Experimental Design: from Agriculture to Industry and
Marketing Research
Utami Dyah Syafitri
Email: [email protected]
Seminar Online : 27 Februari 2021
Program Studi Statistika dan Sains Data
Departemen Statistika FMIPA
IPB University
Departemen Statistika FMIPA IPB 1
Outline
Definition of Experimental Design
Experimental Design in Industry – Factorial Design
Experimental Design in Market Research – Conjoint analysis
Further research
Departemen Statistika FMIPA IPB 2
Methods of collection data
Departemen Statistika FMIPA IPB
survei percobaanobservasi
database
(administrasi, transaksi,
tangkapan aktivitas)
web scraping
sensus
3
Observasi vs Percobaan
Pengamatan terhadap perubahan warna
Apakah jika diberikan warna yang berbeda, maka perubahan warnanya akan
sesuai dengan warna yang diberikan?
Sumber foto: https://i.ytimg.com/vi/KV4YuzuXpjQ/maxresdefault.jpgSumber foto: https://carecorner.weebly.com/uploads/7/0/4/2/7042382/6001608_orig.jpg Departemen Statistika FMIPA IPB 4
Experiment vs Experimental design
Sumber : https://www.scribbr.com/methodology/experimental-design/Departemen Statistika FMIPA IPB 5
Experiment
• An experiment is a type of research method in which you manipulate one or more independent variables and measure their effect on one or more dependent variables.
Experimental Design
• creating a set of procedures to test a hypothesis.
Source : http://www.stat.cmu.edu/~hseltman/309/Book/Book.pdf
Departemen Statistika FMIPA IPB 6
APA ITU PERANCANGAN PERCOBAAN?
• Merupakan suatu metode yang sistematik yang didalamnya terdapat uji atau sederetan uji dimana suatuproses atau sistem mengakibatkan terjadinya perubahanyang cukup berarti dari variabel input, yang dapatdiamati melalui respon yang muncul.
• Perencanaan (planning) suatu percobaan untukmemperoleh informasi yang relevan dengan tujuan daripenelitian
Departemen Statistika FMIPA IPB 7
ProsesOutput
Y1, Y2, …, Yk
Inputs
. . .
Controllable factors
. . .
Uncontrollable factors
X1X2 Xp
Z1 Z2 Zp
Sumber: Montgomery, 2013.Design and analysis of experiments 8th edition. Wiley
Departemen Statistika FMIPA IPB 8
Cause and Effects Diagram
Objective of experiments
Blocking factors
Held constant factors
Controllable factors
Uncontrollable factors
Departemen Statistika FMIPA IPB 9
Experimental Design in Industry
Departemen Statistika FMIPA IPB 10
Syzygium polyanthum (Wight) WalpBay Leaves
as medicinal plant
Another names: Salam|Maselangan|Ubar Serai|Gowok|Kastolam
Pharmacological Effects of
Bay Leaves
Antioxidant Antibacterial AntidiarrhealAlzheimer
treatment
ACTIVITIESCOMPONENTS
Flavonoids
Phenolics
EXTRACTION TECHNIQUE
Microwave-Assisted Extraction (MAE)
♥ Higher yield
♥ Much shorter time
♥ High efficiency in solvent consuming
Factors Levels(Variables, Inputs) (Settings)
Study case: Bay leaves extraction
Time
Power
Solvent to sample ratio
Treatment(s)
30s 45s 60s
30%
30 mL/g
(1) 30s
(4) 45s
(8) 30s
(2) 45s
(6) 60s
(9) 60s
(3) 30s
(7) 45s
(10) 60s
Replications : 3x
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Analysing the data
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1
2
3
One factor at time
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Power : 30%Solvent to sample ratio: 30 ml/gram
Factors Levels(Variables, Inputs) (Settings)
Study case: Bay leave extraction
Time
Power
Solvent to sample ratio
Treatment(s)
45s
10% 30% 50%
30 mL/g
(1) 10%
(4) 30%
(7) 50%
(2) 50%
(5) 10%
(8) 10%
(3) 30%
(6) 50%
(9) 30%
Replications : 3x
Departemen Statistika FMIPA IPB 17
Departemen Statistika FMIPA IPB 18
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• Tidak bisamembandingkan semuakombinasi perlakuan
• Pengacakan lengkaptidak bisa dilakukankarena percobaandilakukan secarabertahap -> pengacakanmerupakan salah satuprinsip dalamperancangan percobaan
Drawbacks
Factorial design
20
•Dalam berbagai bidang penerapan perancanganpercobaan diketahui bahwa respon dari individumerupakan akibat dari berbagai faktor secarasimultan•Percobaan satu faktor akan menjadi sangat tidak
efektif mengingat respon yang muncul akan berbedajika kondisi faktor-faktor lain berubah•Percobaan faktorial dicirikan oleh perlakuan yang
merupakan komposisi dari semua kemungkinankombinasi dari level-level dua faktor atau lebih
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Factors Levels Responses
(Variables, Inputs) (Settings) (Outcomes, characteristics)
Flavonoid
Phenolics
Study case: Bay leaves extraction
Time
Power
Solvent to sample ratio
Treatment(s)
30s 45s 60s
10% 30% 50%
20 mL/g 30 mL/g 40 mL/g
30s
10%
40 mL/g
Departemen Statistika FMIPA IPB 21
Sumber : Anggraini, D.N. 2016. Pengoptimuman kondisi ekstraksi berbantuan mikrogelombang untuk fenol dan flavonoid total daun salam menggunakan metode permukaan. Skripsi. Kimia.
33 Factorial Design - All treatments
Departemen Statistika FMIPA IPB 22
20 ml/gram
10%
30%
50%
30 s
45 s
60 s
30 s
45 s
60 s
30 s
45 s
60 s
30 ml/gram
10%
30%
50%
30 s
45 s
60 s
30 s
45 s
60 s
30 s
45 s
60 s
40 ml/gram
10%
30%
50%
30 s
45 s
60 s
30 s
45 s
60 s
30 s
45 s
60 s
Experimental Region – 33 Factorial Design
Departemen Statistika FMIPA IPB 23
C
A
B
20 mg/liter 40 mg/liter
10%
50%
30%
60%
Prinsip Perancangan Percobaan
Randomization (Pengacakan)
Replication (Ulangan)
Local Control -- Blocking
Factorial design in Complete Randomized Block Design (CRBD)
25
Block I Block II
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RandomizationPengamatan
ke- ...Kondisi ekstraksi
A B C1 1 1 22 3 2 13 3 2 34 1 2 15 2 2 26 1 3 27 3 1 18 2 3 29 3 2 2
10 1 1 111 3 1 212 2 1 113 2 2 114 1 3 115 3 1 316 2 1 217 1 3 318 3 3 219 2 1 320 1 1 321 2 3 322 3 3 123 2 3 124 1 2 225 3 3 326 1 2 327 2 2 3
Pengamatan ke- ...
Kondisi ekstraksiA B C
28 3 2 329 3 3 330 2 3 331 2 2 232 3 3 133 1 2 134 1 3 335 1 1 136 2 3 237 2 2 138 1 3 239 3 2 140 1 1 341 3 1 142 1 2 243 1 2 344 2 1 145 3 2 246 1 1 247 3 3 248 2 1 349 1 3 150 3 1 351 2 3 152 2 1 253 3 1 254 2 2 3Departemen Statistika FMIPA IPB 26
Departemen Statistika FMIPA IPB 27
Response Surface Model
Total Flavonoids
Power 10% Power 30% Power 50%Departemen Statistika FMIPA IPB 28
Sumber : Anggraini, D.N. 2016. Pengoptimuman kondisi ekstraksi berbantuan mikrogelombang untuk fenol dan flavonoid total daun salam menggunakan metode permukaan. Skripsi. Kimia.
Experimental Design in Marketing Research
Departemen Statistika FMIPA IPB 29
Conjoin Analysis
Dalam marketing riset merupakan suatu tehnikpeubah ganda yang dikembangkan secara khusus
untuk mengetahui preferensi dari berbagaiobjek (produk, layanan, atau ide)
Preferences of statistics teaching methods
Preferences of bike
Three important steps in Conjoint Analysis
Design the combination of attributes (experimental design)
Sampling
Analysis
Tahapan (1)
Tahap I
• Definisipermasalahan
Tahap II
• PemilihanMetode Konjoin
• Merancangstimuli
• Merancangbagaimanastimuli diukur
• Merancangkuesioner
Tahap III
• Keterpenuhanasumsi darianalisis konjoin
Tahapan (2)
Tahap IV
• Pendugaanmodel konjoin
• Evaluasikebaikanmodel
Tahap V
• Interpretasihasil baikdalam level umum maupundalam level individu
• Kepentinganrelatif dariatribut
Tahap VI
• Validasi hasil: internal daneksternalvalidity
Tahap VII
• Aplikasikanhasil konjoinanalisis untuksegmentasipelanggan, analisis profit, choice simulator
Desain-Empat pertanyaan
• Atribut mana yang paling penting dalam menilai preferensi dariresponden – pemilihan atribut
• Bagaimana responden tahu mengenai makna dari masing-masingfaktor – pemilihan level
• Apa yang dievaluasi oleh responden – kombinasi dari atribut profil
• Berapa banyak profil yang dievaluasi -- rancangan
Attributes of teaching methods
• The number of students
• Delivering methods
• Teaching equipments
• Material resources
• Giving rewards
• Giving motivation
• Exercise methods
• Assesment methods
Departemen Statistika FMIPA IPB 36
Levels of each attribute
• The number of students : small, medium, large
• Delivering methods : one way, two way
• Teaching equipments : board, projector, board + projector
• Material resources : handouts, slides, textbooks
• Giving rewards : always, sometimes, no
• Giving motivation : always, sometimes, no
• Exercise methods : individual, groups
• Assesment methods : standard, distribution
Departemen Statistika FMIPA IPB 37
Profiles
Departemen Statistika FMIPA IPB 38
BoardHandouts
Rewards : alwaysMotivation : alwaysExercise : individual
Assesment : standard
ProjectorHandouts
Rewards : alwaysMotivation : alwaysExercise : individual
Assesment : standard
ProjectorHandouts
Rewards : sometimesMotivation : alwaysExercise : individual
Assesment : standard
Experimental design
Departemen Statistika FMIPA IPB 39
• The number of students : small, medium, large
• Delivering methods : one way, two way
• Teaching equipments : board, projector, board + projector
• Material resources : handouts, slides, textbooks
• Giving rewards : always, sometimes, no
• Giving motivation : always, sometimes, no
• Exercise methods : individual, groups
• Assesment methods : standard, distribution
All combination:
35x23
=7776
Impossible to run!
Design
•Karena biasanya jumlah level dan faktor banyak maka tidak digunakan rancangan faktorial
•Rancangan yang biasa digunakan adalah fraksional faktorial atau bridging design atau orthogonal array atau rancangan yang optimal berdasarkan kriteria tertentu
Another isu : Response measurementTraditional conjoint
Rank
1 10 1 10 1 10Rating
Response measurementChoice Based Conjoint
Our research : Five atributes/factors*
Delivering methods (A)
One way (1)
Two way (-1)
Teaching equipments (B)
white board (1)
white board + projector (-1)
Material sources (C)
Slides/handout (1)
Textbooks (-1)
Giving rewards (D)
Exist (1)
Not exist (-1)
Giving motivation (E)
Exist (1)
Not exist (-1)
Syafitri U,Afandi FM, Palupi SP (2016). Choice Based Conjoint for Preferences of Statistics Teaching Methods. Proceeding of the 7th Annual Basic Science International Conference-2017. FMIPA Universitas Brawijaya
25 Full Factorial Design NO A B C D E
1 1 1 1 1 1
2 1 1 1 1 -1
3 1 1 1 -1 1
4 1 1 1 -1 -1
5 1 1 -1 1 1
6 1 1 -1 1 -1
7 1 1 -1 -1 1
8 1 1 -1 -1 -1
9 1 -1 1 1 1
.
.
.
32 -1 -1 -1 -1 -1
A profile
One way, using only white board
Sources of material : Slides/handout
Giving rewards for students
Giving motivation in the class
Profile no 1
Profile no 9
One way, using white board + projector
Sources of material : Slides/handout
Giving rewards for students
Giving motivation in the class
25 Full Factorial Design NO A B C D E
1 1 1 1 1 1
2 1 1 1 1 -1
3 1 1 1 -1 1
4 1 1 1 -1 -1
5 1 1 -1 1 1
6 1 1 -1 1 -1
7 1 1 -1 -1 1
8 1 1 -1 -1 -1
9 1 -1 1 1 1
.
.
.
32 -1 -1 -1 -1 -1
A profile
Too Much!!!
Fractional Factorial
8 profiles 8 profiles
8 profiles 8 profiles
Task 1 Task 2
Task 3 Task 4
Task 1No A B C D E
1 1 1 1 1 1
2 1 1 -1 1 -1
3 1 -1 1 -1 -1
4 1 -1 -1 -1 1
5 -1 1 1 -1 -1
6 -1 1 -1 -1 1
7 -1 -1 1 1 1
8 -1 -1 -1 1 -1
Generators : D = AB, E = ABC
+1
Task 2No A B C D E
1 1 1 1 -1 1
2 1 1 -1 -1 -1
3 1 -1 1 1 -1
4 1 -1 -1 1 1
5 -1 1 1 1 -1
6 -1 1 -1 1 1
7 -1 -1 1 -1 1
8 -1 -1 -1 -1 -1
Generators : D = -AB, E = ABC
-1
Task 3No A B C D E
1 1 1 1 -1 -1
2 1 1 -1 -1 1
3 1 -1 1 1 1
4 1 -1 -1 1 -1
5 -1 1 1 1 1
6 -1 1 -1 1 -1
7 -1 -1 1 -1 -1
8 -1 -1 -1 -1 1
Generators : D = -AB, E = -ABC
+1
Task 4
No A B C D E
1 1 1 1 1 -1
2 1 1 -1 1 1
3 1 -1 1 -1 1
4 1 -1 -1 -1 -1
5 -1 1 1 -1 1
6 -1 1 -1 -1 -1
7 -1 -1 1 1 -1
8 -1 -1 -1 1 1
Generators : D = AB, E = -ABC
-1
Sampling Methodology
• Population : Students of Department of Statistics, Faculty of Mathematics and Natural Sciencies , Bogor Agricultural University
• Stratified Random sampling : 2nd batch, 3rd batch, and 4th batch
• Total respondents : 150
• Distribution respondents of each batch
53 50 47
2nd batch 3rd batch 4th batch
Distribution of respondents/task
12 121112 1412
14 1212 15 1212
Task 1 Task 2
Task 3 Task 4
35 38
38 39
Analysis
• The X matrix is a full factorial from 4 tasks
• The response is 1 (yes) and 0 (no)
• Logistic binary regression is used to estimate the coefficients of the model which uses for utility values of product
• Total utility function Coefficient of a logistic regression
Utility value of each level of each attribute
Important relative values
Conclusion
• The preferable teaching method for statistics students were : delivering methods in two ways, using white board and projector, no matter material sources, existing rewards and motivations in the class.
• The most important attribute was teaching equipment. The next two important attributes were delivering methods and motivation. Source of materials was not important attribute at all.
• The preferable teaching method for statistics students were : delivering methods in two ways, using white board and projector, existing rewards and motivations in the class.
• The most important attribute was teaching equipment for visualization.
Further research
Departemen Statistika FMIPA IPB 58
More factors and levels
Blocking factors
Model : linear vs non linear
model
Restricted on budget
Type of factors
Restricted on experimental
region
Availability constraint
Other constraints
Orthogonal array
Optimal Design
The secret of change is to focus all of your energy not on fighting the old,
but building the new
Socrates
The people who are crazy enough to think they can change the world are the
ones who do
Think different
Departemen Statistika FMIPA IPB 60
Thank you
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