"Data quality control is essential for reliable market research outcomes." - Market Research Expert

Conjoint analysis stands out as a trusted method for optimizing product features and pricing through survey-based research. However, the reliability of the results depends on the quality of the data collected. Ensuring the elimination of poor-quality responses is crucial, and employing the Root Likelihood fit score proves to be an effective method.

Quality Assurance in Conjoint Analysis

"The quality of responses determines the quality of the results." - Research Analyst

Conducting conjoint analysis involves survey takers making choices among potential products or services. To ensure high-quality data, it is imperative that respondents carefully consider their selections, reflecting genuine opinions. Verification methods, such as checking completion time, are common, but employing advanced measures like the Root Likelihood fit score enhances the evaluation of data quality.

Root Likelihood Fit Score: A Statistical Measure

"The Root Likelihood serves a similar role that R-squared does in regression." - Statistical Analyst

The Root Likelihood score provides a statistical measure, indicating the probability that a survey respondent would have made specific selections based on their preferences or utility scores. This method involves calculating utility scores and utilizing the logit equation to determine the likelihood of product selections. The Root Likelihood fit score is then derived by assessing the geometric mean of probabilities across multiple choice tasks.

Identifying Poor-Quality Responses

"If a real respondent scores lower than 20% of random bots, their choices may be random and not carefully considered." - Data Quality Specialist

To identify poor-quality responses, a two-step process is recommended. First, create a conjoint exercise with random respondents, generating a dataset akin to 'bots' with no preferences. Run a hierarchical Bayesian conjoint analysis to obtain utility estimates for these 'bots.' Establish an 80th percentile Root Likelihood score as the cutoff, considering random variation. Subsequently, flag every real respondent with a score lower than the threshold, indicating potentially random or hasty choices.

Improving Data Quality

"Cleaning data is crucial for accurate outcomes in features like Willingness to Pay." - Market Research Strategist

To enhance data quality, conduct the conjoint task on random respondents, run an HB conjoint analysis, and determine the 80th percentile Root Likelihood fit score. Set this score as the cutoff for real respondents and flag those below the threshold. Adequate survey questions are essential for effective distinction between genuine and random responders, emphasizing the importance of a well-structured conjoint dataset.

Final Thoughts

Utilizing Root Likelihood fit scores offers a reliable approach to identify and eliminate poor-quality, random responses in conjoint analysis. This method ensures that research outcomes, such as Willingness to Pay and product preferences, accurately reflect genuine opinions, preventing data distortion and inaccuracies.