Editing Conjoint analysis (section)

==Analysis==
[[File:Sample-output-of-conjoint-analysis.png|thumb|Sample output of conjoint analysis with application to marketing]]
Because conjoint designs are complicated, they usually generate substantial measurement error (as indicated by low intra-respondent reliability), which can induce substantial bias in any direction by any amount; this bias must be corrected in statistical analyses of conjoint data.<ref>{{Cite web |last1=Clayton |first1=Katherine |last2=Horiuchi |first2=Yusaku |last3=Kaufman |first3=Aaron R. |last4=King |first4=Gary |last5=Komisarchik |first5=Mayya |date=2023 |title=Correcting Measurement Error Bias in Conjoint Survey Experiments |url=https://gking.harvard.edu/conjointe |access-date=2023-01-31 |website=gking.harvard.edu}}</ref>  Depending on the type of model, different econometric and statistical methods can be used to estimate utility functions. These utility functions indicate the perceived value of the feature and how sensitive consumer perceptions and preferences are to changes in product features. The actual estimation procedure will depend on the design of the task and profiles for respondents and the measurement scale used to indicate preferences (interval-scaled, ranking, or discrete choice). For estimating the utilities for each attribute level using ratings-based full profile tasks, [[linear regression]] may be appropriate, for choice based tasks, [[maximum likelihood estimation]] usually with [[logistic regression]] is typically used. The original utility estimation methods were monotonic analysis of variance or linear programming techniques, but contemporary marketing research practice has shifted towards choice-based models using multinomial logit, mixed versions of this model, and other refinements. [[Bayesian estimator]]s are also very popular. Hierarchical Bayesian procedures are nowadays relatively popular as well.{{Citation needed|date=June 2023}}<ref>{{cite journal |last1=Tso |first1=Ivy F. |last2=Taylor |first2=Stephan F. |last3=Johnson |first3=Timothy D. |title=Applying hierarchical bayesian modeling to experimental psychopathology data: An introduction and tutorial. |journal=Journal of Abnormal Psychology |date=November 2021 |volume=130 |issue=8 |pages=923–936 |doi=10.1037/abn0000707 |url=https://pmc.ncbi.nlm.nih.gov/articles/PMC8634778/ |language=en |issn=1939-1846}}</ref>