Quantitative research relies on structured data that can be quantified and used in statistical analysis or modeling. The data are generally numerical, have larger sample sizes than qualitative studies, and the focus tends to be on finding a stable, projectable understanding of a topic or experience.

Quota is related to overall sample sizes and the make-up on different groups within that sample: quota is the number of respondents or data points needed to create the ideal sample profile. For example, if trying to design a sample that represents the US population, quotas are used to make sure the sample has the same percentage of people in their 20’s as the US population, and they can be used to balance any group of interest — by demographic categories, behaviors, beliefs, attitudes, situations, current statuses, etc. Quotas are the goals for the sample profile.

Ratio is very similar to Interval data, with the added feature of being able to accommodate absolute zeros (for example, a ruler has an absolute zero, so it is a ratio scale, but a thermometer has a relative zero, making it an interval scale). Variable type: Named + Ordered + Proportionate Intervals + Absolute Zeros

Since all percentages are a fraction of some larger base, sometimes it’s helpful to re-define that base to make sense of a percentage — this is called rebasing. For example, if you ask a question to a smaller group of people and want to know how many of them said “yes,” you can either look at them as a percentage of only the people who responded to that question, or you can look at them as a percentage of everyone in the study. Changing the base that the percentage is using is called “rebasing.”

Response Rate is a calculation that illustrates the participation level of those invited to participate in the study. (For example, if 5,000 individuals are invited to participate in a study and 1,427 individuals complete the study, the participation rate is 28.5%.) Average response rates vary broadly by industry and source of participants.

Sample is the smaller group of people or data points that are included in a study, representing the larger population of interest.

Secondary research is essentially any research or info that already exists and can be looked up, purchased, or otherwise acquired.

When working with a sample of the population we’re studying, Sig Testing (short for “Significance Testing”) is used to determine whether or not the differences we see in the smaller sample are likely to be present in the larger population. The difference between two numbers needs to be large enough that when we account for the likelihood of slight variability between the sample and the population (the “margin of error”), the numbers are still distinctly different.

Skips are short for “skip patterns” and these are used skip a respondent over particular questions based on either their responses to a previous question, or based on something we know about the respondent (like information from their sample record).

Stated Importance is how important a respondent says something is in a very straightforward question (for example, “how important is customer service to you?”).