Steps in Designing a Survey

2005-2012 David S. Walonick, Ph.D.

All rights reserved.

Excerpts from Survival Statistics - an applied statistics book for graduate students.


The rules for designing any kind of survey are the same. It doesn't matter if it's a market research survey, public opinion poll, customer satisfaction survey, or employee survey. It doesn't matter if it's survey for program evaluation, government, health care, education, or a nonprofit agency. The steps in designing all surveys are the same. Once you know the rules for designing questionnaires, you can create any kind of customer satisfaction survey or employee opinion survey. (Samples)


How to define the goals of the research

Many people begin a survey by writing the questionnaire items. This is wrong. Don't do it. If you begin by writing the questionnaire items you'll lose focus of what's important. Your survey will become a fishing expedition with a lot of tributaries leading nowhere.

Well-designed surveys always begin by committing your research questions to writing. Research questions are not the same as the questionnaire items. Research questions are global in nature. They are the goals and objectives of the study. Questionnaire items, on the other hand, are designed to help answer the global research questions.

Defining the goals and objectives of a research project is one of the most important steps in the research process. Do not underestimate the importance of this step. Clearly stated goals keep a research project focused. The process of goal definition begins by writing down the broad and general goals of the study. As the process continues, the goals become more clearly defined and the research issues are narrowed.

Here are a few examples of how you might phrase your goals and objectives:

I want to discover if customers are satisfied with ...(service, quality, etc.)

I want to learn how employees feel about ...(policy, pay, hours, etc.)
I want to know if there are relationships between x and y.
I want to understand the differences between x and y.

How to write research questions

The goals of the study are easily transformed into research questions. Once again, research questions are global and broad, and they are not the same as the questionnaire items. There are basically two kinds of research questions: testable and non-testable. Neither is better than the other, and both have a place in customer satisfaction and employee surveys.

"Non-testable" means that the research question cannot be answered by performing a statistical test. The answers to these questions might be important to know, but the decision making criteria does not involve a statistical test.

Examples of non-testable research questions are:

What do customers feel is fair price for the new product?
How do customers feel about our service?
How do customers feel about the quality of our products?
What are employee's attitudes towards the new management?
What changes would improve employee productivity?

Respondents' answers to these questions can be summarized in descriptive tables and the results might be extremely valuable to administrators and planners. Business and social science researchers often ask non-testable research questions. The shortcoming with these types of questions is that they do not provide objective cut-off points for decision-makers.

For example, imagine that we've done our survey, and now we need to decide what constitutes satisfactory service? Each of us might give a different answer. There is no exact cutoff point where we would say "yes" our customers are satisfied, or "no" they are not. When we ask questions like this, it's important to establish a decision making guideline before doing the survey.

How to write testable research questions

It is perhaps more important to ask questions that involve decision making criteria. Business research usually seeks to answer one or more testable research questions. Nearly all testable research questions begin with one of the following two phrases:

   Is there a significant difference between ...?
   Is there a significant relationship between ...?

Examples of testable research questions are:

   Is there a significant relationship between a customer's age  and their level of satisfaction with the service?

   Is there a significant difference between the level of male and female satisfaction with the service?

   Is there a significant relationship between managerial level and support of the new budget?

   Is there a significant difference between the productivity of workers in plant A and workers in plant B?

How to convert research questions into hypotheses and null hypotheses

A research hypothesis is a testable statement of opinion. It is created from the research question by replacing the words "Is there" with the words "There is", and also replacing the question mark with a period. The hypotheses for the four sample research questions would be:

   There is a significant relationship between a customer's age and their level of satisfaction with the service.

   There is a significant difference between the level of male and female satisfaction with the service.

   There is a significant relationship between managerial level and support of the new budget.

   There is a significant difference between the productivity of plant A and plant B.

It is not possible to test a hypothesis directly. Instead, you must turn the hypothesis into a null hypothesis. The null hypothesis is created from the hypothesis by adding the words "no" or "not" to the statement. For example, the null hypotheses for the two examples would be:

   There is no significant relationship between a customer's age and their level of satisfaction with the service.

   There is no significant difference between the level of male and female satisfaction with the service.

   There is no significant relationship between managerial level and support of the new budget.

   There is no significant difference between the productivity of plant A and plant B.

All statistical testing is done on the null hypothesis...never the hypothesis. The result of a statistical test will enable you to either 1) reject the null hypothesis, or 2) fail to reject the null hypothesis. Never use the words "accept the null hypothesis". When you say that you "reject the null hypothesis", it means that you are reasonably certain that the null hypothesis is wrong. When you say that you "fail to reject the null hypothesis", it means that you do not have enough evidence to claim that the null hypothesis is wrong.

How to make sure your survey is valid

Pointers on how to write questionnaire items

There are many different ways to design the questions for a survey. The problem is how can we check the validity of the survey? How can we determine if a survey is actually measuring what it's supposed to measure?

There are no statistical tests for validity. When a survey is "validated" it means that the researcher has come to the opinion that the survey is measuring what it was designed to measure, or the researcher has received a statement from another researcher indicating that they believe the instrument is measuring what it was designed to measure. Validity is an opinion; nothing more.

Here is a quote from our statistics book Survival Statistics

"Validity refers to the accuracy or truthfulness of a measurement. Are we measuring what we think we are? This is a simple concept, but in reality, it is extremely difficult to determine if a measure is valid. Generally, validity is based solely on the judgment of the researcher. When an instrument is developed, each question is scrutinized and modified until the researcher is satisfied that it is an accurate measure of the desired construct, and that there is adequate coverage of each area to be investigated."

Most statistics textbooks will tell you that the way to "debug" a questionnaire is to send the questionnaire to a sample of about 30 potential respondents and evaluate their responses for potential problems. We have found that this rarely yields useful information.

Instead, we recommend the following method to get the kinks out of your survey. It's fast, costs nothing, and you'll get immediate and valuable feedback that can be used to improve your instrument.

  1. Find someone who will act as a respondent. They do not need to be someone from the actual pool of potential respondents. You can ask a spouse or friend to "pretend" they are from the target population. Do not use someone who helped create the survey.
     
  2. Give them a "final" copy of the survey and say something like, "Please complete this survey as if you were a real respondent. You can just make up the answers. Feel free to ask me any questions while you're completing it". Then give them the survey and sit there quietly while they take the survey. The survey you give them should be a "final" copy... exactly the way it will appear on the paper when it is printed. If it's an internet survey, have them take it on the internet. If you use this method while the survey is in "draft" form, do it again after the survey is in final form.
     
  3. Any question they ask you about the survey indicates a defective item. Real respondents will not have an opportunity to ask questions, so you must fix these items now. Modify all items that were mentioned. Then begin the process again with a new respondent, and continue until there are no questions. Usually, you'll be done after two or three "pretend respondents".

How to test the reliability of a survey

Reliability is synonymous with repeatability. A measurement that yields consistent results over time is said to be reliable. When a measurement is prone to random error, it lacks reliability. The reliability of an instrument places an upper limit on its validity. A measurement that lacks reliability will also lack validity. There are three basic methods to test reliability: test-retest, equivalent form, and internal consistency

A test-retest measure of reliability can be obtained by administering the same instrument to the same group of people at two different points in time. The degree to which both administrations are in agreement is a measure of the reliability of the instrument. This technique for assessing reliability suffers two possible drawbacks. First, a person may have changed between the first and second measurement. Second, the initial administration of an instrument might in itself induce a person to answer differently on the second administration.

The second method of determining reliability is called the equivalent-form technique. The researcher creates two different instruments designed to measure identical constructs. The degree of correlation between the instruments is a measure of equivalent-form reliability. The difficulty in using this method is that it may be very difficult (and/or prohibitively expensive) to create a totally equivalent instrument.

The most popular methods of estimating reliability use measures of internal consistency. When an instrument includes a series of questions designed to examine the same construct, the questions can be arbitrarily split into two groups. The correlation between the two subsets of questions is called the split-half reliability. The problem is that this measure of reliability changes depending on how the questions are split. A better statistic, known as Cronbach's alpha, is based on the mean (absolute value) interitem correlation for all possible variable pairs. It provides a conservative estimate of reliability, and generally represents the lower bound to the reliability of a scale of items. For dichotomous nominal data, the KR-20 (Kuder-Richardson) is used instead of Cronbach's alpha.

 

Copyright 2014 StatPac Inc., All Rights Reserved