Posts Tagged ‘sampling’

RKA Blog_Evaluation Design Series_LogoInterviews are a commonly used data collection method in qualitative studies, where the goal is to understand or explore a phenomenon.  They’re an extremely effective way to gather rich, descriptive data about people’s experiences in a program or exhibition, which is one reason we use them often in our work at RK&A.  Figuring out sample size for interviews can sometimes feel trickier than for quantitative methods, like questionnaires because there aren’t tools like sample size calculators to use.  However, there are several important questions to consider that can help guide your decision-making (and while you do so, remember that a cornerstone of qualitative research is that it requires a high tolerance for ambiguity and instinct!):

  1. How much does your population vary? The more homogenous the population, the smaller the sample size. For example, is your population all teachers? Do they all teach the same grade level?  If so, you can use a smaller sample size, since the population and related phenomenon are narrow.  Generally speaking, if a population is very homogeneous and the phenomenon narrow, aim for a sample size of around 10.  If the population is varied or the phenomenon is complex, aim for around 40 to 50.  And if you want to compare populations, aim for 25 to 30 per segment.  In any case, a sample of more than 100 is generally excessive.
  2. What is the scope of the question or phenomenon you are exploring? The more narrow the question being explored or phenomena being studied, the smaller your sample size can be. Are you looking at one program, or just one aspect of a program? Or, are you comparing programs or looking at many different aspects of a program?
  3. At what point will you reach redundancy? This is key for determining sample size for any qualitative data collection method.  You want to sample only to the point of saturation—that is, stop sampling when no new information emerges.  Another way to think about this is that you stop collecting data when you keep hearing the same things again and again.  To be clear, I’m talking about big trends here—while each interview will have its own nuance and the small details might vary from interview to interview, you can stop when the larger trends start to repeat themselves and no new trends arise.

The question of “how many” for qualitative studies might always feel a bit frustrating, since (as illustrated by the questions above) the answer will always be “it depends.”  But remember, as the word “qualitative” suggests, it’s less about exact numbers and more about understanding the quality of responses, including the breadth, depth, and range of responses.  Each study will vary, but as long as you consider the questions above the next time you are deciding on sample size for qualitative methods, you can be confident you’re approaching the study in a systematic and rigorous way.

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RKA Blog_Evaluation Design Series_LogoSampling is a very important consideration for all types of data collection.  For audience research and summative evaluations in particular, it is important that the sample from which data is collected represents the actual population.  That is, the visitors who participate in a questionnaire or interview should match the entire population of visitors.  For instance, if the population of program visitors are 75% female, the sample should include approximately the same percent of females.  When the study sample and the museum’s visiting population are the same, the sample has external validity.  And when there is external validity, we can draw conclusions from a study’s results and generalize them to the entire population.


There are several protocols RK&A follows to work towards external validity.  First, to select study participants, we use a random sampling method, and most often, a continuous random selection method.  To follow the method, we instruct data collectors to position themselves in a designated recruitment location (e.g., museum or exhibition exit) and ask them to visualize an imaginary line on the floor.  Once they are in place, we instruct data collectors to select the first person who crosses the line.  If two people cross the line at the same time, we ask data collectors to select the person closest to them.  After the data collector finishes surveying or interviewing the selected person, the data collector returns to their recruitment location and selects the very next person to cross the line.  It is important for data collectors to follow this protocol every time so as not to introduce bias into the sample.  For instance, data collectors should not move the imaginary line or decide to delay recruiting because the person crossing the line looks unfriendly.


Second, we record observable demographics (e.g., approximate age) and visit characteristics (e.g., presence of children in the group) of any visitor who is invited to participate in the study but declines.  We also record the reason these recruited visitors provide for declining (e.g., parking meter is about to run out).  These data points are important to confirm or reject the external validity of the sample because we compare demographic and visit characteristics of those who participated in the study to the demographic and visit characteristics of those who declined participation.  While the data points for comparison are limited, they are still informative.  For instance, a trend we have observed is that visitors 35 – 54 years are most likely to decline participation, so their voices are often underrepresented.  The same goes for visitors with children, which may be a subset of those in the 35 – 54 year age group; they are often underrepresented in visitor studies.  Knowing where your sample may be lacking is important context when interpreting the results.


For these two reasons, we aim to systematically recruit visitors for audience research and evaluation studies.  Even for studies that use standardized questionnaires, we hire data collectors who use a random selection protocol to recruit participants and track information about those who declined.  As such, we do not recommend using survey kiosks to collect data since visitors self-select to complete the survey and cannot be compared to those who decided not to complete the survey (and if you think kiosks may be preferable because you could boost the number of surveys collected, see my former post on sample sizes).  Again, there are always some exceptions to these general rules described above.  Yet, our goal is always to use protocols that promote external validity as well as document threats to it…because what you don’t know can hurt you.



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