# Elicit Expert Judgement in Five Steps

## Solutions

Developed by Lion Behrens, Duco Veen and Rens van de Schoot

This tutorial is based on Veen, Stoel, Zondervan-Zwijnenburg & van de Schoot (2017) and provides the reader with a basic introduction to specifying prior distributions based on eliciting expert knowledge using an original Shiny app. The reader will be guided through a five-step method with which experts from academia, business, society or other fields can express their beliefs and predictions in the form of a probability distribution. Open the application to perform the exercise!

### Introduction

DataAid is a young start-up company founded in 2015 consisting of a network of young data analysists and programmers. Table 1 displays the generated turnover per year for each employee since the company’s founding in 2015. DataAid started out with nine employees. Over the years, four employees dropped out, while new ones joined. All cells filled with a question mark represent employees that will work for the company in 2018.

Table 1: Individual and Overall Turnover Rates

### Exercise

Open the Shiny app. Consider yourself an expert whose knowledge should be elicited. Your task is to predict the turnover rate created by the company for the upcoming year 2018 based on the prior knowledge that you required by inspecting Table 1.

a) Start by defining the number of employees that will create individual turnover rates for the company in 2018. Fill in this number under “Count”.

b) Consider all individual turnover rates by the employees that will be active in 2018. What is the minimum turnover rate that you expect? Fill in this number under “Minimum Value”. What is the maximum turnover rate that you expect? Fill in this number under “Maximum Value”.

c) The figure on the upper right is showing the scale of individual turnover rates that you defined. Below, your expected turnover rates for the least productive employee (“Minimum”) and the most productive employee (“Maximum”) are displayed as black dots.