If you divide your population into a number of strata (sub-populations), where each stratum is internally homogeneous with respect to the characteristic being studied, and you take a random sample from each stratum you have a stratified random sample. The second one is that the members of the population may be organized in some sort of cyclical or periodical fashion so that every, say, tenth member is different from the others in a systematic way. The first one is that it requires that there be a way to line up all members of the population so you can count them off and choose every k th one. Although many combinations of members are not possible with this method, the characteristics of equal probability and, to an extent, independence, are still present, which means that these samples are essentially equivalent to simple random sampling. If the first member selected is chosen randomly, your sample becomes a systematic sample with a random start, which, although much easier to do, is almost as valuable as a simple random sample.
If you are able to line up all members of your population and move down the line one member at a time, you can draw a systematic sample by taking every k th member (for example, every tenth member) of the population. Because there are many research situations in which such a list doesn?t exist or isn?t available situations in which you thus can?t randomly select a sample from the populationsystematic methods were developed. The main weakness of this method is that it requires that you have a list of all the members of the population and that you are able to get access to any members who may be chosen. Its strengths include its simplicity, lack of bias, and relatively straightforward math. The simple random sample is the ideal against which all other probability samples must be judged. In a simple random sample, all combinations of population members are equally likely. If you construct your sample by randomly choosing members of the population, you will have a simple random sample. Discuss the main types of probability sampling methods and explain their strengths and weaknesses. You can't do this with non-probability sampling methods.Ģ. Because you can do this, you can generalize to the population from which the sample was drawn.
With probability samples you can estimate the accuracy of the sample that is, you can estimate the level of confidence you can have that your sample statistics differ from the population parameters by no more than a given level of error. they have higher external validity) than non-probability samples because there is less bias. Probability samples are usually more representative (i.e. With probability samples you can make valid generalization to the population from which the samples are drawn. If you know the probability of being included in the sample for each and every member of the population, you have a probability sample. In fact, there is no way to tell which population (if any) a non-probability sample represents. You can?t generalize your results to the population. Since you don?t know which members of the population have a chance to be included in your sample, you don?t know whether or not your sample accurately represents the population. Some individuals may have absolutely no chance of being selected.
Some combinations of individuals may be more likely to be selected than others. If you can not specify the probability that any given individual will be in the sample, you have a non-probability sample. Distinguish between probability and non-probability sampling and discuss the advantages and disadvantages of each.
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