LAUSR

Prehospital and Disaster Medicine (PDM) receives manuscripts in which sampling is used for data acquisition. The validity of the sampling method used for research is an important criterion for ranking quality of a study. This editorial provides an overview of data sampling methods that are common among submissions to PDM. Sampling techniques are used to select a portion of individuals as a data source to represent the total individuals in a pre-identified population. An example of sampling would be using a process to select 50 disaster victims (study subjects) that are representative of 500 total victims (target population) for a research project. Important in population-based investigation is that a study sample is a valid representation of the total research target population. Sampling is used when it is not possible to be test every individual or element in a study target population. An important concept in sampling is risk for sampling error, which occurs when the sample selected from among individuals in a general target population erroneously does not represent the entire population. Sampling error can occur due to lack of understanding who should be surveyed within a population (population error), stratification error when an inappropriate qualifier is used to select a sample (sample frame error), selection error in which those selected for a sample are not representative of the target population (selection bias), non-response error in which individuals selected for a sample elect not to participate, variation from or lack of representation of the study target population, and too few subjects in a sample to allow for precision in quantitative measures or complete development of themes. Only when an entire target population cannot be tested is sampling appropriate. When compared to sampling, testing an entire population provides more valid and accurate data for developing research conclusions. To achieve validity for data obtained from awhole population, samplingmust be done in a systematic manner, with an emphasis to avoid: (1) lack of detection of population variation, (2) lack of precision of mean and median measures, (3) lack of detection of important qualitative themes, (4) lack of detection of heterogeneity or homogeneity within the target population, (5) lack of accuracy in inferences made about a target population, and most important, (6) selection bias. Sampling is used in both qualitative and quantitative research. Depending on the type of research or if mixed methods are used, sampling should be designed to achieve the study objective. Qualitative methods are intended to develop understanding of a population by saturation of themes or information. Quantitative methods are intended to achieve generalizability or conclusions that are representative of a study target population. For each type of research, standards for determining the number of subjects required for a sample to be valid vary. Quantitative methods use established formulae to avoid Type I and Type II errors. Established formulae cannot be applied to qualitative studies, and the number needed for a valid sample is determined by the type of analysis proposed, depth of detail expected, and whether homogeneity (needing smaller samples) or heterogeneity (needing larger samples) is being explored. Two categories of sampling exist, probability and non-probability sampling. An overview of these two sampling methods is provided below.