Wednesday, August 28, 2019
ICT Workshop Report
Tuesday, August 27, 2019
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
|
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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