Quantitative Research - IDR Medical

Quantitative Research

We work with global panels and communities of healthcare professionals to provide reliable and responsive quantitative market research

IDR Medical use statistical-based quantitative market research techniques to objectively test hypotheses, measure preferences and provide robust insights to inform decision making.

Web Surveys

Most quantitative projects we complete are executed through our dedicated web research platform, using top of the range software to create intuitive, user-friendly interfaces which help our respondents to answer questions.

Web surveys have increased in importance over the years, being far quicker and more convenient. This enables us to gather much larger sample sizes, and encourages both participation in the first instance, but also improves the likelihood of those surveys being completed.

Techniques such as CATI (computer-assisted telephone interviews) are another improvement…

We work closely with external partners who have extensive panels of healthcare professionals and patients available and willing to share their insights.

Analysis

We utilise a range of statistical analyses, carefully selected to address the objectives of each project.

Conjoint Analysis

Conjoint Analysis is central to many of our projects. We use a range of conjoint methods to, amongst many things, design new products and services, estimate brand equity and measure price sensitivity.

Conjoint analysis is a powerful research technique that evaluates and measures the value customers place on features of a product or service based on trade-offs. Over time the use of conjoint has expanded across markets ranging from consumer goods, B2B and from simple products to multi-dimensional product and service solutions. IDR Medical has utilised a number of conjoint methodologies across medical device and pharmaceutical product development and launch initiatives. Techniques include:

  • Traditional full-profile conjoint surveys simply involve respondents selecting their preferred options from side-by-side product descriptions.
  • Adaptive conjoint analysis follows the same premise as traditional conjoint but will adapt the questions asked based on the answers given previously. It can also allow respondents to rate levels, attribute importance, and indicate the likelihood of purchase.
  • Choice-based conjoint surveys require respondents to make choices regarding different purchasing options based on several attributes provided for each one, thus mimicking a typical buying scenario.
  • Adaptive choice-based conjoint is a combination of the previous two methods, it captures more information at the individual level than traditional surveys and can be used with small samples.
  • Menu-based conjoint analyses discrete choices, allowing respondents to select those attributes they do like, and leave out those they don’t.

Max Diff Analysis

Maximum Difference Analysis is a simple approach for obtaining preference scores for multiple attributes, be that product features or brand image. It is also known as best-worst scaling.

The benefit of MaxDiff when compared to a standard survey which asks respondents to assign a perceived importance to every attribute, is that it ensures respondents consider their answers and compels a hierarchy of importance out of them. This means we can identify even where there are small differences in their preferences, and it is easier to identify those attributes that are most important to them.

The answers provided are then ranked to provide a scale of attribute importance from best to worst. A simple way to analyse MaxDiff results is allocating scores based on preference selections, but this can be highly flawed, so identifying how likely an attribute is to be selected as best or worst (using, for example, an adapted logistical regression) can be more accurate. It can be important to look at the likelihood of an attribute being selected as both best and worst, as it could highlight attributes that are more divisive.