Is it okay to have a 50 respondents in a research?
A sample size consisting of 50-100 respondents will be sufficient for obtaining comprehensive behavioral insights during emotion measurement.
Summary: 40 participants is an appropriate number for most quantitative studies, but there are cases where you can recruit fewer users.
We generally recommend a panel size of 30 respondents for in-depth interviews if the study includes similar segments within the population. We suggest a minimum sample size of 10, but in this case, population integrity in recruiting is critical.
If the research has a relational survey design, the sample size should not be less than 30. Causal-comparative and experimental studies require more than 50 samples. In survey research, 100 samples should be identified for each major sub-group in the population and between 20 to 50 samples for each minor sub-group.
Most statisticians agree that the minimum sample size to get any kind of meaningful result is 100. If your population is less than 100 then you really need to survey all of them.
In within-subjects designs, a small sample size could be defined as having less than 20 observations. A relatively large sample size or pool of participants could defined as more than 50 independent observations in a between-subjects design, more than 100 observations in a mixed or multivariate design.
While some experts in qualitative research avoid the topic of “how many” interviews “are enough,” there is indeed variability in what is suggested as a minimum. An extremely large number of articles, book chapters, and books recommend guidance and suggest anywhere from 5 to 50 participants as adequate.
Ensuring you've hit the right number of participants
In The logic of small samples in interview-based, authors Mira Crouch and Heather McKenzie note that using fewer than 20 participants during a qualitative research study will result in better data.
A sample size of 30 is fairly common across statistics. A sample size of 30 often increases the confidence interval of your population data set enough to warrant assertions against your findings.4 The higher your sample size, the more likely the sample will be representative of your population set.
It has previously been recommended that qualitative studies require a minimum sample size of at least 12 to reach data saturation (Clarke & Braun, 2013; Fugard & Potts, 2014; Guest, Bunce, & Johnson, 2006) Therefore, a sample of 13 was deemed sufficient for the qualitative analysis and scale of this study.
Is 15 respondents enough for qualitative research?
Mason's (2010) analysis of 560 PhD studies that adopted a qualitative interview as their main method revealed that the most common sample size in qualitative research is between 15 and 50 participants, with 20 being the average sample size in grounded theory studies (which was also the type of study I was undertaking).
Sufficient sample size is the minimum number of participants required to identify a statistically significant difference if a difference truly exists. Statistical significance does not mean clinical significance.
Based on studies that have been done in academia on this very issue, 30 seems to be an ideal sample size for the most comprehensive view, but studies can have as little as 10 total participants and still yield extremely fruitful, and applicable, results.
The use of sample size calculation directly influences research findings. Very small samples undermine the internal and external validity of a study.
As a general rule, sample sizes of 200 to 300 respondents provide an acceptable margin of error and fall before the point of diminishing returns.
For small populations you almost need to include the entire population in your sample to get a reasonable margin of error. In this case, for 50 facilities, you will need a sample of 45 for a margin of error of 5% at the 95% confidence level.
The Large Sample Condition: The sample size is at least 30. Note: In some textbooks, a “large enough” sample size is defined as at least 40 but the number 30 is more commonly used.
Depending on what your objectives are, a sample size of less than 60 but more than 30 might not be too small. In any case, having small sample size means your study has less statistical power, and non-parametric tests are used to analyze such data.
A rule-of-thumb is that, for small populations (<500), you select at least 50% for the sample. For large populations (>5000), you select 17-27%. If the population exceeds 250.000, the required sample size hardly increases (between 1060-1840 observations).
The main results should have 95% confidence intervals (CI), and the width of these depend directly on the sample size: large studies produce narrow intervals and, therefore, more precise results. A study of 20 subjects, for example, is likely to be too small for most investigations.
Why is 30 considered a large sample?
It's that you need at least 30 before you can reasonably expect an analysis based upon the normal distribution (i.e. z test) to be valid. That is it represents a threshold above which the sample size is no longer considered "small".
Sample Size for Qualitative Studies
Need to ensure there is enough, but not too much, data (>30 too large; Boddy, 2016). One review identified that samples of 20 and 30 (and multiples of 10) were most common (Mason, 2010), with 25-30 being a typical recommendation (Dworkin, 2012).
Another guide for good sample size suggests : 50 as very poor; 100 as poor, 200 as fair, 300 as good, 500 as very good and 1000 as excellent (Comrey and Lee, 1992; Tabacnik and Fidell, 1996; Vanvoorhis and Morgan, 2007).
When a study's aim is to investigate a correlational relationship, however, we recommend sampling between 500 and 1,000 people. More participants in a study will always be better, but these numbers are a useful rule of thumb for researchers seeking to find out how many participants they need to sample.
Guest et al. (2006) found that in homogeneous studies using purposeful sampling, like many qualitative studies, 12 interviews should be sufficient to achieve data saturation.
Big sample needed: quantitative research requires a large sample of the population to deliver reliable results. The larger the sample of people, the more statistically accurate the outputs will be.
Although one researcher's “small” is another's large, when I refer to small sample sizes I mean studies that have typically between 5 and 30 users total—a size very common in usability studies.
For example, when we are comparing the means of two populations, if the sample size is less than 30, then we use the t-test. If the sample size is greater than 30, then we use the z-test.
This is not a problem if the sample size is 30 or greater because of the central limit theorem. However, if the sample is small (<30) , we have to adjust and use a t-value instead of a Z score in order to account for the smaller sample size and using the sample SD.
Since Guest et al.'s publication in 2006, other researchers have confirmed that 6-12 interviews seem to be a sweet spot for the number of qualitative interviews needed to reach saturation.
What is the minimum number of respondents for reliability?
For reliability analysis the standard advice is to have at least 10 participants per item on your scale.
You have a symmetric distribution or unimodal distribution without outliers: a sample size of 15 is “large enough.” You have a moderately skewed distribution, that's unimodal without outliers; If your sample size is between 16 and 40, it's “large enough.”
Effective Sample (ESS) should be as large as possible, altough for most applications, an effective sample size greater than 1,000 is sufficient for stable estimates (Bürkner, 2017). The ESS corresponds to the number of independent samples with the same estimation power as the N autocorrelated samples.
Whatever be the aim, one can draw a precise and accurate conclusion only with an appropriate sample size. A smaller sample will give a result which may not be sufficiently powered to detect a difference between the groups and the study may turn out to be falsely negative leading to a type II error.
The sample size of 30 or greater requires the use of a z test.
A standard deviation of 0.5 is a safe level to pick to ensure that the sample size is large enough.
Sampling. The most obvious strategy is simply to sample more of your population. Keep your survey open, contact more potential participants, or consider widening the population.
The higher the sample size, the higher the power of the test and the better the external validity of the research findings. Survey research generally accepts for quantitative studies, therefore, it is ideal to achieve a number of respondents exceeding 200.
Mason's (2010) analysis of 560 PhD studies that adopted a qualitative interview as their main method revealed that the most common sample size in qualitative research is between 15 and 50 participants, with 20 being the average sample size in grounded theory studies (which was also the type of study I was undertaking).
I don't think you could proceed a quantitative research with only 15 respondents. It's an insufficient amount of respondents even for a questionnaire pretest. There's a relation between sample size and the number of items. Hair et al (1998) recommend between 5 and 10 respondents per item.
Why is 30 the minimum sample size?
A sample size of 30 often increases the confidence interval of your population data set enough to warrant assertions against your findings.4 The higher your sample size, the more likely the sample will be representative of your population set.
A rule-of-thumb is that, for small populations (<500), you select at least 50% for the sample. For large populations (>5000), you select 17-27%. If the population exceeds 250.000, the required sample size hardly increases (between 1060-1840 observations).
Chi Square and the Large Enough Sample Condition
To know if your sample is large enough to use chi-square, you must check the Expected Counts Condition: if the counts in every cell is 5 or more, the cells meet the Expected Counts Condition and your sample is large enough.