10-04-2017, 08:42 PM
Data Preparation
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Three Common Mistakes
Before you dive into analyzing your survey results, take a look back at the big picture. What objectives were you trying to accomplish when you created your survey? Did your survey instrument meet those objectives? Is the data you collected the right data? Do you have sufficient data to properly reach a conclusion?
Although data analysis is the wrong time to try and rewrite your survey instrument, it is important to remember the scope of your project and stick to it. Many first time surveyors attempt to read "between the lines" while analyzing data. They attempt to answer questions that were not asked by making inferences and assumptions from those that were asked. Doing so amounts to nothing more than guesswork. To avoid this temptation, remember this simple rule:
Rule 1: If you did not ask you do not know.
Another common mistake that many first time surveyors make is to attempt to change data to compensate for poor question design. For example, if a question asked a respondent to indicate his total household income using a scale of values, a mean and median cannot be calculated. Many people try to get around this by assigning each response a value representing the range. Even if the adjustment is made consistently across all responses, the resulting calculations will be wrong. Similarly, trying to analyze a multiple-choice question as if it was a single-select question will often provide erroneous information. In order to avoid this pitfall, remember this simple rule:
Rule 2: Do not alter data to compensate for bad survey design.
A second mistake inexperienced surveyors make is to project the findings to an audience that was not either part of the survey population or not adequately represented. For example, if an HR manager conducts a benefits survey and invites all employees to participate, most people would assume that the results represent all employees since everyone had an opportunity to participate.
Survey Analysis
Analyzing any survey, web or traditional, consists of a number of interrelated processes that are intended to summarize, arrange, and transform data into information. If your survey objective was simply to collect data for your database or data warehouse, you do not have to do any analysis of the data. On the other hand, if your objective was to understand the characteristics of typical customers, then you must transform you raw results in to information that will enable you to paint a clear picture of your customers.
Quick Review
Read all your results. Although, this seems like an obvious thing to do, many surveyors think that they can skip this step and dive right in to data analysis. A quick review can tell you lots about your project, including any flaws in questionnaire design or response population, before you spend hours of time in analyzing the data.