Data Preparation Guidelines for BHATLIB

This document provides clear, step-by-step guidelines for preparing your data for use with the BHATLIB library in GAUSS.

Overview

BHATLIB requires your data to be structured in a clear, consistent way to enable seamless estimation of discrete choice models. This guide will help you prepare your dataset correctly.

File Format

  • Your data file should be in any format compatible with the GAUSS loadd() procedure (e.g., .csv, .gdat, .xls, .dta, etc.).
  • Ensure the file is clean and formatted with column headers for easy reference.

Rows: Observations

  • Each row should represent one observation (choice situation).
  • Each row corresponds to an individual’s decision in a specific scenario.

Columns: Alternatives and Choices

  • Create separate columns for each possible alternative.
  • Use binary coding to represent choices:
    • The column for the chosen alternative should be 1.
    • All other alternative columns for that row should be 0.
  • Only one alternative should be marked as 1 per row.

Example:

If a person chooses transit (TR) over driving alone (DA) or shared ride (SR):

Alt1_ch, Alt2_ch, Alt3_ch
0,       0,       1

Availability of Alternatives

BHATLIB allows for flexibility in specifying the availability of alternatives for each individual.

  • If all alternatives are available to all individuals (no restrictions):
    • No additional availability columns are needed in your dataset.
    • The system will assume all alternatives are fully available for each observation.
  • If availability varies across individuals:
    • Create one column per alternative to indicate availability.
    • Use binary coding:
      • 1 if the alternative is available to that individual.
      • 0 if the alternative is unavailable.
    • Example column names: alt1_avail, alt2_avail, alt3_avail, etc.

Example:

If Alt2 is unavailable to a respondent:

alt1_avail, alt2_avail, alt3_avail
1,          0,          1

Columns: Individual-Specific Variables

  • You can include additional columns for individual-specific variables (e.g., income, age, etc.) that may influence the choice.

Summary Checklist

  • Use a GAUSS loadd() compatible file with clear column headers.
  • Each row contains one observation/choice situation.
  • Separate columns for each alternative with binary choice coding (1 for chosen, 0 for non-chosen).
  • Only one 1 per row in choice columns.
  • No availability columns needed if all alternatives are available.
  • If availability varies, add one binary availability column per alternative.

Following these guidelines will ensure that your data is ready for BHATLIB analysis without additional restructuring, enabling a smooth and efficient estimation process.