There are a lot of terms that market researchers need to know, whether it be specific to market research, product development, marketing, or other disciplines. However, there are also several very important definitions to market researchers that are frequently thrown around that everyone else should know. So whether you’re a market researcher or not, brushing up on these four terms can make it a bit easier in dealing with the research process and insights.
1. Incidence Rate
If someone’s only reading research reports, it’s likely they’ve never heard or had to deal with this term before. In the research world, the incidence rate (IR) is defined as the percent of people in a sample, or the number of people the study was sent to, that qualify for a study. There are two types of IR:
- Predicted IR is the anticipated percentage of respondents that are likely to qualify for a study based on the screening criteria. The predicted IR often fluctuates throughout the field time and helps anticipate how many respondents the study should be sent to before and during the study.
- Actual IR is the actual number of respondents that qualified for the study based on the final number of respondents the study was sent to.
It’s very important to understand the incidence rate since it guides which panel providers or samples are used. The IR can even identify when there are errors in the study and anticipate timing. For example, if the actual IR is greater than the predicted IR than the project should field and close sooner than expected. Further, when the actual IR is less than the predicted IR it’s likely timing will have to be extended or the screening criteria is too narrow and will need to change to allow more respondents to qualify for the study. Most importantly, incidence rate factors heavily into feasibility—another important term in market research.
Like incidence rate, feasibility is important to panel providers, but also research vendors. It is one of the first factors to be considered in the research process as it directly determines timing and cost. Unlike the incidence rate, feasibility takes into account more than just how many respondents are likely to qualify for a study. The study methodology, when the study is being conducted, and who the respondents are, and how many are needed, all make up feasibility. As you can probably guess, the more robust the methodology or more specific the target audience, the more likely timing and the cost will be affected and the less likely the study will be feasible.
3. Statistically Significant
Perhaps the most popular phrase in market research, especially during analysis and reporting, is the phrase “statistically significant.” The question most often asked when it comes to this term is whether a data point is significant or not. Significance is incredibly important as it’s frequently used to determine insights. There are two parts to statistical significance:
- How many respondents are needed to test for statistical significance
- What results are actually statistically significant
Determining the number of respondents needed for statistical significance involves a more robust education in statistics, and things like the z-score or confidence level, standard deviation, and margin of error—so we’ll skip that for now. Understanding how significant data points are found is an easier process: it requires testing at a confidence level to see where the significant differences between groups arise. We use t-tests at the 90 and 80 percent confidence intervals for our significance testing since we’re often comparing between just two groups (i.e. male and female, Millennials and Gen X, etc.). As significant results arise between groups, it becomes easier to start to understand the bigger picture and capture insights.
As mentioned, statistically significant results arise when comparing two groups, often done through a cross-tab analysis. Cross-tabulation is special in that it provides a quick and easy means for analyzing statistical significance in a dataset. When looking at significant differences between two groups, a cross-tab will output the raw data into columns and rows. For example, columns could contain various age groups while the rows could include the purchase intent levels of those age groups. The significance is then called out in letters or colors to show where a significant difference lies, like whether purchase intent is lower or greater than another age group.
While these definitions encompass only 4 of the many terms market researchers use, they can be commonly lost in translation or misconstrued. Having a good understanding of what these terms mean and why they are important is the first step in gaining a better understanding of the research process and insights.
To learn more about market research terminology and best practices when it comes to utilizing qualitative and quantitative research, check out the eGuide below. You’ll learn differences between qual and quant research, what findings each methodology provides, and how using them together creates more impactful insights.