Y
incorrect term is subjects) are those individuals you will use in your study. They are the adults or children who will receive your treatment, take your surveys, or be under your close observation. The technique you use to select your participants is based on the kind of research you choose to conduct.
Sampling in Qualitative Research
Qualitative researchers select their participants based on their characteristics and knowledge as they relate to the research questions being investigated. As you al-ready know, the researchers’ primary concern is to explore individuals in their nat-ural context, and they have little interest in generalizing the results beyond the participants in the study. The sampling procedure most often used in qualitative research is purposeful sampling. According to Patton (1990), “The logic and power of purposeful sampling lies in selecting information-rich cases for study in depth. Information-rich cases are those from which one can learn a great deal about issues of central importance to the purpose of the research” (p. 169). So, purposeful sampling is a procedure where the researcher identifies key infor-mants: persons who have some specific knowledge about the topic being inves-tigated. For example, let us say that you are interested in discovering how an inner-city after-school program influences the lives of a group of youth of color.
To get the insider’s perspective, you would select a group of participants who had participated in the particular after-school program under study and who have in-depth knowledge of the program. Qualitative researchers have identified many different types of purposeful sampling procedures. In fact, Miles and Huberman (1994) identify 17 variations, and Patton (1990) identifies 15 strategies for pur-poseful sampling. Although all of these strategies are useful, Table 6.1 identifies the ones we believe to be most useful to beginning researchers.
If your study is a qualitative one, you will want to carefully consider the types of purposeful sampling discussed and which one will best answer your research questions.
Sampling in Quantitative Research
Unlike their qualitative counterparts, quantitative researchers are interested in generalizing from their group of participants, the sample, to the larger popula-tion from which the sample was drawn. Various decisions regarding the partici-pants must be made by the researcher in order to maximize the generalizability of the study.
A population is the wider group of individuals about which the researcher wants to make statements. Fifth-grade teachers in the United States, high school
Working with Research Participants: Sampling and Ethics 141
TABLE 6.1 SUMMARY OF PURPOSEFUL SAMPLING TECHNIQUES.
Type of
Purposeful Sampling Explanation Example
Convenience Least desirable sampling; samples Students from a graduate class sampling those who are convenient in educational research would
be used by the professor as participants
Critical case Samples those who can “make Individuals who have had a sampling a point dramatically” (Patton, loved one die in the Iraq War
1990, p. 174) will represent the antiwar position
Extreme case Individuals selected who Participants who have the high-sampling represent the extremes est number of absences and
those who have the lowest will be used for the study
Homogeneous Individuals with only similar Participants will be children sampling attributes are used in the sample who have attended preschool
and come from similar socio-economic backgrounds Intensity sampling Samples individuals with the Participants who are highly
strongest feelings about critical of the problems
associ-something ated with NCLB will be included
in the sample
Maximum Sample includes individuals Participants who hold opinions variation sampling with different views on the issue ranging from pro-stem cell
being studied or who represent research to anti-stem cell the widest possible range of research will be used in the the characteristics being studied study
Purposeful One of the other purposeful Use intensity sampling to random sampling sampling procedures is used, identify persons who are
anti-followed by a randomization NCLB. Then a random sample
procedure of 15% will be selected from
this group
Snowball or Participants who possess certain Teachers who infuse technology network sampling characteristics are selected and in the classroom will be asked
asked to refer others with similar to nominate colleagues who do characteristics the same; these colleagues will
be included in the study Typical case Individuals selected because Teenage participants who enjoy sampling they represent the norm and music will be included in the
are in no way atypical study
principals on the East Coast, or parents who have children in day care in Chicago are all examples of populations. Let us say that a researcher was interested in con-ducting a survey study. Ideally, the survey researcher would want to send surveys to every member or individual within these populations. Although these large pop-ulations are referred to as ideal poppop-ulations, sampling every person in these populations is not realistic or doable. Time, money, and other resources such as staffing typically make it impossible for the average survey researcher to reach all members of an ideal population. Therefore, the researcher has to forgo these grand expectations and select a smaller or realistic population. Presented below are samples of ideal populations, followed by those of realistic populations.
• Ideal population: Fifth-grade teachers in the United States Realistic population: Fifth-grade teachers in New York City
• Ideal population: High-school principals on the East Coast Realistic population: High school principals in Pennsylvania
• Ideal population: Parents who have children in day care in Chicago
Realistic population: Parents who have children in day care in three neighborhoods on the west side of Chicago.
In some situations, even these realistic populations may not be so realistic. For example, surveying all the fifth-grade teachers in New York City or high school principals in Pennsylvania might exceed the study’s resources. In addition, the re-searcher must be able to obtain a complete list of persons in the realistic popula-tion. Some researchers refer to this as the sampling frame. For teachers, administrators, and school districts, researchers can consult their state education department and state educational organizations to obtain these lists. For other TABLE 6.1 SUMMARY OF PURPOSEFUL SAMPLING TECHNIQUES, Cont’d.
Type of
Purposeful Sampling Explanation Example
Theory-based Participants are selected in an Sufficient preoperational sampling ongoing way, e.g., the researcher children are selected to test
identifies participants, analyzes Piaget’s theory; researcher next data, and then decides who to selects formal operational collect data from next as the thinkers to test other parts of theoretical framework emerges the theory
Note: NCLB = No Child Left Behind Act.
populations, local, state, and national organizations may help identify possible participants. For some studies, such lists do not exist. For example, a college coun-seling center might want to identify participants for a study of students who are depressed but have not previously sought services. In this case, they might run an advertisement in the student newspaper asking for participants for their study.
After you have identified a list of possible participants, the next step is to se-lect a sample. A sample is a smaller group sese-lected from a larger population (in this case, a realistic population) that is representative of the larger population.
Samples allow researchers to work with a smaller, more manageable subgroup of the realistic population.
Types of Random Sampling
The most important aspect of sampling is that the sample must represent the larger population from which it is drawn. Random sampling is a technique or tool that produces essentially a miniversion of the initial population. Random sampling is conducted in such a way that every person in the population has an equal and independent chance of being selected. This means that when a person is selected, it does not affect the chances of anyone else being selected. Take, for example, a technique often used in grade school spelling lessons. Remember your elementary teacher asking everyone in your class to form a line and to count off by ones and twos to establish teams of spellers? Is this an example of random sam-pling? No, the moment you got in line with your classmates and the teacher said,
“Okay, count off by ones and twos,” your group membership was decided. If your best friend, immediately to your left, was a one, then you had to be a two—and there was no chance that the two of you would be on the same spelling team. This scenario violates the requirement that the selection of one person cannot have an effect on whether another person is chosen or not.
Simple Random Sampling. Simple random sampling involves the random selec-tion of individuals from the realistic populaselec-tion as a whole. First, the researcher must obtain a complete list of names for all individuals who make up the realistic population. To select a simple random sample, each person on the list of the real-istic population is assigned a number. For example, if the list contains the names of 20 individuals, then the number 01 is assigned to the first person on the list, and number 02 to the second person on the list, and so on, until all 20 people on the list have been assigned a number. Next, a random sampling table (usually gener-ated by computer) is used, similar to the one in the third column in Table 6.2.
Random number tables present clusters of number strings that have been randomly generated. To use the table, simply begin anywhere you want and select
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a number. If number 05 is selected, person 05, Derrick, is then selected for your sample. At this point, you can move up or down or in any direction on the table that you wish to select your next number because, after all, the table is random.
For example, if you move down the column of random numbers, the next num-ber is 18, so Richard would be added to the sample. If a numnum-ber is selected that is not on your list of individuals in the population (such as 48 in our example), simply move to the next line of the random numbers. Continue this process until you have your entire sample.
Stratified Random Sampling. There may be times that a simple random selec-tion will not generate the type of participants needed in a sample. For example, if a researcher conducting exit polling during an election uses a simple random sampling procedure to choose individuals to survey as they leave the polls, just by chance he or she would not get a representative sample of Democrats,
Republi-TABLE 6.2 SIMPLE RANDOM SELECTION.
Possible Participants Random Numbers
01 Steve 22
02 Tonya 05
03 Ramone 18
04 Juan 04
05 Derrick 07
06 Tony 24
07 Liz 16
08 Jean 14
09 Dean 48
10 Margie 41
11 Kate 17
12 Maria 12
13 Ismael 12
14 Jim 10
15 Donna 19
16 Heta 45
17 Aviva 27
18 Richard 32
19 Ron 08
20 Travis 18
cans, and Independents. For such a poll, a researcher would want to obtain a sam-ple that was proportional to the number of Democrats, Republicans, and Inde-pendents in the geographic area being studied. A stratified random selection procedure would allow the researcher to stratify along the variable of party af-filiation—that is, to select a sample that was more representative of the popula-tion. If possible, the researcher would obtain a list of eligible voters according to political party and then randomly select the appropriate proportional number of participants from each group. So whenever subgroups are critical to creating a sample that represents the entire population, stratified random sampling is the most precise sampling technique. Variables that are used to stratify a sample in educational research might include, race, ethnicity, socioeconomic status, years of teaching experience, grade level, or school location: urban, suburban, and rural.
Cluster Random Selection. In the field of education, simple random selection is often not possible. For example, if you are surveying teachers in a particular state, you might not be able to obtain a list of individual teacher names, but you can get a list of school buildings for the state. In this case, cluster random selec-tion may be useful. Instead of assigning numbers to individuals, in cluster ran-dom selection, numbers are assigned to the cluster or subgroup within the realistic population. In the example above, each school building would include a subgroup (or cluster) of teachers. The building would be assigned a number, and buildings would be randomly selected using the random number table described previously.
By selecting clusters, you reduce the number of schools you need to visit or the number of contact persons you need to identify. The important thing to remem-ber is that cluster random selection is a procedure where entire groups and not individuals are randomly selected. This procedure allows the researcher to select clusters randomly and is actually a simpler technique than selecting individuals randomly. However, to be certain that the sample accurately reflects the popula-tion, a researcher would likely have to select multiple clusters. The number of clus-ters you select would be determined by the number of clusclus-ters in the population.
Nonrandom Samples
Random selection of the sample allows a researcher to generalize the results of the study back to the entire population from which the sample was drawn. Be-cause of limited time, resources, or purpose (or all of these), a researcher may con-duct a study where teachers or students in one school building or one district are included in the study. This type of nonrandom sampling is referred to as conve-nience sampling. Although a sample of conveconve-nience requires fewer resources, it severely limits a study’s generalizability. In this case, the study’s results are only
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“good for” or generalizable back to the teachers in the school building or district, whichever the case may be. Depending on the overall purpose of the study and how the results of the study will be used and disseminated, lack of generalizability might not be an issue. If the purpose of the study is to use the results to make decisions at either the school building or district level, then such sampling methods are adequate.
However, if the intent of the study is to add to the academic or professional knowl-edge base on a subject by publishing on a national level, such sampling techniques would be considered a major flaw in the study design. Census sampling is an-other nonrandom sampling technique used in quantitative research. In census sam-pling, the researcher surveys the entire realistic population without drawing a random sample from the population. This technique may be used when the study has unlimited resources or the realistic population is not too large. Census sampling is frequently used by educators who are only trying to obtain data on their own school or district. Such data can be useful in learning about that particular school or district; however, remember that the results cannot be generalized to other schools or districts because the sample was not chosen randomly.
Sample Size and Survey Response Rates
Although random selection certainly plays a pivotal role in a study’s credibility, the size of the sample that one selects from the realistic population is also important.
If the sample is too small, it may not fully represent the population from which it was drawn, and therefore, the findings from the study cannot be generalized back to the wider audience, even though random sampling practices were used.
Even though there are no “hard or fast” rules for determining sample sizes, there are some general guidelines to consider when planning a study. For survey research, if the population is fewer than 200 individuals, the entire population should be sampled. This would be considered census sampling. At around a pop-ulation of 400, approximately 50% of the poppop-ulation should make up the sample, and populations over 1,000 require about 20% for an appropriate sample. For large populations of 5,000 or more, samples of 350 to 500 persons are often adequate.
For correlational studies, a minimum of 30 participants should be tested. Experi-mental research studies generally require at least 30 participants per group. These generalizations are based on the work of Krejcie and Morgan (1970), and their ar-ticle should be consulted for more precise information about sample size.