How does a researcher decide on the appropriate sample size for a given study?

What will be an ideal response?


Factors taken into consideration when determining the sample size include the characteristics of the population, the nature of the analysis to be conducted, the desired precision of the estimates, the resources available, the study design, and the anticipated response rate.

Population characteristics such as heterogeneity and size have a significant impact on the size of a representative sample. In general, the more heterogeneous a population, the larger the sample size required, and the less heterogeneous the population, the smaller the sample size required. When all population elements are different, a census of every element is required. When there is no heterogeneity among population elements, a sample of one is sufficient. The accuracy of a sample estimate may be indicated by its standard error, which reflects the magnitude of differences in the measured variable among study subjects. The more heterogeneous the population is, the larger the sample size must be to minimize the standard error. Given the same heterogeneity, a larger population requires a larger sample size than a smaller population. However, sample size does not need to increase in proportion to population size. Only when population is small does the sampling fraction have a significant impact on standard error.

Sample size is also determined by the nature of the analysis to be performed. The types of analyses to be conducted, the number of comparisons that will be made, and the number of variables that has to be examined simultaneously have a significant influence on sample size. In general, the more comparisons or subgroup analysis to be performed, the larger the sample size should be. The number of variables to be analyzed at one time also influences sample size. Typically, in quasi-experimental research, relevant variables have to be controlled statistically because groups differ by factors other than chance. The more variables that need to be analyzed simultaneously, the larger the sample size should be to make sure the investigator will have sufficient cases representing the variables considered. Before deciding on the sample size, researchers should know the type of analysis they are going to conduct with the data. A rule of thumb is to include at least 30 to 50 cases for each subcategory.

The more precise the estimates, the larger the sample size required. Generally, the level of accuracy of estimates hinges on the importance of the research findings. If important decisions are going to be based on research findings, then decision makers demand a very high level of confidence in the data and estimates. In such cases, a larger sample size would be needed. If there are few, if any, major decisions to be based on the research findings or only rough estimates are required by the sponsor, then the sample size would be correspondingly small.

Important resources, such as time, money, and staff support, can also influence sample size, and the sample size may be prespecified by the sponsor through available funding. The amount of the budget may dictate the upper limit of a sample because the budgeted research funding is needed not just for data collection, but also research preparation, data analysis, and reporting. The time element is important if decisions based on the research have to be made at a certain time. Then research activities have to be planned around this deadline, and a smaller sample size may be necessary. Staff support is particularly important in interview surveys where the number of interviewers available is directly correlated with the number of subjects that can be studied given a particular time period, or the speed at which data can be collected given the number of interviews to be conducted.

Different study designs tend to have different demands for sample size. If the variables in an experiment are controlled, researchers can use a relatively smaller sample size. In quasi-experimental designs, a larger sample size is generally required to statistically control for extraneous factors. For the same reason, stratified, cluster, and quota sampling methods generally require a smaller sample size than simple random or systematic sampling methods.

Less-than-perfect response rates can cause the ideal and actual sample sizes to differ. The sample size is always smaller than the initial plan due to incomplete, unusable, or missing questionnaires. Researchers need to anticipate these factors and make necessary adjustments at an early stage. Issues such as relevance of the topic to respondents, number of and personal relationships with contacts, time of survey administration, question complexity, and questionnaire design all influence response rates. Response rate also impacts the validity of the research in that if a systematic bias exists that affects the response, then the results of the study may not be generalizable to the whole population.

Health Professions

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