9.1 From Completed Survey to Analyzable Data

It can be very exciting to receive those first few completed surveys back from respondents. Hopefully you will get more than a few back, however once you have a handful of completed questionnaires, your feelings may go from initial euphoria to dread. Data are fun and can also be overwhelming. The goal with data analysis is to be able to condense large amounts of information into usable and understandable chunks. Here we will describe just how that process works for survey researchers.

As mentioned, the hope is that you will receive a good portion of completed, readable and usable surveys. The number of completed surveys you receive divided by the number of surveys you distributed is your response rate. For example, suppose your sample included 100 people and you sent surveys to each of those people. It would be wonderful if all 100 returned fully completed surveys, but the chances of that happening are about zero. If you are lucky, perhaps 75 or so will return completed surveys. In this case, your response rate would be 75% (75 divided by 100). Though response rates vary, and researchers do not always agree about what makes a good response rate, having three-quarters of your surveys returned would be considered good, even excellent, by most survey researchers.

Lots of research has been done on how to improve a survey’s response rate. We covered some of these previously, but suggestions include personalizing surveys by addressing them to specific respondents rather than to some generic recipient such as “madam” or “sir”; enhancing the survey’s credibility by providing details about the study, contact information for the researcher, and perhaps partnering with agencies likely to be respected by respondents such as universities, hospitals, or other relevant organizations; sending out pre-survey notices and post-survey reminders; and including some token of appreciation with mailed surveys, even if small, such as one dollar.

The major concern with response rates is that a low rate of response may introduce non-response bias into a study’s findings. What if only those who have strong opinions about your study topic return their surveys? If that is the case, you may well find that your findings don’t at all represent how things really are or, at the very least, you are limited in the claims you can make about patterns found in your data.

Regardless of your survey’s response rate, the major concern of survey researchers, once they have their nice, big stack of completed surveys, is condensing their data into manageable and analyzable bits. One major advantage of quantitative methods such as survey research is that they enable researchers to describe large amounts of data because they can be represented by and condensed into numbers. In order to condense your completed surveys into analyzable numbers, you will first need to create a codebook. A codebook is a document that outlines how a survey researcher has translated her or his data from words into numbers. An excerpt from the codebook, related to a survey by Saylor Academy (2012) regarding older workers, can be seen in Table 9.1, “Codebook excerpt from survey of older workers”. As you will see in the table, in addition to converting response options into numerical values, a short variable name is given to each question. This shortened name comes in handy when entering data into a computer program for analysis.

For those who will be conducting manual data entry, there probably is not much to be said about this task that will make you want to perform it other than pointing out the reward of having a database of your very own analyzable data. We will not get into too many of the details of data entry, but we will mention a few programs that survey researchers may use to analyze data once it has been entered. The first is SPSS, or the Statistical Package for the Social Sciences. SPSS is a statistical analysis computer program designed to analyze just the sort of data quantitative survey researchers collect. It can perform everything from very basic descriptive statistical analysis to more complex inferential statistical analysis. SPSS is touted by many for being highly accessible and relatively easy to navigate (with practice). Excel, which is far less sophisticated in its statistical capabilities, is relatively easy to use and suits some researchers’ purposes just fine.

In analyzing data, it is important to differentiate between aggregate data and disaggregating data.

Aggregate data refers to numerical or non-numerical information that is (1) collected from multiple sources and/or on multiple measures (variables or individuals) and (2) compiled into data summaries or summary reports to examine trends or statistical analysis. On the other hand, disaggregate data breaks down aggregated data into component parts or smaller units of data.