Surveys are a method of gathering information from people. Surveys have a variety of purposes and can be conducted in many ways. Surveys may be conducted to gather information through a printed questionnaire, over the telephone, by mail, in person, by diskette, or on the web. Create a transparent, unbiased, and complete survey to get related suggestions without being tough for the respondent to finish and return. The right selection of phrases and linear scale settings are essential traits.
The key to understanding qualitative analysis is that the survey question responses will be in the form of words instead of numbers.
These types of surveys may contain questions that are:
- Open-Ended Questions
- Binary Questions
- Categorical Preference Questions
Questions that offer the most freedom to respond have the benefit of obtaining a higher level of detail but also take the most time to decipher and analyze. These also run the risk of needing to follow-up (if possible) or possible misinterpretation.
Sample questions are:
- Why did you choose this product/service?
- What improvements would you like to see in this product?
- What do you like about the product?
- What do you dislike about the product?
Review the first question. There could be the following responses:
- Great price
- Referral from a friend/family
- Already own one, good reputation and trust
- Good warranty
- Higher quality and fair price compared to competition
- Nothing else on the market
These responses would be reviewed by the team and broken down into categories such as Quality, Price, Brand, Delivery, and Customer Service. Then organize the responses into the categories, and it is possible that one response may get multiple categories (such as question #5) which are the benefit of open-ended questions.
The following is a methodical way to break down the responses to open-ended questions.
- Have the team review the responses and try group consensus to categorize the response.
- Create possible categories for answers and there will likely be some responses that don't seem to fit, or can be labeled as miscellaneous. If there are too many of these, it is possible the question needed to be more specific.
- Determine the number (count) of responses in each category.
- Display counts in each category and now the BB/GB can provide the team results regarding percentages, mode, rankings, or just the count of each category.
Sample questions are:
- Do you prefer this product HOT or COLD?
- What is your gender?
- Do you prefer the glass-half-full or half-empty?
- Would you refer this product to a friend/family? YES or NO
These are simply categorized and then analyzed
CATEGORICAL PREFERENCE "WORDED" QUESTIONS
Many surveys use numbers to represent categories or classifications and the difference between each number has meaning. This is ORDINAL data and is one level higher than the lowest level of data, known as NOMINAL data.
- 1 = POOR
- 2 = FAIR
- 3 = AVERAGE
- 4 = GOOD
- 5 = EXCELLENT
Other questions are:
- What is most important to you? Price, Quality, Warranty, or Service (Nominal Data)
- What color do you most prefer in the product? Blue, Red, or Green (Nominal Data)
- When you provide the possible answers it is easier to categorize and count but sometimes the responses may not be exactly the respondents best choice or ideal answer.
They may feel obligated to pick one and this can lead to wrong decisions from the statistical analysis. This is non-metric or qualitative data and requires the use of non-parametric tests to evaluate statistically.
- Mann-Whitney: Test the differences between categories
- Chi-Squared: Test the independence between categories
- Spearman's Rho: Test the relationship between categories.