# MConjoint-package: Perform Conjoint Analysis using multiple designs In MConjoint: Conjoint Analysis through Averaging of Multiple Analyses

## Description

The Multiple Conjoint Analysis package changes the meaning and use of traditional holdout cases. Rather than using the holdout cases to check a single design, the "holdouts" are used to create a large set of designs, each of which is analyzed. The average result is used

## Details

 Package: MConjoint Type: Package Version: 0.1 Date: 2013-05-14 License: GPL-3

The use of the routines centers around something I call a "despack" a design package. A despack contains despack\$cards: a list of the m cards for which ranks are obtained; despack\$designs: a list of designs each with n cards drawn from the list of m cards; despack\$samples, a list of samples of length n, drawn from 1:m, corresponding to the cards used in the design; despack\$coeffs: a list of matrices of linear coefficients; despack\$all.utils: a list of lists of utility values, on for each column of the coeffs matrices; despack\$all.imps, a list of matrices of importances, one column for each utility; despack\$utils: a list of utilities (average taken over first index of the list of lists; despack\$imps: the average of the list of importance matices

Start with a data set, full.design, with all possible cards. (This may be the full factorial design (all combinations of levels)) or some combinations may be removed.

Obtain a "good" design of n cards (for information on what makes a design good see the documentation for mc.good.desgins). To this you add extra.cards cards in such a way that you maximize the number of subsets of the m=n + extra.cards of length n that lead to "good" designs.

Both operations can be done by calling

orig.design = mc.get.initial.design(full.design)

orig.design\$design will be the m cards for which you will collect data

You then obtain your data, data, a matrix with each column corresponding the the ranks given to the cards by one subject. Then run

despack = good.designs(orig.design\$design)

This will give an initial despack, with \$cards, \$samples, and \$designs

Fill the other elements of despack by calling

despack=M.Conjoint(despack,data)

This will print a summary with the utilites and the importances averaged over the subjects (an operation that may or may not be useful)

## Author(s)

William Hughes

Maintainer: William Hughes <William.Hughes@rogers.com>

## Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 # A simple conjoint problem. Managers can make hiring descisions # based on the factors # University: Prestige, Excellent, Good; Sex: Male, Female; # Dress: smart, messy; Hair: long, short. # We want to determine the importance of these factors. # We interview two managers. The first picks first by # University, then by sex, male over female, then # by dress, smart over messy, and does not care about hair # length. The second is like the first except that # this manager picks female over male. # start with the full factorial design data(hire.candidates) #get a questionaire hire.questionaire = mc.get.initial.design(hire.candidates,max.trials=10) #collect the data data(hire.data) #get a design pack for the analyis hire.despack=mc.good.designs(hire.questionaire\$design, size=20) #do the conjoint analysis hire.despack=M.Conjoint(hire.despack,hire.data) # (note this illustrates the danger of averaging utilities. # The average utility for both Male and Female is small, but # Sex is important to both managers)

MConjoint documentation built on May 1, 2019, 7:56 p.m.