The researcher must first know what her resources are. The main resources regarding sample collection are money, time and access to the population in question. By first knowing his resources, the researcher can set an interval for the possible number of data points to be collected. This allows a researcher to know beforehand the feasibility of the project.
The sample size of a study is directly related to the margin of error of the study. Because of this, the researcher must take into account the acceptable margin of error for the results before deciding on a sample size. You will need a larger sample to yield a smaller margin of error. Researchers prefer smaller margins of error, because the margin of error represents the sampling error present in the conclusion of a study.
The sample is a subsection of a larger population meant to represent that population. Being so, the sample must be both random yet representative of the larger population under study. This means that the researchers must rigorously define the population. One additional thing researchers must take into account is how the population size will affect the sample size. Studies investigating large populations tend to require larger sample sizes.
The diversity of the population of interest also has a large effect on sample size estimation. This diversity is handled through a statistic known as variance. Populations that are more homogeneous have lower variance statistics; populations that are more heterogeneous, or diverse, have higher variance statistics. Variance plays a direct role in sample size estimation because a study using a more diverse sample requires a larger sample to provide for variance. This control helps the statistical analysis yield statistically significant results.