Chair of Marketing and Market ResearchDepartment of Business AdministrationUniversität Zürich, Switzerland © Zürich, 2016/2017. All rights reserved.
SyllabusEach Fall SemesterLast edit: 05.09.2016
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
Marketing Analytics II - Syllabus - 2
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-Yu 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 Workshop 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 3.3.
Language:
English
Marketing Analytics II - Syllabus - 3
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-5-29/31
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.
Marketing Analytics II - Syllabus - 4
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.
Marketing Analytics II - Syllabus - 5
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.
Marketing Analytics II - Syllabus - 6
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…
Marketing Analytics II - Syllabus - 7
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
Marketing Analytics II - Syllabus - 8
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.
Mar
ketin
g A
naly
tics
II - S
ylla
bus
- 9
Plan
ned
cour
se s
ched
ule[1]
:
Day
Topi
cTe
xt C
hapt
ers
Read
ings
1 (9
h30
- 11h
00)
Intr
oduc
tion
to M
ulti
vari
ate
Met
hods
(mul
tivar
iate
re
gres
sion
, an
ova,
di
scrim
inan
t an
alys
is,
expl
orat
ory
and
confi
rm-a
tory
fac
tor
anal
ysis
, lo
gist
ic
regr
essi
on,
conj
oint
mea
sure
men
t, st
ruct
ural
equ
atio
n m
odel
ing,
clu
ster
ana
lysi
s, s
ocia
l ne
twor
k an
alys
is a
nd
mac
hine
lear
ning
).
Exer
cise
: ¹
Intr
oduc
tion
to th
e st
atis
tical
pro
gram
R 3
.3
Dat
aset
s:
¹H
BAT
.csv
Requ
ired
read
ing
¹A
F, p
p. 1-
61.
¹H
BB
A, p
p. 1-
30.
Reco
mm
ende
d re
adin
g ¹
AF,
pp.
63-
115
(esp
ecia
lly fo
r tho
se, w
ho d
id n
ot v
isit
Qua
ntita
tive
Mar
ket R
esea
rch
I). ¹
Page
, Sco
tt (2
011)
, “Re
gres
sing
you
r inn
er h
edge
hog,
” (ac
cess
ed A
ugus
t 12t
h, 2
014
),
[ava
il-ab
le a
t htt
p://
ww
w.c
scs.
umic
h.ed
u/~s
page
/tea
chin
g_fil
es/m
odel
ing_
lect
ures
/MO
DEL
5/Le
c1.p
df].
Follo
w-u
p re
adin
g ¹
Coh
en, J
. (19
90),
“Thi
ngs
I hav
e le
arne
d (s
o fa
r),”
Am
eric
an P
sych
olog
ist,
45(12
), 13
04–
1312
. ¹
Coh
en, J
. (19
94),
“The
ear
th is
roun
d (p
< .0
5),”
Am
eric
an P
sych
olog
ist,
49 (1
2), 9
97-1
003
. ¹
Coh
en,
J. (
200
9),
“Mis
sion
Im
prob
able
: A
Con
cise
and
Pre
cise
Defi
nitio
n of
p-v
alue
,” Sc
ienc
eNow
Dai
ly, (
acce
ssed
Oct
ober
30
th, 2
00
9), [
avai
labl
e at
htt
p://
scie
ncen
ow.s
ci-
ence
mag
.org
/cgi
/con
tent
/ful
l/20
09/
1030
/1].
¹N
uzzo
, R. (
2014
), “S
tatis
tical
err
ors,
” Nat
ure,
vol
. 50
6 (F
ebru
ary
13th
), 15
0-1
52.
¹Ke
inin
gham
, T. L
., B
. Coo
il, T
.W. A
ndre
asse
n, a
nd L
. Aks
oy (2
00
7), “
A L
ongi
tudi
nal E
xam
ina-
tion
of N
et P
rom
oter
and
Firm
Rev
enue
Gro
wth
,”, J
ourn
al o
f Mar
ketin
g, 7
1 (Ju
ly),
39-5
1. Ex
empl
ary
read
ing
¹Re
ichh
eld,
Fre
deric
k F.
(20
03),
“The
onl
y nu
mbe
r you
nee
d to
gro
w,”
Har
vard
Bus
ines
s Re
-vi
ew, D
ecem
ber,
Repr
int:
R03
12C
.
1(14
h00
-15h3
0)M
ulti
vari
ate
regr
essi
on (O
LS)
(ord
inar
y le
ast s
quar
es (o
ls),
tota
l sum
of s
quar
es, m
odel
su
m o
f sq
uare
s, r
esid
ual s
um o
f sq
uare
s, m
ean
sum
of
squa
res,
roo
t m
ean
squa
re e
rror
, r2,
adj
uste
d r2
, f-t
est,
t-te
st, l
inea
rity,
hom
osce
dast
icity
, aut
ocor
rela
tion,
mul
ti-co
lline
arity
, Coo
k’s
dist
ance
, lev
erag
e, m
ahal
anob
is d
2,
dffit,
dfbet
a)
Exer
cise
: ¹
R ex
ampl
e of
reg
ress
ion
diag
nost
ics
in p
redi
ctin
g sa
les
of m
usic
alb
ums.
Dat
aset
s:
albu
m_s
ales
_dat
aset
.csv
Requ
ired
read
ing
¹A
F, p
p. 2
45-2
66.
Reco
mm
ende
d re
adin
g ¹
Gar
son,
G. D
avid
(20
14),
“Mul
tiple
reg
ress
ion”
, Sta
tistic
al A
ssoc
iate
s Pu
blis
hing
, [av
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,
” Mar
ketin
g Sc
ienc
e, 2
9 (4
), 75
6-76
9.
1. A
rtic
les
are
liste
d in
the
orde
r of p
ublic
atio
n ye
ar, f
ollo
wed
by
book
s. T
he o
nly
exce
ptio
ns a
re re
adin
gs in
the
AF
or H
BB
A b
ooks
that
are
alw
ays
on to
p.
Mar
ketin
g A
naly
tics
II - S
ylla
bus
- 10
Day
Topi
cTe
xt C
hapt
ers
Read
ings
2(9
h30
- 11h
00)
OLS
ass
umpt
ions
(line
arity
, ho
mos
ceda
stic
ity,
auto
corr
elat
ion,
mul
ticol
-lin
earit
y).
Exer
cise
:
¹Te
stin
g as
umpt
ions
.
Dat
aset
s:
¹al
bum
_sal
es_d
atas
et.c
sv
Requ
ired
read
ing
¹A
F, p
p. 2
66-3
11.
Reco
mm
ende
d re
adin
g ¹
Gar
son,
G. D
avid
(20
14),
“Mul
tiple
reg
ress
ion”
, Sta
tistic
al A
ssoc
iate
s Pu
blis
hing
, [av
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.
¹So
yer,
E.,
and
Hog
arth
, R. M
. (20
12),
“The
illu
sion
of p
redi
ctab
ility
: How
regr
essi
on s
tatis
tics
mis
lead
exp
erts
,” In
tern
atio
nal J
ourn
al o
f For
ecas
ting,
Jul
y-Se
ptem
ber,
28 (3
), 69
5-71
1. ¹
Ritt
er, D
. (20
14),
“Whe
n to
act
on
corr
elat
ion,
and
whe
n no
t,” H
arva
rd B
usin
ess
Revi
ew,
19.3
.20
14, r
ecei
ved
from
: ww
w.b
logs
.hbr
.com
, las
t acc
esse
d: A
ugus
t 12t
h, 2
014
.
Follo
w-u
p re
adin
g ¹
Aks
oy, L
erza
n et
al.
(20
08)
, “Th
e Lo
ng T
erm
Sto
ck M
arke
t Val
uatio
n of
Cus
tom
er S
atis
fac-
tion,
” Jou
rnal
of M
arke
ting,
72,
105-
122.
¹B
radl
ey E
. (19
79),
“Boo
tstr
ap M
etho
ds: A
noth
er L
ook
at t
he J
ackk
nife
,” Th
e A
nnal
s of
Sta
-tis
tics,
7, 1,
1979
, 1–2
6. ¹
Litt
le, R
. J. A
. and
D.B
. Rub
in (2
00
2), S
tatis
tical
Ana
lysi
s w
ith M
issi
ng D
ata,
2nd
ed.
, Hob
o-ke
n: J
ohn
Wile
y &
Son
s. ¹
Wilc
ox, R
. R. (
200
5), I
ntro
duct
ion
to R
obus
t Es
timat
ion
and
Hyp
othe
sis
Test
ing,
2nd
ed.
, B
urlin
gton
, MA
: Els
evie
r.
Exem
plar
y re
adin
g ¹
The
Am
eric
an C
usto
mer
Sat
isfa
ctio
n In
dex,
htt
p://
ww
w.th
eacs
i.org
/.
2(14
h00
-15h3
0)O
LS c
halle
nges
(mul
ticol
linea
rity,
het
eros
keda
stic
ity, o
utlie
rs, e
rror
s in
va
riabl
es,
endo
gene
ity,
self-
sele
ctio
n, g
raph
ical
pro
fil-
ing,
uni
varia
te p
rofil
ing
with
his
togr
ams,
biv
aria
te p
rofil
-in
g w
ith b
oxpl
ots
and
scatt
erpl
ots)
Exer
cise
:
¹O
utlie
r and
mul
ticol
linea
rity
dete
ctio
n, a
sses
sing
im-
pact
of o
mitt
ed v
aria
ble
bias
.
Dat
aset
s:
¹al
bum
_sal
es_d
atas
et.c
sv
Requ
ired
read
ing
¹A
F, p
p. 2
66-3
11.
¹W
oold
ridge
J.M
. Int
rodu
ctor
y Ec
onom
etric
s, (2
00
9),
Sout
h-W
est-
ernC
enga
ge L
earn
ing,
4th
edi
tion,
C
h.3,
8, 1
5, (h
ttp:
//nc
bae.
yola
site
.co
m/r
esou
rces
/Int
rodu
ctor
yEco
no-
met
rics_
AM
oder
nApp
roac
h_Fo
ur-
thEd
ition
_Jeff
rey_
Woo
ldrid
ge.p
df)
Reco
mm
ende
d re
adin
g ¹
Ang
rist,
J. a
nd P
isch
ke, J
-S, (
200
8), M
ostly
Har
mle
ss E
cono
met
rics:
An
Empi
ricis
tís C
om-
pani
on, h
ttp:
//w
ww
.dev
elop
men
t.wne
.uw
.edu
.pl/
uplo
ads/
Mai
n/re
crut
_eco
nom
etric
s.pd
f.
Mar
ketin
g A
naly
tics
II - S
ylla
bus
- 11
Day
Topi
cTe
xt C
hapt
ers
Read
ings
3(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 P
ublis
hing
, [av
aila
ble
at
http:
//w
ww
.sta
tistic
alas
soci
ates
.com
/boo
klis
t.htm
], la
st a
cces
sed:
Aug
ust 1
2th,
20
14.
¹H
BB
A, p
p. 4
39-4
76.
Follo
w-u
p re
adin
g ¹
AF,
pp.
457
-50
5 (R
epea
ted
Mea
sure
s).
¹A
F, p
p. 5
06-
538
(Mix
ed D
esig
ns).
¹Ke
nny,
D. A
. and
C.M
. Jud
d (19
86),
“Con
sequ
ence
s 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
.
3(14
h00
-15h3
0)Ex
plor
ator
y Fa
ctor
Ana
lysi
s (E
FA)
(exp
lora
tory
fa
ctor
an
alys
is,
alph
a fa
ctor
ing,
A
nder
son-
Rubi
n m
etho
d,
com
mon
va
rianc
e,
com
mun
ality
, Cro
nbac
h’s
Alp
ha, d
irect
obl
imin
, fa
ctor
load
ing,
fact
or m
atrix
, fac
tor s
core
s, in
tra-
clas
s co
rrel
atio
n co
effici
ent
(icc)
, Kai
ser-
Mey
er-
Olk
in (k
mo)
mea
sure
of s
ampl
ing
adeq
uacy
, Kai
-se
r cr
iterio
n, l
aten
t va
riabl
e, o
bliq
ue r
otat
ion,
pr
inci
pal
com
pone
nt
anal
ysis
(p
ca),
rand
om
varia
nce,
rota
tion,
scr
ee p
lot,
sing
ular
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
, int
erpr
eta
EFA
mod
els.
Dat
aset
s:
¹EF
A_W
illia
ms.
csv
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 A
naly
sis”
, Sta
tistic
al A
ssoc
iate
s Pu
blis
hing
, [av
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
BB
A, p
p. 9
1-15
2.
Follo
w-u
p re
adin
g ¹
Chu
rchi
ll, G
. A. (
1979
): A
Par
adig
m f
or D
evel
opin
g B
etter
Mea
sure
s of
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 i
s C
oeffi
cien
t Alp
ha?
An
Exam
inat
ion
of T
heor
y an
d A
pplic
atio
ns,”
Jour
nal o
f App
lied
Psyc
holo
gy, 7
8, 9
8-10
4. ¹
Ger
bing
, D.W
. and
J.C
. And
erso
n (19
88),
“An
Upd
ated
Par
adig
m fo
r Sc
ale
Dev
elop
men
t Inc
or-
pora
ting
Uni
dim
ensi
onal
ity a
nd I
ts A
sses
smen
t,” J
ourn
al o
f M
arke
ting
Rese
arch
, 25
(May
), 18
6-19
2. ¹
Gor
such
, R. L
. (19
90),
“Com
mon
Fac
tor
Ana
lysi
s Ve
rsus
Com
pone
nt A
naly
sis:
Som
e W
ell a
nd
Litt
le K
now
n Fa
cts,
” Mul
tivar
iate
Beh
avio
ral R
esea
rch,
25,
33-
39.
¹St
ewar
t, D
. W. (
1981
), “T
he A
pplic
atio
n an
d M
isap
plic
atio
n of
Fac
tor
Ana
lysi
s in
Mar
ketin
g Re
-se
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
stig
atio
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
anua
ry),
1-21
. 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
-Use
r 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
- 12
Day
Topi
cTe
xt C
hapt
ers
Read
ings
4(9
h30
-11h3
0)C
onfir
mat
ory
Fact
or A
naly
sis
(CFA
)(la
tent
var
iabl
e, o
bser
ved
varia
ble,
fac
tor
load
ings
, pa
th c
oeffi
cien
t, fa
ctor
cor
rela
tions
, fixe
dpa
ram
eter
s, c
onst
rain
ed p
aram
eter
s, fr
eepa
ram
eter
s, r
ando
m m
easu
rem
ent
erro
r, no
nrad
om
mea
sure
men
t err
or, f
orm
ativ
e in
dica
tors
,re
flect
ive
indi
cato
rs,
estim
ator
s, m
odel
fit
indi
ces,
pa
ram
eter
crit
eria
(co
nstr
uct
valid
ity,
conv
erge
nt
valid
ity, d
iscr
imin
ant
valid
ity, n
omol
ogic
al a
nd f
ace
valid
ity, s
tand
ardi
zed
resi
dual
s, r
esid
ual
varia
nces
, m
odifi
catio
n in
dice
s).
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
(20
14).
“Con
firm
ator
y Fa
ctor
A
naly
sis”
, St
atis
tical
A
ssoc
iate
s Pu
blis
hing
, [a
vaila
ble
at h
ttp:
//w
ww
.sta
-tis
tical
asso
ciat
es.c
om/b
ookl
ist.h
tm],
last
ac
cess
ed: A
ugus
t 12t
h, 2
014
.
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
m
etho
ds, 3
7(4)
, 672
-676
. ¹
Bro
wn,
Tim
othy
A.
(20
06)
, C
onfir
mat
ory
Fact
orA
naly
sis
for
App
lied
Rese
arch
. Lo
ndon
: Th
eGui
lford
Pre
ss, 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:
Th
eGui
lford
Pre
ss, c
hapt
er 5
and
9.
¹M
uthé
n, L
inda
K. a
nd B
engt
O. M
uthé
n (2
010
), M
plus
Use
r’s G
uide
(6
ed.).
Los
Ang
eles
: M
uthé
n &
Mut
hén,
cha
pter
5, 1
5, 16
, 17
and
18.
Follo
w-u
p re
adin
g 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
en-
sitiv
ity t
o U
nder
para
met
eriz
ed M
odel
Mis
spec
ifica
tion,
” Ps
ycho
logi
cal M
etho
ds, 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
ruc-
ture
Ana
lysi
s: C
onve
ntio
nal C
riter
ia V
ersu
s N
ew A
ltern
ativ
es,”
Stru
ctur
al E
quat
ion
Mod
-el
ing,
6 (1
), 1-
55.
¹M
arsh
, Her
bert
W.,
Kit-
Tai H
au, J
ohn
R. B
alla
, and
Dav
id G
rays
on (1
998)
, “Is
Mor
e Ev
er T
oo
Muc
h? T
he N
umbe
r of I
ndic
ator
s Pe
r Fac
tor i
n C
onfir
mat
ory
Fact
or A
naly
sis,
” Mul
tivar
i-at
e Be
havi
oral
Res
earc
h, 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
a-to
ry F
acto
r Ana
lysi
s Mod
els o
f 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
the
End
-Use
r 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
Vers
ions
of
the
Mar
low
e–C
row
ne S
ocia
l Des
irabi
lity
Scal
e,”
Jour
nal o
f A
pplie
d So
cial
Ps
ycho
logy
, 34(
11), 2
343-
2352
. ¹
Yoo,
Mah
n H
ee a
nd J
aebe
om S
uh (2
003
), “O
rgan
izat
iona
l citi
zens
hip
beha
vior
s and
ser
vice
qu
ality
as
exte
rnal
effe
ctiv
enes
s of
con
tact
em
ploy
ees,
” Jo
urna
l of B
usin
ess
Rese
arch
, 56
(8),
p. 5
97-6
11.
Mar
ketin
g A
naly
tics
II - S
ylla
bus
- 13
Day
Topi
cTe
xt C
hapt
ers
Read
ings
4(14
h00
- 15h
30)
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
od-
el,
stru
ctur
al m
odel
, m
easu
rem
ent
erro
rs,
anal
ysis
of co
varia
nce
stru
ctur
es,
path
an
alys
is,
nest
ed
mod
els,
non
-nes
ted
mod
els,
mod
el s
peci
ficat
ion,
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 (
2014
). “S
truc
tura
l Equ
a-tio
ns M
odel
ing”
, St
atis
tical
Ass
ocia
tes
Publ
ishi
ng, [
avai
labl
e at
htt
p://
ww
w.s
ta-
tistic
alas
soci
ates
.com
/boo
klis
t.htm
], la
st
acce
ssed
: Aug
ust 1
2th,
20
14.
¹M
uthé
n, L
inda
K.
and
Ben
gt O
. M
uthé
n (2
010
): M
plus
Use
r’s G
uide
(6
ed.).
Los
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
and
Chr
istia
n H
ombu
rg (1
996)
, “A
pplic
atio
ns o
f Str
uctu
ral E
quat
ion
Mod
el-
ing
in M
arke
ting
and
Con
sum
er R
esea
rch:
A R
evie
w,”
Inte
rnat
iona
l Jou
rnal
of
Rese
arch
in
Mar
ketin
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 (
200
0), “
On
the
Use
of S
truc
tura
l Equ
a-tio
n M
odel
s fo
r Mar
ketin
g M
odel
ing,
” Int
erna
tiona
l Jou
rnal
of R
esea
rch
in M
arke
ting,
17 (2
-3),
195-
202.
Follo
w-u
p re
adin
g ¹
Asp
arou
hov,
T. a
nd B
. Mut
hén
(20
09)
, “Ex
plor
ator
y St
ruct
ural
Equ
atio
n M
odel
ing,
” St
ruct
ural
Eq
uatio
n M
odel
ing:
A M
ultid
isci
plin
ary
Jour
nal,
16(3
), 39
7-43
8.
¹B
olle
n, K
enne
th A
. (19
89),
Stru
ctur
al E
quat
ions
with
Lat
ent V
aria
bles
. New
Yor
k: J
ohn
Will
ey &
So
ns, c
hapt
er 1.
¹
Bol
len,
Ken
neth
A. a
nd W
. R. D
avis
(20
09)
, “Tw
o Ru
les
of Id
entifi
catio
n fo
r St
ruct
ural
Equ
atio
n M
odel
s,” S
truc
tura
l Equ
atio
n M
odel
ing:
A M
ultid
isci
plin
ary
Jour
nal,
16(3
), 52
3 - 5
36.
¹Ja
rvis
, Che
ryl B
., Sc
ott B
. Mac
Kenz
ie, a
nd P
hilip
M. P
odsa
koff
(20
03) “
A C
ritic
al R
evie
w o
f Con
-st
ruct
Indi
cato
rs a
nd M
easu
rem
ent
Mod
el M
issp
ecifi
catio
n in
Mar
ketin
g an
d C
onsu
mer
Re-
sear
ch,”
Jour
nal o
f Con
sum
er R
esea
rch,
30
(2),
199-
218.
¹
Mar
sh, H
. W.,
B. M
uthé
n, e
t al
. (20
09)
, “Ex
plor
ator
y St
ruct
ural
Equ
atio
n M
odel
ing,
Inte
grat
ing
CFA
and
EFA
: App
licat
ion
to S
tude
nts’
Eval
uatio
ns o
f Uni
vers
ity T
each
ing,
” St
ruct
ural
Equ
a-tio
n M
odel
ing:
A M
ultid
isci
plin
ary
Jour
nal,
16(3
), 43
9-47
6.
¹Sc
hrei
ber,
Jam
es B
. et a
l. (2
00
6), “
Repo
rtin
g St
ruct
ural
Equ
atio
n M
odel
ing.
The
Jou
rnal
of E
du-
catio
nal R
esea
rch,
99(
6), 3
23-3
37. C
onfir
mat
ory
Fact
or A
naly
sis
Resu
lts: A
Rev
iew
,” Th
e Jo
ur-
nal o
f Edu
catio
nal R
esea
rch,
99(
6), 3
23-3
37.
Exem
plar
y re
adin
g ¹
Alg
eshe
imer
, R.,
U. D
hola
kia
and
A. H
errm
ann
(20
05)
: The
Soc
ial I
nflue
nce
of B
rand
Com
mun
ity:
Evid
ence
from
Eur
opea
n C
ar C
lubs
, Jou
rnal
of M
arke
ting,
69(
7), p
. 19-
34.
5(9
h30
- 17h
30)
WO
RKSH
OP
Mar
ketin
g A
naly
tics
II - S
ylla
bus
- 14
Day
Topi
cTe
xt C
hapt
ers
Read
ings
6(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
(e
xp(b
)), in
com
plet
e in
form
atio
n, c
ompl
ete
sepa
-ra
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 (2
00
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
Line
ar L
ogit
Mod
els
in M
arke
ting
Rese
arch
,” Jo
urna
l of
Mar
ketin
g Re
sear
ch, 2
1 (1/
Feb.
), 20
-31.
¹Pe
ng, C
.-Y. J
., K
.L. L
ee, a
nd G
.M. I
nger
soll
(20
02)
, “A
n In
trod
uctio
n to
Log
istic
Reg
ress
ion
Ana
lysi
s an
d Re
port
ing,
” The
Jou
rnal
of E
duca
tiona
l Res
earc
h, 9
6 (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 Im
ran
S. C
urri
m (
200
2), “
An
Empi
rica
l Com
pari
son
of L
ogit
Cho
ice
Mod
els
with
Dis
cret
e V
ersu
s C
ontin
uous
Rep
rese
ntat
ions
of
Het
erog
enei
ty,”
Jour
nal o
f M
arke
ting
Rese
arch
, 39
(4),
479-
487.
¹Sw
ait,
Joffr
e an
d Jo
rdan
Lou
vier
e (19
93),
“The
Rol
e of
the
Sca
le P
aram
eter
in t
he E
stim
atio
n an
d C
ompa
riso
n of
Mul
tinom
ial L
ogit
Mod
els,
” Jou
rnal
of M
arke
ting
Rese
arch
, 30
(3/A
ug.),
30
5-31
4.
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 F
requ
ently
Pur
chas
ed P
rod-
ucts
,” Jo
urna
l of C
onsu
mer
Res
earc
h, 13
(2/S
ep.),
250
-256
.
6(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 s
imul
ator
, cho
ice-
base
d co
njoi
nt a
nal-
ysis
(cbc
), co
mpo
sitio
n ru
le, c
ompo
sitio
nal m
odel
, co
njoi
nt
anal
ysis
, de
com
posi
tiona
l m
odel
, fu
ll pr
ofile
met
hod,
full
fact
oria
l exp
erim
enta
l des
ign,
ho
ldou
t pro
files
, mai
n eff
ects
, mar
ket s
imul
atio
ns,
orth
ogon
al d
esig
n, p
lanc
ards
, rev
ersa
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 Ana
lysi
s: R
eflec
tions
and
Pr
ospe
cts,
” Int
erfa
ces,
31(
3), 5
6-73
. ¹
Hau
ser,
J. R
. and
V.R
. Rao
(20
03),
“Con
join
t Ana
lysi
s, R
elat
ed M
odel
ing,
and
App
licat
ions
,” in
Mar
ket
Rese
arch
and
Mod
elin
g: P
rogr
ess
and
Pros
pect
s: A
Tri
bute
to P
aul E
. Gre
en (I
nter
natio
nal S
erie
s 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 Se-
ries i
n 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 Ana
lysi
s: H
ow w
e go
t her
e an
d w
here
we
are
(an
upda
te),
Saw
toot
h So
ft-w
are
Rese
arch
Pap
er S
erie
s, 1-
15.
Exem
plar
y re
adin
g ¹
Qua
ltrix
.com
(20
11): C
onjo
int A
naly
sis:
Exp
lain
ing
Full
Profi
le a
nd S
elf E
xplic
ated
App
roac
hes,
[ava
il-ab
le a
t: htt
p://
ww
w.q
ualt
rics
.com
/doc
s/C
onjo
intO
verv
iew
]. ¹
Witt
ink,
D. a
nd P
. Catt
in (1
989)
, “C
omm
erci
al U
se o
f Con
join
t Ana
lysi
s: A
n U
pdat
e,”
Jour
nal o
f Mar
-ke
ting,
53
(Jul
y), 9
1–96
.
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 the
est
imat
ion
of th
e 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 F
ittin
g th
e M
ultin
omia
l Pro
bit M
odel
“, Jo
urna
l of
Sta
tistic
al S
oftw
are,
14(3
), 1-3
2.
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)
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
- 16
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.
7(14
h00
- 15h
30)
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
- 17
Day
Topi
cTe
xt C
hapt
ers
Read
ings
8(9
h30
- 11h
00)
Mac
hine
Lea
rnin
g (D
ecis
ion
Tree
s)(m
achi
ne le
arni
ng, c
lass
ifica
tion
tree
s, b
aggi
ng,
rand
om fo
rest
).
Exer
cise
: ¹
tba
Dat
aset
s:
¹tb
a
Requ
ired
read
ing
¹Ja
mes
, G.,
Witt
en, D
., H
astie
, T. a
nd T
ibsh
irani
, R.
(20
15),
AnI
intr
oduc
tion
to S
tatis
tical
Lea
rn-
ing
with
App
licat
ions
in R
, Cha
pter
8. (
http:
//w
ww
-bcf
.usc
.edu
/~ga
reth
/ISL
/ISL
R%
20Si
xth%
20Pr
intin
g.pd
f) ¹
Pan
g-N
ing
Tan,
Mic
hael
Ste
inba
ch, V
ipin
Ku-
mar
,(20
14):
Intr
oduc
tion
to D
ata
Min
ing,
Cha
p-te
r 4 (h
ttps
://w
ww
-use
rs.c
s.um
n.ed
u/~k
umar
/dm
book
/ch4
).
Reco
mm
ende
d re
adin
g
¹Fr
iedm
an, J
erom
e, T
revo
r Has
tie a
nd R
ober
t Tib
shira
ni, (
2001
): Th
e el
emen
ts o
f sta
tistic
al le
arni
ng,
Cha
pter
9 (h
ttps
://w
eb.st
anfo
rd.e
du/~
hast
ie/l
ocal
.ftp/
Sprin
ger/
OLD
/ESL
II_pr
int4
). ¹
The
rnea
u, T
erry
, and
Eliz
abet
h A
tkin
son,
(20
15):
An
intr
oduc
tion
to r
ecur
sive
par
titio
ning
us-
ing
the
RPA
RT r
outin
es, (
https
://cr
an.r-
proj
ect.o
rg/w
eb/p
acka
ges/
rpar
t/vi
gnett
es/l
ongi
ntro
.pd
f).
¹Li
aw, A
ndy
and
Matt
hew
Wie
ner,
(200
2): C
lass
ifica
tion
and
regr
essi
on b
y ra
ndom
Fore
st,
(htt
p://
ww
w.b
ios.u
nc.e
du/~
dzen
g/BI
OS7
40/r
ando
mfo
rest
).
8(14
h00
- 15h
30)
Mac
hine
Lea
rnin
g (S
VM
)(m
achi
ne le
arni
ng, s
uppo
rt v
ecto
rs, s
epar
atin
g hy
perp
lane
, lin
ear
kern
el,
poly
nom
ial
kern
el,
RB
F ke
rnel
).Ex
erci
se:
¹tr
aini
ng a
sup
port
vec
tor m
achi
neD
atas
ets:
¹
sim
ulat
ed d
atas
et
Requ
ired
read
ing
¹G
unn,
S.R
.,(19
98):
Supp
ort
Vect
or M
achi
nes
for
Cla
ssifi
catio
n an
d Re
gres
sion
, Te
chni
cal
Re-
port
, (htt
p://
ww
w.e
cs.s
oton
.ac.
uk/s
rg/p
ublic
a-tio
ns/p
df/S
VM
). ¹
Jam
es G
., W
itten
, G
., H
astie
, T.
and
Tib
shira
ni,
R.,(
2015
): A
n in
trod
uctio
n to
Sta
tistic
al L
earn
-in
g w
ith A
pplic
atio
ns i
n R
,Chp
ater
3,
8 an
d 15
, Sp
ringe
r Sc
ienc
e+B
usin
ess
Med
ia
New
Yo
rk,
(htt
p://
ww
w-b
cf.u
sc.e
du/~
gare
th/I
SL/
ISLR
%20
Sixt
h%20
Prin
ting.
pdf)
.
Reco
mm
ende
d re
adin
g
¹B
ernh
ard
E. B
oser
and
Isab
elle
M. G
uyon
and
Vla
dim
ir Va
pnik
. (19
92).
Proc
eedi
ngs
of t
he fi
fth
annu
al w
orks
hop
on C
ompu
tatio
nal l
earn
ing
theo
ry (C
OLT
). ¹
S. M
ika,
C. S
chaf
er, P
. Las
kov,
D. T
ax a
nd K
.-R. M
ulle
r, Su
ppor
t Vec
tor
Mac
hine
s, B
ookc
hapt
er :
Han
dboo
k of
Com
puta
tiona
l Sta
tistic
s (C
once
pts
and
Met
hods
). ¹
Mac
hine
Lea
rnin
g w
ith R
, ¹
http:
//w
ww
.ker
nel-m
achi
nes.
org/
¹htt
p://
ww
w.s
uppo
rt-v
ecto
r.net
/ ¹
http:
//w
ww
.lear
ning
-with
-ker
nels
.org
/ ¹
http:
//w
ww
.pas
cal-n
etw
ork.
org/
¹htt
p://
ww
w.r-
proj
ect.o
rg/
(pac
kage
s e1
071
,ker
nlab
)
9(9
h30
- 17h
30)
WO
RKSH
OP
10(9
h30
- 14h
30)
Gro
up p
rese
ntat
ions
, fee
dbac
k an
d cl
osin
g
Marketing Analytics II - Syllabus - 18
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 (50%)
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 50% of your final grade.
4.2 Group Participation on and around the Workshops (50%)
Will randomly form groups and will randomly distribute topics to them. Each group will be linked to one specific lecture and will receive two method-specific tasks. These tasks are: 1) create a realistic marketing application with your method, collect data to this, apply your method, present the results in the presentation block, 2) create “role-model“ output tables per method, code them in R. We will grade the contribution of your group work which will count to 50% of the overall grade (40% application and R 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. We reserved time for group work throughout our workshops.
4.3 Bonus for exceptional individual participation (+.25 to final grade)
Overall, less than 5% of the students will most likely perceive this bonus. It is given for exceptional performance and adds 0.25 to the final grade, conditional on passing 4.1 and 4.2.
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.
Marketing Analytics II - Syllabus - 19
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.
Marketing Analytics II - Syllabus - 20
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!