Yes, positive sampling is still used for quantitative analysis. Because purposive sampling is a non-probability sampling approach, I favor non-parametric procedures such the Mann-Whitney test, Wilcoxon test, Fisher Exact Test, Kruskal-Wallis test, and others. These tests do not make any assumptions about the distribution of the data and are therefore appropriate for any sample size.
The only requirement for these tests is that there be at least two groups. If this condition is not met, then you can't run any statistical tests; thus, no conclusions can be drawn from your data set. However, if your study has some form of qualitative component to it (i.e., interviews or focus groups), then probability sampling may be used with these methods.
For example, let's say that you wanted to know how many men and women work in technology. Because this is a qualitative study that seeks to understand the motivations behind actions, questions about gender would need to be asked of both men and women. Thus, we would need to ask both men and women working in technology what their motivation was for doing so. Only after getting responses from several individuals within each group could an inference about the overall population be made.
In conclusion, yes, you can use purposive sampling within quantitative research studies as long as you include all relevant variables in your analysis and apply appropriate statistical tests.
Purposive sampling selects non-probability samples depending on the features existing in a given population group and the overall research. Purposive sampling may be used in a variety of ways to collect information by academics. For example, a researcher might purposefully select students in an introductory psychology course to participate in a study investigating factors that influence student interest in the field. The primary advantage of this method over probability sampling is that it allows the investigator to select subjects based on certain characteristics such as age, gender, race, religion, etc., which would otherwise be impossible if all eligible participants were selected at random.
In addition to being able to select subject characteristics, another advantage of purposive sampling is that it can be used when there is no clear relationship between variables involved in the study. For example, if the researcher wants to learn about how college students feel about racism but cannot identify any schools where this problem exists, he or she could use purposive sampling to select institutions where students report experiencing racism and then contact individuals from these schools to ask them about their views on racism. Subjects who agree to participate will help ensure that results are relevant to the population under study.
The primary disadvantage of purposive sampling is that the researcher cannot assume that the sample represents the population as a whole.
The study also demonstrates that, while convenience sampling may be utilized in both qualitative and quantitative studies, it is most commonly employed in quantitative studies, whereas purposive sampling is more commonly used in qualitative investigations. Nonprobability Sampling Methods include self-selecting samples, snowball samples, and volunteer samples.
Convenience sampling is often considered inferior to probability sampling for generating representative samples because the selection of participants is not based on any scientific criteria. However, convenience samples can be useful in exploratory research projects or when there is no budget for probability sampling techniques. In addition, convenience samples are easy to obtain and can provide insight into issues that would otherwise remain unexplored.
Purposeful sampling is used when we want to ensure that our sample represents some specific group in the population. For example, if we wanted to find out what food items people like best, we could call up a few restaurants and ask them, or go to several grocery stores and take notes on which products people buy most. This method is called "criteria-based sampling."
When choosing between convenience and purposeful sampling methods, consider how much control you need over your sample. If you do not need to make sure that your sample is a good representation of the whole population, then using a convenient sample is enough.
The convenience sampling approach may be used in both qualitative and quantitative research, however it is more commonly employed in quantitative studies, whereas purposive sampling is more commonly used in qualitative studies. Convenience sampling is a method for selecting participants for study who are chosen by chance from a larger group (i.e., not necessarily a random sample). In this type of sampling, researchers select potential participants based on some criterion that should yield a representative sample. For example, researchers might select all women over 21 years old living in a certain housing complex or all physicians working at a particular hospital. Participants are usually asked to provide only their names and email addresses, so as not to jeopardize the anonymity of the sample.
Convenience samples can be very useful tools for gaining insight into sensitive topics that might otherwise be difficult or impossible to study. Or if you want to understand how people feel about organ donation after reading no more than one article on the subject, you could contact all the organ donor registries in your state and ask them to send you information on anyone who has registered as an organ donor.
Convenience samples also have several drawbacks.
Purposive sampling would be the selection of a sample of universities in the United States that represents a cross-section of universities in the United States, using expert knowledge of the population first to decide which characteristics are important to be represented in the sample and then to identify a sample of...
The following example uses demographic data from the U.S. Census Bureau's American Community Survey (ACS) 5-year estimates to select colleges for inclusion in a sample of public universities in California. The example assumes that the researcher is interested in comparing the demographics of four-year institutions with those of two-year institutions.
First, the analyst should understand what factors are likely to influence the probability of being selected into the sample. In this case, it makes sense to include small and large schools as well as private and public institutions.
Second, the analyst should review available data on these factors. In this case, the only data source that appears relevant is the ACS. The Census Bureau publishes five-year average statistics at the national level, by state, and by type of institution (four-year vs. two-year).
Third, the analyst should consider how these factors might influence the probability of being selected into the sample.
Although the phrases "purposive sampling" and "convenient sampling" are sometimes used interchangeably, they do not signify the same thing. Convenience sampling is when researchers use persons who can be discovered and reached with the least amount of effort. For example, if you were doing a study on children's attitudes toward animals at a local zoo, you would use individuals who visited the zoo recently enough that their names were still fresh in their minds but old enough that they no longer exhibited any behavioral problems related to their dementia. The sample would therefore be representative of the general population.
Purposive sampling is another name for random sampling. In purposive sampling, researchers decide what characteristics they want to investigate by looking at the data they have available. They then select participants from among those with these characteristics based on certain criteria (such as age or income). This method is often used when there is no way to predict which members of a group will have an interest in taking part in a study and which ones won't. For example, if you wanted to learn more about how people feel about animals in zoos, you could not possibly know which of the people at the zoo this would affect most strongly - infants vs. adults vs. seniors. So, you would use a sample method such as purposive sampling to choose participants.
There are two types of purposive samples: qualitative and quantitative.