Sample size determination in Qualitative and Quantitative Research
Sample size determination in Qualitative and Quantitative Research discuss the difference between qualitative and quantitative research, various sampling techniques used in either case and how to determine the ideal sample size.
Many times those that undertake a research project often find they are not aware of the differences between Qualitative Research and Quantitative Research methods. Many mistakenly think the two terms can be used interchangeably.
So what is the difference between Qualitative Research and Quantitative Research?
Qualitative Research is primarily exploratory research. It is used to gain an understanding of underlying reasons, opinions, and motivations. It provides insights into the problem or helps to develop ideas or hypotheses for potential quantitative research. Qualitative Research is also used to uncover trends in thought and opinions, and dive deeper into the problem. Qualitative data collection methods vary using unstructured or semi-structured techniques. Some common methods include focus groups (group discussions), individual interviews, and participation/observations. The sample size is typically small, and respondents are selected to fulfil a given quota.
Quantitative Research is used to quantify the problem by way of generating numerical data or data that can be transformed into useable statistics. It is used to quantify attitudes, opinions, behaviours, and other defined variables – and generalize results from a larger sample population. Quantitative Research uses measurable data to formulate facts and uncover patterns in research. Quantitative data collection methods are much more structured than Qualitative data collection methods. Quantitative data collection methods include various forms of surveys – online surveys, paper surveys, mobile surveys and kiosk surveys, face-to-face interviews, telephone interviews, longitudinal studies, website interceptors, online polls, and systematic observations.
However, determining the ideal survey sample size and population can prove tricky. In other words, who will you be surveying and how many people? Say you’re a market research manager at EMDOM Company and you are planning to launch a new product line by the end of the first quarter of 2017. However, before you launch the new line you wish to conduct an online survey on whether your new product will perform well in the Lagos market. So far, so good. Yet, the following question will almost instantly arise: “What is the population that I would like to survey?”. Or, who do I need to survey to gain valuable insights into the success of your new product line? In this case, the answer is rather straightforward. Assuming that you are launching the new product line on the Abakaliki Metropolises, you will understand that goats do not buy your product and that your new product is reasonably priced, your population consists of everyone in Abakaliki town.
What is Sampling and Sample Size?
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 question(s). For example, if you are interested in studying adult survivors of childhood sexual abuse, interviewing a random sample of 10 people may yield only one adult survivor, thus, you will essentially have a sample size of one and need to continue to randomly sample people until you have interviewed an appropriate number of who have survived childhood sexual abuse. This is not a wise use of your time.
The difference in sampling strategies between quantitative and qualitative studies is due to the different goals of each research approach. Recall that typical quantitative research seeks to infer from a sample to a population (for example, a relationship or a treatment effect). In general, you want to include a variety of types of people in a quantitative study so that it generalizes beyond those in your study. Thus, the goal of quantitative approaches can be stated as, ”empirical generalization to many.”
Qualitative research, on the other hand, typically starts with a specific group, type of individual, event, or process. As in the qualitative study of adult survivors of childhood sexual abuse example above, you would choose your sample very purposefully and include in your study only those with this particular experience. The goal of qualitative research can be stated as “in-depth understanding.”
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, crises. This strategy tries to select particular cases that would glean the most information, given the research question. One example of an extreme/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, one 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 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
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 Sampling
Looks at cases that will produce critical information. 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___?” 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.
- Criterion Sampling
Selects all cases that meet some criterion. This strategy is typically applied when considering quality assurance issues. In essence, you choose information-rich cases and that might reveal a major system weakness that could be improved.
- Theory-Based or Operational Construct or Theoretical Sampling
Identifies manifestations of a theoretical construct of interest 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 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 (examples that do not fit and are 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 is 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, 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 of 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 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 a consensus on a set of cases that you would sample.
Sample Sizes: Considerations
When determining sample size for qualitative studies, it is important to remember that there are no hard and fast rules. There are, however, at least four considerations:
- What sample size will reach saturation or redundancy? That is, how large does the sample need to be to allow for the identification of consistent patterns? Some researchers say the size of the sample should be large enough to leave you with “nothing left to learn.” In other words, you might conduct interviews, and after the tenth one, realize that no new concepts are emerging. That is, the concepts, themes, etc. begin to be redundant.
- How large a sample is needed to represent the variation within the target population? That is, how large must a sample be in order to assess an appropriate amount of diversity or variation that is represented in the population of interest?
You may estimate sample size, based on the approach of the study or the data collection method used. For each category, there are some related rules of thumb, represented in the tables below.
Rules of Thumb Based on Approach
Research Approach Rule of Thumb Biography/Case Study Selects one case or one person. Phenomenology Assess 10 people. If you reach saturation before assessing ten people you may use fewer. Grounded theory/ethnography/action research Assess 20-30 people, which typically is enough to reach saturation.
Rules of Thumb Based on Data Collection Method
Data Collection Method Rule of Thumb Interviewing key informants Interview approximately five people. In-depth interviews
Interview approximately 30 people. Focus groups
Create groups that average 5-10 people each. In addition, consider the number of focus groups you need based on “groupings” represented in the research question. That is, when studying males and females of three different age groupings, plan for six focus groups, giving you one for each gender and three age groups for each gender. Ethnographic surveys
Select a large and representative sample (purposeful or random based on purpose) with numbers similar to those in a quantitative study.
However, after choosing one or two case studies in the case study approach you might want to know the exact sample size from the given population. In that case, there are few other techniques to determine your sample size. The image below gives us a mirror of getting sample size depending on the number of margins you are creating.
Calculating Sample size
The mathematical statistician Taro Yamane devised a formula for estimating or determining the sample size of the population under study so that the inferences and conclusions drawn from the survey can be applied to the complete population from which the sample was drawn.
For example: For instance, am researching the impact of economic recession on low-income earners in Abakaliki metropolises in Ebonyi state. Let say my population of the study is 52% of the Abakaliki population (141437). Statistics revealed that 52% of Abakaliki residents live below the US $1.50 poverty line constituting our low-income group. Taking 52% of the total population gives me 73,547.
Using the Taro Yamane formula to arrive at the sampling size. The calculation of the sample size is as shown below.
Therefore our sample size is 207 low-income earners that will be drawn randomly from Abakaliki metropolises.
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