With these methods, each study unit has an equal or at least a known probability of being selected in the sample. This is the simplest form of probability sampling. To select a simple random sample you need to: Simple random sampling can be used for the weekly illness recall method and when selecting facilities for simulated client visits see Chapter 3.
In systematic sampling, individuals or households are chosen at regular intervals from the sampling frame. For this method we randomly select a number to tell us where to start selecting individuals from the list. For example, a systematic sample is to be selected from 1, students at a school. The sample size selected is The sampling interval is therefore The number of the first student to be included in the sample is chosen randomly, for example, by blindly picking one out of 12 pieces of paper, numbered 1 to If number 6 is picked, then every twelfth student will be included in the sample, starting with student number 6, until students are selected.
The numbers selected would be 6, 18, 30, 42, etc. Systematic sampling is usually less time-consuming and easier to perform than simple random sampling. However, there is a risk of bias, as the sampling interval may coincide with a systematic variation in the sampling frame.
For instance, if we want to select a random sample of days on which to count clinic attendance, systematic sampling with a sampling interval of 7 days would be inappropriate, as all study days would fall on the same day of the week, which might, for example, be a market day.
The simple random sampling method described above does not ensure that the proportion of some individuals with certain characteristics will be included. If it is important that the sample includes representative groups of study units with specific characteristics for example, residents from urban and rural areas, or different age groups , then the sampling frame must be divided into groups, or strata, according to these characteristics.
Random or systematic samples of a predetermined size will then have to be obtained from each group stratum. This is called stratified sampling. Stratified sampling is only possible when we know what proportion of the study population belongs to each group we are interested in. An advantage of stratified sampling is that it is possible to take a relatively large sample from a small group in the study population.
This makes it possible to get a sample that is big enough to enable researchers to draw valid conclusions about a relatively small group without having to collect an unnecessarily large and hence expensive sample of the other, larger groups. However, in doing so, unequal sampling fractions are used and it is important to correct for this when generalizing our findings to the whole study population. It is suspected that in urban areas self-medication is less common due to the vicinity of health centres.
A decision is made to include urban households out of 4,, which gives a 1 in 40 sample and rural households out of 16,, which gives a 1 in 80 sample. Non-probability Sampling — Does not rely on the use of randomization techniques to select members. This is typically done in studies where randomization is not possible in order to obtain a representative sample. Bias is more of a concern with this type of sampling. The different types of non-probability sampling are as follows:. The following Slideshare presentation, Sampling in Quantitative and Qualitative Research — A practical how to, offers an overview of sampling methods for quantitative research and contrasts them with qualitative method for further understanding.
Examples of Data Collection Methods — Following is a link to a chart of data collection methods that examines types of data collection, advantages and challenges. Qualitative and Quantitative Data Collection Methods - The link below provides specific example of instruments and methods used to collect quantitative data. Sampling and Measurement - The link below defines sampling and discusses types of probability and nonprobability sampling.
Principles of Sociological Inquiry — Qualitative and Quantitative Methods — The following resources provides a discussion of sampling methods and provides examples. This pin will expire , on Change. This pin never expires. Select an expiration date. About Us Contact Us. Search Community Search Community. Sampling Methods Sampling and types of sampling methods commonly used in quantitative research are discussed in the following module.
In particular, I may be interested to know whether socio-economic status influences American adolescent females' mental health. There are a variety of ways I could consider to obtain a representative sample. A sample is representative when it allows the results of the sample to be generalized to the population. The sampling frame is the group of individuals who had a real chance of being selected for the sample. For example, if I use as my sampling frame the lists of students held by public and private schools in America from which to select a sample of adolescent females, then only students on those lists have a real chance of being selected.
This may differ from the population to which I wish to generalize the results of my study all adolescent females in the United States. In this case, my sample will almost certainly be bias because adolescent females with poor mental and with lower socio-economic status are probably less likely to be on school lists than other students. As you can see from this example, the process of sampling , even when done systematically, can introduce potentially critical biases into a research study.
Due our bias sampling technique we may enhance the risk of incorrectly concluding that there is no relationship between socioeconomic status and the mental health of adolescent females in the United States because we did representatively sample from adolescent females who weren't on school registers. There are two main types of sampling - the key is whether or not the selection involves randomization.
Randomization means that each unit within a sampling frame has an equal chance of being selected. By selecting randomly from a sampling frame , probability theory says that our sample, more often than not, should approximately represent the whole population.
You can read more about sampling terminology and probability theory in social science research on Bill Trochim's Sampling Terminology page. Probability sampling involves the use of randomization. These are the main types of probability sampling:
Sampling Methods. Sampling and types of sampling methods commonly used in quantitative research are discussed in the following module. Learning Objectives: Define sampling and randomization. Explain probability and non-probability sampling and describes the different types of each.
Quantitative Research Sampling Methods There are two main sampling methods for quantitative research: Probability and Non-probability sampling. Probability sampling: A theory of probability is used to filter individuals from a population and create samples in probability sampling.
While there are certainly instances when quantitative researchers rely on nonprobability samples (e.g., when doing exploratory or evaluation research), quantitative researchers tend to rely on probability sampling techniques. In the practical exercise (Quantitative Exam Task 1 - 5%) you developed three quantitative research designs (experimental, quasi-experimental, and non-experimental) for your research question, and considered the strengths and weaknesses, as well as practical issues, for each of these designs.