Conjoint Methods
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[edit] Conjoint Methods
Conjoint analysis methodology has withstood intense scrutiny from both academics and professional researchers over the past 25 years and is widely used in consumer products, durable goods, pharmaceutical, transportation and service industries. There are many methodologies for conducting conjoint analysis, including two-attribute trade-off, full profile, adaptive conjoint analysis, choice-based conjoint, self explicated conjoint, and Hierarchical Bayes (HB).
[edit] Two Attribute Tradeoff Analysis
One early conjoint data collection method presented a series of attribute-by-attribute (two attributes at a time) tradeoff tables where respondents ranked their preferences of the different combinations of the attribute levels. For example, if each attribute had three levels, the table would have nine cells and the respondents would rank their tradeoff preferences from 1 to 9. The two-factor-at-a-time approach makes few cognitive demands of the respondent and is simple to follow but it is both time-consuming and tedious. Moreover, respondents often lose their place in the table or develop some stylized pattern just to get the job done. Most importantly, however, the task is unrealistic in that real alternatives do not present themselves for evaluation two attributes at a time.
[edit] Full-profile conjoint analysis
Full-profile conjoint analysis has been a popular approach to measure attribute utilities. In the full-profile conjoint task, different product descriptions (or even different actual products) are developed and presented to the respondent for acceptability or preference evaluations. Each product profile is designed as part of a fractional factorial experimental design that evenly matches the occurrence of each attribute with all other attributes. By controlling the attribute pairings, the researcher can estimate the respondent’s utility for each level of each attribute tested.
[edit] Adaptive Conjoint Analysis
Adaptive Conjoint Analysis was developed to handle larger problems that required more descriptive attributes and levels. A unique contribution of ACA was to adapt each respondent’s interview to the evaluations provided by each respondent. Early in the interview, the respondent is asked to eliminate attributes and levels that would not be considered in an acceptable product under any conditions. The attributes are then presented for evaluation, followed by sets of full profiles, two at a time, for evaluation. The choice pairs are presented in an order that increasingly focuses on determining the utility associated with each attribute.
[edit] Choice-based Conjoint
Choice-based conjoint requires the respondent to make a choice of their preferred full-profile concept. This choice is made repeatedly from sets of 3–5 full profile concepts. This choice activity is thought to simulate an actual buying situation, thereby mimicking actual shopping behavior.
[edit] Self-explicated Conjoint Analysis
Self-explicated conjoint analysis offers a simple but surprisingly robust approach that is very simple to implement and does not require the development of full-profile concepts. First, factors and levels are presented to respondents for elimination if they are not acceptable in products under any condition. The attribute levels retained in the analysis are then evaluated for desirability. Finally, the relative importance of attributes is measured using a constant sum scale to allocate 100 points between the most desirable levels of each attribute. The attribute level desirabilities are then weighted by the attribute importances to provide utility values for each attribute level. This approach does not require regression analysis or aggregated solution required in many other conjoint approaches. This approach has been shown to provide results equal or superior to full-profile approaches, and places fewer demands on the respondent.
[edit] Hierarchical Bayes Conjoint Analysis (HB)
Hierarchical Bayes Conjoint Analysis (HB) is similarly used to estimate attribute level utilities from choice data. HB is particularly useful in situations where the data collection task is so large that the respondent cannot reasonably provide preference evaluations for all attribute levels. The HB approach uses averages (information about the distribution of utilities from all respondents) as part of the procedure to estimate attribute level utilities for each individual. This approach again allows more attributes and levels to be estimated with smaller amounts of data collected from each individual respondent.