Data Collection and Sampling Techniques Objectives: Identify the four basic sampling techniques.

Difference between census and sample If the method of sample selection was successful, statistics such as median and range should have similar values for both the sample and the population.

BIAS IN SAMPLING A biased sample is one in which the data has been unfairly influenced by the collection process and is not truly representative of the whole population.

Population… …the larger group from which individuals are selected to participate in a study

The purpose for sampling… To gather data about the population in order to make an inference that can be generalized to the population

The sampling process… POPULATION INFERENCE SAMPLE

Steps in sampling... 1. Define population (N) to be sampled 2. Determine sample size (n) 3. Control for bias and error 4. Select sample

Random sampling methods... 1. Simple random sampling 2. Stratified sampling 3. Cluster sampling 4. Systematic sampling

1) Random Sampling Random samples are selected by using chance methods or random numbers. One such method is to number each subject in the population. Then place numbered cards in a bowl, mix them thoroughly, and select as many cards as needed. The subjects whose numbers are selected constitute the sample.

advantages… …easy to conduct …strategy requires minimum knowledge of the population to be sampled

disadvantages… …need names of all population members …may over- represent or under- estimate sample members …there is difficulty in reaching all selected in the sample

2. Stratified sampling the process of selecting a sample that allows identified subgroups in the defined population to be represented in the same proportion that they exist in the population

advantages… …more precise sample …can be used for both proportions and stratification sampling …sample represents the desired strata

disadvantages… …need names of all population members …there is difficulty in reaching all selected in the sample …researcher must have names of all populations

3. Cluster sampling The process of randomly selecting intact groups, not individuals, within the defined population sharing similar characteristics. some means such as geographic area or schools in a large school district, etc.

advantages… …efficient …researcher doesn’t need names of all population members …reduces travel to site …useful for educational research

disadvantages… …fewer sampling points make it less like that the sample is representative

4. Systematic sampling The process of selecting individuals within the defined population from a list by taking every K th name.

For example, suppose there were 2000 subjects in the population and a sample of 50 subjects were needed. Since 2000 50 40, then k 40, and every 40th subject would be selected; however, the first subject (numbered between 1 and 40) would be selected at random. Suppose subject 12 were the first subject selected; then the sample would consist of the subjects whose numbers were 12, 52, 92, etc., until 50 subjects were obtained.

advantages… …sample selection is simple

disadvantages… …all members of the population do not have an equal chance of being selected …the Kth person may be related to a periodical order in the population list, producing unrepresentativeness in the sample

assignment Work in groups of 4. create a sway that summarize the four sampling techniques that we studied today. Mention three example for each type. Submit your work in your Class Note Collaboration Section