Different Types of Sample
There are 5 different types of sample you should be able to define. You should also understand when to use them, and what their advantages and disadvantages are.
Simple Random Sample
Obtaining a genuine random sample is difficult. We usually use Random Number Tables, and use the following procedure;
- Number the population from 0 to n
- Pick a random place I the number table
- Work in a random direction
- Organise numbers into the required number of digits (e.g. if the size of the population is 80, use 2 digits)
- Reject any numbers not applicable (in our example, numbers between 80 and 99)
- Continue until the required number of samples has been collected
- [ If the sample is "without replacement", discard any repetitions of any number]
The sample will be free from Bias (i.e. it's random!)
Difficult to obtain
Due to its very randomness, "freak" results can sometimes be obtained that are not representative of the population. In addition, these freak results may be difficult to spot. Increasing the sample size is the best way to eradicate this problem.
With this method, items are chosen from the population according to a fixed rule, e.g. every 10th house along a street. This method should yield a more representative sample than the random sample (especially if the sample size is small). It seeks to eliminate sources of bias, e.g. an inspector checking sweets on a conveyor belt might unconsciously favour red sweets. However, a systematic method can also introduce bias, e.g. the period chosen might coincide with the period of faulty machine, thus yielding an unrepresentative number of faulty sweets.
Can eliminate other sources of bias
Can introduce bias where the pattern used for the samples coincides with a pattern in the population.
The population is broken down into categories, and a random sample is taken of each category. The proportions of the sample sizes are the same as the proportion of each category to the whole.
Yields more accurate results than simple random sampling
Can show different tendencies within each category (e.g. men and women)
Nothing major, hence it's used a lot
As with stratified samples, the population is broken down into different categories. However, the size of the sample of each category does not reflect the population as a whole. This can be used where an unrepresentative sample is desirable (e.g. you might want to interview more children than adults for a survey on computer games), or where it would be too difficult to undertake a stratified sample.
Simpler to undertake than a stratified sample
Sometimes a deliberately biased sample is desirable
Not a genuine random sample
Likely to yield a biased result
Used when populations can be broken down into many different categories, or clusters (e.g. church parishes). Rather than taking a sample from each cluster, a random selection of clusters is chosen to represent the whole. Within each cluster, a random sample is taken.
Less expensive and time consuming than a fully random sample
Can show "regional" variations
Not a genuine random sample
Likely to yield a biased result (especially if only a few clusters are sampled)