1. Chapter 12: Surveys Introduction 12.1 The method 12.2 Random samples
12.3 Designing questionnaires
12.4 Pilot studies
12.5 Collecting data
12.6 Processing data
12.7 Getting professional help

2. Introduction Counts things in a group (population)
Members in group with a specific characteristic are counted and compared to members without that characteristic
Three aspects
Population is often too large to count: Count a sample and generalise
If human population, count opinions
Surveys are not done to determine current status of population, but to prove theories
Problems
You might miss target group
Poorly formulated questions (ambiguous, etc.)

3. 12.1 The method The method To assist Start with a theory
Formulate a hypothesis
Select a sample
Collect the data
Analyse the data
Test hypothesis
Confirm or modify theory
To assist
Know the broad problem, e.g. ease of use
Know dimensions, e.g. ease of learning, ease of use under pressure, etc.
Identify indicators. E.g. ease of learning may be measured by questioning people directly, comparing duration of training courses with that of other systems, obtain statistics on support systems used, etc.
Determine relative weight of various indicators

4. 12.2 Random samples Know exactly what the population is
Select a sample in such a way that every member of the population has equal chance of being in the population
E.g don’t use telephonic interviews / internet if every member of population is not reachable by telephone / internet
If necessary, redefine population, e.g. refer to computer users in a specific country/ language/etc.

5. 12.3 Design questionnaires
Distinguish between questionnaires and schedules
Ask precisely and unambiguously
Don’t ask questions that participants are unlikely to know, e.g. “How many minutes did you spend on the Internet during the past month?”
Don’t ask questions that participants do not want to answer or will most likely not answer honestly, e.g. “What is the weakest point in your company’s computer security measures?”
Keep the questionnaire short and simple.
Pay attention to professional appearance.

6. 12.3 Types of questionaires
Nominal measures (example p 82)
Ordinal measures
Using numbers to express preferences but weight of number does not bear any meaning
Ratio measures
Measurement in a unit where 0 means zero
Interval measures
Measurement in a unit where 0 does not mean zero
Likert scales
Specify degree to which specific statement applies
Example p 83
LPC scales
Respondent indicate preference between two explicit alternatives
Open questions
Format of response depends on respondent

7. 12.4 Pilot studies
Once the survey begins, you will experience problems with respondents not understanding instructions, certain questions that prove to be ambiguous, etc.
Do a pilot study
Select a small group from the population – need not be representative but it will help
Conduct the survey
Results are not important - look for hitches
Adapt questionnaire
If many problems were encountered, repeat pilot study

8. 12.5 Collecting data
If you use assistants, ensure that they all collect the data in the same way.
If they explain the questions, the explanation might influence the answers
Use detailed protocol
If you depend on cooperation of people in the sample
People might not be honest
Pay them?
Promise copy of research report
Make results available on the internet
Create a sense that participants are contributing to something worthwhile
Conduct the survey under auspices of an organisation with a reputation of conducting researches
Personal contact

9. 12.6 Processing the data
Sometimes easy, e.g. you are only interested in the totals or averages for every question.
Sometimes you need more statistical analysis (chapter 13).

10. 12.7 Getting professional help
Statistical consultation service
Don’t contact them only after you have collected the data
The data might have an inherent flaw and all the effort, time, cost is wasted
Contact them prior to setting up experimental design