﻿ Setting ACA Control Parameters

# Setting ACA Control Parameters

You control the way the ACA questionnaire is implemented using the Design tab on the ACA Exercise Settings dialog.

This dialog provides the following areas:

Priors Randomization

Pairs Settings

Importance Settings

The controls on each dialog are described below.

Priors Randomization

Randomize Level Order in Rating Questions

You can randomize the order of attribute levels (such as levels of brand, color, style, etc.) that are displayed in ACARAT (level ratings) questions.  This can control for order bias.

Randomize Attribute Order in Rating and Importance Questions

If you select this option, the order that attributes are presented in the ACARating (level ratings) and ACAImportance (importance) questions is randomized.  (The same randomized order is used for both sections, for a given respondent.)

Pairs Settings

Maximum Attributes for Pairs Section

This controls how many attributes will be carried forward to the pairs questions.  If you specify as many attributes as are in your study, all attributes will be used within the pairs questions.  If you specify a number n which is less than your total number of attributes, only the n most important attributes (as rated by the respondent in the Importance question) will be used in the Pairs section.  You can take as many as 20 attributes forward to the Pairs section.  We generally recommend taking all attributes (up to 20) forward to pairs questions if you are using ACA/HB estimation.  If using the standard OLS estimation, we suggest you bring around 8 to 12 attributes into Pairs.  However, we have not conducted any research to confirm that about 8 to 12 is optimal, and it depends on the number of Pairs questions you plan to ask.  The more Pairs questions asked, the more attributes you might consider carrying into the Pairs section.

Number of Pairs Questions (0-50)

This number controls how many total conjoint pairs are to be asked.  We suggest that you ask a number of pairs equal to 3 (N - n - 1) - N, where:

N = total number of levels taken into the Pairs section

n = total number of attributes taken into the Pairs section

There are many times when the number of pairs suggested by the above formula are more than respondents can manage.  (Most ACA surveys will have between 15 to 30 pairs questions.)  We suggest you determine how many pairs questions respondents can reliably answer, and not try to ask more pairs than is prudent.

There are also instances with very small ACA designs in which the above formula leads to very few suggested pairs (i.e. sometimes six or fewer).  In that case, we recommend that you increase the number of pairs questions to a number that your respondents can reasonably answer.

Number of Pairs Questions at Each Stage

This value controls how many pairs questions are asked before the next degree of complexity is implemented.  For example, if you want the first 10 pairs questions to show two attributes at a time, and the next 10 pairs to show three at a time, you should specify "10."

Number of Attributes in Each Pairs Question in the First Stage (2-5)

This value specifies how many attributes will be presented at a time throughout the first stage.  We recommend showing two attributes at a time in the first few pairs.

Number of Attributes in Each Pairs Question in the Last Stage (2-5)

Controls the maximum complexity (number of attributes shown) in conjoint pairs questions.  We generally recommend that three be the maximum number of attributes in the last pairs.  Although, if attribute text is particularly short and you feel that respondents would not become overly confused or fatigued, you may wish to display four or five attributes in the last pairs.

Calibration Settings

Number of Calibration Concepts (0,3-8)

Controls how many calibration concept questions will be shown.  If you want to use the Purchase Likelihood model during analysis, you should include at least three Calibration Concepts.  We recommend using at least five if you plan to include this section.  If you plan to use ACA/HB to compute part-worths (hierarchical Bayes) and if you do not need to run the purchase likelihood model, you should probably not include a Calibration Concepts section.

The total number of calibration concepts cannot exceed the number of attributes (or the number of attributes in each concept--see below) plus one.

Number of Attributes in Each Concept (2-8)

Specifies how many attributes are to appear within each Calibration Concept question.  Only the most important attributes are included (as rated in the Importances section).  We recommend including no more than about six attributes.

If you have specified prohibited pairs, there are situations in which fewer attributes than the number you specify are actually displayed within the Calibration Concept section.

Importance Settings

Use Default ACA Importance Questions

By default, an importance question is asked for each attribute.

Advanced users may wish to skip the importance questions (ACA/HB estimation required).  We strongly suggest you read the "Omitting the 'Importance' Question" documentation within this online manual and also the article in the Technical Papers section of our website entitled "The 'Importance' Question in ACA: Can It Be Omitted?"  If you select this option, you then choose among the following advanced options:

Use Group Means for Prior Importances

You can use importances based on previous respondents (collected on the same server or CAPI installation).  These importances are used for selecting the attribute level combinations in the conjoint pairs.  ACA/HB estimation is required.

Number of Preliminary Respondents before Using Group Means

This defines how many respondents must complete the survey prior to using group means.  Before group means are used, we assign prior equal importances.

Set Prior Importances Equal for All Attributes

If you select this option, all attributes will be assumed to have equal prior importances.  The order of attributes used in the pairs section will be randomized.  Utility balance for conjoint pairs will be based on the level ratings/rankings already established in the questionnaire or by the author when setting a priori attribute level order, and then updated by the respondent's subsequent pairs questions.  ACA/HB estimation is required.

Set Prior Importances Based on other Questions

Some users may wish to customize the importance questions.  You can use any series of questions or variables that reflects positive values (decimal values allowed).  Click the Specify Questions... button to associate a question/variable with each attribute importance.