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Probability and Non-Probability Sampling

COLLAPSE

      The proper sample for the study needs to be chosen once the research question and research strategy have been decided upon. The “Sampling Process” refers to the process through which the researcher chooses the sample. Basically, there are two categories of sampling techniques: 1) Probability sampling is based on random events (such as picking numbers at random or tossing a coin), and 2) Non-probability sampling is based on the researcher’s preference for accessible and available populations. Purposive sampling, convenience sampling, and quota sampling are a few examples of non-probability sampling techniques. Probability sampling is a type of random sampling technique (such as a basic random sample or stratified random sample). It’s critical to comprehend the various sampling techniques used in clinical trials and to explicitly describe each one in the report(Setia, 2016).

Discuss in detail the characteristics of probability and nonprobability sampling.

     There are two main types of sampling techniques: probability sampling techniques, where each subject in the target population has an equal chance of being chosen for the sample, and non-probability sampling techniques, where the sample population is chosen in an ad hoc manner without a guarantee that each subject in the target population will have an equal chance of being chosen. Probability sampling techniques were used to choose samples that are more representative of the intended audience(Elfil & Negida, 2017).

Probability sampling method

Simple random sampling

     When the entire population is reachable and the researchers have a list of every subject in this target population, they will apply this strategy. The “sampling frame” is a list of every person in this population. We select a random sample from this list by a lottery system or a computer-generated random list.

Stratified random sampling

     Since this technique modifies simple random sampling, it also necessitates the availability of the sample frame. Yet, this approach divides the entire population into uniform strata or subgroups based on a demographic feature (e.g. gender, age, religion, socio-economic level, education, or diagnosis etc.). The researchers then choose a random sample from among the various strata. Minority populations would continue to be underrepresented in the sample if the researchers used simple random sampling. The basic random approach typically represents the entire target population, to put it simply. In this situation, it is preferable for investigators to employ a stratified random sample to get sufficient samples from each demographic stratum.

Non-probability sampling method

Convenience sampling

     It is the most relevant and often used sampling technique in clinical research, despite the fact that it is not based on chance. With this approach, respondents are enrolled by the researchers based on their accessibility and availability. As a result, this approach is simple, affordable, and practical. When the researcher chooses the sample components based on their easy accessibility and closeness, the practice is known as convenient sampling.

Snow-ball sampling

   This approach is utilized when it is difficult to access a population because it cannot be found in a single location. Using this approach, the researcher requests access to each subject’s coworkers who belong to the same population. This issue frequently arises in social science research. For instance, if we were doing a survey on street children, there would be no list of homeless children, making it challenging to find this demographic in one location(Elfil & Negida, 2017).

Quota sampling

     The non-probability equivalent of stratified sampling is quota sampling. The investigator first determines the strata and the frequency of each in the population. Following that, the necessary number of participants from each stratum are chosen using convenience sampling(Tyrer & Heyman, 2016).

Discuss why researchers would use conditional probability instead of unconditional probability in their study.

The concept of conditional probability is a recognized mathematical theory that offers a method for updating probabilities in light of new information(Bissiri & Walker, 2018). Conditional probabilities are probabilities whose value depends on another probability’s value. Such probabilities are pervasive(Westbury, 2010). Conditional probabilities come into play when deciding how much confidence to assign to a given belief, such as “this patient will respond to this intervention” or “this person should receive this specific diagnosis” or “incorporating this method into my clinical practice is worthwhile.”

References:                                                                              

Bissiri, P. G., & Walker, S. G. (2018). A Definition of Conditional Probability with Non-Stochastic Information. Entropy, 20(8), 572.

Elfil, M., & Negida, A. (2017). Sampling methods in Clinical Research; an Educational Review. Emergency, 5(1), e52.

Setia, M. S. (2016). Methodology Series Module 5: Sampling Strategies. Indian Journal of Dermatology, 61(5), 505–509.

Tyrer, S., & Heyman, B. (2016). Sampling in epidemiological research: Issues, hazards and pitfalls. BJPsych Bulletin, 40(2), 57–60.

thuis is was qoustion 

This week covers probability and non-probability sampling. Discuss in detail the characteristics of probability and nonprobability sampling. Discuss why researchers would use conditional probability instead of unconditional probability in their study.