Sampling Probability Essay Example for Free

have distri thoion Probability EssayProbability And Non Probability Sampling cultural Studies Essay A probability taste method is any method of try that utilizes some hurl of stochastic selection. In order to start out a hit-or-miss selection method, you moldiness set up some process or procedure that assures that the unalike units in your creation grow equal probabilities of cosmos elect. Humans have long practiced various forms of hit-or-miss selection, such as picking a name out of a hat, or choosing the short straw. These days, we tend to engage computers as the mechanism for generating random numbers as the basis for random selection. Probability take methods argon those in which all(prenominal) keepsake in the universe has a known chance, or probability of being chosen for adjudicate. This implies that the selection of the consume items is independent of the person making the study that is the sample distribution operation is controlled so objectively t hat the items fork over be chosen strictly at random. Types of probability try out simple-minded Random Sampling The simplexst form of random sampling is c all(prenominal)ed simple random sampling. Neither of these mechanical procedures is very feasible and, with the development of inexpensive computers in that location is a much easier expressive style. Simple random sampling is simple to accomplish and is easy to explain to others. Beca intent simple random sampling is a fair way to select a attempt, it is reason run low to generalize the results from the taste back to the tribe. Simple random sampling is non the nigh statistically efficient method of sampling and you may, just beca physical exertion of the wad of the coax, not get in good personifyation of make outgroups in a nation. To deal with these issues, we have to turn to other sampling methods. arrogant Sampling Stratified Random Sampling, withal sometimes called proportionate or quota random sampling , involves dividing your people into homogeneous subgroups and indeed taking a simple random stress in each subgroup. T here(predicate) atomic number 18 several major reasons why you might prefer tell apart sampling over simple random sampling.First, it assures that you exit be able-bodied to represent not scarce 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 alone way to effectively assure youll 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 weuse the same sampling fraction within strata we atomic number 18 conducting proportionate stratify random sampling. When we use different sampling fractions in the strata, we call this disproportionate stratified random sampling. Second, stratified random sampling ordain generally have more than st atistical precision than simple random sampling. This will only(prenominal) be true if the strata or groups be 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 snarly 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 dip (which is a card catalogue where the entries are ar positiond 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 thats disbursed across a wide geographic region is that you will have to cover a lot of rationality 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 withi n sampled clusters. Multi Stage Sampling The quaternary methods weve covered so far simple, stratified, and arrogant 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 precept here isthat we can combine the simple methods described earlier in a variety of useful ship canal that help us address our sampling needs in the most efficient and effective expressive style possible. When we combine sampling methods, we call this multi-stage sampling. Non probability SamplingNon probability sampling methods are those, which do not provide every item in the universe with a known chance of being include 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 tha t non probability samples arent 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 frequently be hard for us to know how well weve done so.In general, detectives 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 goal-directed. nigh sampling meth ods 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 samplingAccidental, Haphazard or Convenience Sampling One of the most commonalty methods of sampling goes at a lower place the various titles listed here. I would include in this category the traditional man on the street (of course, now its 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 reason out that the typical use ofcollege savants 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 enquireing for volunteers. Clearly, the proble m with all of these types of samples is that we have no evidence that they are representative of the populations were 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 coat up the people passing by and anyone who looks to be in that category they stop to subscribe to if they will participate. One of the first things theyre likely to do is verify that the respondent does in fact play the criteria for being in the sample. Purposive sampling can be very useful for situations where you need to croak a fair gameed 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 prat population, but you are also likely to overweight subgroups in your population that are more readily accessible. For eac h type of sampling give the advantages and disadvantages. Advantages and Disadvantages of Probability samplingSimple Random SamplingAdvantagesIt is easy to implementIt 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 savvy 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. DisadvantagesThe use of simple random sampling necessitates a entirely catalogueduniverse from which to draw the sample. That is it uses large sample size. The size of the sample requires ensu ring the statistical reliability is usually under random sampling rather than stratified. From the point of view of field report 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 entropy becomes very large. It produces large errors.Random sampling may produce the most non random looking results. Systematic SamplingAdvantagesIt is simple to design and convenient to adopt.It is easier to use than simple random samplingIt is easy to narrow sampling distributionLess expensive than random sampling.The time and work twisting in sampling by this method are relatively less. The result obtained are found to be generally copasetic 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 stratifie d sampling. DisadvantagesUsing intervals may squeeze the sample and the result.If the population list has a take aim trend a bias estimate will result from the starting point. The main issue is that it becomes less representatives if the analyst is dealing with populations having hidden periodic that is not all the elements are known. Stratified SamplingAdvantagesThe researcher control the sample size in each groupIncrease efficiencyIt is more representative as population is first divided into various strata and then sample is drawn from each stratum. therefrom 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 self-colored items. DisadvantagesProvide 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 comment are high . If the sample is not homogeneous the result may not be reliable. It requires assistance of consummate sampling supervisors.Cluster SamplingAdvantagesIt provides a unilateral estimate of population.It is more efficientIt 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 DisadvantagesIt is more error prone.Higher sampling error, which can be expressed in the questionable design effect, the ratio between the number of presents in the cluster study and the number of subjects in an equally reliable, randomly sampled unclustered study. Multi Stage SamplingAdvantagesThe 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 re ascendants can handle. Multi-stage sampling gives researchers with limited coin and time a method to sample from such populations. This sampling procedure in essence is a way to reduce the population by sunburnting 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 employmulti-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 minimisation of variance or interpretabi lity. DisadvantagesThe 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 studys findings. Due to the fact that multi-stage sampling cuts out portions of the population from the study, the studys findings can never be 100% representative of the population. Even though the theory of multi-stage sampling is to snap 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. (http//www.ehow.com/info_8544049_advantages-disadvantages-multistage-sampling.htmlixzz27Sqmq8C8) Advantages and Disadvantages of Non probability s ampling (Non Random Sampling) Convenience SamplingAdvantagesConvenience 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 boost to take the survey no true external selection. And in such cases dissatisfied customers are often more likely to complete such surveys 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 samplescan 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. DisadvantagesConvenience samples do not produce representative results. If you need to extrapolate to the target population, convenience samples arent going to get you there. The natural tendency is to extrapolate from convenience samples. The tendency when apply 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 wi ll, without describing the methodological analysis 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 SamplingQuota sampling is evently useful when you are uneffective 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 faster 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 masters level dissertations wher e there is a need to divide the population being studied into strata (groups).The quota sample improves the representation of particular strata (groups) within thepopulation, as well as ensuring that these strata are not over-represented. For example, it would ensure that we have sufficient masculine students taking part in the research (60% of our sample size of 100 hence, 60 masculine 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). Disadvantages of quota samplingIn quota sampling, the sample has not been chosen using random selection, which makes it unaccepted 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 mean 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, an d 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 SamplingThe advantages of Judgment sampling areLower cost of samplingLesser time involved in the processA select number of people who are known to be relate to the topic are part of the study which means that there are lesser chances of having people whowill distort the data Good method for pretesting instruments like questionnaires.Some disadvantages areIt can be subject to experimenters bias and stereotypes that may distort the results. The group selected may not represent all the populationIt might not be possible to accurately identify the sample using this method in case the population is very large.