Predict Consumer Decicions with Choice-Based Conjoint: 中国

Showing you how to run Choice Based Conjoint experiments to predict people's decisions. Includes Chinese 中国 translations
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  • Lectures 34
  • Length 4.5 hours
  • Skill Level All Levels
  • Languages English, captions
  • Includes Lifetime access
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About This Course

Published 7/2015 English Closed captions available

Course Description

Choice-Based Conjoint, also often called Discrete Choice Experimentation, is a powerful research and management tool that allow us to understand and predict people's preferences. Whether it is a manager wanting to predict product preferences, a health researcher wanting to explore the treatment preferences of patients, or a transport engineer examining people's choices of public transport this tool can provide the insight needed.

離散選擇分析,也常常被稱為離散的選擇模型,是一個強大的研究和管理工具讓我們瞭解和預測人的喜好。無論是企業管理者想要預測市場對產品的喜好,學者想要探索患者的治療喜好,或交通工程師想預測乘客對公共交通工具的選擇,離散選擇分析亦可以提供所需的洞察力。

This course starts with an introduction to the capabilities and applications of Choice Based Conjoint that is suitable for all audiences. It explains the basic requirements of a CBC research project, and details the outputs that can be obtained. The course then continues on to more advanced topics where you will get training and hands on experience developing and running a CBC project. This includes design, data collection, analysis and reporting of results.

本課程先介紹離散選擇分析的功能和應用,適合所有人。它說明一個CBC研究項目的基本要求,並詳細說明瞭可以得到的結果。本課程然後進深到更高級的主題,你會得到開發和運行CBC項目的培訓和實踐經驗。這包括設計,數據收集,分析和報告結果。

This course is suitable for managers, marketing/business researchers, and academic researchers interested in building an understanding of CBC.

本課程適用於經理,營銷/商業研究人員,並有意打造CBC的學術研究人員。

If you are a PhD student I am happy to provide a discounted rate for this course. Please contact me through the Udemy messaging service. Provide your university email address, your name, and a link to your supervisor's profile on your university website.

如果你是一個博士研究生,我們很高興為你提供折扣。請通過Udemy與我聯繫, 提供您的大學電子郵件地址,名字,大學網站及鏈接到你上司的個人資料。

This version of the course includes Chinese language support, with all materials translated from English to Chinese.

這個課程當然也包括中文的支援,亦包括由英語翻譯成中文的課程材料。

What are the requirements?

  • There is no assumed knowledge for the early topics in this course
  • 本課程早期的主題沒有任何假設己知的知識
  • For those progressing to the advanced topics in this course an understanding of introductory level statistics is recommended.
  • 對於那些進展到本課程高級的主題,建議先有對入門級統計數據的理解
  • Access to MS Excel for running analysis is essential for those progressing to advanced topics. Additional topics covering analysis using SPSS are also provided but they are not essential
  • 對於那些進展到本課程高級的主題,MS Excel會是運行分析必要的工具。本課程還提供了附加的主題教授採用SPSS覆蓋分析但它們不是必需
  • Some English language skills are needed, but all videos have Chinese language subtitles and Chinese language versions of the slides are available
  • 需要一些英語語言技能,但所有視頻都有中文字幕和中文版本的幻燈片可供選擇

What am I going to get from this course?

  • Get Chinese language support when learning Choice-Based Conjoint
  • 學習離散選擇分析時獲得中國語言支持。
  • Understand the capabilities of Choice-Based Conjoint and Discrete Choice Experiments
  • 了解離散選擇分析和離散選擇實驗的能力
  • Recognise the requirements of running a CBC and DCE research project
  • 明白運行CBC和DCE研究項目的要求
  • Be able to develop and run your own small scale CBC and DCE project
  • 能夠開發並運行自己的小規模CBC和DCE項目
  • Practice your design and analysis skills with real data sets and extra examples
  • 用真實數據集和額外的例子練習設計和分析技巧

What is the target audience?

  • Chinese speakers needing help to understand a course taught in English
  • 來自中國的講師但需要了解用英語授課的課程
  • Managers wanting to understand the capabilities of CBC and DCEs for measuring people’s preferences
  • 管理者希望了解CBC和的DCE的功能以用於測量人的喜好
  • Researchers wanting to learn how to implement CBC and DCE projects
  • 研究人員希望了解如何實現CBC和DCE項目

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

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Certificate of completion.

Curriculum

Section 1: Introduction and Overview
02:57

Welcome and Introduction to the course.

歡迎,課程簡介。


The Chinese language version of the slides can be found in the downloads section

幻燈片的中文版本可以在下載部分找到



00:00 - Caption instruction (turn on subtitles)

00:02 - Title

00:12 - Welcome

01:05 - The lesson

02:13 - What you should come away with

02:52

An overview of how you can best learn Choice Based Conjoint.

您如何能夠最好地學習離散選擇分析。


The Chinese language version of the slides can be found in the downloads section

幻燈片的中文版本可以在下載部分找到


00:00 - Caption instruction (turn on subtitles)

00:02 - Title

00:25 - This course's learning approach

01:15 - Understanding learning style

02:11 - Your learning objective

Section 2: An Overview of Choice Based Conjoint (An ideal summary for Managers)
05:16

Explains how 'utility' is the basis of choice based conjoint.


The Chinese language version of the slides can be found in the downloads section

幻燈片的中文版本可以在下載部分找到


00:00 - Title

00:14 - Conjoint and utility

00:49 - Utility as a dependant variable

02:43 - (Academic) utility - Random Utility Theory

03:40 - What utility does....

04:44 - Conjoint is about measuring utility

09:30

Describes the different ways to measure utility, highlighting why measuring using 'Choice' offers the biggest advantages.


The Chinese language version of the slides can be found in the downloads section

幻燈片的中文版本可以在下載部分找到



00:00 - Title
00:10 - Utility recap
00:33 - The different ways to measure utility
02:16 - Rating scale measurement
03:48 - Ranking based conjoint measurement
06:00 - Choice based conjoint measurement
07:55 - A side note on combined measurement




The adaptive conjoint link from the last slide in the lecture:
http://www.sawtoothsoftware.com/products/acbc/

03:39

Explains each of the elements of a Choice Based Conjoint experiment.


The Chinese language version of the slides can be found in the downloads section

幻燈片的中文版本可以在下載部分找到


00:00 - Caption instruction (turn on subtitles)
00:02 - Title
00:23 - Experiencing a survey
00:47 - An 'Alternative'
01:02 - An 'Attribute'
01:26 - An 'Attribute level'
02:42 - 'Choice sets'
03:23 - The whole Choice Based Conjoint

09:07

Provides examples of what Choice Based Conjoint can be used for.


The Chinese language version of the slides can be found in the downloads section

幻燈片的中文版本可以在下載部分找到



00:00 - Title
00:31 - What problems does CBC solve?
01:37 - Some examples
02:02 - Cost management in health example
04:53 - Brand equity measurement example
07:07 - Product design example

09:33

An example of how a Choice Based Conjoint study is developed, run, and analysed.


The Chinese language version of the slides can be found in the downloads section

幻燈片的中文版本可以在下載部分找到



00:00 - Title
00:36 - Digital camera context
00:59 - Target features to investigate
02:03 - Designing the experiment
02:59 - The survey
03:41 - The data collected
04:34 - Analysing the data
05:36 - The regression output
06:54 - The results
07:56 - Using the results

Section 3: Designing a Choice Based Conjoint Experiment
11:38

Explains the three main types of experimental design.

00:00 - Title
00:30 - The challenge of design
01:23 - The purpose of design
02:01 - The types of design
03:19 - Alternative design
05:23 - Choice set design
07:55 - Information/context design
09:43 - The reality of design
10:36 - The benefits of design

19:44

Explains how to use factorial designs to generate the alternatives in the Choice Based Conjoint experiment.



00:00 - Title
00:13 - Alternative design
00:43 - Factorials (definition)
02:01 - Full factorial (creation)
05:43 - Full factorial (size and interactions)
07:20 - Fractional factorials (definitions)
10:10 - Fractional factorials (creation)
15:56 - Fractional factorials (correlation test)
19:02 - Factorial designs recap




Note: I explain one mechanism here to create fractional factorial designs. There are other ways of generating fractionals that can produce different results. With further study in this area you can start to learn these different ways and anticipate when one design method may offer advantages over another.




A website that examines different ways to automate the creation of factorial designs:
http://harvest.nps.edu/software.html#DOE



A good overview of fractional factorials can be found here:
http://methodology.psu.edu/ra/most/fefaq#t41n270

08:08

Explains how to create and Orthogonal Main Effects Plan (OMEP) to generate the alternatives in your CBC.



00:00 - Title
00:13 - OMEPs
01:33 - Advanced OMEPs
02:34 - Generating OMEPs in SPSS




If you need any help with creating an OMEP in SPSS this help website can talk you through. It also provides sample coding:
http://publib.boulder.ibm.com/infocenter/spssstat/v20r0m0/index.jsp?topic=%2Fcom.ibm.spss.statistics.help%2Fidh_orth.htm

05:20

Introduces an alterantive to orthogonal designs (Full/Fractional factorials and OMEPs); natural designs.



00:00 - Title
00:10 - Orthogonal designs
00:45 - Natural designs
01:26 - Some effects to design in...
04:17 - Natural designs (conclusion)

12:33

Explains how to construct a choice set using a random design.



00:00 - Title
00:13 - Choice set design
00:38 - Random design generation
08:24 - The quality of random design

08:07

Details how to use a combinatorial design to create choice sets.



00:00 - Title
00:04 - Combinatorial Designs
01:33 - Generating them in Excel
03:59 - Generating them online
06:53 - Design Quality

04:59

Explains how to construct choice sets using BIBDs, and highlights other potential designs available.



00:00 - Title
00:07 - BIBDs
01:04 - BIBD generation
02:27 - Design quality
03:31 - Other block designs





I have attached an Excel file containing some BIBDs to help you start building your library of designs.

Section 4: Laying Out The Survey
04:44

Details the main issues to consider when choosing how to present your choice sets to participants (the survey layout).



00:00 - Title
00:13 - Survey layout
00:51 - Classic matrix
01:29 - Shop layout
02:29 - Horizontal vs vertical
03:25 - Text vs visual
03:44 - Interaction
04:06 - Choice set layout

05:11

Gives some helpful hints and tips about how to give instructions in your survey that will improve your data quality.



00:00 - Title
00:08 - Instructions
00:27 - Survey instructions
03:21 - Choice set instructions

10:42

Introduces some of the software packages that can be used to design your CBC experiment.



00:00 - Title
00:36 - Packages: Advanced and DIY
01:23 - Advanced: Confirmit
03:01 - DIY: SurveyGizmo and similar
04:44 - A look at SurveyGizmo



SurveyGizmo: http://www.surveygizmo.com/
Confirmit: http://www.confirmit.com/

Section 5: A Brief Introduction to Analysing Your Data
10:59

Introduces some of the major issues to consider when choosing an analysis approach for your CBC project.


00:00 - Title
00:34 - Analysis = Discrete DV models
01:45 - Why are there different models?
05:24 - Discrete DV models
06:54 - The link to design
07:39 - Software



Stata: www.stata.com
Regression Models for Categorical Dependent Variables Using Stata, 2nd Edition, by J. Scott Long and Jeremy Freese

10:39

Provides an overview of the main analysis methods available to CBC researchers.



00:00 - Title
00:18 - What is simple?
01:07 - Linear regression
03:27 - Linear probability models
05:41 - Logit and probit
07:40 - (Conditional) Multinomial logit model
09:54 - Many more

07:48

Gives a top line overview of some of the more elaborate analysis methods available to CBC researchers.

00:00 - Title
00:30 - What is complex?
01:19 - The big three
02:11 - Mother logit
03:49 - Latent class
05:32 - Bayesian estimation
06:40 - A warning



For a technical introduction to some of the more complex analysis methods try this website:
http://elsa.berkeley.edu/~train/distant.html

Section 6: Analysing Your Data: Regression using MS Excel
03:19

Gives the background to the research problem being addressed in this example of regression analysis.



00:00 - Title
00:13 - Research background
00:43 - Keeping it simple
01:20 - Alternatives
01:41 - Experimental design
02:48 - The data

05:39

Describes the way to structure the data in Excel so that the analysis can be completed.



00:00 - Title
00:05 - The survey data
00:33 - What it needs to look like
01:56 - Excel

03:50

Gives step by step instructions on how to run the regression in Excel.



00:00 - Title
00:13 - MS Excel

06:37

Gives an overview of how to interpret the regression model to be able to predict choice frequency.



00:00 - Title
00:58 - Summary
02:55 - ANOVA
04:30 - Coefficients
05:55 - Regression equation

10:03

Explains how to calculate the choice probabilities for the alternatives that you are interested in. These choice probabilities are analogous to market shares and let you predict the market.



00:00 - Title
00:11 - Using the regression equation
00:30 - Calculating the choice probabilities in Excel

Section 7: Analysing Your Data: The (c)MNL in SPSS
04:21

Introduces the conditional Multinomial Logit (cMNL)model for analysing CBC data. Gives you some background on the data we will be using for this exercise.

00:00 - Title

01:03 - Research background

01:40 - Keeping it simple

02:12 - Alternatives

02:37 - Experimental design

03:51 - The data

11:57

Describes how to prepare the data set so you can run the cMNL analysis. This is a very important stage as most of the difficulty with this analysis is in the data setup.


00:00 - Title

00:18 - The survey data

01:17 - What the data set needs

02:02 - The data in Excel



The xls and SPSS files are in the download materials.

03:53

Gives step by step instructions on how to run the analysis in SPSS. A written version of these instructions can be found in word file attached to the "Brief Overview" lecture at the start of this section.

00:00 - Title

00:08 - Analysing in SPSS

08:54

Goes through each of the major elements of the output of the analysis and explains how to interpet them meaningfully.

00:00 - Title

00:25 - Case processing summary

01:13 - Stratum status

02:01 - Omnibus tests

02:58 - McFadden's R-square

05:10 - Variables in the equation

07:30 - Regression equation

10:00

Explains how to use the results to calculate choice probabilities, which are generally much more meaningful for decision makers.


00:00 - Title

00:12 - Using the regression equation

00:41 - Calculating in Excel

Section 8: Building Decision Support Systems with the Results
03:38

Decision Support System help decision makers make sense of results and can be a great way to add value to a CBC project. This lecture explains the basic elements of a DSS.

00:00 - Title

00:11 - Definition

01:07 - Aims

02:44 - Reasons for having one

06:38

Talks through a simple example of a DSS. The sky is the limit with how elaborate you wish to make a DSS, but even the most simple one can make a lot of difference when helping a decision maker understand CBC results.


00:00 - Title

00:10 - The example

Section 9: Thank You and Good Night
07:16

Lists some of the resources available to help you move into more advanced forms of CBC.

00:00 - Title

00:12 - The challenges

01:31 - What you know

02:03 - Experimental design

02:49 - Analysis methods

04:08 - Presentation of results

05:21 - State of the art

06:02 - Hunt around and build your resources

08:21

Provides the details of some of the key supplies in the industry that can help you with your CBC projects.

00:00 - Title

00:21 - Survey software

01:04 - Panel providers

05:30 - Data analysis

06:10 - Complete solutions


As a heads up the Centre for the Study of Choice (CenSoC) has now moved and is called the Institute for Choice. You can find their details here:

https://www.unisa.edu.au/Research/Institute-for-Choice/

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Instructor Biography

Luke Greenacre, Assistant Professor of Marketing and Market Research

I obtained my PhD in Marketing several years ago, having specialised in studying advanced research methods for understanding consumer purchase decisions and word of mouth behavior. Since then I have worked as an Assistant Professor at several universities around the world running various industry and academic research projects.

I have taught advanced research methods in an industry setting and also to both undergraduate and postgraduate students at universities.

My full bio is available at: lukegreenacre. com

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