Proportional allocation is advisable when all we know of the strata is their sizes. In situations where the standard deviations of the strata are known it may be advantageous to make a disproportionate allocation. Suppose that, once again, we had stratum A and stratum B, but we know that the individuals assigned to stratum A were more varied with respect to their opinions than those assigned to stratum B. Optimum allocation minimises the standard error of the estimated mean by ensuring that more respondents are assigned to the stratum within which there is greatest variation.
But it does mean that nonprobability samples cannot depend upon the rationale of probability theory. At least with a probabilistic sample, we know the odds or probability that we have represented the population well.
We are able to estimate confidence intervals for the statistic. In general, researchers prefer probabilistic or random sampling methods over nonprobabilistic ones, and consider them to be more accurate and rigorous. However, in applied social research there may be circumstances where it is not feasible, practical or theoretically sensible to do random sampling.
Here, we consider a wide range of nonprobabilistic alternatives. We can divide nonprobability sampling methods into two broad types: Most sampling methods are purposive in nature because we usually Quota sampling in research the sampling problem with a specific plan in mind.
The most important distinctions among these types of sampling methods are the ones between the different types of purposive sampling approaches. Accidental, Haphazard or Convenience Sampling One of the most common methods of sampling goes under the various titles listed here.
I would also argue that the typical use of college students in much psychological research is primarily a matter of convenience. In clinical practice,we might use clients who are available to us as our sample. In many research contexts, we sample simply by asking for volunteers.
Purposive Sampling In purposive sampling, we sample with a purpose in mind. We usually would have one or more specific predefined groups we are seeking. For instance, have you ever run into people in a mall or on the street who are carrying a clipboard and who are stopping various people and asking if they could interview them?
Most likely they are conducting a purposive sample and most likely they are engaged in market research. They might be looking for Caucasian females between years old.
They size up the people passing by and anyone who looks to be in that category they stop to ask if they will participate. Purposive sampling can be very useful for situations where you need to reach a targeted sample quickly and where sampling for proportionality is not the primary concern.
With a purposive sample, you are likely to get the opinions of your target population, but you are also likely to overweight subgroups in your population that are more readily accessible. All of the methods that follow can be considered subcategories of purposive sampling methods.
We might sample for specific groups or types of people as in modal instance, expert, or quota sampling. We might sample for diversity as in heterogeneity sampling.
Or, we might capitalize on informal social networks to identify specific respondents who are hard to locate otherwise, as in snowball sampling.
In all of these methods we know what we want -- we are sampling with a purpose. Modal Instance Sampling In statistics, the mode is the most frequently occurring value in a distribution.
In sampling, when we do a modal instance sample, we are sampling the most frequent case, or the "typical" case.
In a lot of informal public opinion polls, for instance, they interview a "typical" voter. There are a number of problems with this sampling approach.
First, how do we know what the "typical" or "modal" case is? We could say that the modal voter is a person who is of average age, educational level, and income in the population.
And, how do you know that those three variables -- age, education, income -- are the only or even the most relevant for classifying the typical voter?
What if religion or ethnicity is an important discriminator? Clearly, modal instance sampling is only sensible for informal sampling contexts. Expert Sampling Expert sampling involves the assembling of a sample of persons with known or demonstrable experience and expertise in some area.
Often, we convene such a sample under the auspices of a "panel of experts.QUOTA SAMPLING In quota sampling the selection of the sample is made by the interviewer, who has been given quotas to fill from specified sub-groups of the population.
For example, an interviewer may be told to sample 50 females between the age of 45 and Sampling In Research In research terms a sample is a group of people, objects, or items that are taken from a larger population for measurement.
The sample should be representative of the population to ensure that we can generalise. Research has shown that increasing your sample size can reduce haphazard selection bias. This sounds easy, but in practice your choices can be influenced by factors you aren’t aware of.
For example, you might unconsciously choose a name because of the way a name looks, or you might show a hidden gender preference. 1 RSMichael Sampling Procedures Y Strategies for Educational Inquiry Robert S Michael RSMichael Terms Population (or universe) – The group to which inferences are made based on a sample drawn from the population.
Targeted sampling (Watters and Biernacki ) is a non-probability sampling method that combines extensive ethnographic mapping with sampling quotas, time and location quotas, and peer-referrals constituting network sampling.
This is a careful, pragmatic, though non-probabilistic approach, designed to gather a representative sample of a hard. In sociology and statistics research, snowball sampling (or chain sampling, chain-referral quota sampling in research sampling, referral sampling) is a nonprobability king customer needs to wake up sampling technique where.