Educational Research Chapter 4: Selecting A Sample

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Chapter 4: Selecting a Sample
I.Quantitative Sampling: Define the term sampling from a quantitative perspective and identify its purpose.

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Sampling: the process of selecting a number of individuals for a study
Purpose: To identify participants from whom to seek some information

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Chapter 4: Selecting a Sample
1.2 Define the terms target population, accessible population, sample, and subject.

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Population: All members of a specified group
Target population: the population that the researcher would like to generalize results to
Accessible population or Available population: the population that the researcher realistically selects from
Sample: individuals that represent the larger group from which they were selected
Subject: a specific individual participation in a study

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Chapter 4: Selecting a Sample
Differentiate the term target population from accessible population and give examples of each. (1.3)

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A distinction is made between the population to which the researcher would ideally like to generalize study results, the target population, and the population from which the researcher can realistically select subjects, which is known as the accessible population of available population. In most studies the chosen population is generally a realistic choice (i.e., accessible), not an idealistic one (i.e., target).
Example: Studies of high school principals’ opinions about having their students attend school 6 days a week. It is unrealistic to collect a sample of all principals in the United States so you choose principals in your own state but then this limits the generalization to your state only.

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Chapter 4: Selecting a Sample
Discuss the concepts of representativeness and generalization as related to quantitative sampling. (1.4)

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Representation: the extent to which the sample is representative of the population: demographic characteristics, personal characteristics and specific traits.
Generalization: the extent to which the results of the study can be reasonably extended from the sample to the population.

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Chapter 4: Selecting a Sample
Describe the three fundamental steps in selecting a sample regardless of the specific technique being used. (1.5)

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Identifying the population: example-all 10th graders in the United States
Determining required sample size: Depends on the type of study
Selecting the sample:

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Chapter 4: Selecting a Sample
Explain why a population must be defined and each member of the population identified in order to select a random sample. (1.6)

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The selection of subjects so that all members of a population have an equal and independent chance of being selected.

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Chapter 4: Selecting a Sample
Identify four random sampling techniques (1.7)

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Random
Stratified random
Cluster
Systematic

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Chapter 4: Selecting a Sample
Identify the unique characteristic of a random sample (2.1)

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Simple Random Sampling: the process of selecting a sample in such a way that all individuals in the defined population have equal and independent chance of being selected for the sample. The selection of the sample is completely out of the researcher’s control.

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Chapter 4: Selecting a Sample
Identify three advantages of using random samples (2.2)

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• Easy to conduct
• High probability of achieving a representative sample
• meets assumptions of many statistical procedures

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Chapter 4: Selecting a Sample
Describe the seven steps involved in selecting a random sample and use these to select a random sample. (2.3)

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1) Identify and define the population
2) Determine the desired sample size
3) List all members of the population
4) Assign all members on the list a consecutive number
5) Select an arbitrary starting point from a table of random numbers and read the appropriate number of digits
6) If the number corresponds to a number assigned to an individual in the population, that individual is in the sample; if not, ignore the number
7) Continue until the desired number of subjects have been selected

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Chapter 4: Selecting a Sample
Define stratified random sampling and describe a situation in which you would use this technique. (3.1)

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Stratified sampling: the process of selecting a sample in such a way that identified subgroups in the population represented in the sample in the same proportion in which they exist in the population. Example: A strata represents a variable on which the researcher would like to see representation in the sample (gender, ethnicity, grade level). Comparing the achievement of students of different ability levels (high, average, and low) being taught by two methods of mathematics instruction (teacher and computer).

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Chapter 4: Selecting a Sample
Describe the reason it is important to use stratified random sampling. (3.2)

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The purpose of stratified sampling is to guarantee desired representation of relevant subgroups within the sample.

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Chapter 4: Selecting a Sample
Identify three common variables used as strata. (3.3)

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A strata represents a variable on which the researcher would like to see representation in the sample. Example: gender, ethnicity, and grade level.

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Chapter 4: Selecting a Sample
Differentiate proportional stratified random sampling from equal sample size stratified random sampling (i.e., non-proportional) (3.4)

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1) Proportional: Same proportion of subgroups in the sample as in the population. Example: if a population has 45% females and 55% males, the sample should have 45% females and 55% males.
2) Non-proportional: Different, often equal, proportions of subgroups. Example: selecting the same number of children from each of the five grades in a school even though there are different numbers of children in each grade.

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Chapter 4: Selecting a Sample
Describe the five steps involved in taking a proportional stratified random sample.(4.1)

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1) Identify and define the population.
2) Determine the desired sample size.
3) Identify the variable and subgroups (i.e., strata) for which you want to guarantee appropriate representation.
4) Classify all members of the population as members of one of the identifies subgroups.
5) Randomly select a number of individuals from each subgroup so the proportion of these individuals in the sample is the same as that in the population.

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Chapter 4: Selecting a Sample
Describe the five steps involved in taking an equal sample size stratified sample (4.1)

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1) Identify and define the population.
2) Determine desired sample size.
3) Identify the variable and subgroups (strata) for which you want to guarantee appropriate representation.
4) Classify all members of the population as members of one of the identified subgroups.
5) Randomly select (using a table of random numbers) an “appropriate” number of individuals from each of the subgroups. Appropriate in this case means an equal number of individuals.

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Chapter 4: Selecting a Sample
Define cluster sampling, identify several common clusters, and describe a situation in which you would use this technique. (4.3)

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Cluster sampling: Selecting subjects by using groups that have similar characteristics and in which subjects can be found.
Cluster: locations within which an intact group of members of the population can be found.
Examples: Neighborhoods, School districts, schools, classrooms. Instead of choosing some fifth graders for a study you would use all fifth graders.

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Chapter 4: Selecting a Sample
Describe the eight steps involved in cluster sampling. (4.4)

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1) Identify and define the population. 2) Determine the desired sample size. 3) List all clusters (or obtain a list) that make up the population of clusters. 4) List all clusters (or obtain a list) that make up the population of clusters. 5) Estimate the average number of population members per cluster. 6) Determine the number of clusters needed by dividing the sample size by the estimated size of a cluster. 7) Randomly select the needed number of clusters (using a table of random numbers). 8) Include in your study all population members in each selected cluster.

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Chapter 4: Selecting a Sample
Describe the steps to select a cluster sample. (4.5)

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1) The population is all 5,000 teachers in the superintendent’s school system. 2) The desired sample size is 500 3) A logical, useful cluster is a school. 4) The superintendent has a list of all the schools in the district, there are 100 schools. 5) Although the schools vary in the number of teachers per school, there is an average of 50 teachers per school. 6) The number of clusters (schools) to be selected equals the desired sample size, 500, divided by the average size of a cluster, 50. Thus, the number of schools needed is (500/50) = 10. 7) Therefore, 10 of the 100 schools are randomly selected by assigning a number to each school and using a table of random numbers.
8) All the teachers in each of the 10 schools are in the sample (10 schools, 50 teachers per school on average, equals the desired sample size)

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Chapter 4: Selecting a Sample
Explain the term multi-stage sampling in the context of cluster sampling. (4.6)

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Multistage sampling involves the use of two or more sets of clusters
•Randomly select a number of school districts from a population of districts
•Randomly select a number of schools from within each of the school districts
•Randomly select a number of classrooms from within each school

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Chapter 4: Selecting a Sample
Define systematic sampling and describe a situation in which you would use this technique. (4.7)

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Systemic Sampling: Selecting every Kth subject from a list of the members of the population

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Chapter 4: Selecting a Sample
Describe the seven steps involved in systematic sampling. (4.8)

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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. Close your eyes and stick your finger on a name. 6) Starting at that point, take every Kth name on the list until the desired sample size is reached. 7) If the end of the list is reached before the desired sample is reached, go back to the top of the list.

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Chapter 4: Selecting a Sample
Identify the advantages and disadvantages of simple random

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Select desired number of sample members using a table of random numbers Advantages: Easy to conduct; strategy requires minimum knowledge of the population to be sampled
Disadvantages: Need names of all population members; may over-or under represent sample members; difficult to reach all selected in sample

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Chapter 4: Selecting a Sample
Identify the advantages and disadvantages of Stratified random sampling

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Divide population into separate levels, or strata, and randomly sample from the separate strata.
Advantage: More precise sample; can be used for both proportions and stratification sampling; sample represents the desired strata
Disadvantages: Need names of all population members; difficult to reach all selected in sample; researcher must have names of all populations.

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Chapter 4: Selecting a Sample
Identify the advantages and disadvantages of Cluster sampling

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Cluster sampling Select groups, not individuals; identify clusters and randomly select them to reach desired sample size. Efficient; clusters are most likely to be used in school research, don’t need names of all population members; reduces travel to sites. Fewer sampling points make it less likely to produce a representative sample.

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Chapter 4: Selecting a Sample
Identify the advantages and disadvantages of Systematic sampling

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Systematic sampling Using list of population, pick a name on list at random and select each Kth person on the list to the desired sample size. Sample selection is simple All members of population do not have an equal chance to be selected; Kth person may be related to a periodic order in the population list, producing unrepresentativeness in the sample

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Chapter 4: Selecting a Sample
Determining Sample Size
Explain the general rules for determining sample size for correlational, causl-comparitive, experimental, and descriptive research. (1.8)

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The minimum sample size depends on the type of research involved. Some cite a sample size of 30 as a guideline for correlational, causal-comparative, and true experimental research. For descriptive research 10% to 20% of the population.

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Chapter 4: Selecting a Sample
Identify five guidelines for determining sample sizes selected from given population sizes. (1.9)

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1)The larger the population size, the smaller the percentage of the population required to get a representative sample
2)For smaller populations, say, N = 100 or fewer, there is little point in sampling; survey the entire population
3)If the population size is around 500 (give or take 100), 50% should be sampled
4)If the population size is around 1,500, 20% should be sampled
5)Beyond a certain point (about N = 5,000), the population size is almost irrelevant and a sample size of 400 will be adequate.

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Chapter 4: Selecting a Sample
Avoiding Sampling Error and Bias
Define the term sampling error and discuss the means by which it can be controlled to some extent. (6.1)

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Sampling error: The chance occurrence that a randomly selected sample is not representative of the population due to errors inherent in the sampling technique. If there is a variable for which the sample is greatly underrepresented, the researcher should stratify on that variable because stratification can provide proportional or equal-sized samples. Sampling errors can be controlled by selecting large samples .

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Chapter 4: Selecting a Sample
Define the term sampling bias, identify the major source of it, and provide several examples of it. (6.2)

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Sampling bias: A systemic sampling error that is generally the fault of the researcher. It occurs when some aspect of the sampling creates a bias in the data. Example a researcher asks college students their attitudes toward alcohol stood outside a bar and asked patrons leaving about their attitude toward alcohol.

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Chapter 4: Selecting a Sample
Explain the importance of discussing sampling bias in a final research report in which nonrandom sampling techniques were used. (5.6)

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If it is not possible to avoid sampling bias, you must decide whether the bias is so severe that the study results will be seriously affected. If you decide to continue with the study, with full awareness of the existing bias, such bias should be completely reported in the final research report. This allows the consumers of the research to decide for themselves how serious the bias is

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Chapter 4: Selecting a Sample
Selecting a Nonrandom Sample 112
Explain why random sampling techniques cannot always be used in educational research, and describe several situations in which you would use nonrandom sampling techniques. (5.1)

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Non-probability sampling: the process of selecting a sample using a technique which does not permit the researcher to specify the probability, or chance, that each member of a population has of being selected for the sample. This sampling is useful when the population cannot be described. Administrators may place restrictions that prevent good research. Securing administrative approval to involve students in educational research studies is not easy forcing researchers to use whatever samples they can get.

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Chapter 4: Selecting a Sample
Discuss the concerns related to representativeness and Generalizability resulting from the use of nonrandom sampling techniques. (5.2)

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When nonrandom samples are used if is usually difficult to describe the population from which a sample was drawn and to whom results can be generalized.

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Chapter 4: Selecting a Sample
Define convenience sampling and describe a situation in which you would use this technique. (5.3)

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Convenience sampling (accidental sampling and haphazard sampling): the process of using as the sample whoever happens to be available at the time. In a grocery store you volunteer to answer survey questions. Volunteers and nonvolunteers are not the same. Data from volunteers is not generalizable to the entire population.

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Chapter 4: Selecting a Sample
Define purpose sampling and describe a situation in which you would use this technique. (5.4)

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Purposive sampling (judgment sampling): Selection based on the researcher’s experience and knowledge of the individuals being sampled. Usually selected for some specific reason
•Knowledge and use of a particular instructional strategy
•Experience
•Being in a specific setting such as a school changing to a teacher-based decision-making process
Clear criteria provide a basis for describing and defending purposive samples. The main weakness of purposive sampling is the potential for inaccuracy in the researcher’s criteria and resulting sample selections.

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Chapter 4: Selecting a Sample
Define quota sampling and describe a situation in which you would use this technique. (5.5)

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Quota sampling: is the process of selecting a sample based on required, exact numbers, or quotas, of persons of varying characteristics. This sampling techniques is widely used in large-scale surveys.

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Chapter 4: Selecting a Sample
Define the term sampling from a qualitative perspective and identify its purpose. (7.1)

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Qualitative sampling: is the process of selecting a small number of individuals for a study in such a way that the individuals chosen will be able to help the researcher understand the phenomenon under investigation. The purpose of qualitative sampling is to choose participants who will be good “key informants” who will contribute to the researcher’s understanding or a given phenomenon.

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Chapter 4: Selecting a Sample
Explain why the characteristics of qualitative research require alternative sampling strategies. (7.2)

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Unique characteristics of qualitative research:
•In-depth inquiry
•Immersion in the setting
•Importance of context
•Appreciation of participant’s perspective
•Description of a single setting
Qualitative research samples are generally different, smaller, and les “representative” compared to those of quantitative research because the tow approaches have different aims and needs. Because of the interest in participants’ perspectives, immersion in the setting, and the research topic being studied, qualitative research requires more in-depth data collection that that typically needed in quantitative research. Sampling in qualitative research is almost always purposive. The researcher relies on experience and insight to select a sample; randomness is rarely part of the process.

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Chapter 4: Selecting a Sample
Define intensity sampling and describe a situation in which you would use this technique. (7.3)

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Definition: Selecting participants who permit study of different levels of the research topic.
Example: the researcher might select some good and poor students, experienced and inexperienced teachers, or teachers with small and large classes.
Sample Strategy: compare differences of two or more levels of the topic; select two groups of about 20 participants from each of the two levels.

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Chapter 4: Selecting a Sample
Define homogeneous sampling and describe a situation in which you would use this technique. (7.4)

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Definition: Selecting participants who are very similar in experience, perspective, or outlook, this produces a narrow, homogeneous sample and makes data collection and analysis simple.
Sample Strategy: select a small group of participants who fit a narrow, homogeneous topic; collect data from the chosen participants.

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Chapter 4: Selecting a Sample
Define criterion sampling and describe a situation in which you would use this technique. (7.5)

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Definition: Selecting all cases that meet some set of criteria or have some characteristic.
Example: the researcher might pick students who have been held back in two successive years or teachers who left the profession to raise children and then returned to teaching.

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Chapter 4: Selecting a Sample
Define snowball sampling and describe a situation in which you would use this technique. (7.6)

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Definition: selecting a few people who fit a researcher’s needs, then using those participants to identify additional participants, and so on , until the researcher has a sufficient number of participants. (Snowballing is most useful when it is difficult to find participants of the type needed.)
Sample Strategy: Decide how many participants are needed; let initial participants recruit additional participants that fit the researcher’s requirements until the desired number is reached.

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Chapter 4: Selecting a Sample
Define random purposive sampling and describe a situation in which you would use this technique. (7.7)

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Definition: Selecting more participants than needed for the study.
Example: if 25 participants were purposively selected by the researcher but only 10 participants could take part in the study, a random sample of 10 from 25 potential participants would be chosen; this strategy adds credibility to the study, although the initial sample is passed on purposive selection (this approach is typically used with small samples)
Sample Strategy: Given a pool of participants, decide how many of them can reasonably be dealt with in the study, and randomly select this number to participate. (This strategy is intended to deal with small samples)

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Chapter 4: Selecting a Sample
Discuss concerns related to representativeness and generalizability when using purposive samples in a qualitative research study. (7.8)

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When choosing a sampling technique and the sample itself, researchers need to remember a primary goal: selecting participants who can best add to the understanding of the phenomenon under study, not participants who necessarily represent some larger population. The participant’s perspectives, as described by the researcher, form the very core of a qualitative research study.

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Chapter 4: Selecting a Sample
Identify two guides for determining an appropriate sample size in a qualitative study. (7.9)

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Generally very small samples given the nature of the data collection methods and the data itself
Two general guidelines
1. Redundancy of the information collected from participants
2. Representation of the range of potential participants in the setting

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Chapter 4: Selecting a Sample
Differentiate probability sampling from purposive sampling. (7.10)

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Probability sampling begins with a population and selects a sample from it. Generalizability to the population is relatively easy.
Non-probability and purposive sampling begins with a sample that is NOT selected from some larger population. Must consider the population hypothetical as it is based on the characteristics of the sample. Generalizability is often very limited