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# stratified sampling vs systematic sampling

The systemic sampling method is comparable to the simple random sampling method; however, it is less complicated to conduct. The lecture is available below, and a transcript of the lecture is also available. With Example 1: Stratified sampling would be preferred over cluster sampling, particularly if the questions of interest are affected by time zone. In these situations non-probability samples can be used. The selection often follows a predetermined interval (k). In stratified sampling, a two-step process is followed to divide the population into subgroups or strata. Systematic sampling is the selection of specific individuals or members from an entire population. There are many situations in which it is not possible to generate a sampling frame, and the probability that any individual is selected into the sample is unknown. Sampling individuals from a population into a sample is a critically important step in any biostatistical analysis, because we are making generalizations about the population based on that sample. return to top | previous page | next page, Content ©2016. As opposed, in cluster sampling initially a partition of study objects is made into mutually exclusive and collectively exhaustive subgroups, known as a cluster. Link to transcript of lecture on basics probability. Wayne W. LaMorte, MD, PhD, MPH, Boston University School of Public Health, Link to transcript of lecture on basics probability. Some examples of non-probability samples are described below. Selecting every tenth person (or any even-numbered multiple) would result in selecting all males or females depending on the starting point. If the population order is random or random-like (e.g., alphabetical), then this method will give you a representative sample that can be … Systematic sampling is a probability sampling method in which researchers select members of the population at a regular interval (or k) determined in advance.. Excel, for example, has a built-in function that can be used to generate random numbers. Consequently, if we were to select a sample from a population in order to estimate the overall prevalence of obesity, we would want the educational level of the sample to be similar to that of the overall population in order to avoid an over- or underestimate of the prevalence of obesity. All Rights Reserved. For example, you can choose every 5th person to be in the sample. For example, studies have shown that the prevalence of obesity is inversely related to educational attainment (i.e., persons with higher levels of education are less likely to be obese). In probability sampling, each member of the population has a known probability of being selected. If the desired sample size is n=175, then the sampling fraction is 1,000/175 = 5.7, so we round this down to five and take every fifth person. Once the first person is selected at random, every fifth person is selected from that point on through the end of the list. For example, suppose that the population of interest consisted of married couples and that the sampling frame was set up to list each husband and then his wife. With stratified random sampling, these breaks may not exist*, so you divide your target population into groups (more formally called "strata"). For example, suppose our desired sample size is n=300, and we wish to ensure that the distribution of subjects' ages in the sample is similar to that in the population. Quota sampling is different from stratified sampling, because in a stratified sample individuals within each stratum are selected at random. For example, if a population contains 70% men and 30% women, and we want to ensure the same representation in the sample, we can stratify and sample the numbers of men and women to ensure the same representation. This is similar to stratified sampling in that we develop non-overlapping groups and sample a predetermined number of individuals within each. Quota sampling achieves a representative age distribution, but it isn't a random sample, because the sampling frame is unknown. Advantages. Note: Much of the content in the first half of this module is presented in a 38 minute lecture by Professor Lisa Sullivan. This sampling strategy is most useful for small populations, because it requires a complete enumeration of the population as a first step. Systematic sampling. The selection process begins by selecting the first person at random from the first ten subjects in the sampling frame using a random number table; then 10th subject is selected. Convenience samples are useful for collecting preliminary or pilot data, but they should be used with caution for statistical inference, since they may not be representative of the target population. This is an extreme example, but one should consider all potential sources of systematic bias in the sampling process. Stratified Sampling. Published on October 2, 2020 by Lauren Thomas. Many introductory statistical textbooks contain tables of random numbers that can be used to ensure random selection, and statistical computing packages can be used to determine random numbers. With systematic sampling like this, it is possible to obtain non-representative samples if there is a systematic arrangement of individuals in the population. Due to practical difficulties it will not be possible to make use of data from a whole population when a hypothesis is tested. 2. For example, if the desired sample size is n=200, then n=140 men and n=60 women could be sampled either by simple random sampling or by systematic sampling. In convenience sampling, we select individuals into our sample based on their availability to the investigators rather than selecting subjects at random from the entire population. In stratified sampling, we split the population into non-overlapping groups or strata (e.g., men and women, people under 30 years of age and people 30 years of age and older), and then sample within each strata. The purpose is to ensure adequate representation of subjects in each stratum. Stratified Sampling: In Stratified Sampling, we divide the population into non-overlapping subgroups called strata and then use Simple Random Sampling method to select a proportionate number of individuals from each strata. Systematic sampling: Systematic sampling involves choosing items at regular intervals e.g. In quota sampling, we determine a specific number of individuals to select into our sample in each of several specific groups. Systematic sampling also begins with the complete sampling frame and assignment of unique identification numbers. In stratified sampling, we split the population into non-overlapping groups or strata (e.g., men and women, people under 30 years of age and people 30 years of age and older), and then sample within each strata. A stratified survey could thus claim to be more representative of the population than a survey of simple random sampling or systematic sampling. Simple Random Sample vs Systematic Random Sample Data is one of the most important things in statistics. The reasons to use stratified sampling rather than simple random sampling include For example, if the population size is N=1,000 and a sample size of n=100 is desired, then the sampling interval is 1,000/100 = 10, so every tenth person is selected into the sample. Therefore, the sample may not be representative of the population. Each of these is assigned a unique identification number, and elements are selected at random to determine the individuals to be included in the sample. Systematic sampling is an extended implementation of the same old probability technique in which each member of the group is selected at regular periods to form a sample. For example, we might approach patients seeking medical care at a particular hospital in a waiting or reception area. The spacing or interval between selections is determined by the ratio of the population size to the sample size (N/n). This is an extreme example, but one should consider all potential sources of systematic bias in the sampling process. Stratified sampling In the image below, let's say you need a sample size of 6. What is most important, however, is selecting a sample that is representative of the population. Systematic Sample; Systematic Sampling is when you choose every “nth” individual to be a part of the sample. As a result, each element has an equal chance of being selected, and the probability of being selected can be easily computed. In non-probability sampling, each member of the population is selected without the use of probability. How to perform systematic sampling. 3. Sampling within each stratum can be by simple random sampling or systematic sampling. In stratified sampling, a sample is drawn from each strata (using a random sampling method like simple random sampling or systematic sampling). When selecting a sample from a population, it is important that the sample is representative of the population, i.e., the sample should be similar to the population with respect to key characteristics.

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