Ipsative questionnaires of personality have been attacked as necessarily inferior to normative approaches. Some even go so far as to accuse the authors of ipsative scales of reporting spurious statistics and of ‘cheating at patience'. Despite previous papers which claim to demonstrate that ipsative scales overestimate reliabilities, cannot be factored soundly and yield uninterpretable validity coefficients, this investigation shows with synthetic data that these generalizations are ill‐advised. The results demonstrate with simulated data that ipsative scores can be factored soundly, that reliability data are not overestimated, and that under moderate conditions of central tendency bias in normative items, ipsative scores actually correlate better with hypothetical ‘true’ scores than the normative form. When replicated on real data from a sample of 243 subjects, a high correlation was found between ipsative and normative scale scores, ipsative scaling did not produce spuriously high reliabilities, and both normative and ipsative data showed sensible and significant correlations with external rating criteria.

Having spent a lot of money collecting data to better understand the satisfaction of their customers, many clients want to know, with some certainty, what will happen if...? The traditional statistical techniques used to answer this question frequently struggle to cope with the complexity of real survey data, and in particular the interrelationships that exist between the various measures which make up 'satisfaction'. In providing a solution, some analysts venture where others fear to tread, and many clients are obliged to follow whether they know the risks or not. The authors have developed a practical and intuitive solution to building 'what if' scenarios using an empirical approach, which overcomes many of the technical problems associated with analysing complex customer satisfaction data. Results are transparent to the client and can be explained without compromising the truth. As a consequence, the authors feel that clients will make better investment decisions. This paper describes the analytical approach to creating 'what if' scenarios and provides case studies, using real datasets, which had previously proved trouble some. Implications for the ubiquitous key driver analysis are also discussed.