By default, we use Ordinary Least Squares to compute part-worth utilities, though we recommend the more advanced HB utility estimation procedure. The resulting part-worths are saved to a STUDYNAME.utl file, which is a text-only file with a specific format. (Please see Appendix A for more information on interpreting conjoint analysis results.)
Select Analysis | Analysis Manager and then choose OLS as the Analysis Type. Click the Settings icon 
to adjust the settings. You are able to specify a number of options on this dialog:
The default settings should work well for almost every application, but we review them here.
Respondent ID
If estimating utilities using Latent Class or HB, you should select the Respondent ID to use for identifying the respondents in the utility files and segment membership files.
Respondent Filter
Filters allow you to perform analysis on a subset of the data. Categories of the sys_RespStatus (Incomplete, Disqualified, or Qualified/Complete) variable may be selected for inclusion in the analysis. Using the Manage Filters... additional filters may be selected from variables in your study, or new variables that you build based on existing variables or combinations of existing variables.
HB Fit Model Settings
When using HB estimation, there are different ways the model may be fit:
Fit Pairs Only fits only the paired-comparison (conjoint) information, staying more true to the conjoint data information, but not utilizing the self-explicated information for utility estimation.
Use Respondent Importances as Constraints uses the self-explicated importance scores as utility constraints.
Fit Pairs & Priors fits both the self-explicated and paired comparison information. Since no constraints are used, this method of estimation is very similar to earlier versions of ACA. It works reasonably well, but generally not quite so well as when employing constraints.
Output
When utility estimation is completed, the output is displayed in a report window. The first (Summary) tab displays the average part-worth utilities across respondents (normalized by the zero-centered diffs method), and the average attribute importances. The second tab displays a report of the settings used. On the third (Individual Utilities) tab are the raw utility data for each respondent, organized as one row per respondent. Click the Save Report... button to save the data as an Excel (.XLS) file.
Zero-Centered Diffs Method: The Summary tab displays the average rescaled utilities (as well as the standard deviations) under the zero-centered "diffs" method. The diffs method rescales utilities so that for each individual the total sum of the utility differences between the worst and best levels of each attribute across attributes is equal to the number of attributes times 100. This normalizes the data so that each respondent has equal impact when computing the population average.
A studyname OLS.utl file is also generated, which is a text-only file with a specific format.