conjoint-analysis

To estimate Average Marginal Component Effects (AMCE) in a conjoint analysis, you model forced-choice selections across randomized attributes to quantify the causal impact of changing each attribute level relative to a baseline.

Calculating clustered standard errors for conjoint analysis subgroup effects requires grouping data at the respondent level to account for repeated choices, ensuring accurate variance estimation for heterogeneous preference testing.

Yes, you can run diagnostic checks on forced-choice conjoint experiment data to verify randomization, detect order effects, and identify respondent fatigue or satisficing biases that might compromise causal inference.

Python statsmodels supports AMCE estimation with respondent-level clustering by allowing you to fit choice models and compute robust standard errors grouped by respondent for accurate causal attribute effect testing.

The best way to test subgroup-specific preferences in a forced-choice conjoint design is to estimate interaction terms or run subgroup-specific AMCE estimations using randomized profile choices to evaluate heterogeneous effects.

You need to specify explicit baseline levels for conjoint analysis attribute estimation whenever computing AMCEs, because the causal impact of each attribute level is measured as a change relative to that predefined baseline.