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Steps In Running A Survey
Step 1: Planning a survey
Step 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 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.
Step 3: Organising data
|After you have collected the data, it needs to be organised so that it is useful and ready to display.
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.
Figure 1: Frequency table of number of TVs per household
|NUMBER OF TVs||TALLY||FREQUENCY (f)|
|0||l l l l||4|
|2|l l l l l |6|
|3|l l l l l l l |8|
Figure 2: Example of a frequency table showing five minute class intervals
|TIME TAKEN TO GET TO SCHOOL (min)||TALLY||FREQUENCY (f)|
|1 - 5|l l l l l l |7|
|6 - 10|l l l l l l l l |9|
|11 - 15|l l l l l l l l |9|
|16 - 20||1||1|
Step 4: Displaying information
Step 5: Analysing the data
Step 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?
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.
This page last updated 19 June 2013