# Probability And Non Probability Sampling Cultural Studies Essay

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A probability sampling method is any method of sampling that utilizes some form of random selection. In order to have a random selection method, you must set up some process or procedure that assures that the different units in your population have equal probabilities of being chosen. Humans have long practiced various forms of random selection, such as picking a name out of a hat, or choosing the short straw. These days, we tend to use computers as the mechanism for generating random numbers as the basis for random selection.

Probability sampling methods are those in which every item in the universe has a known chance, or probability of being chosen for sample. This implies that the selection of the sample items is independent of the person making the study that is the sampling operation is controlled so objectively that the items will be chosen strictly at random.

Types of probability sampling

Simple Random Sampling: The simplest form of random sampling is called simple random sampling. Neither of these mechanical procedures is very feasible and, with the development of inexpensive computers there is a much easier way. Simple random sampling is simple to accomplish and is easy to explain to others. Because simple random sampling is a fair way to select a sample, it is reasonable to generalize the results from the sample back to the population. Simple random sampling is not the most statistically efficient method of sampling and you may, just because of the luck of the draw, not get good representation of subgroups in a population. To deal with these issues, we have to turn to other sampling methods.

Systematic Sampling: Stratified Random Sampling, also sometimes called proportional or quota random sampling, involves dividing your population into homogeneous subgroups and then taking a simple random sample in each subgroup. There are several major reasons why you might prefer stratified sampling over simple random sampling. First, it assures that you will be able to represent not only the overall population, but also key subgroups of the population, especially small minority groups. If you want to be able to talk about subgroups, this may be the only way to effectively assure you'll be able to. If the subgroup is extremely small, you can use different sampling fractions within the different strata to randomly over-sample the small group. When we use the same sampling fraction within strata we are conducting proportionate stratified random sampling. When we use different sampling fractions in the strata, we call this disproportionate stratified random sampling. Second, stratified random sampling will generally have more statistical precision than simple random sampling. This will only be true if the strata or groups are homogeneous. If they are, we expect that the variability within-groups are lower than the variability for the population as a whole. Stratified sampling capitalizes on that fact.

Stratified Sampling: For this to work it is essential that the units in the population are randomly ordered, at least with respect to the characteristics you are measuring. For one thing, it is fairly easy to do. You only have to select a single random number to start things off. It may also be more precise than simple random sampling. Finally, in some situations there is simply no easier way to do random sampling. For instance, I once had to do a study that involved sampling from all the books in a library. Once selected, I would have to go to the shelf, locate the book, and record when it last circulated. I knew that I had a fairly good sampling frame in the form of the shelf list (which is a card catalogue where the entries are arranged in the order they occur on the shelf). To do a simple random sample, I could have estimated the total number of books and generated random numbers to draw the sample.

Cluster Sampling: The problem with random sampling methods when we have to sample a population that's disbursed across a wide geographic region is that you will have to cover a lot of ground geographically in order to get to each of the units you sampled. Imagine taking a simple random sample of all the residents of New York State in order to conduct personal interviews. By the luck of the draw you will wind up with respondents who come from all over the state. Your interviewers are going to have a lot of travelling to do. It is for precisely this problem that cluster or area random sampling was invented.

In cluster sampling, we follow these steps: divide population into clusters (usually along geographic boundaries), randomly sample clusters, and measure all units within sampled clusters.

Multi Stage Sampling: The four methods we've covered so far -- simple, stratified, and systematic and cluster -- are the simplest random sampling strategies. In most real applied social research, we would use sampling methods that are considerably more complex than these simple variations. The most important principle here is that we can combine the simple methods described earlier in a variety of useful ways that help us address our sampling needs in the most efficient and effective manner possible. When we combine sampling methods, we call this multi-stage sampling.

Non probability Sampling

Non probability sampling methods are those, which do not provide every item in the universe with a known chance of being included in the sample. The selection process is to some extent

The difference between non probability and probability sampling is that non probability sampling does not involve random selection and probability sampling does. Does that mean that non probability samples aren't representative of the population? Not necessarily. But it does mean that non probability 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. With non probability samples, we may or may not represent the population well, and it will often be hard for us to know how well we've done so. In general, researchers prefer probabilistic or random sampling methods over non probabilistic 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 non probabilistic alternatives.

We can divide non probability sampling methods into two broad types: accidental or purposive. Most sampling methods are purposive in nature because we usually approach 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.

Types of non probability sampling

Accidental, Haphazard or Convenience Sampling: One of the most common methods of sampling goes under the various titles listed here. I would include in this category the traditional "man on the street" (of course, now it's probably the "person on the street") interviews conducted frequently by television news programs to get a quick (although non representative) reading of public opinion. 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. Clearly, the problem with all of these types of samples is that we have no evidence that they are representative of the populations we're interested in generalizing to -- and in many cases we would clearly suspect that they are not.

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. They size up the people passing by and anyone who looks to be in that category they stop to ask if they will participate. One of the first things they're likely to do is verify that the respondent does in fact meet the criteria for being in the sample. 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.

Simple Random Sampling:

It is easy to implement

It requires a listing of population element.

Since selection of its items in the sample depends on change there is no possibility of personal bias affecting the result.

As compared to judgment sampling a random sample represents the universe in a better way. As the size of the sample increases, it becomes increasingly representative of the population.

The analyst can easily assess the accuracy of the estimates because sampling errors follows the principle of chance. The theory of random sampling is further developed than that of any type of sampling, which enables the researcher to provide the most reliable information at least cost.

The use of simple random sampling necessitates a completely catalogued universe from which to draw the sample. That is it uses large sample size.

The size of the sample requires ensuring the statistical reliability is usually under random sampling rather than stratified.

From the point of view of field survey it has been claimed that the cases selected by random sampling tend to be too widely dispersed geographically and that the time and the cost of collecting data becomes very large.

It produces large errors.

Random sampling may produce the most non random looking results.

Systematic Sampling

It is simple to design and convenient to adopt.

It is easier to use than simple random sampling

It is easy to determine sampling distribution

Less expensive than random sampling.

The time and work involved in sampling by this method are relatively less.

The result obtained are found to be generally satisfactory provided care is taken to see that there are no periodic features associated with the sampling intervals.

If the population are sufficiently large, systematic sampling can often be expected to yield results similar to those obtained by proportional stratified sampling.

Using intervals may squeeze the sample and the result.

If the population list has a monotonic trend a bias estimate will result from the starting point.

The main issue is that it becomes fewer representatives if the analyst is dealing with populations having hidden periodic that is not all the elements are known.

Stratified Sampling

The researcher control the sample size in each group

Increase efficiency

It is more representative as population is first divided into various strata and then sample is drawn from each stratum. Thus there is little chance that any essential group of the population is being completely excluded.

There is greater accuracy as each stratum will consist of uniform or homogenous items.

Provide data to represent and analyse sub groups.

Increase error in reason if sub group are selected at different rate.

It is expensive because it is widely distributed geographically and the sample costs per observation are high.

If the sample is not homogeneous the result may not be reliable.

It requires assistance of skilled sampling supervisors.

Cluster Sampling

It provides a unilateral estimate of population.

It is more efficient

It is easy to do without population unit.

It enables each sub division of the population to be used at various stages and permits the fieldwork to be more concentrated.

It is valuable in surveys of underdeveloped areas.

Can be cheaper than other methods - e.g. fewer travel expenses, administration costs

It is more error prone.

Higher sampling error, which can be expressed in the so-called "design effect", the ratio between the number of subjects in the cluster study and the number of subjects in an equally reliable, randomly sampled unclustered study.

Multi Stage Sampling

The main purpose of the creation and present-day use of multi-stage sampling is ti avoid the problems of randomly sampling from a population that is larger than the researcher's resources can handle. Multi-stage sampling gives researchers with limited funds and time a method to sample from such populations. This sampling procedure in essence is a way to reduce the population by cutting it up into smaller groups, which then can be the subject of random sampling. As long as the groups have low between-group variance, this form of sampling is a legitimate way to simplify the population.

The multi-stage form of sampling is flexible in many senses. First, it allows researchers to employ random sampling or cluster sampling after the determination of groups. Second, researchers can employ multi-stage sampling indefinitely to break down groups and subgroups into smaller groups until the researcher reaches the desired type or size of groups. Last, there are no restrictions on how researchers divide the population into groups/ This allows a large number of possibilities for methods of convenience, the maximization or minimization of variance or interpretability.

The flexibility of multi-stage sampling is a double-edged sword. Because of the lack of restrictions on the decision processes involved in choosing groups, multi-stage sampling has a level of subjectivity. Thus, there will always be questions as to whether the chosen groups were optimal. Researchers must find a way to justify their choices when presenting the study's findings.

Due to the fact that multi-stage sampling cuts out portions of the population from the study, the study's findings can never be 100% representative of the population. Even though the theory of multi-stage sampling is to focus on the within-group variance and de-emphasize the between-group variance (which should be minimized), there is no way to know if the demographics cut from the study could have provided any useful information to the researchers.

Convenience Sampling

Convenience samples are cheap.

Convenience samples can be used to intervene to satisfy dissatisfied customers. A key, often forgotten aspect of probability sampling is its dependence on external selection: inviting and then repeatedly reminding people to take a survey, which helps ensure representativeness. Putting a survey postcard with every bill presented at a restaurant is a convenience sample, since there is no follow-up and encouragement to take the survey: no true external selection. And in such cases dissatisfied customers are often more likely to complete such surveys ââ‚¬" the survey does provide an opportunity to hear from such customers and ask them for contact information in order to take action to improve their satisfaction.

Convenience samples can provide rich qualitative information. When illustrative quotes are important, surveys to convenience samples can be a great source of rich verbatim comments on specific topics. The survey can also provide detailed demographic profiles to shed further light on the comments.

Convenience samples may provide accurate correlations. Some argue that correlation research is accurate enough with convenience samples, since the study is not of proportions of the target audience but of the relationship between variables.

Convenience samples do not produce representative results. If you need to extrapolate to the target population, convenience samples arenââ‚¬â„¢t going to get you there.

The natural tendency is to extrapolate from convenience samples. The tendency when using convenience samples is to treat the results as representative, even though they are not. Many people do not understand the theoretical underpinnings of probability sampling and treat any survey results as accurate representations of the target audience. While mainstream media outlets often will not publicize the results of surveys that used convenience samples, small media organizations often will, without describing the methodology as a convenience sample.

The results of convenience samples are hard to replicate. If you analyze the results of a convenience survey by list source, you will often find dramatic differences in the answers from the different lists, often in ways that confound easy explanation

Quota Sampling

Quota sampling is particularly useful when you are unable to obtain a probability sample, but you are still trying to create a sample that is as representative as possible of the population being studied. In this respect, it is the non-probability based equivalent of the stratified random sample.

Unlike probability sampling techniques, especially stratified random sampling, quota sampling is much quicker and easier to carry out because it does not require a sampling frame and the strict use of random sampling techniques (i.e. probability sampling techniques). This makes quota sampling popular in undergraduate and masterââ‚¬â„¢s level dissertations where there is a need to divide the population being studied into strata (groups).

The quota sample improves the representation of particular strata (groups) within the population, as well as ensuring that these strata are not over-represented. For example, it would ensure that we have sufficient male students taking part in the research (60% of our sample size of 100; hence, 60 male students). It would also make sure we did not have more than 60 male students, which would result in an over-representation of male students in our research.

The use of a quota sample, which leads to the stratification of a sample (e.g. male and female students), allows us to more easily compare these groups (strata).

In quota sampling, the sample has not been chosen using random selection, which makes it impossible to determine the possible sampling error. Indeed, it is possible that the selection of units to be included in the sample will be based on ease of access and cost considerations, resulting in sampling bias. It also means that it is not possible to make generalisations (i.e. statistical inferences) from the sample to the population. This can lead to problems of external validity.

Also, with quota sampling it must be possible to clearly divide the population into strata; that is, each unit from the population must only belong to one stratum. In our example, this would be fairly simple, since our strata are male and female students. Clearly, a student could only be classified as either male or female. No student could fit into both categories (ignoring transgender issues).

Furthermore, imagine extending the sampling requirements such that we were also interested in how career goals changed depending on whether a student was an undergraduate or postgraduate. Since the strata must be mutually exclusive, this means that we would need to sample four strata from the population: undergraduate males, undergraduate females, postgraduate males, and postgraduate females. This will increase overall sample size required for the research, which can increase costs and time to carry out the research

Purposive or Judgemental Sampling

The advantages of Judgment sampling are:

Lower cost of sampling

Lesser time involved in the process

A select number of people who are known to be related to the topic are part of the study which means that there are lesser chances of having people who will distort the data

Good method for pretesting instruments like questionnaires.