﻿ CVA Monotone Regression Settings

# CVA Monotone Regression Settings

This dialog controls the settings used for estimating part-worths using Monotone Regression.  Monotone Regression is recommended for card-sort designs involving rank-order data, where respondents sorted individual conjoint cards from best to worst.  Please see the section entitled How CVA Calculates Utilities for more information about Monotone Regression.

Minimum Scale Value:

Specify the lowest value used in the rank-order scale.  This is usually 1 when using card-sort data.  If using pairwise comparison presentation, it is the lowest value used in the response scale.

Maximum Scale Value:

Specify the highest value used in the rank-order scale.  This is usually equal to the number of cards (conjoint questions) in your study.  For example, if you have 18 cards in your study, then specify 18.  If using pairwise comparison presentation, it is the highest value used in the response scale.

Scale Direction:

Single-Concept (Card-Sort) Designs:  If using card-sort data, there are two choices: "Lowest Number Best" and "Highest Number Best."  With rank-order data, if a "1" in your data set indicates the best card, you should choose "Lowest Number Best."  If you used CVA to import rank-order data from a text file and specified that the values were card numbers, CVA internally coded the values so that a "1" is associated with the best card, a "2" with the next-best card, etc.  If higher numbers indicate greater preference, choose "Highest Number Best."

Pairwise Comparison Designs: It is unusual to use Monotone Regression in pairwise comparison CVA designs, but if you do wish to try it, you should specify the correct scale direction.  The selections are: "Highest Number on Right" and "Lowest Number on Right."  Monotone regression only retains the ordinal information about which concepts are preferred to others.  The magnitudes of the differences are ignored.  For this reason, part-worth estimation from pairwise rating questionnaires using monotone regression may result in noisier estimates than when preserving the additional information of the ratings scale under OLS.

Constraints:

This dialog lets you specify that certain levels within certain attributes have known a priori preference order and that the part-worths should be constrained accordingly.  All rank-order relationships you originally specified when entering your list of attributes and levels are carried forward to the Additional Utility Constraints dialog.  But, you can modify those selections.