Probability sampling pdf

Each member of the population has a known chance of being selected. Non-probability sampling is most useful for exploratory studies like a pilot survey deploying a survey to a smaller sample compared to pre-determined sample size. Researchers use this method in studies where it is impossible to draw random probability sampling due to time or cost considerations. Researchers choose these samples just because they are easy to recruit, and the researcher did not consider selecting a sample that represents the entire population.

Ideally, in research, it is good to test a sample that represents the population. But, in some research, the population is too large to examine and consider the entire population. It is one of the reasons why researchers rely on convenience sampling, which is the most common non-probability sampling method, because of its speed, cost-effectiveness, and ease of availability of the sample. Here, the researcher picks a single person or a group of a sample, conducts research over a period, analyzes the results, and then moves on to another subject or group if needed.

There are employees in the organization, also known as the population. To understand better about a population, the researcher will need only a sample, not the entire population. Further, the researcher is interested in particular strata within the population. Here is where quota sampling helps in dividing the population into strata or groups.

In other words, researchers choose only those people who they deem fit to participate in the research study. Judgmental or purposive sampling is not a scientific method of sampling, and the downside to this sampling technique is that the preconceived notions of a researcher can influence the results. Thus, this research technique involves a high amount of ambiguity. Researchers use this technique when the sample size is small and not easily available. This sampling system works like the referral program.

Once the researchers find suitable subjects, he asks them for assistance to seek similar subjects to form a considerably good size sample. Non-probability sampling examples Here are three simple examples of non-probability sampling to understand the subject better.

An example of convenience sampling would be using student volunteers known to the researcher. Researchers can send the survey to students belonging to a particular school, college, or university, and act as a sample. In an organization, for studying the career goals of employees, technically, the sample selected should have proportionate numbers of males and females. The three characteristics Sampling procedures used in quantitative survey of a sample frame a researcher should evaluate are research studies seen in the healthcare peer-reviewed comprehensiveness, probability of selection of units, literature often contain methodological errors in the and efficiency 4.

The statement underpins the fact selection of the sampling frame and the sampling that a sample can only represent the sample frame — units. Quality concerns in conducting surveys arise in this case the police population—that actually had from poor design or execution of survey research a chance to be selected.

It would be important to and ineffective reporting 4,5,7. These errors may consider the comprehensiveness of the selection in an have ramifications in public health and the broader evaluation of a sampling approach to determine the healthcare literature. The sampling error is the variable around the The authors in this study did not indicate if the true value of what is being measured and is often police register was scrutinized for possible errors.

For example, the composition of the police force or transferred from other parts of the country. Are female officers or senior behavior patterns of the target population of officers officers listed separately?

Such questions could and could be excluded by selecting the next unit in the only be answered by scrutinizing how the list was sample frame. Another possible selection issue could compiled with a view of detecting any clustering of arise if the officers who declined participation and people with peculiar characteristics that will affect were replaced were more inclined to consuming more the randomness of the selection.

The authors do not alcohol, had something to hide, or simply avoided an mention if the police register was scrutinized for any embarrassing interview. A reorganization of the list—especially Sample Size Determination if computerized—could be used to create a more The optimum sample size has a relation to the type of representative sampling frame.

The total number of police officers in the police force list sample frame was reported Probability of Selection as approximately 2, individuals but the authors Another concern is the ability of a researcher to know do not elaborate on how they arrived at a sample size or calculate the probability of the selection of each of participants 8. Using a suggested guideline, selected individual.

It has also sample statistics and the population sampled the been suggested that one should have observations sampling frame if the probability of the selection for each major subgroup in survey research and 20 of each individual selected cannot be determined to 50 for minor groups 2. Using the online sample 4. One possible way to determine the probability size calculator, a sample size of was derived for of selection of each individual officer is to examine this study. The small sample of selected by the closely the list of units in the sampling frame of the authors may limit the generalizability of the findings police officers 8.

Such an examination was probably to the whole Ugandan police force or the general performed but not reported in the article. However, population. Yet, the sample size and population size the authors clearly stated that the first respondent may not often be correlated as generally believed 4. Another point to find the correct sample size, the sampling interval could out the probability of selection would be during data be calculated by dividing the sample frame with the collection as incidences of double entry into the list sample size.

Therefore, the correct sampling interval should have been every 7th officer on the list and not every Efficiency 20th officer as calculated by the authors. The usefulness of the sample would also be Rules of thumb can be used to approximate the sample determined by the rate at which members of the size, but the practice may not be universally accepted police force can be found among those in the sampling by researchers.

It has been suggested that new frame, a characteristic of a sample frame referred to researchers should use approximations to get a feel as efficiency. It could happen that sampling frames for sample sizes 2. The amount of variability on the sometimes include units or individuals who are dependent measure within the sample determines the not part of the target population.

The importance ability to detect statistically significant differences 2. Therefore, a chance to be selected has been stressed as the there is a need to determine the optimal sample size ability to generalize from a sample depends on the that will take variability into account and still be sample frame 4. Moreover, it is also important to sensitive to detect statistical significance.

The authors find out any distinctive attributes of those omitted. Fowler FJ. Survey Research Methods commonly used sampling size estimation techniques. However, where it is not possible to use probability sampling, non-probability sampling at least provides a viable alternative that can be used. This could significantly diminish the potential for researchers to study certain types of population, such as those populations that are hidden or hard-to-reach e. Here, snowball sampling , a type of non-probability sampling technique, provides a solution.

Non-probability sampling can also be particularly useful in exploratory research where the aim is to find out if a problem or issue even exists in a quick and inexpensive way. After all, you may have a theory that such a problem or issue exists, but there is limited or no research that currently supports such a theory. Where your main desire is to find out is if such a problem or issue even exists, the potential sampling bias of certain non-probability sampling techniques can be used as a tool to help you.

For example, you may choose to select only those units to be included in your sample that you feel will exhibit the problem or issue you are interested in finding. If this problem or issue does not exist even in your biased sample , it is unlikely to be present if you selected a relatively unbiased sample whether using another non-probability sampling technique; or even a probability sampling technique.

This would help you to avoid a potentially more time consuming and expensive piece of research looking into a potential problem or issue that actually doesn't exist. It may also be considered an ethical approach to finding out whether a problem or issue is worth examining in more depth, since fewer participants are subjected to a research project unnecessarily.

If you are considering whether to use non-probability sampling, it is important to consider how your choice of research strategy will influence whether this is an appropriate decision.

Even if you know that non-probability sampling fits with the research strategy guiding your dissertation, it is important to choose the appropriate type of non-probability sampling techniques. These non-probability sampling techniques are briefly set out in the next section. There are five types of non-probability sampling technique that you may use when doing a dissertation at the undergraduate and master's level: quota sampling , convenience sampling , purposive sampling , self-selection sampling and snowball sampling.

To get a sense of what these five types of non-probability sampling technique are, imagine that a researcher wants to understand more about the career goals of students at a single university. Let's say that the university has roughly 10, students. These 10, students are our population N. Each of the 10, students is known as a unit although sometimes other terms are used to describe a unit; see Sampling: The basics.

In order to select a sample n of students from this population of 10, students, we could choose to use quota sampling , convenience sampling , purposive sampling , self-selection sampling and snowball sampling :. With proportional quota sampling , the aim is to end up with a sample where the strata groups being studied e. If we were to examine the differences in male and female students, for example, the number of students from each group that we would include in the sample would be based on the proportion of male and female students amongst the 10, university students.

To understand more about quota sampling, how to create a quota sample, and the advantages and disadvantages of this non-probability sampling technique, see the article: Quota sampling. A convenience sample is simply one where the units that are selected for inclusion in the sample are the easiest to access. In our example of the 10, university students, if we were only interested in achieving a sample size of say students, we may simply stand at one of the main entrances to campus, where it would be easy to invite the many students that pass by to take part in the research.

To understand more about convenience sampling, how to create a convenience sample, and the advantages and disadvantages of this non-probability sampling technique, see the article: Convenience sampling. Purposive sampling, also known as judgmental , selective or subjective sampling , reflects a group of sampling techniques that rely on the judgement of the researcher when it comes to selecting the units e.

These purposive sampling techniques include maximum variation sampling , homogeneous sampling, typical case sampling , extreme or deviant case sampling , total population sampling and expert sampling. Each of these purposive sampling techniques has a specific goal, focusing on certain types of units, all for different reasons.

The different purposive sampling techniques can either be used on their own or in combination with other purposive sampling techniques. To understand more about purposive sampling, the different types of purposive sampling, and the advantages and disadvantages of this non-probability sampling technique, see the article: Purposive sampling.

Self-selection sampling is appropriate when we want to allow units or cases, whether individuals or organisations, to choose to take part in research on their own accord. The key component is that research subjects or organisations volunteer to take part in the research rather than being approached by the researcher directly.

To understand more about self-selection sampling, how to create a self-selection sample, and the advantages and disadvantages of this non-probability sampling technique, see the article: Self-selection sampling. To understand more about snowball sampling, how to create a snowball sample, and the advantages and disadvantages of this non-probability sampling technique, see the article: Snowball sampling. Non-probability sampling Non-probability sampling represents a group of sampling techniques that help researchers to select units from a population that they are interested in studying.

Principles of non-probability sampling Types of non-probability sampling.