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Sampling Methods in Educational Research

The document discusses various sampling methods used in research to ensure that selected individuals represent a larger population accurately. It covers definitions of sample types, sources of sampling bias, various sampling techniques (such as random, stratified, cluster, and convenience sampling), and highlights considerations for sample size and representativeness. Additionally, it explores the challenges of obtaining reliable qualitative and quantitative data within practical constraints.

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Summarized by: Dr. Maribel R. Gaite
 The process of selecting a number of individuals for a study in such a way that the individuals represent the larger group from which they were selected
Sample Sample… …the representatives selected for a study whose characteristics exemplify the larger group from which they were selected
Population Population… …the larger group from which individuals are selected to participate in a study
 To gather data about the population in order to make an inference that can be generalized to the population
POPULATION SAMPLE INFERENCE
POPULATION (N) SAMPLE (n) IS THE SAMPLE REPRESENTATIVE?
POPULATION (N) SAMPLE (n) INFERENCE IS THE INFERENCE GENERALIZABLE?
2. Sampling bias …which threaten to render a study’s findings invalid 1. Sampling error
Sampling error Sampling error… …the chance and random variation in variables that occurs when any sample is selected from the population …sampling error is to be expected
…to avoid sampling error, a census census of the entire population must be taken …to control for sampling error, researchers use various sampling methods
Sampling bias Sampling bias… …nonrandom differences, generally the fault of the researcher, where sample over-represents individuals or groups within the population and which lead to invalid findings …sources of sampling bias include the use of volunteers and available groups
2. Determine sample size (n) 3. Control for bias and error 4. Select sample 1. Define population (N) to be sampled
 Identify the group of interest and its characteristics to which the findings of the study will be generalized …called the “target target” population (the ideal selection) …oftentimes the “accessible accessible” or “available available” population must be used (the realistic selection)
 The size of the sample influences both the representativeness of the sample and the statistical analysis of the data …larger samples are more likely to detect a difference between different groups …smaller samples are more likely not to be representative
2. For smaller samples (N ‹ 100), there is little point in sampling. Survey the entire population. 1. The larger the population size, the smaller the percentage of the population required to get a representative sample
 Be aware of the sources of sampling bias and identify how to avoid it Decide whether the bias is so severe that the results of the study will be seriously affected In the final report, document awareness of bias, rationale for proceeding, and potential effects
 A process by which the researcher attempts to ensure that the sample is representative of the population from which it is to be selected …requires identifying the sampling method that will be used
2. Nonrandom Nonrandom (“nonprobability”): does not have random sampling at any state of the sample selection; increases probability of sampling bias 1. Random Random: allows a procedure governed by chance to select the sample; controls for sampling bias
2. Stratified sampling 3. Cluster sampling 4. Systematic sampling 1. Simple random sampling
1. Simple random sampling Simple random sampling: the process of selecting a sample that allows individual in the defined population to have an equal and independent chance of being selected for the sample
2. Determine the desired sample size. 3. List all members of the population. 4. Assign all individuals on the list a consecutive number from zero to the required number. Each individual must have the same number of digits as each other individual. 1. Identify and define the population.
6. For the selected number, look only at the number of digits assigned to each population member. 5. Select an arbitrary number in the table of random numbers.
8. Go to the next number in the column and repeat step #7 until the desired number of individuals has been selected for the sample. 7. If the number corresponds to the number assigned to any of the individuals in the population, then that individual is included in the sample.
advantages advantages… …easy to conduct …strategy requires minimum knowledge of the population to be sampled
disadvantages 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 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
2. Determine the desired sample size. 3. Identify the variable and subgroups (strata) for which you want to guarantee appropriate, equal representation. 1. Identify and define the population.
5. Randomly select, using a table of random numbers) an “appropriate” number of individuals from each of the subgroups, appropriate meaning an equal number of individuals 4. Classify all members of the population as members of one identified subgroup.
advantages advantages… …more precise sample …can be used for both proportions and stratification sampling …sample represents the desired strata
disadvantages 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 Cluster sampling: the process of randomly selecting intact groups, not individuals, within the defined population sharing similar characteristics
2. Determine the desired sample size. 3. Identify and define a logical cluster. 4. List all clusters (or obtain a list) that make up the population of clusters. 1. Identify and define the population. 5. Estimate the average number of population members per cluster.
7. Randomly select the needed number of clusters by using a table of random numbers. 6. Determine the number of clusters needed by dividing the sample size by the estimated size of a cluster. 8. Include in your study all population members in each selected cluster.
advantages advantages… …efficient …researcher doesn’t need names of all population members …reduces travel to site …useful for educational research
disadvantages disadvantages… …fewer sampling points make it less like that the sample is representative
4. Systematic sampling Systematic sampling: the process of selecting individuals within the defined population from a list by taking every K Kth name.
2. Determine the desired sample size. 3. Obtain a list of the population. 4. Determine what K K is equal to by dividing the size of the population by the desired sample size. 1. Identify and define the population.
6. Starting at that point, take every K Kth name on the list until the desired sample size is reached. 5. Start at some random place in the population list. Close you eyes and point your finger to a name. 7. If the end of the list is reached before the desired sample is reached, go back to the top of the list.
advantages advantages… …sample selection is simple
disadvantages disadvantages… …all members of the population do not have an equal chance of being selected …the K Kth person may be related to a periodical order in the population list, producing unrepresentativeness in the sample
2. Purposive sampling 3. Quota sampling 1. Convenience sampling
1. Convenience sampling Convenience sampling: the process of including whoever happens to be available at the time …called “accidental” or “haphazard” sampling
disadvantages disadvantages… …difficulty in determining how much of the effect (dependent variable) results from the cause (independent variable)
2. Purposive sampling Purposive sampling: the process whereby the researcher selects a sample based on experience or knowledge of the group to be sampled …called “judgment” sampling
disadvantages disadvantages… …potential for inaccuracy in the researcher’s criteria and resulting sample selections
3. Quota sampling Quota sampling: the process whereby a researcher gathers data from individuals possessing identified characteristics and quotas
disadvantages disadvantages… …people who are less accessible (more difficult to contact, more reluctant to participate) are under-represented
…qualitative research is characterized by in-depth inquiry, immersion in a setting, emphasis on context, concern with participants’ perspectives, and description of a single setting, not generalization to many settings
…because samples need to be small and many potential participants are unwilling to undergo the demands of participation, most qualitative research samples are purposive
…representativeness is secondary to the quality of the participants’ ability to provide the desired information about self and setting
2. Homogeneous sampling Homogeneous sampling: selecting participants who are very similar in experience, perspective, or outlook 1. Intensity sampling Intensity sampling: selecting participants who permit study of different levels of the research topic
4. Snowball sampling Snowball sampling: selecting a few individuals who can identify other individuals who can identify still other individuals who might be good participants for a study 3. Criterion sampling Criterion sampling: selecting all cases that meet some pre-defined characteristic
5. Random purposive sampling Random purposive sampling: with a small sample, selecting by random means participants who were purposively selected and are too numerous to include all in the study
MIXED METHODS SAMPLING STRATEGIES Relationship between QUAL and QUANT samples Description Identical sampling The same participants participate in QUAL and QUANT study phases Parallel sampling Different samples for QUAL and QUANT study phases but participants drawn from same population Nested sampling A subset of the entire sample participate in an additional study Multilevel sampling Two or more samples recruited from different levels of the population of interest. Table adapted from Onwuegbuzie and Collins (2007) These sampling strategies can be used in concurrent and sequential designs
QUAL: Difficulties of capturing representative samples of qualitative life experiences QUANT: Difficulties of obtaining data from a representative sample Validity and legitimization (dependability and confirmability) Ensuring sufficient sample size within the pragmatic constraints of resources (financial, staff, etc) Attrition and/or incomplete information in the different study phases MIXED METHODS SAMPLING ISSUES Jacqui Smith, MCUAAAR workshop 5/2012
Jacobs, R. Educational Research: Sampling a Population. Educational Research. n.d. Reference: