SAMPLES in research methodology quantitative AND qualititative approach Thushara RANASINGHE (ttr) FACULTY OF LAW INTAKE 29 RESEARCH METHODOLOGY
What is sampling Once the researcher has chosen a hypothesis to test in a study, the next step is to select a pool of participants to be in that study. Sampling, as it relates to research, refers to the selection of individuals, units, and/or settings to be studied. Whereas quantitative studies strive for random sampling, qualitative studies often use purposeful or criterion- based sampling, that is, a sample that has the characteristics relevant to the research questions.
Why Sample for researches? First, it is usually too costly to test the entire population The second reason to sample is that it may be impossible to test the entire population. The third reason to sample is that testing the entire population often produces error. The final reason to sample is that testing may be destructive To draw conclusions about populations from samples.
Sampling methods in qualitative and quantitative research Assumptions of quantitative sampling. We want to generalize to the population. Random events are predictable. We can compare random events to our results. Therefore Probability sampling is the best approach. Assumptions of qualitative sampling Social actors are not predictable like objects. Randomized events are irrelevant to social life. Probability sampling is expensive and inefficient. therefore Non-probability sampling is the best approach.
size of the Sample sample size not matter in qualitative research Because of the assumptions that qualitative researchers make, namely, that the social world is not predictable. Qualitative researchers believe that people are not like molecules or other objects; peoples actions are not predictable. But quantitative researchers believe that social activity is predictable. So when they compare their observations of social activity to what would happen in purely random results, the difference says something.
Types of Samples 6 Probability (Random) Samples Simple random sample Systematic random sample Stratified random sample Multistage sample Multiphase sample Cluster sample Non-Probability Samples Convenience sample Purposive sample Quota Snowball Theoretical
Simple random sample Simple random sampling is the most straight forward of the random sampling strategies. We use this strategy when we believe that the population is relatively homogeneous for the characteristic of interest. For example, let's say you were surveying first-time parents about their attitudes toward mandatory seat belt laws. You might expect that their status as new parents might lead to similar concerns about safety. On campus, those who share a major might also have similar interests and values; we might expect psychology majors to share concerns about access to mental health services on campus.
Systematic sampling Systematic sampling yields a probability sample but it is not a random sampling strategy (it is one of our exceptions). Systematic sampling strategies take every nth person from the sampling frame. For example, you choose a random start page and take every 45th name in the directory until you have the desired sample size. Its major advantage is that it is much less cumbersome to use than the procedures outlined for simple random sampling.
Stratified random sampling Stratified random sampling is used when we have subgroups in our population that are likely to differ substantially in their responses or behavior. This sampling technique treats the population as though it were two or more separate populations and then randomly samples within each. For example, you are interested in visual-spatial reasoning and previous research suggests that men and women will perform differently on these types of task. So, you divide your sample into male and female members and randomly select equal numbers within each subgroup (or "stratum"). With this technique, you are guaranteed to have enough of each subgroup for meaningful analysis.
Multistage sampling Multistage sampling. This is our most sophisticated sampling strategy and it is often used in large epidemiological studies. To obtain a representative national sample, researchers may select zip codes at random from each state. Within these zip codes, streets are randomly selected. Within each street, addresses are randomly selected. While each zip code constitutes a cluster, which may not be as accurate as other probability sampling strategies, it still can be very accurate.
Cluster sampling Cluster sampling is useful when it would be impossible or impractical to identify every person in the sample. Suppose a college does not print a student directory. It would be most practical in this instance to sample students from classes. Rather than randomly sample 10% of students from each class, which would be a difficult task, randomly sampling every student in 10% of the classes would be easier. Sampling every student in a class is not a random procedure. However, by randomly selecting the classes, you have a greater probability of capturing a representative sample of the population. Many students believe that it is not possible to gather a representative sample for a class project or a thesis. However, this type of cluster sampling is easily done, especially since all colleges publish lists of classes for registration.
Convenience sampling Convenience sampling selects a particular group of people but it does not come close to sampling all of a population. Convenience sampling is widely used in student research projects. Students contact professors that they know and ask if they can use their classes to recruit research subjects. Convenience sampling looks just like cluster sampling. The major difference is that the clusters of research participants are selected by convenience rather than by a random process.
Purposive sampling Purposive sampling targets a particular group of people. When the desired population for the study is rare or very difficult to locate and recruit for a study, purposive sampling may be the only option. For example, you are interested in studying cognitive processing speed of young adults who have suffered closed head brain injuries in automobile accidents. This would be a difficult population to find. Your city has a well-established rehabilitation hospital and you contact the director to ask permission to recruit from this population. The major problem with purposive sampling is that the type of people who are available for study may be different from those in the population who can't be located and this might introduce a source of bias. For example, those available for study through the rehabilitation hospital may have more serious injuries requiring longer rehabilitation, their families may have greater education and financial resources (which resulted in their choosing this hospital for care).
Theoretical Sampling Theory-Based or Operational Construct or Theoretical Samplingdentifies manifestations of a theoretical construct of interest so as to elaborate and examine the construct. This strategy is similar to criterion sampling, except it is more conceptually focused. This strategy is used in grounded theory studies. You would sample people/incidents, etc., based on whether or not they manifest/represent an important theoretical or operational construct. For example, if you were interested in studying the theory of resiliency in adults who were physically abused as children, you would sample people who meet theory-driven criteria for resiliency.
Snowball or Chain Sampling Snowball or Chain SamplingIdentifies cases of interest from people who know people who know what cases are information-rich, that is, who would be a good interview participant. Thus, this is an approach used for locating information-rich cases. You would begin by asking relevant people something like: Who knows a lot about ? For example, you would ask for nominations, until the nominations snowball, getting bigger and bigger. Eventually, there should be a few key names that are mentioned repeatedly.
Advantages & disadvantages of samples. Technique advantages disadvantages Quota Ensures selection of adequate numbers of subjects with appropriate characteristics Not possible to prove that the sample is representative of designated population Snowball Possible to include members of groups where no lists or identifiable clusters even exist (e.g., drug abusers, criminals) No way of knowing whether the sample is representative of the population convenience Inexpensive way of ensuring sufficient numbers of a study Can be highly unrepresentative
Technique Advantages disadvantages Simple random Highly representative if all subjects participate; the ideal Not possible without complete list of population members; Stratified random Can ensure that specific groups are represented, even proportionally, in the sample(s) (e.g., by gender), by selecting individuals from strata list More complex, requires greater effort than simple random; strata must be carefully defined Cluster Possible to select randomly when no single list of population members exists, but local lists do; data collected on groups may avoid introduction of confounding by isolating members Clusters in a level must be equivalent and some natural ones are not for essential characteristics (e.g., geographic: numbers equal, but unemployment rates differ) Purposive Ensures balance of group sizes when multiple groups are to be selected Samples are not easily defensible as being representative of populations due to potential subjectivity of researcher
Process 18 The sampling process comprises several stages: Defining the population of concern Specifying a sampling frame, a set of items or events possible to measure Specifying a sampling method for selecting items or events from the frame Determining the sample size Implementing the sampling plan Sampling and data collecting Reviewing the sampling process
Sampling Frame The list or procedure defining the POPULATION. (From which the sample will be drawn.) Distinguish sampling frame from sample. Examples: Telephone book Voter list Random digit dialing Essential for probability sampling, but can be defined for non-probability sampling
The time factor of sampling A sample may provide to us with needed information quickly. Ex: you are a Doctor and a disease has broken out in a village within your area of jurisdiction, the disease is contagious and it is killing within hours nobody knows what it is. You are required to conduct quick tests to help save the situation. If you try a census of those affected, they may be long dead when you arrive with your results. In such a case just a few of those already infected could be used to provide the required information. Accuracy and sampling A sample may be more accurate than a census. A sloppily conducted census can provide less reliable information than a carefully obtained sample
Sampling Problems missing elements - individuals who should be on the list but for some reason are not on the list. Ex: if my population consists of all individuals living in a particular city and I use the phone directory as my sampling frame or list, I will miss individuals with unlisted numbers or who can not afford a phone. Foreign elements Elements which should not be included in population and sample appear on the sampling list. Ex: if I were to use property records to create my list of individuals living within a particular city, landlords who live elsewhere would be foreign elements. In this case, renters would be missing elements. Duplicates These are elements who appear more than once on the sampling frame. Ex: if I am a researcher studying patient satisfaction with emergency room care, I may potentially include the same patient more than once in my study. If the patients are completing a patient satisfaction questionnaire, I need to make sure that patients are aware that if they have completed the questionnaire previously, they should not complete it again. If they complete it more that once, their second set of data respresents a duplicate.
References Camic, P. M, Rhodes, J. E., & & Yardley, L. (Ed.). (2003). Qualitative research in psychology: Expanding perspectives in methodology and design. Washington, DC: American Psychological Association. Creswell, J. W. (1998). Qualitative Inquiry & Research Design: Choosing Among Five Traditions. Thousand Oaks: CA. Sag Publications, Inc. Dey, I. (1999). Grounding grounded theory: Guidelines for qualitative inquiry. San Diego, CA: Academic Press. Harter, S. (1978). Effectance motivation reconsidered: Toward a developmental model. Human Development, 21, 3464. Harter, S. (1999). The construction of the self: A developmental perspective. New York: Guilford. Hitchcock, J. H, Nastasi, B. K., Dai, D. C., Newman, J., Jayasena, A., Bernstein-Moore, R., Sarkar, S., & Varjas, K. (2004). Illustrating a mixed- method approach for identifying and validating culturally specific constructs. Accepted for publication in Journal of School Psychology.
Thushara RANASINGHE (ttr) FACULTY OF LAW INTAKE 29 RESEARCH METHODOLOGY