10 votes

R gives me the error "contrasts can be applied only to factors with 2 or more levels" running an mlogit model, but all my factors have 2 levels

See Answer Here - https://stackoverflow.com/questions/18171246/error-in-contrasts-when-defining-a-linear-model-in-r There are factors that you are using that either have only 1 distinct value or are ...
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  • 201
4 votes
Accepted

How to interpret D-Efficiency in Experimental Design in R OptFederov?

I found answer myself. Design efficiency is judged by Ge. It should be 1 or close to 1. Below links have some explanation and i referred the book "Design and Analysis of Experiments with R". Thought ...
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4 votes

Hierarchical Bayes estimation in Conjoint Analysis

I think you're looking for the ChoiceModelR package. You specify a utility function, and the package will estimate coefficients, or part-worth utilities, using a hierarchical multinomial logit model.
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3 votes
Accepted

Conjoint Analysis in R and SPSS result in Different Standard Errors using Same Data

The R implementation seems to be giving results for the separate subjects stacked on top of one another and run in a single regression using all 54 observations (nine from each of the six subjects). ...
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3 votes

'No choice' option in conjoints

You should add another column in your dataset. Put in 0 for any response that is an actual alternative. 1 for any response that is a "none of the above". Then in your model, you will have an ...
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  • 315
3 votes
Accepted

Preparing a survey questionnaire

Below is a point by point breakdown of the use and interpretation of conjoint analysis. I would suggest that you read carefully through the details to ensure the questions you are asking are relevant ...
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2 votes

Conjoint Packages for R

Faisal Conjoint Model (FCM) is an integrated model of conjoint analysis and random utility models, developed by Faisal Afzal Sid- diqui, Ghulam Hussain, and Mudassir Uddin in 2012. Its algorithm was ...
2 votes

Conjoint Packages for R

If you are looking for models other than logit, you can use 'survival' package to build conditional multinomial logit model. you can use 'bayesm' package to build hierarchical bayesian(HB) model. ...
2 votes

CBC analysis using choicemodelr - interpretation of the output attribute values

You need to find the covariance matrix and correlation matrix for the attributes price and brand amongst others. Suppose the output of ChoiceModelR is hb.post. To find the covariance matrix and ...
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  • 21
2 votes

How do I do a conjoint analysis in R? I don't think the conjoint package is appropriate

The mlogit package can analyse this data, but you need to make sure that the data is transformed to the appropriate shape (either long or wide, depending on how your data look). See the mlogit.data ...
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2 votes

Choice based conjoint latent class analysis in R

flexmix would do the job but (so far I remember) only if you model binary (Yes/No) or pairwise (A vs B) choices (Last time I checked the authors were working on an extension to multinomial (MNL) ...
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  • 919
2 votes

conjoint or something else

What you are looking for is discrete choice modelling. I would start with a multinomial logit (MNL) model which will model the choice probability as a function of product attributes. This type of ...
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  • 919
1 vote
Accepted

How to do a choice-based conjoint analysis with multiple regression and 3 levels per attribute?

In my data, I also have one column per level instead of one column per attribute like they did. The problem is that, since they only use 2 levels per attribute in their example, they have no problem ...
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1 vote
Accepted

How to calculate average importance of factors (attributes) correctly in "conjoint" package (R)?

Can you improve your answer, please. There are several mistakes, such as in the first attribute calculation. Also, what is y,type = "score" supposed to be? Usually you should be able to ...
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1 vote

Conjoint analysis on split-sample?

I use choiceModelR package in R for my conjoint analysis. Check the documentation for this package to see if it can work for you. There is also a great ...
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1 vote

Choice Conjoint-Analysis design for 2-way interactions

Few comments: 1/ A full factorial design is likely to be an inefficient approach - It will in fact allow you to do more than needed (e.g. to investigate three-ways interaction effects, so on). You ...
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  • 919
1 vote
Accepted

How should I shuffle a sample into multiple choice options?

It seems that the (main) objective of your analysis is to measure the influence of the different product features on customers' decisions. This is exactly what conjoint analysis (or choice-based ...
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  • 919
1 vote

'No choice' option in conjoints

[I wanted to comment on K-zar previous comment, but my answer is too long]. "None of the above" is the model intercept (This is why these parameters are called alternative specific constants (ASC))....
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  • 919
1 vote

Experimental (factorial) design not exploiting the variance

As mentioned in the comments, you need to do the following: Convert all categorical factors into dummy variables (make sure that you apply correct dummy coding: if a factor has three levels you only ...
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  • 315
1 vote

Hierarchical bayes

This depends on how you structure your matrix. But keep in mind that "None of the above" is a choice that is different from your baseline choice. For example, of you have two attributes with two ...
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  • 315
1 vote

For conjoint attribute importance calculation, should insignificant attribute levels be included in the calculation?

The difference formula gives a point estimate snapshot and as such is not a statistical tool. Therefore you should use all variables, even the ones with insignificant coefficients. See example here, ...
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  • 315
1 vote

D-efficiency in choice-based conjoint analysis

For those who want to learn about designing of choice experiments (CEs), I would strongly advise to read the documentation of the NGENE software (https://dl.dropboxusercontent.com/u/9406880/...
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  • 919
1 vote

Conjoint analysis in R

One strategy would be to break each task into a series of head-to-head comparisons. Create a dataset with one row for each comparison in each task (i.e. watch A vs. watch B in task t) with the ...
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1 vote
Accepted

Conjoint Analysis - Incorporating individual-specific intercept

How many scenarios did each respondent see? I typically use hierarchical Bayes to estimate individual random effect discrete choice models, especially if I have a small sample. You can use covariates ...
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1 vote

How do I do a conjoint analysis in R? I don't think the conjoint package is appropriate

The conditional logit is the type of multinomial logit model used for analyzing discrete choice data. Use clogit in R. See Appled Choice Analysis 1st and 2nd edition.
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  • 284
1 vote

For a conjoint analysis in SPSS, I have 16 cards, but some combinations are unrealistic. Do I still include them in my survey and analysis?

Dropping the unrealistic combinations may appear to spoil a nice, neat computational system. But that has to take a back seat. You do not want to waste your survey respondents' time or risk ...
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  • 11.7k
1 vote
Accepted

Probability in choice experiments

This situation relates to the so called softmax function that assigns probabilities based on scores. Let the scores be $s_1,s_2,\dots,s_n$ any real numbers. The softmax calculates the probability of ...
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  • 2,503

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