed, in a way that allows the student to really fall in love with numbers and apply them to different settings. When I was a student, statistics was something about axioms, definitions, propositions, evidence, and occasionally it was also about software programming. I was often interested in WHY I should apply this statistical test and HOW it works, what the outputs mean and what we can conclude from the results. For me, statistics has always been a way to support higher order real life situations or managerial decisions with the advantage of offering practical insights and recommendations. Therefore, our market research courses should help students to solidify and ground the knowledge gained in basic marketing courses and also help them to develop effective marketing thinking on their own. This is done by exposing students to a variety of old, new, and sometimes unusual, instruments of quantitative marketing methods. Furthermore, the course will motivate and encourage students to practice these concepts in practical exercises as well as exposing them to the neces-sary software packages while simultaneously developing a spirit of problem solving. I understand my courses as “hands-on” and so should you.

Second, I believe that quantitative thinking is supportive to logical and structured business thinking, a capacity that is very often necessary in organizations. Therefore, I hope that the classes will not only enhance your quantitative knowledge, but also your ability to think in business terms.

This course should (a) sensitize students to typical quantitative marketing problems; (b) introduce students to quantitative marketing methods that are typically used in marketing management; (c) develop students’ abilities to identify, apply and evaluate these methods; (d) develop students’ skills in gathering information, drawing conclu-sions from it, and presenting the material, and (e) develop student’s hands-on compe-tence in quantitative marketing research methods.


Course Contribution towards Marketing Management

The course includes a comprehensive presentation of the main quantitative methods that are typically used in marketing management. These elements are discussed in class and supported by relevant examples, taken either from specialized academic and professional literature, or from the personal experience of the teaching staff. The approach adopted encourages students to critically evaluate given marketing situa-tions, and methods to question and discuss their applicability as well as to solve given marketing decision problems.

Course Contribution towards Analytical Competence

The course presents the main quantitative marketing instruments that are applied in the professional world, and which help marketing managers to analyze marketing situations, to formulate marketing strategies and plans, and to evaluate their impact. The student’s understanding of these analytical instruments, taught to them from basics, is realized through theoretical discussions, examples, exercises, and practical assignments. While many books separate different methods and tests, the approach in this course is to build a unique perspective that draws similarities across several statistical methods and tests.

Course Contribution towards Correctly Understanding and Applying Marketing Instruments

One course objective is to show how analytical marketing instruments can support marketing decisions. The quantitative methods presented and discussed in class will be instruments providing students with an image of the complexity and pitfalls of typi-cal marketing problems. These instruments have to be correctly applied by students in order to successfully solve their assignments and to answer the questions included in the final exam.

Course Contribution towards Critical Thinking, and Problem Solving Skills

As all instruments are directly applied to realistic marketing situations, students need to formulate the related marketing problem and marketing questions to these given situations. Problem solving skills are developed as a consequence of applying quanti-tative methods, and alternatives are also discussed in class. The results of quantitative marketing methods are interpreted and critically analyzed in order to foster critical thinking.

Course Contribution towards Ethical and Social Responsibility

The cases that are presented in class integrate ethical questions in order to develop a sense of ethical and social responsibility and to actively generate an understanding of different cultural perspectives. An open minded, tolerant, and respectful atmosphere within class is necessary to maintain this. The pedagogical approach adopted in this course encourages students to participate contributing their opinions, experience, and comments to the discussions developed around the presented marketing meth-ods and to seriously consider and discuss other’s opinions.


Course Contribution towards the Development of Good Teamwork and Communication Skills (depending on group size)

The capability to effectively work in teams and to communicate during the work-ing process is an essential skill for modern marketing managers. The pedagogical approach adopted in this course encourages students to participate forwarding their opinions, experience, and comments to the discussions developed around the presented marketing methods. The in class exercises are also conducted in groups on our workshop days so that the course encourages students to develop interpersonal communication skills, as well as to debate and negotiate ideas and decisions during their group work. Finally, students are obliged to use both verbal and written commu-nication during their course work and evaluation, which reinforces these skills.

Course Description

The course presents popular quantitative marketing methods with practical exercises to familiarize students both with the theoretical and practical aspects of marketing methods.

2. COURSE MATERIAL

Students have access to our web-based e-learning platform on OLAT to download the slides presented in class, participate in self-learning modules, find relevant mate-rial, datasets and literature, discuss with your classmates the latest topic in class and much more thus benefiting from complementary information available online and in the library.

A system of different learning abilities has been developed. The following procedure is strongly recommended as preparation for the classes.

Overview of classes

On the webpage an overview of all classes given by our team can be found. Develop an idea of the classes and how they best fit into your personal agenda. Keep in mind that quantitative marketing research classes are only offered once a year. It is also necessary to have successfully completed the prior course to proceed with the fol-lowing.

Hands-on guides

Several files have been prepared that should provide you with background knowledge of my expectations in the classroom and some tips concerning “How to give presenta-tions in class”, “How to write in an academic style”… If you read them prior to class, then you’ll obtain a good understanding of what is expected from you.

Syllabus

For each course, a detailed syllabus exists with all details concerning that specific course. This is your guideline for the class and a MUST read. You’ll find everything in here concerning the grading of the course, the agenda, the planned topics, the work-load, readings and much more…


The main materials used in this course are:

The Slides

The slides presented and discussed in class are available in a digital format, on the e-learning platform. You can download the slides to each class. The slides don’t com-pletely cover the entire syllabus, therefore it is necessary to participate in the class. All slides will be uploaded after each module and contain lecture notes as well.

All our slides follow our detailed standardized slide format. All presentations in the classroom also have to follow this format.

The Reading List

The reading list is split into three categories depending on your time and involvement in the class. REQUIRED readings are necessary readings before each class and pre-pare you for the actual content. RECOMMENDED readings are articles that go into more details and widen your knowledge. FOLLOW-UP readings will help you to draw together your newly acquired knowledge of the content or solve some troubles if you are in the middle of your own practical work. EXEMPLARY articles apply the learned knowledge within different marketing areas and allow you to establish utilization of the learned methods.

The Exercises and workshops

For each class you’ll find about 10 multiple choice questions on the eLearning plat-form that you can solve on your own. Furthermore, you’ll receive a bunch of practical lessons combined with exercises that you will solve with my TAs during practical ses-sions. On the workshop days, group-work is given to you to practise your skills!

Additional Materials

The academic and professional papers published online or in marketing journals can also be used by students to obtain additional information about marketing concepts, theories and methods. The following journals are reputable and are therefore strongly recommended to the students:

Marketing journals:

º Marketing Science

º Journal of Marketing Research

º Journal of Marketing

º Journal of Consumer Research

º Quantitative Marketing and Economics

º International Journal of Research in Marketing

º Journal of the Academy of Marketing Science

º Journal of Interactive Marketing

º Journal of Service Research

º Journal of Product and Innovation Management

º Harvard Business Review


º Sloan Management Review

º McKinsey Quarterly

3. COURSE CONTENTS

The course will potentially cover the following topics: introduction into quantitative market research, definitions of basic concepts, introduction into R 3.11 and Gephi 0.8.2, exploring data with graphs, key assumptions in quantitative research, correla-tion and causation, descriptive statistics. You will find a detailed schedule below.

Required readings in class:

Field, Andy (2012): Discovering Statistics Using R, 1st ed., London et al.: Sage. [AF]

Hair, Joseph F. Jr.; Black, William C.; Babin, Barry J. & Anderson, Rolph E. (2010): Multivariate Data Analysis. A Global Perspective, 7th ed., Upper Saddle River et al.: Pearson.[HBBA]

Recommended readings in class:

Hanssens, Dominique M., Parsosns, Leonard J. & Schultz, Randall L. (2003): Market Response Models: Econometric and Time Series Analysis, 2nd ed., Boston et al.: Kluwer Academic Publishers.

Iacobucci, Dawn (2012): Marketing Models, Cengage Learning. [I]

Lilien, Gary L. & Rangaswamy, Arvind (2006): Marketing Engineering, revised 2nd ed., Victoria: Trafford Publishing.

Leeflang, Peter S.H., Wittink, Dick R., Wedel, Michel & Naert, Philippe A. (2000): Building Models for Marketing Decisions, Boston et al.: Kluwer Academic Publish-ers.

Weiss, Neil A. (2007): Introductory Statistics, 8th ed., Boston et al.: Addison Wesley.

Wierenga, Berend (2008): Handbook of Marketing Decision Models, New York et al.; Springer.

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aila

ble

at h

ttp:

//w

ww

.sta

tistic

alas

soci

ates

.com

/boo

klis

t.htm

], la

st a

cces

sed:

Aug

ust 1

2th,

20

14.

¹H

eckm

an,

J. (

1979

), “S

ampl

e Se

lect

ion

Bia

s as

a S

peci

ficat

ion

Erro

r,” E

cono

met

rica,

47,

15

3–61

. ¹

Oze

r-B

alli,

H. a

nd B

.E. S

oren

sen

(20

10),

“Int

erac

tion

effec

ts in

Eco

nom

etric

s,” W

orki

ng P

a-pe

r, U

nive

rsity

of M

asse

y an

d U

nive

rsity

of H

oust

on.

Follo

w-u

p re

adin

g ¹

Alg

eshe

imer

, R. S

. Bor

le, U

.M. D

hola

kia,

and

S. S

iddh

arth

(20

10),

“The

Impa

ct o

f Cus

tom

er

Com

mun

ity P

artic

ipat

ion

on C

usto

mer

Beh

avio

rs: A

n Em

piric

al In

vest

igat

ion,

” M

arke

t-in

g Sc

ienc

e, 2

9 (4

), 75

6-76

9. ¹

Bra

dley

E. (

1979

), “B

oots

trap

Met

hods

: Ano

ther

Loo

k at

the

Jac

kkni

fe,”

The

Ann

als

of S

ta-

tistic

s, 7,

1, 19

79, 1

–26.

¹Li

ttle

, R. J

. A. a

nd D

.B. R

ubin

(20

02)

, Sta

tistic

al A

naly

sis

with

Mis

sing

Dat

a, 2

nd e

d., H

obo-

ken:

Joh

n W

iley

& S

ons.

¹W

ilcox

, R. R

. (20

05)

, Int

rodu

ctio

n to

Rob

ust

Estim

atio

n an

d H

ypot

hesi

s Te

stin

g, 2

nd e

d.,

Bur

lingt

on, M

A: E

lsev

ier.

Mar

ketin

g A

naly

tics

II - S

ylla

bus

- 11

Day

Topi

cTe

xt C

hapt

ers

Read

ings

3(9

h30

-11h0

0)Lo

gist

ic R

egre

ssio

n

(logi

stic

regr

essi

on, m

axim

um li

kelih

ood

estim

a-tio

n, lo

g-lik

elih

ood,

bas

elin

e m

odel

, r, H

osm

er

and

Lem

esho

w’s

r2l,

r-st

atis

tic, C

ox a

nd S

nell’

s r2

cs, N

agel

kerk

e’s

r2n,

Wal

d st

atis

tic, o

dds

ratio

(exp

(b)),

inco

mpl

ete

info

rmat

ion,

com

plet

e se

para

tion,

ove

rdis

pers

ion)

.

Exer

cise

:

¹R

exam

ple

of lo

gist

ic re

gres

sion

. ¹

Test

ing

logi

stic

regr

essi

on a

ssum

ptio

ns.

Dat

aset

s:

¹H

BAT

.csv

Requ

ired

read

ing

¹A

F, p

p.31

2-35

8. ¹

HB

BA

, 20

10, p

p. 2

61-3

30.

Reco

mm

ende

d re

adin

g

¹G

arso

n, G

. Dav

id (

200

9), “

Logi

stic

Reg

ress

ion,

” fr

om S

tatn

otes

: Top

ics

in M

ultiv

aria

te A

naly

sis,

(ac-

cess

ed F

ebru

ary

19th

, 20

10),

[ava

ilabl

e at

htt

p://

facu

lty.c

hass

.ncs

u.ed

u/ga

rson

/pa7

65/s

tatn

ote.

htm

l]. ¹

Mal

hotr

a, N

ares

h K

. (19

84),

“The

Use

of L

inea

r Log

it M

odel

s in

Mar

ketin

g Re

sear

ch,”

Jour

nal o

f Mar

-ke

ting

Rese

arch

, 21 (

1/Fe

b.),

20-3

1. ¹

Peng

, C.-Y

. J.,

K.L

. Lee

, and

G.M

. Ing

erso

ll (2

00

2), “

An

Intr

oduc

tion

to L

ogis

tic R

egre

ssio

n A

naly

sis

and

Repo

rtin

g,” T

he J

ourn

al o

f Edu

catio

nal R

esea

rch,

96

(1),

3-14

.

Follo

w-u

p re

adin

g ¹

And

rew

s, R

ick

L., A

ndre

w A

insl

ie, a

nd I

mra

n S.

Cur

rim

(20

02)

, “A

n Em

piri

cal C

ompa

riso

n of

Log

it C

hoic

e M

odel

s w

ith D

iscr

ete

Ver

sus

Con

tinuo

us R

epre

sent

atio

ns o

f H

eter

ogen

eity

,” Jo

urna

l of

Mar

ketin

g Re

sear

ch, 3

9 (4

), 47

9-48

7. ¹

Swai

t, Jo

ffre

and

Jord

an L

ouvi

ere

(1993

), “T

he R

ole

of t

he S

cale

Par

amet

er i

n th

e Es

timat

ion

and

Com

pari

son

of M

ultin

omia

l Log

it M

odel

s,” J

ourn

al o

f Mar

ketin

g Re

sear

ch, 3

0 (3

/Aug

.), 3

05-

314.

Exem

plar

y re

adin

g ¹

Reic

hhel

d, F

. (20

03),

“The

Onl

y N

umbe

r Yo

u N

eed

to G

row

,” H

arva

rd B

usin

ess

Revi

ew, D

ecem

ber,

46-5

4. ¹

Win

er, R

usse

ll S.

(198

6), “

A R

efer

ence

Pri

ce M

odel

of B

rand

Cho

ice

for

Freq

uent

ly P

urch

ased

Pro

d-uc

ts,”

Jour

nal o

f Con

sum

er R

esea

rch,

13 (2

/Sep

.), 2

50-2

56.

3(14

h00

-15h3

0)C

onjo

int M

easu

rem

ent (

CO

NJO

INT)

(late

nt A

dapt

ive

conj

oint

ana

lysi

s (a

ca),

choi

ce

set,

choi

ce

sim

ulat

or,

choi

ce-b

ased

co

njoi

nt

anal

ysis

(cb

c),

com

posi

tion

rule

, co

mpo

sitio

nal

mod

el, c

onjo

int

anal

ysis

, dec

ompo

sitio

nal m

od-

el, f

ull p

rofil

e m

etho

d, fu

ll fa

ctor

ial e

xper

imen

tal

desi

gn,

hold

out

profi

les,

mai

n eff

ects

, m

arke

t si

mul

atio

ns, o

rtho

gona

l des

ign,

pla

ncar

ds, r

ever

-sa

ls, t

rade

-off

met

hod,

(par

t-w

orth

) util

ity).

Exer

cise

: ¹

R ex

ampl

e of

dev

elop

ing

effec

tive

prod

uct

desi

gn w

ith C

ON

JOIN

T an

alys

isD

atas

ets:

¹

RB

CRe

spon

ses.

csv.

Requ

ired

read

ing

¹H

BB

A, 2

010

, pp.

261

-330

.Re

com

men

ded

read

ing

¹G

reen

Pau

l E. a

nd V

. Sri

niva

san

(1990

), “C

onjo

int

Ana

lysi

s in

Mar

ketin

g: N

ew D

evel

opm

ents

With

Im

plic

atio

ns fo

r Res

earc

h an

d Pr

actic

e,” J

ourn

al o

f Mar

ketin

g, 5

4(4)

, 3-1

9.

¹G

reen

Pau

l E.,

A.M

. Kri

eger

, and

Y. W

ind

(20

01),

“Th

irty

Yea

rs o

f Con

join

t A

naly

sis:

Refl

ectio

ns a

nd

Pros

pect

s,” I

nter

face

s, 3

1(3)

, 56-

73.

¹H

ause

r, J.

R. a

nd V

.R. R

ao (2

003

), “C

onjo

int A

naly

sis,

Rel

ated

Mod

elin

g, a

nd A

pplic

atio

ns,”

in M

arke

t Re

sear

ch a

nd M

odel

ing:

Pro

gres

s an

d Pr

ospe

cts:

A T

ribu

te to

Pau

l E. G

reen

(Int

erna

tiona

l Ser

ies

in

Qua

ntita

tive

Mar

ketin

g), Y

oram

(Jer

ry) W

ind

and

Paul

E. G

reen

(eds

.), IS

QM

, Int

erna

tiona

l Ser

ies

in Q

uant

itativ

e M

arke

ting,

Klu

wer

Aca

dem

ic P

ublis

hers

.

Follo

w-u

p re

adin

g ¹

Hub

er, J

. (20

04)

, Con

join

t A

naly

sis:

How

we

got

here

and

whe

re w

e ar

e (a

n up

date

), Sa

wto

oth

Soft-

war

e Re

sear

ch P

aper

Ser

ies,

1-15

.Ex

empl

ary

read

ing

¹Q

ualtr

ix.c

om (2

011)

: Con

join

t Ana

lysi

s: E

xpla

inin

g Fu

ll Pr

ofile

and

Sel

f Exp

licat

ed A

ppro

ache

s, [a

vail-

able

at:

http:

//w

ww

.qua

ltri

cs.c

om/d

ocs/

Con

join

tOve

rvie

w.p

df].

¹W

ittin

k, D

. and

P. C

attin

(198

9), “

Com

mer

cial

Use

of C

onjo

int A

naly

sis:

An

Upd

ate,

” Jou

rnal

of M

arke

t-in

g, 5

3 (J

uly)

, 91–

96.

Con

join

t Pac

kage

s ¹

Aiz

aki,

H. a

nd N

ishi

mur

a, K

. (20

08)

, “D

esig

n an

d A

naly

sis

of C

hoic

e Ex

peri

men

ts U

sing

R: A

Bri

ef

Intr

oduc

tion“

, Agr

icul

tura

l Inf

omat

ion

Rese

arch

, 17(

2), 8

6-94

. ¹

Cro

issa

nt, Y

. (20

09)

, “m

logi

t: a

R pa

ckag

e fo

r th

e es

timat

ion

of t

he m

ultid

imen

sion

al lo

git“

, Pre

sent

a-tio

n at

LET

Uni

vers

ity L

yon

II, J

uly

9, 2

00

9. ¹

Imai

, K. a

nd v

an D

.A.D

yk (2

00

5), “

MN

P: R

Pac

kage

for

Fitt

ing

the

Mul

tinom

ial P

robi

t Mod

el“,

Jour

nal

of S

tatis

tical

Soft

war

e, 14

(3),

1-32.

Mar

ketin

g A

naly

tics

II - S

ylla

bus

- 12

Day

Topi

cTe

xt C

hapt

ers

Read

ings

4(9

h30

- 11h

00)

Ana

lysi

s of

Var

ianc

e (A

NO

VA)

(gen

eral

lin

ear

mod

els

(glm

), t-

test

s, i

ndep

en-

dent

t-t

est,

depe

nden

t t-

test

, an

ova,

Hot

ellin

g t2

, f-t

est,

Bro

wn-

Fors

ythe

f-te

st, W

elch

s f-

test

, pl

anne

d co

mpa

rison

, pl

anne

d co

ntra

st,

post

-ho

c te

sts,

tre

nd a

naly

sis,

gra

nd m

ean,

gro

up

mea

n, w

ithin

-gro

up v

aria

nce,

bet

wee

n-gr

oups

va

rianc

e, e

ta s

quar

ed, o

meg

a sq

uare

d).

Exer

cise

: ¹

Lear

n ho

w to

con

duct

GLM

in R

, und

erst

and

GLM

with

sev

eral

pre

dict

ors,

und

erst

and

how

to a

sses

s th

e fit

of a

GLM

mod

el a

nd

inte

rpre

t the

resu

lts.

Dat

aset

s:

¹Ic

eCre

am.c

sv, P

urch

ase.

csv

Requ

ired

read

ing

¹A

F, p

p. 3

59-6

52.

¹A

F, p

p. 6

96-7

48.

¹H

BB

A, p

p. 4

39-4

47.

Reco

mm

ende

d re

adin

g ¹

Gar

son,

G. D

avid

(20

14),

“Gen

eral

Lin

ear M

odel

s”, S

tatis

tical

Ass

ocia

tes

Publ

ishi

ng, [

avai

labl

e at

htt

p://

ww

w.s

tatis

tical

asso

ciat

es.c

om/b

ookl

ist.h

tm],

last

acc

esse

d: A

ugus

t 12t

h, 2

014

. ¹

HB

BA

, pp.

439

-476

.Fo

llow

-up

read

ing

¹A

F, p

p. 4

57-5

05

(Rep

eate

d M

easu

res)

. ¹

AF,

pp.

50

6-53

8 (M

ixed

Des

igns

). ¹

Kenn

y, D

. A. a

nd C

.M. J

udd

(1986

), “C

onse

quen

ces

of v

iola

ting

the

inde

pend

ence

ass

umpt

ion

in

anal

ysis

of v

aria

nce,

” Psy

chol

ogic

al B

ulle

tin, 9

9(3)

, 422

-431

. ¹

Oze

r-B

alli,

H. a

nd B

.E. S

oren

sen

(20

10),

“Int

erac

tion

effec

ts in

Eco

nom

etric

s,”

Wor

king

Pap

er,

Uni

vers

ity o

f Mas

sey

and

Uni

vers

ity o

f Hou

ston

.Ex

empl

ary

read

ing

¹U

rban

y, J

. E.;

Bea

rden

, W. O

.; W

eilb

aker

, D. C

. (19

88):

The

Effec

t of P

laus

ible

and

Exa

gger

ated

Re

fere

nce

Pric

es o

n C

onsu

mer

Per

cept

ions

and

Pric

e Se

arch

, Jou

rnal

of C

onsu

mer

Res

earc

h,

15(1)

, 95–

110

.

4(14

h00

-15h3

0)M

easu

ring

Diff

eren

ces

in P

urch

ases

w

ith

Mul

tiva

riat

e A

naly

sis

of V

ari-

ance

(M

AN

OVA

); D

iscr

imin

ant

Ana

lysi

s (D

A)

(Bar

tlett

’s te

st o

f sph

eric

ity, b

ox’s

test

, dis

crim

i-na

nt a

naly

sis,

dis

crim

inan

t fun

ctio

n va

riate

s, d

is-

crim

inan

t sc

ores

, ssc

p, e

, h, h

e-1,

hom

ogen

eity

of

cov

aria

nce

mat

rices

, m

ultiv

aria

te n

orm

ality

, Pi

llai-B

artle

tt t

race

(v),

Roy’

s la

rges

t roo

t, W

ilk’s

lam

bda,

dis

crim

inan

t sc

ore,

eig

enva

lue,

eig

en-

vect

or).

Exer

cise

: ¹

Lear

n ho

w to

con

duct

GLM

in R

, und

erst

and

GLM

with

sev

eral

pre

dict

ors,

und

erst

and

how

to a

sses

s th

e fit

of a

GLM

mod

el a

nd

inte

rpre

t the

resu

lts.

Dat

aset

s:

¹H

BAT

.csv

Requ

ired

read

ing

¹A

F, p

p. 3

59-6

52.

¹A

F, p

p. 6

96-7

48.

¹H

BB

A, p

p. 4

39-4

47.

Reco

mm

ende

d re

adin

g ¹

Gar

son,

G. D

avid

(20

14),

“Gen

eral

Lin

ear M

odel

s”, S

tatis

tical

Ass

ocia

tes

Publ

ishi

ng, [

avai

labl

e at

htt

p://

ww

w.s

tatis

tical

asso

ciat

es.c

om/b

ookl

ist.h

tm],

last

acc

esse

d: A

ugus

t 12t

h, 2

014

. ¹

HB

BA

, pp.

439

-476

.Fo

llow

-up

read

ing

¹A

F, p

p. 4

57-5

05

(Rep

eate

d M

easu

res)

. ¹

AF,

pp.

50

6-53

8 (M

ixed

Des

igns

). ¹

Hub

erty

, C. J

. and

J.D

. Mor

ris (1

989)

, “M

ultiv

aria

te A

naly

sis

Vers

us M

ultip

le U

niva

riate

Ana

ly-

sis,

” Psy

chol

ogic

al B

ulle

tin, 1

05

(2),

302-

308.

Exem

plar

y re

adin

g ¹

Moh

r, J.

Nev

in, J

. R. (

1990

): C

omm

unic

atio

n St

rate

gies

in M

arke

ting

Cha

nnel

s: A

The

oret

ical

Pe

rspe

ctiv

e, J

ourn

al o

f Mar

ketin

g, 5

9 (O

ctob

er),

36-5

1.

Mar

ketin

g A

naly

tics

II - S

ylla

bus

- 13

Day

Topi

cTe

xt C

hapt

ers

Read

ings

5(9

h30

-17h3

0)W

ORK

SHO

P

6(9

h30

-11h0

0)Ex

plor

ator

y Fa

ctor

Ana

lysi

s (E

FA)

(exp

lora

tory

fact

or a

naly

sis,

alp

ha fa

ctor

ing,

And

er-

son-

Rubi

n m

etho

d, c

omm

on v

aria

nce,

com

mun

ality

, C

ronb

ach’

s A

lpha

, di

rect

obl

imin

, fa

ctor

loa

ding

, fa

ctor

mat

rix,

fact

or s

core

s, i

ntra

clas

s co

rrel

atio

n co

effici

ent (

icc)

, Kai

ser-

Mey

er- O

lkin

(km

o) m

easu

re

of s

ampl

ing

adeq

uacy

, Kai

ser

crite

rion,

lat

ent

vari

-ab

le, o

bliq

ue r

otat

ion,

prin

cipa

l com

pone

nt a

naly

sis

(pca

), ra

ndom

var

ianc

e, ro

tatio

n, s

cree

plo

t, si

ngul

ar-

ity, u

niqu

e va

rianc

e, V

arim

ax).

Exer

cise

: ¹

Und

erst

and

EFA

mod

els,

how

to d

o EF

A in

R,

inte

rpre

ta E

FA m

odel

s.D

atas

ets:

¹

EFA

_Will

iam

s.cs

v

Requ

ired

read

ing

¹A

F, p

p. 7

49-8

11.

¹H

BB

A, 2

010

, pp.

50

5-56

4.

Reco

mm

ende

d re

adin

g ¹

Gar

son,

G. D

avid

(20

14).

“Fac

tor

Ana

lysi

s”, S

tatis

tical

Ass

ocia

tes

Publ

ishi

ng, [

avai

labl

e at

htt

p://

ww

w.s

tatis

tical

asso

ciat

es.c

om/b

ookl

ist.h

tm],

last

acc

esse

d: A

ugus

t 12t

h, 2

014

. ¹

HB

BA

, pp.

91-

152.

Follo

w-u

p re

adin

g ¹

Chu

rchi

ll, G

. A. (

1979

): A

Par

adig

m fo

r Dev

elop

ing

Bett

er M

easu

res o

f Mar

ketin

g C

onst

ruct

s,

Jour

nal o

f Mar

ketin

g Re

sear

ch, 1

6 (F

ebru

ary)

, 64-

73.

¹C

ortin

a, J

. M. (

1993

), “W

hat

is C

oeffi

cien

t A

lpha

? A

n Ex

amin

atio

n of

The

ory

and

App

lica-

tions

,” Jo

urna

l of A

pplie

d Ps

ycho

logy

, 78,

98-

104.

¹G

erbi

ng, D

.W. a

nd J

.C. A

nder

son

(1988

), “A

n U

pdat

ed P

arad

igm

for

Sca

le D

evel

opm

ent

Inco

rpor

atin

g U

nidi

men

sion

ality

and

Its

Ass

essm

ent,”

Jou

rnal

of

Mar

ketin

g Re

sear

ch,

25 (M

ay),

186-

192.

¹G

orsu

ch, R

. L. (

1990

), “C

omm

on F

acto

r A

naly

sis

Vers

us C

ompo

nent

Ana

lysi

s: S

ome

Wel

l an

d Li

ttle

Kno

wn

Fact

s,” M

ultiv

aria

te B

ehav

iora

l Res

earc

h, 2

5, 3

3-39

. ¹

Stew

art,

D. W

. (19

81),

“The

App

licat

ion

and

Mis

appl

icat

ion

of F

acto

r A

naly

sis

in M

arke

ting

Rese

arch

,” Jo

urna

l of M

arke

ting

Rese

arch

, 18

(Feb

ruar

y), 5

1-62

.

Exem

plar

y re

adin

g ¹

Cav

usgi

l, S.

T. a

nd S

. Zou

(199

4), “

Mar

ketin

g St

rate

gy-P

efor

man

ce R

elat

ions

hip:

An

Inve

sti-

gatio

n of

the

Empi

rical

Lin

k in

Exp

ort M

arke

t Ven

ture

s,” J

ourn

al o

f Mar

ketin

g, 5

8 (J

anu-

ary)

, 1-2

1. ¹

Dol

l, W

. J.,

W. X

ia, a

nd G

. Tor

kzad

eh (1

994)

, “A

Con

firm

ator

y Fa

ctor

Ana

lysi

s of

the

End

-U

ser C

ompu

ting

Satis

fact

ion

Inst

rum

ent,

MIS

Qua

rter

ly, 1

(2),

453-

461.

Mar

ketin

g A

naly

tics

II - S

ylla

bus

- 14

Day

Topi

cTe

xt C

hapt

ers

Read

ings

6(14

h00

- 15h

30)

Con

firm

ator

y Fa

ctor

Ana

lysi

s (C

FA)

(late

nt v

aria

ble,

obs

erve

d va

riabl

e, f

acto

r lo

ad-

ings

, pat

h co

effici

ent,

fact

or c

orre

latio

ns, fi

xed

para

met

ers,

con

stra

ined

par

amet

ers,

free

para

met

ers,

ran

dom

mea

sure

men

t er

ror,

nonr

a-do

m m

easu

rem

ent e

rror

, for

mat

ive

indi

cato

rs,

refle

ctiv

e in

dica

tors

, es

timat

ors,

mod

el fi

t in

-di

ces,

pa

ram

eter

cr

iteria

(c

onst

ruct

va

lidity

, co

nver

gent

val

idity

, dis

crim

inan

t va

lidity

, nom

o-lo

gica

l and

fac

e va

lidity

, sta

ndar

dize

d re

sidu

als,

re

sidu

al v

aria

nces

, mod

ifica

tion

indi

ces)

.

Exer

cise

: ¹

R ex

ampl

e of

ope

ratio

naliz

ing

Prod

uct

¹Im

age

and

Prod

uct L

oyal

ityD

atas

ets:

¹

HB

AT_S

EM.c

sv

Requ

ired

read

ing

¹G

arso

n, G

. Dav

id (

2014

). “C

onfir

mat

ory

Fact

or A

naly

sis”

, St

atis

tical

Ass

oci-

ates

Pub

lishi

ng,

[ava

ilabl

e at

htt

p://

ww

w.s

tatis

tical

asso

ciat

es.c

om/b

ook-

list.h

tm],

last

acc

esse

d: A

ugus

t 12

th,

2014

.

Reco

mm

ende

d re

adin

g ¹

Mile

s, J

. N. V

. (20

05)

, “C

onfir

mat

ory

fact

or a

naly

sis

usin

g M

icro

soft

Exce

l,” B

ehav

ior

rese

arch

met

hods

, 37

(4),

672-

676.

¹

Bro

wn,

Tim

othy

A. (

200

6), C

onfir

mat

ory

Fact

orA

naly

sis f

or A

pplie

d Re

sear

ch. L

ondo

n: T

heG

uilfo

rd

Pres

s, c

hapt

er 3

and

4.

¹K

line,

Rex

B. (

2010

), Pr

inci

ples

and

Pra

ctic

e of

Stru

ctur

al E

quat

ion

Mod

elin

g. N

ew Y

ork:

The

Gui

lford

Pr

ess,

cha

pter

5 a

nd 9

. ¹

Mut

hén,

Lin

da K

. and

Ben

gt O

. Mut

hén

(20

10),

Mpl

us U

ser’s

Gui

de (6

ed.

). Lo

s A

ngel

es: M

uthé

n &

M

uthé

n, c

hapt

er 5

, 15,

16, 1

7 an

d 18

.Fo

llow

-up

read

ing

¹B

olle

n, K

enne

th A

. (19

89),

Stru

ctur

al E

quat

ions

with

Lat

ent

Varia

bles

. New

Yor

k: J

ohn

Will

ey &

So

ns, c

hapt

er 7

. ¹

Hu,

Li-t

ze a

nd P

eter

M. B

entle

r (19

98),

“Fit

Indi

ces

in C

ovar

ianc

e St

ruct

ure

Mod

elin

g: S

ensi

tivity

to

Und

erpa

ram

eter

ized

Mod

el M

issp

ecifi

catio

n,” P

sych

olog

ical

Met

hods

, 3 (4

), 42

4-53

. ¹

Hu,

Li-t

ze a

nd P

eter

M. B

entle

r (19

99),

“Cut

off C

riter

ia fo

r Fit

Inde

xes

in C

ovar

ianc

e St

ruct

ure

Ana

l-ys

is: C

onve

ntio

nal C

riter

ia V

ersu

s N

ew A

ltern

ativ

es,”

Stru

ctur

al E

quat

ion

Mod

elin

g, 6

(1),

1-55

. ¹

Mar

sh, H

erbe

rt W

., K

it-Ta

i Hau

, Joh

n R

. Bal

la, a

nd D

avid

Gra

yson

(199

8), “

Is M

ore

Ever

Too

Muc

h?

The

Num

ber

of In

dica

tors

Per

Fac

tor

in C

onfir

mat

ory

Fact

or A

naly

sis,

” M

ultiv

aria

te B

ehav

iora

l Re

sear

ch, 3

3 (2

), 18

1 - 2

20.

¹Re

illy,

Ter

ence

(199

5), “

A N

eces

sary

and

Suffi

cien

t Con

ditio

n fo

r Ide

ntifi

catio

n of

Con

firm

ator

y Fa

c-to

r Ana

lysi

s M

odel

s of

Fac

tor C

ompl

exity

One

,” So

ciol

ogic

al M

etho

ds R

esea

rch,

23

(4),

421-

41.

Exem

plar

y re

adin

g ¹

Dol

l, W

. J.,

W. X

ia, G

. Tor

kzad

eh (1

994)

, “A

Con

firm

ator

y Fa

ctor

Ana

lysi

s of t

he E

nd-U

ser C

ompu

ting

Satis

fact

ion

Inst

rum

ent,”

MIS

Qua

rter

ly, 1

(2),

453-

461.

¹Lo

o, R

. and

P. L

oew

en (2

00

4), “

Con

firm

ator

y Fa

ctor

Ana

lyse

s of

Sco

res

From

Ful

l and

Sho

rt V

er-

sion

s of

the

Mar

low

e–C

row

ne S

ocia

l Des

irabi

lity

Scal

e,”

Jour

nal o

f App

lied

Soci

al P

sych

olog

y, 34

(11),

2343

-235

2.

¹Yo

o, M

ahn

Hee

and

Jae

beom

Suh

(20

03),

“Org

aniz

atio

nal c

itize

nshi

p be

havi

ors

and

serv

ice

qual

ity

as e

xter

nal e

ffect

iven

ess

of c

onta

ct e

mpl

oyee

s,” J

ourn

al o

f Bus

ines

s Res

earc

h, 5

6(8)

, p. 5

97-6

11.

Mar

ketin

g A

naly

tics

II - S

ylla

bus

- 15

Day

Topi

cTe

xt C

hapt

ers

Read

ings

7(9

h30

- 11h

00)

Stru

ctur

al E

quat

ion

Mod

elin

g (S

EM)

(Lat

ent v

aria

ble,

man

ifest

var

iabl

e, e

ndog

enou

sva

riabl

e, e

xoge

nous

var

iabl

e, m

easu

rem

ent m

odel

,st

ruct

ural

mod

el, m

easu

rem

ent e

rror

s, a

naly

sis

ofco

varia

nce

stru

ctur

es, p

ath

anal

ysis

, nes

ted

mod

-el

s, n

on-n

este

d m

odel

s, m

odel

spe

cific

atio

n, m

odel

iden

tifica

tion,

par

amet

er e

stim

atio

n, g

oodn

ess-

offit

asse

ssm

ent,

mod

el m

odifi

catio

n).

Exer

cise

: ¹

R ex

ampl

e of

kee

ping

loya

l cus

tom

ers.

Dat

aset

s:

¹H

BAT

_SEM

.csv

Requ

ired

read

ing

¹G

arso

n,

G.

Dav

id

(20

14).

“Str

uctu

ral

Equa

tions

Mod

elin

g”, S

tatis

tical

Ass

o-ci

ates

Pub

lishi

ng, [

avai

labl

e at

htt

p://

ww

w.s

tatis

tical

asso

ciat

es.c

om/b

ook-

list.h

tm],

last

acc

esse

d: A

ugus

t 12

th,

2014

. ¹

Mut

hén,

Lin

da K

. and

Ben

gt O

. Mut

hén

(20

10):

Mpl

us U

ser’s

Gui

de (6

ed.

). Lo

s A

ngel

es: M

uthé

n &

Mut

hén,

cha

pter

5,

15, 1

6, 17

and

18.

Reco

mm

ende

d re

adin

g ¹

Bau

mga

rtne

r, H

ans a

nd C

hris

tian

Hom

burg

(199

6), “

App

licat

ions

of S

truc

tura

l Equ

atio

n M

odel

ing

in M

arke

ting

and

Con

sum

er R

esea

rch:

A R

evie

w,”

Inte

rnat

iona

l Jou

rnal

of R

esea

rch

in M

arke

t-in

g, 13

(2),

139-

61.

¹Ia

cobu

cci,

Daw

n (2

00

9), “

Ever

ythi

ng y

ou a

lway

s w

ante

d to

kno

w a

bout

SEM

(str

uctu

ral e

qua-

tions

mod

elin

g) b

ut w

ere

afra

id to

ask

,” Jo

urna

l of C

onsu

mer

Psy

chol

ogy,

19(4

), 67

3-68

0.

¹Ia

cobu

cci,

Daw

n (2

010

), “S

truc

tura

l equ

atio

ns m

odel

ing:

Fit

Indi

ces,

sam

ple

size

, and

adv

ance

d to

pics

,“ Jo

urna

l of C

onsu

mer

Psy

chol

ogy,

20(1)

, 90

-98.

¹

Klin

e, R

ex B

. (20

10),

Prin

cipl

es a

nd P

ract

ice

of S

truc

tura

l Equ

atio

n M

odel

ing.

New

Yor

k: T

he

Gui

lford

Pre

ss, c

hapt

er 2

, 4, 5

and

10.

¹St

eenk

amp,

Jan

-Ben

edic

t E.M

. and

Han

s B

aum

gart

ner

(20

00)

, “O

n th

e U

se o

f Str

uctu

ral E

qua-

tion

Mod

els

for M

arke

ting

Mod

elin

g,” I

nter

natio

nal J

ourn

al o

f Res

earc

h in

Mar

ketin

g, 17

(2-3

), 19

5-20

2.Fo

llow

-up

read

ing

¹A

spar

ouho

v, T

. and

B. M

uthé

n (2

00

9), “

Expl

orat

ory

Stru

ctur

al E

quat

ion

Mod

elin

g,”

Stru

ctur

al

Equa

tion

Mod

elin

g: A

Mul

tidis

cipl

inar

y Jo

urna

l, 16

(3),

397-

438.

¹

Bol

len,

Ken

neth

A. (

1989

), St

ruct

ural

Equ

atio

ns w

ith L

aten

t Var

iabl

es. N

ew Y

ork:

Joh

n W

illey

&

Sons

, cha

pter

1.

¹B

olle

n, K

enne

th A

. and

W. R

. Dav

is (2

00

9), “

Two

Rule

s of

Iden

tifica

tion

for

Stru

ctur

al E

quat

ion

Mod

els,

” Str

uctu

ral E

quat

ion

Mod

elin

g: A

Mul

tidis

cipl

inar

y Jo

urna

l, 16

(3),

523

- 536

. ¹

Jarv

is, C

hery

l B.,

Scott

B. M

acKe

nzie

, and

Phi

lip M

. Pod

sako

ff (2

003

) “A

Crit

ical

Rev

iew

of C

on-

stru

ct In

dica

tors

and

Mea

sure

men

t M

odel

Mis

spec

ifica

tion

in M

arke

ting

and

Con

sum

er R

e-se

arch

,” Jo

urna

l of C

onsu

mer

Res

earc

h, 3

0 (2

), 19

9-21

8.

¹M

arsh

, H. W

., B

. Mut

hén,

et

al. (

200

9), “

Expl

orat

ory

Stru

ctur

al E

quat

ion

Mod

elin

g, In

tegr

atin

g C

FA a

nd E

FA: A

pplic

atio

n to

Stu

dent

s’ Ev

alua

tions

of U

nive

rsity

Tea

chin

g,”

Stru

ctur

al E

qua-

tion

Mod

elin

g: A

Mul

tidis

cipl

inar

y Jo

urna

l, 16

(3),

439-

476.

¹

Schr

eibe

r, Ja

mes

B. e

t al.

(20

06)

, “Re

port

ing

Stru

ctur

al E

quat

ion

Mod

elin

g. T

he J

ourn

al o

f Edu

-ca

tiona

l Res

earc

h, 9

9(6)

, 323

-337

. Con

firm

ator

y Fa

ctor

Ana

lysi

s Re

sults

: A R

evie

w,”

The

Jour

-na

l of E

duca

tiona

l Res

earc

h, 9

9(6)

, 323

-337

.Ex

empl

ary

read

ing

¹A

lges

heim

er, R

., U

. Dho

laki

a an

d A

. Her

rman

n (2

00

5): T

he S

ocia

l Infl

uenc

e of

Bra

nd C

omm

unity

: Ev

iden

ce fr

om E

urop

ean

Car

Clu

bs, J

ourn

al o

f Mar

ketin

g, 6

9(7)

, p. 1

9-34

. ¹

Alg

eshe

imer

, R.,

U. D

hola

kia

and

C. G

urău

(20

11): V

irtu

al T

eam

Per

form

ance

in a

Hig

hly-

Com

pet-

itive

Env

ironm

ent,

fort

hcom

ing

in: G

roup

and

Org

aniz

atio

n M

anag

emen

t, 20

11.

Mar

ketin

g A

naly

tics

II - S

ylla

bus

- 16

Day

Topi

cTe

xt C

hapt

ers

Read

ings

7(14

h00

- 15h

30)

Soci

al N

etw

ork

Ana

lysi

s (S

NA

)(s

ocia

l net

wor

k an

alys

is, d

yadi

c da

ta, n

ode,

tie,

full-

netw

ork

met

hods

, sno

wba

ll m

etho

ds, e

go-c

entr

icne

twor

ks w

ith a

lters

, ego

-cen

tric

net

wor

ks, e

goon

ly, c

entr

ality

, bet

wee

nnes

s, b

ridge

, clo

sene

ss,

clus

terin

g-co

effici

ent,

cohe

sion

, deg

ree,

den

sity

,pa

th le

ngth

, str

uctu

ral h

ole)

.

Exer

cise

:

Exam

ple

of S

ocia

l Net

wor

k A

naly

sis

Dat

aset

s:

¹tb

a

Requ

ired

read

ing

¹B

orga

tti,

S. P

. and

P. F

oste

r (2

003

), “T

he

New

Par

adig

m i

n O

rgan

izat

iona

l Re

-se

arch

: A R

evie

w a

nd T

ypol

ogy,

” Jou

r-na

l of M

anag

emen

t, 29

(6),

991-

1013

. ¹

Iaco

bucc

i, D

. and

S. W

asse

rman

(19

88),

“A G

ener

al F

ram

ewor

k fo

r th

e St

a-tis

tical

Ana

lysi

s of

Seq

uent

ial D

yadi

c In

tera

ctio

n D

ata,

” Ps

ycho

logi

cal

Bul

-le

tin, 1

03 (3

), 37

9-39

0.

Reco

mm

ende

d re

adin

g ¹

Scott

, J. (

200

0), S

ocia

l Net

wor

k A

naly

sis,

New

bury

Par

k C

A: S

age.

¹W

asse

rman

, S. a

nd K

. Fau

st (

1994

), So

cial

Net

wor

k A

naly

sis:

Met

hods

and

App

licat

ions

, Cam

-br

idge

Uni

vers

ity P

ress

.

Follo

w-u

p re

adin

g ¹

Adl

er, P

. and

S. K

won

(20

02)

, “So

cial

cap

ital:

Pros

pect

s fo

r a n

ew c

once

pt,”

Aca

dem

y of

Man

age-

men

t Rev

iew

, 27(

1), 17

-40

. ¹

Car

ringt

on, S

. and

S. W

asse

rman

(Eds

) (20

05)

, Mod

els

and

Met

hods

in S

ocia

l Net

wor

k A

naly

sis,

C

ambr

idge

Uni

vers

ity P

ress

. ¹

Han

nem

an, R

. A. a

nd M

. Rid

dle

(20

05)

, Int

rodu

ctio

n to

Soc

ial N

etw

ork

Met

hods

. Riv

ersi

de, C

A:

Uni

vers

ity o

f Cal

iforn

ia, R

iver

side

, [av

aila

ble

at: h

ttp:

//fa

culty

.ucr

.edu

/~ha

nnem

an/]

. ¹

Kild

uff, M

. and

W. T

sai (

2003

), So

cial

Net

wor

ks a

nd O

rgan

izat

ions

. Lon

don:

Sag

e.

Exem

plar

y re

adin

g ¹

Dod

ds, P

., R

. Muh

amad

, and

D. W

atts

(20

03),

“An

Expe

rimen

tal S

tudy

of S

earc

h in

Glo

bal S

ocia

l N

etw

orks

,” Sc

ienc

e, 3

01 (

5634

), 82

7-82

9. ¹

Gol

denb

erg,

J.,

B. L

ibai

, E. M

ulle

r, an

d S.

Str

emer

ch (2

010

), “D

atab

ase

Subm

issi

on -

The

Evol

ving

So

cial

Net

wor

k of

Mar

ketin

g Sc

hola

rs,”

Mar

ketin

g Sc

ienc

e, 2

9(3)

, 561

-567

. ¹

Goy

al, S

. (20

07)

, Con

nect

ions

: An

Intr

oduc

tion

to th

e Ec

onom

ics

of N

etw

orks

, Prin

ceto

n U

nive

r-si

ty P

ress

. ¹

Gra

nove

tter

, M. (

1973

), “T

he S

tren

gth

of W

eak

Ties

,” A

mer

ican

Jou

rnal

of S

ocio

logy

, 78

(6),

1360

-13

80.

¹H

anak

i, N

., A

. Pet

erha

nsl,

P. D

odds

, and

D.J

. Watt

s (2

00

7), “

Coo

pera

tion

in E

volv

ing

Soci

al N

et-

wor

ks,”

Man

agem

ent S

cien

ce, 2

00

7, 5

3 (7

), 10

36-1

050

. ¹

New

man

, M.,

A. B

arab

asi,

and

D.J

. Watt

s (2

00

6), T

he S

truc

ture

and

Dyn

amic

s of

Net

wor

ks, P

rinc-

eton

Uni

vers

ity P

ress

. ¹

Van

den

Bul

te, C

. and

Y. J

oshi

(20

07)

, “N

ew P

rodu

ct D

iffus

ion

with

Infl

uent

ials

and

Im

itato

rs,”

Mar

ketin

g Sc

ienc

e, 2

6(3)

, 40

0-4

21.

¹W

atts,

D. J

., an

d S.

H. S

trog

atz

(1998

): “C

olle

ctiv

e D

ynam

ics

of ‘S

mal

l-Wor

ld’ N

etw

orks

,” N

atur

e,

393

(668

4), 4

09–

10.

¹W

atts,

D.J

. and

J. P

erett

i (20

07)

, “V

iral M

arke

ting

in t

he R

eal W

orld

,” H

arva

rd B

usin

ess

Revi

ew,

200

7, M

ay, 2

2-23

. Po

pula

r rea

ding

¹B

arab

asi,

A.-L

. (20

02)

, Lin

ked:

The

New

Sci

ence

of N

etw

orks

, Cam

brid

ge, M

A: P

erse

us.

¹B

ucha

nan,

M. (

200

2), S

mal

l Wor

ld: U

ncov

erin

g N

atur

e’s

Hid

den

Net

wor

ks, L

ondo

n: W

iede

nfel

d an

d N

icol

son.

¹

Watt

s, D

unca

n J.

(199

9), S

mal

l Wor

lds:

The

Dyn

amic

s of

Net

wor

ks b

etw

een

Ord

er a

nd R

ando

m-

ness

. Prin

ceto

n, N

J: P

rince

ton

Uni

vers

ity P

ress

.

Mar

ketin

g A

naly

tics

II - S

ylla

bus

- 17

Day

Topi

cTe

xt C

hapt

ers

Read

ings

Key

Soft

war

e ¹

Gep

hi -

free

war

e - g

ener

al s

oftw

are,

htt

p://

ww

w.g

ephi

.org

. ¹

Keyp

laye

r – fr

eew

are

– ide

ntify

impo

rtan

t nod

es w

hose

elim

inat

ion

may

dis

rupt

net

wor

ks, h

ttp:

//w

ww

.an

alyt

icte

ch.c

om/p

rodu

cts.

htm

. ¹

Net

draw

– fr

eew

are

– vis

ual a

naly

sis (

pack

aged

with

UC

INET

), htt

p://

ww

w.a

naly

tict

ech.

com

/net

draw

/ne

tdra

w.h

tm.

¹PA

JEK

– fr

eew

are-

sui

tabl

e fo

r la

rge

netw

orks

and

clu

ster

ing

algo

rithm

s, h

ttp:

//vl

ado.

fmf.u

ni-lj

.si/

pub/

netw

orks

/paj

ek/d

efau

lt.h

tm.

¹U

CIN

ET 6

– fr

ee fo

r 60

days

- exc

elle

nt g

ener

al p

acka

ge, h

ttp:

//w

ww

.ana

lyti

ctec

h.co

m/u

cine

t/.

Soci

al N

etw

ork

Bib

liogr

aphy

¹htt

p://

ww

w.s

ocia

lnet

wor

ks.o

rg/.

Jour

nals

on

Net

wor

ks a

nd S

ocia

l Str

uctu

re

¹So

cial

Net

wor

ks: h

ttp:

//ee

s.el

sevi

er.c

om/s

on/.

¹C

onne

ctio

ns: h

ttp:

//w

ww

.ana

lyti

ctec

h.co

m/c

onne

ctio

ns/.

¹Jo

urna

l of S

ocia

l Str

uctu

re: h

ttp:

//w

ww

.cm

u.ed

u/jo

ss/.

¹N

etw

ork

Scie

nce:

htt

p://

jour

nals

.cam

brid

ge.o

rg/N

WS.

8(9

h30

- 11h

00)

Clu

ster

Ana

lysi

s (C

A)

(clu

ster

ana

lysi

s, c

lust

er s

olut

ion,

den

drog

ram

, het

-er

ogen

eity

, ho

mog

enei

ty,

hier

arch

ical

m

etho

ds,

nonh

iera

rchi

cal

met

hods

, in

tero

bjec

t si

mila

rity,

di

stan

ce m

easu

res,

agg

lom

erat

ive

met

hods

, di

vi-

sive

met

hods

, abs

olut

e Eu

clid

ean

dist

ance

, ave

rage

lin

kage

, cen

troi

d m

etho

d, c

ity-b

lock

dis

tanc

e, c

lus-

ter

cent

roid

, sin

gle

linka

ge, k

-mea

ns, m

ahal

anob

is

dist

ance

(d2)

, War

d’s

met

hod)

.

Exer

cise

: ¹

Rexa

mpl

e of

con

sum

er s

egm

enta

tion

with

CA

.D

atas

ets:

¹

HB

AT.c

sv

Requ

ired

read

ing

¹A

F, p

p. 7

49-8

11.

¹H

BB

A, 2

010

, pp.

50

5-56

4.

Reco

mm

ende

d re

adin

g ¹

Abo

nyi,

J. a

nd B

. Fei

l (20

07)

, Clu

ster

Ana

lysi

s fo

r Dat

a M

inin

g an

d Sy

stem

Iden

tifica

tion.

Bos

ton

and

Bas

el, S

witz

erla

nd: B

irkhä

user

Bas

el.

¹G

arso

n, G

. D

avid

(20

09)

, “C

lust

er A

naly

sis,

” St

atis

tical

Ass

ocia

tes

Publ

ishi

ng,

[ava

ilabl

e at

htt

p://

ww

w.s

tatis

tical

asso

ciat

es.c

om/b

ookl

ist.h

tm],

last

acc

esse

d: A

ugus

t 12t

h, 2

014

.Fo

llow

-up

read

ing

¹Ed

elbr

ock,

C. (

1979

), “C

ompa

ring

the

Acc

urac

y of

Hie

rarc

hica

l Clu

ster

ing

Alg

orith

ms:

The

Pro

b-le

m o

f Cla

ssify

ing

Ever

ybod

y,” M

ultiv

aria

te B

ehav

iora

l Res

earc

h, 14

, 367

-384

. ¹

Mill

igan

, G. W

. and

M.C

. Coo

per

(1985

), “A

n Ex

amin

atio

n of

Pro

cedu

res

for

Det

erm

inin

g th

e N

umbe

r of C

lust

ers

in a

Dat

a Se

t,” P

sych

omet

rika,

50

(29)

; 159

-179.

Exem

plar

y re

adin

g ¹

Ketc

hen,

D.J

. and

C.L

. Sho

ok (1

996)

, “Th

e A

pplic

atio

n of

Clu

ster

Ana

lysi

s in

Str

ateg

ic M

anag

e-m

ent R

esea

rch:

An

Ana

lysi

s an

d C

ritiq

ue,”

Stra

tegi

c M

anag

emen

t Jou

rnal

, 17,

441

-458

. ¹

Punj

, G. a

nd D

. Ste

war

t (19

83),

“Clu

ster

Ana

lysi

s in

Mar

ketin

g Re

sear

ch: R

evie

w a

nd S

ugge

stio

ns

for A

pplic

atio

n,” J

ourn

al o

f Mar

ketin

g Re

sear

ch, 2

0 (M

ay),

134-

148.

Mar

ketin

g A

naly

tics

II - S

ylla

bus

- 18

Day

Topi

cTe

xt C

hapt

ers

Read

ings

8(14

h00

- 15h

30)

Mul

tidi

men

sion

al S

calin

g (M

DS)

(Mul

tidim

ensi

onal

Sca

ling

(MD

S), p

refe

renc

e, p

ref-

eren

ce d

ata,

sim

ilarit

y da

ta, p

erce

ptua

l map

, spa

-tia

l map

, agg

rega

te a

naly

sis,

dis

aggr

egat

e an

alys

is,

com

posi

tiona

l m

etho

d, d

ecom

posi

tiona

l m

etho

d,

cont

inge

ncy

tabl

e, C

orre

spon

danc

e A

naly

sis,

de-

rived

mea

sure

s, d

imen

sion

s, d

ispa

ritie

s, id

eal p

oint

, in

itial

dim

ensi

onal

ity, p

roje

ctio

ns).

Exer

cise

: ¹

tba

Dat

aset

s:

¹tb

a

Requ

ired

read

ing

¹H

BB

A, 2

010

, pp.

565

- 62

6.Re

com

men

ded

read

ing

¹G

arso

n, D

avid

G. (

200

9), “

Mul

tidim

ensi

onal

Sca

ling“

, Sta

tistic

al A

ssoc

iate

s Pu

blis

hing

, [av

ail-

able

at h

ttp:

//w

ww

.sta

tistic

alas

soci

ates

.com

/boo

klis

t.htm

], la

st a

cces

sed:

Aug

ust 1

2th,

20

14.

¹G

reen

, P.E

. (19

75),

Mar

ketig

n A

pplic

atio

ns o

f MD

S: A

sses

smen

t and

Out

look

, Jou

rnal

of M

ar-

ketin

g, 3

9(Ja

nuar

y), 2

4-31

. ¹

Coo

per,

L.G

. (19

83),

A R

Evie

w o

f Mul

tidim

ensi

onal

Sca

ling

in M

arke

ting

Rese

arch

, App

lied

Psyc

holo

gica

l Mea

sure

men

t, 7(

4), 4

27-4

50.

¹C

arro

ll, J

.D. &

Grr

en, P

.E. (

1997

), Ps

ycho

met

ric M

etho

ds in

Mar

ketin

g Re

sear

ch_:

Part

II, M

ulti-

dim

ensi

onal

Sca

ling,

Jou

rnal

of M

arke

ting

Rese

arch

, XX

XIV

(May

), 19

3-20

4.

Follo

w-u

p re

adin

g

¹Ra

msa

y, J

.O. (

1988

), Is

Mul

tidim

ensi

onal

Sca

ling

Mag

ic o

r Sci

ence

?, C

onte

mpo

rary

Psy

chol

ogy,

33

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. ¹

Bor

g, I.

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roen

en, P

. (19

97),

Mod

ern

Mul

tidim

ensi

onal

Sca

ling.

The

ory

and

App

licat

ions

. New

Yo

rk: S

prin

ger.

Exem

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y re

adin

g ¹

Shoc

ker,

A.D

. & S

riniv

asan

, V. (

1997

), A

Con

sum

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ased

Met

hodo

logy

for t

he Id

entifi

catio

on

of N

ew P

rodu

ct Id

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Man

agem

ent S

cien

ce, 2

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Shug

an, S

.M. (

1987

), Es

timat

ing

Bra

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g.

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WO

RKSH

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30)

Gro

up

pres

enta

tion

s,

feed

back

an

d cl

osin

g


4. EVALUATION The time of the exams is over. Why? Because I believe that learning for an exam is inefficient. Rather I would like to motivate you to learn for life. So, how does your grad-ing take place? The course consists of three formal assessment oppotunities.

4.1 Contributions to the Multiple Choice Questions (25%)

Each day, during the first exercise session, a set of multiple choice questions (MCQ) will be handed out based on last day s class content. You will have fifteen minutes to solve the MCQs. Overall, the MCQ average counts for 25% of your final grade.

4.2 Group Paticipation on and around the Workshops (50%)

We will randomly form groups. One data set will be distributed to all the groups. Us-ing one or more methods presented in the first week of the course and one/more methods from the second week, each group has to create a realistic marketing appli-cation with the methods, select a model and then present the results and the market-ing implications. Students should try to combine insights from the methods used to a better overall evaluation of the situation. The results should be presented in “role-model” output tables. We will grade the contribution of your group work which will count to 50% of the overall grade (30% application, 10% code). By doing this we take into account peer scores (10%), i.e. each group member has to grade the participation of each group member. This helps us avoid and identify free-riders.

4.3 Individual Participation (25%)

Credits are awarded for thoughtful and active participation in class and in exercise discussions throughout the course. Credits will be given for knowledge of readings, cogent articulation of arguments and comments, and contribution to case discussion. Participation will be evaluated for quality as well as consistency. Attending the class and the exercises regularly and on time is an indication of professionalism and will also improve your participation grade.

In addition, each student can individually contribute by writing a paper on a specific topic from a list he hand out in the beginning of the semster, by preparing a presenta-tion, by collecting data, or by coding. We especially offer this opportunity to those students who do not feel comfortable actively interacting in the classroom.

We strongly recommend that you participate in all exercises, do the readings and fol-low our instructions. The conduct of this course is based on student inquiry, experi-ence, opinion and reflection realted to the readings and other assignments.

The individual participation scores account for 25% of the overall grade.

5. ACADEMIC FRAUDThe Honor Code of the University of Zurich applies to all work in this course, and will be strictly enforced. The intent of the Honor Code in this course is to ensure that each student claims and receives credits for his/her own efforts. Violations to this are considered academic fraud.


Definition

Academic fraud is an act by a student, which may result in a false academic evaluation of that student or of another student.

All documents you will hand-in are going to be checked by software and manually for plagiates. Documents with a score above 10% are going to be intensively validated and in suspicious cases we hand-out penalties for fraud behavior.

6. ADMINISTRATIVE COMMENTS

6.1 Students with disabilities

Any student with a documented disability needing academic adjustment or accommodations is requested to speak with me during the first two days of class. All discussion will remain confidential. Students with disabilities will need to also contact the directors of the school.

6.2 Getting in contact with me

Emails should be short and to the point. I don’t have time to read novels and to search for the point. Before sending an email, make clear that email is the appropriate instrument for your task. Maybe a telephone call is much easier and more personal. Or just ask me in class.

6.3 Registration cards

Right in the beginning of the class you will receive a Word file that we ask you to fill-out. In this file we ask you to add a personal picture and personal address information. Each information is kept confidential and is only accessible to our team. The reasons for doing this are 1) we would like to learn your names by pictures, 2) we use pictures later on if you ask reference letters to better remind ourselves, and 3) we need your contact information for the administration. Delivering these files if of course volun-tary. However, we would highly appreciate your cooperation on this. Many thanks in advance.

6.4 Name cards

Please use name cards regularly in class throughout the term so I can learn your names. I usually have large numbers of students across my class, so this will make it easier for me. If you don’t use name cards, I assume you do not care if I know who you are.

6.5 Class dismissal

You are asked to remain seated and attentive until class is dismissed by me.


6.6 Sound-emitting devices

It is expected that you turn off/mute all devices that emit sounds and noises that may interrupt the class (e.g., mobile phones, pagers, watch alarms). If an aoccasion arises in which you may need to receive a telephone call, please inform mw before the class. If you leave a class to answer a call or pager without previously notifying me, you will not be allowed to return to class.

6.7 Laptops and calculators

Laptops and programmable calculators are allows in class if you are asked for them and as far as their usage supports the individual learning process.

6.8 Group formation

We randomly assign groups and topics. If you are not satisfied with the group forma-tion, you can individually discuss and manage this with your classmates If you found a match for changing group affiliations with another student, you are invited to contact us and ask for change in the FIRST semester week. Later on we cannot consider this.

6.9 Important deadlines and class schedule

All important deadlines and the class schedule are communicated in lecture 1. If you can t participate in this class, it is your duty to inform yourself on the process.

05/09 - 09/09 - First course week

12/09 - 16/09 - Second course week

16/09 - Group presentation Workshop

We are very much looking forward to meeting you in class !

Enjoy!

Prof. Dr. René Algesheimer Marketing analytics ii

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