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Steps In Running A Survey

Hide details for Step 1: Planning a surveyStep 1: Planning a survey

a) Identifying your question
One of the first things you need to clarify when designing a survey is exactly what you want to find out. Start by writing your question as clearly as you can. Include as much detail as possible so that everyone else will interpret the question in the same way as you.

For example, if you wanted to find out "What times do students get up in the morning?" you would need to clarify:

  • Is it a normal school day, a weekend or a holiday e.g. “What time do students get up on a normal school day?”
  • Will it matter if students have different school starting times? Compare the question “How long before school starts do students get up?”
  • What units do you want to use to collect the data? “How many minutes before school starts do students get up?”
  • How will part units be reported? Do you want data to the closest whole number? If you plan to have fractions can these be decimals?

State your definitions
You will also need to state some definitions:
  • ‘student’ is defined as Year 7 and Year 11 Australian school students
  • ‘getting up time’ is the time students get out of bed on a school day.

It is important that you maintain these definitions throughout your investigation and in any report. If your question is not clearly defined, the participants in your survey may interpret the question differently and your results won't be accurate.

b) Deciding who to include in your sample

Participant characteristics
Next you need to specify the scope of your sample. For example, are you looking at a particular age group or year level or location? In these cases. your question might be “How many minutes before school starts do Year 7 students get up compared with Year 11 students?” or "How many minutes before school starts do students in Queensland get up compared with students in South Australia?".

Sample size
Estimates are made about the total population and subgroups based on the information from the sample. Generally, larger samples will give a more accurate representation of the population. However, it can be difficult to obtain accurate information on smaller groups within the population if the sample size is small.
In addition, the level of accuracy can usually be measured. There are formulae to determine the size of the sample that should be taken depending on the level of confidence required. One of the simplest is:
Sample size = √n
(where n is the size of the population)

To allow predictions to be confidently made about the total population, samples need to be randomly selected as well as of sufficient size. For data to be selected randomly, each data item must have the same chance of being selected as any other. Pulling data items from a hat or using the random number generator on a calculator are common ways of ensuring that data are selected randomly. Data not selected randomly may be biased towards a particular outcome.

Types of Sampling
There are a number of ways that a sample can be randomly drawn from a population. For example, you may want to ensure that each subgroup of a population is represented in the same proportion as in the general population.
For more information on types of sampling see our Glossary page.

Hide details for Step 2: Collecting dataStep 2: Collecting data

Once you have decided on your question, how many people will be in your sample, and randomly selected them, you will need to consider how to collect the data. Will it be through an interview or will you collect written responses? The data you collect will also need to be in a form that is easily organised in order to analyse it. For example, you need consistency in units and fractional answers so request that the data be recorded to the nearest centimetre or half centimetre.

In an interview, a participant can ask questions if they haven’t understood something.

Written response
Written responses can be completed by many participants at the same time and are quicker than interviews.

A variable is any measurable characteristic or attribute that can have different values for different subjects – for example, eye colour, distance from school etc.
Characteristic is another way of saying variable. For example, height, age or country of birth are all characteristics or variables of people.

Hide details for Step 3: Organising dataStep 3: Organising data

After you have collected the data, it needs to be organised so that it is useful and ready to display.

Frequency tables
A useful way to record raw data is a tally table or frequency table.
A frequency table counts the number of times – or frequency – a value occurs in the data. For example, twenty people are asked "How many TVs do you have in your household?" If 2 households have 1 TV, the frequency of households with 1 TV is 2.

Frequency tables with class intervals
When a variable has a large spread, the values can be grouped together to make the data easier to manage and present.
For example, if you asked students how much time it takes them to get to school each day, their responses may vary considerably. In this case, you can group the responses together in 5 minute intervals. These intervals are called class intervals. All class intervals should have an equal range. Class intervals are usually in groups of 5, 10, 20, 50 etc.

0l l l l4
1l l2
2l l l l l6
3l l l l l l l8
Figure 1: Frequency table of number of TVs per household

1 - 5l l l l l l7
6 - 10l l l l l l l l9
11 - 15l l l l l l l l9
16 - 2011
Figure 2: Example of a frequency table showing five minute class intervals

Show details for Step 4: Displaying informationStep 4: Displaying information
Show details for Step 5: Analysing the dataStep 5: Analysing the data
Hide details for Step 6: Drawing conclusionsStep 6: Drawing conclusions

Communicating the results of your investigation is a critical part of the survey process. Ensuring the accuracy of any interpretations and avoiding misinterpretations are crucial. Keeping in mind the purpose of the investigation and your audience will help to keep your conclusions on track and avoid including unnecessary information.

Accuracy of Data
All your calculations need to be accurate, verifiable from the data and clearly communicated using simple language.

To effectively communicate your results, you will need to be aware of avoiding any misinterpretation of the data such as using the mean when the median is more appropriate or not taking seasonal variation into account.

Stating your conclusions
With statistics, there is always a risk that the results you have do not tell the whole story. You can use the following checklist to help judge the reliability of your statistical information.

  • Do your conclusions communicate the message told by the data?
  • Are your conclusions based on results rather than on your opinions?
  • Have you considered alternative explanations for the same results?
  • Is your report set out logically including using an organisational framework such as headings and sub headings?
  • Have you included the source of any information you have used or referred to?
  • Have you included relevant tables and graphs?
  • Are your findings clear, related to your aim and only contain necessary information?

  • Have you considered your audience and used appropriate language?
  • Have you anticipated questions your reader might have? For example, have you explained unusual or unexpected results? Have you justified your choice of analysis, indicated your sampling process etc?
  • Can your reader check your conclusions by viewing your analysis?

Sampling Error
Finally, if you intend for your results to be applicable in other contexts, it is important to understand the limits that might apply.
The difference between an estimate based on a sample survey and the true value that would result if a census of the whole population was taken is called the sampling error. Sampling error can be measured mathematically and is influenced by the size of the sample. In general, the larger the sample size the smaller the sampling error.
The way a sample is drawn is also important. In general, a random sample will result in data that is more able to be generalised to the population.

Commonwealth of Australia 2008

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