Saturday, August 24, 2019

Study notes on Qualitative Research



Qualitative Research: Sampling & Sample Size Considerations
                                                                                       
The goal of qualitative research is to provide in-depth understanding and therefore, targets a specific group, type of individual, event or process. There are three main types of qualitative sampling: purposeful sampling, quota sampling, and snowballing sampling.

Purposeful Sampling
Purposeful sampling is the most common sampling strategy. In this type of sampling, participants are selected or sought after based on pre-selected criteria based on the research question. For example, the study may be attempting to collect data from lymphoma patients in a particular city or country. The sample size may be predetermined or based on theoretical saturation, which is the point at which the newly collected longer provides additional insights. Click on the following link for a description of purposeful sampling: Types of Purposeful Sampling.
 Quota Sampling
It is a sampling technique whereby participant quotas are preset prior to sampling. Typically, the researcher is attempting to gather data from a certain number of participants that meet certain characteristics that may include things such as age, sex, class, marital status, HIV status, etc. Click here for more information on this type of sampling: Quota Sampling.

 Snowball Sampling
It is also known as chain referral sampling. In this method, the participants refer the researcher to others who may be able to potentially contribute or participate in the study. This method offer helps researchers find and recruit participants that may otherwise be hard to reach. For more information, click here: Snowball Sampling.
Figure1. Snowball sampling
Common Qualitative Sampling Strategies
Extreme or Deviant Case Sampling. Looks at highly unusual manifestations of the phenomenon of interest, such as outstanding success/notable failures, top of the class/dropouts, exotic events, and crises. This strategy tries to select particular cases that would glean the most information, given the research question. One example of an extreme/ the deviant case related to battered women would be battered women who kill their abusers.

Intensity Sampling. Chooses information-rich cases that manifest the phenomenon intensely, but not extremely, such as good students/poor students, above average/below average. This strategy is very similar to extreme/deviant case sampling as it uses the same logic. The difference is that the cases selected are not as extreme. This type of sampling requires that you have prior information on the variation of the phenomena under study so that you can choose intense, although not extreme, examples. For example, heuristic research uses the intense, personal experience(s) of the researcher. If one were studying jealousy, you would need to have had an intense experience with this particular emotion; a mild or pathologically extreme experience would not likely elucidate the phenomena in the same way as an intense experience.

            Maximum Variation Sampling. Selects a wide range of variations on dimensions of interest. The purpose is to discover/uncover central themes, core elements, and/or shared dimensions that cut across a diverse sample while at the same time offering the opportunity to document unique or diverse variations. For example, to implement this strategy, you might create a matrix (of communities, people, etc.) where each item on the matrix is as different (on relevant dimensions) as possible from all other items.

            Homogeneous Sampling. Brings together people of similar backgrounds and experiences. It reduces variation, simplifies the analysis, and facilitates group interviewing. This strategy is used most often when conducting focus groups. For example, if you are studying participation in a parenting program, you might sample all single-parent, female heads of households.

     Typical Case Sampling. It focuses on what is typical, normal, and/or average. This strategy may be adopted when one needs to present a qualitative profile of one or more typical cases. When using this strategy you must have a broad consensus about what is “average.” For example, if you were working to begin development projects in Third World countries, you might conduct a typical case sampling of “average” villages. Such a study would uncover critical issues to be addressed for most villages by looking at the ones you sampled.

            Critical Case. Look at cases that will produce critical information. In order to use this method, you must know what constitutes a critical case. This method permits logical generalization and maximum application of information to other cases because if it’s true of this one case, it’s likely to be true of all other cases. For example, if you want to know if people understand a particular set of federal regulations, you may present the regulations to a group of highly educated people (“If they can’t understand them, then most people probably cannot”) and/or you might present them to a group of under-educated people (“If they can understand them, then most people probably can”).

            Snowball or Chain Sampling. Identifies 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(‘Sampling in Qualitative Research’, n.d.).

            Criterion Sampling. Selects all cases that meet some criteria. This strategy is typically applied when considering quality assurance issues. In essence, you choose cases that are information-rich and that might reveal a major system weakness that could be improved. For example, if the average length of stay for a certain surgical procedure is three days, you might set a criterion for being in the study as anyone whose stay exceeded three days. Interviewing these cases may offer information related to aspects of the process/system improved that could be based or Operational Construct or Theoretical Sampling defines 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.”
           
            Confirming and Disconfirming Sampling.  Seeks cases that are both “expected” and the “exception” to what is expected. In this way, this strategy deepens initial the analysis seeks exceptions and tests variation. In this strategy, you find both confirming cases (those that add depth, richness, credibility) as well as disconfirming cases (an example that does not fit and is the source of rival interpretations). This strategy is typically adopted after initial fieldwork has established what a confirming case would be. For example, if you are studying certain negative academic outcomes related to environmental factors, like low SES, low parental involvement, high teacher to student ratios, lack of funding for a school, etc. you would look for both confirming cases (cases that evidence the negative impact of these factors on academic performance) and disconfirming cases (cases where there is no apparent negative association between these factors and academic performance).

            Stratified Purposeful Sampling. Focuses on characteristics of particular subgroups of interest; facilitates comparisons. This strategy is similar to stratified random sampling (samples are taken within samples), except the sample size is typically much smaller. In stratified sampling you “stratify” a sample based on a characteristic. Thus, if you are studying academic performance, you would sample a group of below-average performers, average performers, and above-average performers. The main goal of this strategy is to capture major variations (although common themes may emerge).

            Opportunistic or Emergent Sampling.  Follows new leads during fieldwork, takes advantage of the unexpected, and are flexible. This strategy takes advantage of whatever unfolds as it is unfolding and may be used after fieldwork has begun and as a researcher becomes open to sampling a group or person they may not have initially planned to interview. For example, you might be studying 6th grade students’ awareness of a topic and realize you will gain additional understanding by including 5th-grade students’ as well.

            Purposeful Random Sampling. Looks at a random sample. This strategy adds credibility to a sample when the potential purposeful sample is larger than one can handle. While this is a type of random sampling, it uses small sample sizes, thus the goal is credibility, not representativeness or the ability to generalize. For example, if you want to study clients at a drug rehabilitation program, you may randomly select 10 of 300 current cases to follow. This reduces judgment within a purposeful category because the cases are picked randomly and without regard to the program outcome.

            Sampling Politically Important Cases. Seeks cases that will increase the usefulness and relevance of information gained based on the politics of the moment. This strategy attracts attention to the study (or avoids attracting undesired attention by purposefully eliminating from the sample politically sensitive cases). This strategy is a variation on critical case sampling. For example, when studying voter behaviour, one might choose the 2000 election, not only because it would provide insight, but also because it would likely attract attention.

            Convenience Sampling.  Selects cases based on ease of accessibility. This strategy saves time, money, and effort, however, it has the weakest rationale along with the lowest credibility. This strategy may yield information-poor cases because cases are picked simply because they are easy to access, rather than on a specific strategy/rationale. Sampling your co-workers, family members or neighbours simply because they are “there” is an example of convenience sampling.

            Combination or Mixed Purposeful Sampling. Combines two or more strategies listed above. Basically, using more than one strategy above is considered combination or mixed purposeful sampling. This type of sampling meets multiple interests and needs. For example, you might use chain sampling in order to identify extreme or deviant cases. That is, you might ask people to identify cases that would be considered extreme/deviant and do this until you have consensus on a set of cases that you would sample.
Table 1.
Sampling methods in qualitative and quantitative research
Assumptions of quantitative sampling
                
Assumptions of qualitative sampling
·  We want to generalize to the population 
·       Random events are predictable
·    We can compare random events to our results      
·    Probability sampling is the best approach     
         

·  Social actors are not predictable like  objects   
·   Randomized events are irrelevant to    social life
·    Probability sampling  is expensive and inefficient
·  Non-probability sampling is the best approach



Qualitative Research Sampling Presentation

 






Voice note on Qualitative Sampling in Research




References                   
Sampling in Qualitative Research. (n.d.). Retrieved 20 August 2019, from https://saylordotorg.github.io/text_principles-of-sociological-inquiry-qualitative-and-quantitative-methods/s10-02-sampling-in-qualitative-resear.html



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