# CHAPTER 5 EDUCATIONAL RESEARCH SELECTING A SAMPLE LEARNING

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CHAPTER 5 EDUCATIONAL RESEARCH SELECTING A SAMPLE

LEARNING OUTCOMES: To Define sampling in both: QUALITATIVE RESEARCH & QUANTITATIVE RESEARCH

KEY TERMS: SAMPLE, POPULATION, QUANTITATIVE, QUALITATIVE,

SAMPLE A sample is a selected group (that when properly selected) provides information the same as the population. The representation of the information from the sample group is intended to be the same as the population.

POPULATION The entire group of interest which the researcher would like to get their study results from. A population may be of any size, and usually begins with the word “ALL”

Every member of the population has an equal and independent chance of selection for the sample. The researcher has no control over the selection. PROBABILITY IS EQUALIZED ERROR & BIAS ARE MINIMALIZED

Why do a sample, and not a whole population? Generally, it is not possible to conduct an experiment on all the units of a population. An entire population is usually not available. To save the time and money of the researcher, a portion of the population is used. The results collected from a study on a sample are generalizable to the entire population.

RANDOM SAMPLING STRATEGIES SIMPLE STRATIFIED CLUSTER SYSTEMATIC

SIMPLE RANDOM SAMPLING Everyone in the population has an equal chance of selection for the sample. The researcher has no control over the selection. The selection of an individual does not effect the selection of any other individual (independent) • • You should have at least 30 samples. The sample size should be less than 10% of the entire sample.

Random Numbers

The sample size formula for the infinite population is given as :

THE SAMPLE SIZE FORMULA FOR THE FINITE POPULATION IS GIVEN AS : Here, SS = Sample size. Z = Given z value p = Percentage of population C = Confidence level Pop = Population

STRATIFIED RANDOM SAMPLING Stratified RS is a way to guarantee representation of relevant subgroups within a sample. Population are subdivided into subgroups (strata) on a certain variable. From each group proportional or equal numbers of subjects are selected randomly to form a sample

STEPS IN STRATIFIED RS 1. Identify and define the population. 2. Determine the desired sample size. 3. Identify the variables and subgroups (strata). 4. Classify all members of the population into subgroups. 5. Randomly select an equal or proportional number of individuals from each subgroup (using table of random numbers).

CLUSTER SAMPLING intact groups (clusters), not GROUPS are randomly selected. All the individuals of the selected clusters are included. May be the only feasible method of selecting a sample when the researcher is unable to obtain a list of all members of an intended population.

CLUSTER: STEPS 1. Identify and define the population. 2. Determine the desired sample size. 3. Identify and define a logical cluster. 4. List all clusters. 5. Estimate the average number of population members per cluster. 6. Divide the sample size by the estimated size of cluster to determine the number of clusters. 7. Randomly select the needed number of clusters. 8. Include all population members in each selected cluster.

SYSTEMATIC SAMPLING Selecting every Kth individual from the list of the population. K = Number of Individuals on the list/Number of individuals desired for the sample All members don’t have an independent chance of selection. It is considered random sampling if the list of the population is randomly ordered. Process may cause certain subgroups of the population to be excluded from the sample * NOT USED VERY OFTEN

STEPS: SYSTEMATIC SAMPLING 1. Identify and define the population. 2. Determine the desired sample size. 3. Obtain a list of the population. 4. Determine K by dividing the size of the population by the desired sample size. 5. Start at some random place in the population list. 6. Take ever K th individual on the list. 7. If the end of the list is reached before the desired sample is reached, go back to the top of the list.

NONRANDOM SAMPLING STRATEGIES CONVENIENCE SAMPLING PURPOSIVE SAMPLING QUOTA SAMPLING AKA: non-probability sampling. Independent or biased free selection of the individuals will not happen. Useful when the population can’t be described.

CONVENIENCE SAMPLING AKA accidental Sampling or haphazard sampling. Sample includes available individuals; “whoever is available” Volunteers Pre-existing groups Difficult to describe the population from which the sample was drawn and to whom results can be generalized

PURPOSIVE SAMPLING AKA judgment sampling. Selection based on the researcher’s experience and knowledge of the individuals being sampled. Researcher select the criteria to select the individuals. Main weakness is the imperfections in the researcher’s criteria.

QUOTA SAMPLING Process based on required, exact numbers, or quotas of individuals or groups with varying characteristics. Mostly used in wide-scale survey research when listing all members of the population is not possible. Data obtained from easily accessible individuals. People who are less accessible are underrepresented due to their own unavailability.

QUALITATIVE SAMPLING INTENSITY SAMPLING HOMOGENOUS SAMPLING CRITERION SAMPLING SNOWBALL SAMPLING RANDOM PURPOSIVE SAMPLING

QUALITATIVE SAMPLING In qualitative research the sampling is mainly purposive. Selecting process designed to select a small number of individuals that will be good key informants. “QUALITY instead of Quantity” The researcher first identifies the potential participants of the research. Participants are selected on some criteria according to their knowledge, experience, characteristics and willingness

Intensity Sampling- good and poor, experienced and inexperienced Homogenous Sampling- similar subjects in experience, perspective & outlook Criterion Sampling- according to some specific criterion Snowball Sampling- first select small number, then get additional people from them. Random Purposive Sampling- Select more participants than needed.

Intact groups are randomly selected: a. Simple Sampling b. stratified Sampling c. cluster sampling d. systematic

Intact groups are randomly selected: a. Simple Sampling b. stratified Sampling c. cluster sampling * d. systematic

Everyone has an equal chance of being selected: a. Simple Sampling b. stratified Sampling c. cluster sampling d. systematic

Everyone has an equal chance of being selected: a. Simple Sampling * b. stratified Sampling c. cluster sampling d. systematic

Similar subjects in experience, perspective, & outlook: a. Intensity b. Homogenous c. Criterion Sampling d. Snowball e. Random Purpose

Similar subjects in experience, perspective, & outlook: a. Intensity b. Homogenous * c. Criterion Sampling d. Snowball e. Random Purpose

Select more participants than needed: a. Intensity b. Homogenous c. Criterion Sampling d. Snowball * e. Random Purpose

Good & Poor, Experienced & Inexperienced: a. Intensity b. Homogenous c. Criterion Sampling d. Snowball e. Random Purpose

Good & Poor, Experienced & Inexperienced: a. Intensity * b. Homogenous c. Criterion Sampling d. Snowball e. Random Purpose

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