Introduction to Open Source Conjoint

---

## .center[Introduction to Open Source Conjoint]

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### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M272 304h-96C78.8 304 0 382.8 0 480c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32C448 382.8 369.2 304 272 304zM48.99 464C56.89 400.9 110.8 352 176 352h96c65.16 0 119.1 48.95 127 112H48.99zM224 256c70.69 0 128-57.31 128-128c0-70.69-57.31-128-128-128S96 57.31 96 128C96 198.7 153.3 256 224 256zM224 48c44.11 0 80 35.89 80 80c0 44.11-35.89 80-80 80S144 172.1 144 128C144 83.89 179.9 48 224 48z"/></svg> John Paul Helveston, Ph.D.
### <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M243.4 2.6l-224 96c-14 6-21.8 21-18.7 35.8S16.8 160 32 160v8c0 13.3 10.7 24 24 24H456c13.3 0 24-10.7 24-24v-8c15.2 0 28.3-10.7 31.3-25.6s-4.8-29.9-18.7-35.8l-224-96c-8.1-3.4-17.2-3.4-25.2 0zM128 224H64V420.3c-.6 .3-1.2 .7-1.8 1.1l-48 32c-11.7 7.8-17 22.4-12.9 35.9S17.9 512 32 512H480c14.1 0 26.5-9.2 30.6-22.7s-1.1-28.1-12.9-35.9l-48-32c-.6-.4-1.2-.7-1.8-1.1V224H384V416H344V224H280V416H232V224H168V416H128V224zm128-96c-17.7 0-32-14.3-32-32s14.3-32 32-32s32 14.3 32 32s-14.3 32-32 32z"/></svg> The George Washington University | Dept. of Engineering Management and Systems Engineering
### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M152 64H296V24C296 10.75 306.7 0 320 0C333.3 0 344 10.75 344 24V64H384C419.3 64 448 92.65 448 128V448C448 483.3 419.3 512 384 512H64C28.65 512 0 483.3 0 448V128C0 92.65 28.65 64 64 64H104V24C104 10.75 114.7 0 128 0C141.3 0 152 10.75 152 24V64zM48 248H128V192H48V248zM48 296V360H128V296H48zM176 296V360H272V296H176zM320 296V360H400V296H320zM400 192H320V248H400V192zM400 408H320V464H384C392.8 464 400 456.8 400 448V408zM272 408H176V464H272V408zM128 408H48V448C48 456.8 55.16 464 64 464H128V408zM272 192H176V248H272V192z"/></svg> June 15, 2023

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---

# .center[Target audience]

## You are familiar with:

<br>

## - Conjoint analysis / discrete choice experiments

## - Choice modeling / utility models

## - R / programming in general

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# Install Software!

### https://jhelvy.github.io/2023-qux-conf-conjoint/software

---

# Hello World!

]]

### John Helveston, Ph.D.

Assistant Professor, Engineering Management & Systems Engineering

- 2016-2018 Postdoc at [Institute for Sustainable Energy](https://www.bu.edu/ise/), Boston University
- 2016 PhD in Engineering & Public Policy at Carnegie Mellon University
- 2015 MS in Engineering & Public Policy at Carnegie Mellon University
- 2010 BS in Engineering Science & Mechanics at Virginia Tech
- Website: [www.jhelvy.com](http://www.jhelvy.com/)

]]

---

## Technology Change Lab

> I study how consumers, firms, markets, and policy affect technological change, with a focus on accelerating the transition to low-carbon technologies

### .center[Electric & Sustainable Vehicle Technologies]

]

### .center[Market & Policy Analysis]

]

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background-color: #000
class: center, middle, inverse

# How can you find out know what people want?

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## Directly asking people what they want isn't always helpful

### (People want everything)

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## Which feature do you care more about?

## .center[Battery Life?]

]

## .center[Brand?]

]

## .center[Signal quality?]

]

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## **Conjoint Analysis**:
## Use choice data to model preferences

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### .center[Use random utility framework to predict probability of choosing phone _j_]

<br>

### 1. `$u_j = \beta_1\mathrm{price}_j + \beta_2\mathrm{brand}_j + \beta_3\mathrm{battery}_j + \beta_4\mathrm{signal}_j + \varepsilon_j$`

### 2. Assume `$\varepsilon_j \sim$` iid Gumbel distribution

### 3. Probability of choosing phone _j_: `$P_j = \frac{e^{\beta'x_j}}{\sum_k^J e^{\beta'x_k}}$`

### 4. Estimate `$\beta_1$`, `$\beta_2$`, `$\beta_3$`, `$\beta_4$` via maximum likelihood estimation

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## **Willingness to Pay**

<br>

]]

## **Make predictions**

### `$P_j = \frac{e^{\hat{\beta}'x_j}}{\sum_k^J e^{\hat{\beta}'x_k}}$`

]

---

## .center[Choice-Based Conjoint Analysis Steps]

<br>

## 1. Design a survey (design of experiment)
## 2. Implement it online
## 3. (Collect data) <- not covering this today
## 4. Estimate models

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background-color: #fff

### .center[**Software** for Choice-Based Conjoint Analysis]

- **.red[Licenses cost $$$]**

- **.red[Not reproducible]**

---

# **FOSS** for Choice-Based Conjoint Analysis

## .center[Experiment Design]

- {cbcTools}
- {ExpertChoice}
- {support.CEs}
- {idefix}
- {choiceDes}

]

## .center[Online Surveys]

- formr

]

## .center[Model Estimation]

- {logitr}
- {apollo}
- {mlogit}
- {gmnl}
- {mixl}

Other:

- Python: {xlogit}
- Stan

]

---

# **FOSS** for Choice-Based Conjoint Analysis

## .center[Experiment Design]

<br>

by John Paul Helveston
</center>

]

## .center[Online Surveys]

<br>

## .center[.font200[<span style="font-family: Roboto,Arial,sans-serif;"><span style="color: #8dc63f;">f</span>orm<span style="color: #8dc63f;">{`r}</span></span>]]

<center>
by Ruben C. Arslan and Cyril S. Tata
<br><br>
Conjoint adaptation by John Paul Helveston
</center>
]

## .center[Model Estimation]

<br>

by John Paul Helveston
</center>

]

---

<br>

## .center[Back to workshop website:<br><br> https://jhelvy.github.io/2023-qux-conf-conjoint/]

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