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In order to make inferences from a study, sampling is required in research. This is true for both qualitative and quantitative research because it enables researchers to precisely quantify the outcomes of their studies. It is an important study tool because it enables researchers to gather data from a vast and diverse community in order to obtain accurate findings that can be used to make informed choices.
Qualitative Research
Purposive Sampling
The purposeful sampling technique entails selecting participants based on predetermined criteria pertinent to the study at hand. Purposive selection is frequently used in qualitative research to find individuals who can provide the most incisive views on particular topics or experiences.
This technique allows researchers to discover and recruit individuals with the necessary input, perspectives, and experiences for their study, making it a helpful tool for data analysis and interpretation. It may also facilitate access to individuals with specialised knowledge of particular subjects or events.
Theoretical Sampling
The goal of theoretical sampling is to determine how a selection of individuals or occurrences falls into the theoretical paradigm given. It is an essential part of a qualitative study.
It involves analysing and comparing data in order to find patterns and hypotheses. A researcher investigating how pre-existing notions impact new knowledge, for example, may sample groups subjected to different types of information.
To ensure that the group is truly representative of the theory, the researcher must keep track of both those who match and those who do not. This allows for alternative ideas or answers to the one being assessed, resulting in more accurate findings and real implementation effects.
Quantitative Research
Simple Random Sampling
Simple random sampling is used to pick a sample from a community. This ensures that every person of the community has an equal chance of being selected. It enables researchers to choose participants at random for their investigations. The data from these samples is then analysed by the researcher, who makes inferences about the target community.
To produce accurate findings that reflect the complete community, quantitative and qualitative researchers use basic random sampling. Using basic random samples, quantitative researchers can compute confidence intervals, test theories, and assess regression models with precise parameter values.
While fundamental random sampling guarantees that each member of the target community has an equal chance of being selected for inclusion in a sample, due to statistical limits and real size limitations, not all people will have an equivalent number of choices.
As a result, when conducting complicated research studies, scholars may profit from merging data from various summarising groups.
Stratified Sampling
Stratified sampling is a probability sampling method that divides a community into smaller subgroups or strata and then randomly selects elements or units from each subgroup to create the total sample. The method ensures that all groups in the sample are optimally represented, allowing researchers to achieve greater accuracy in their quantitative research studies.
It is most commonly used when researchers want to target a particular demographic, such as age, gender, race, profession, salary level, or level of schooling. This method serves two purposes: first, it improves accuracy by ideally dispersing each sample member; second, it restricts prejudice by ensuring that an equivalent number of respondents from each subset are included in the general sample.
Systematic Sampling
This technique divides the community and chooses a representative group in a systematic manner. This approach gives approximations as well as a more accurate representation of the community. Either finding an obvious pattern or system in the population or performing random choices with a break between each selection is required for effective systematic sampling.
It also simplifies data analysis over a cluster or stratified random sampling because the entire data set can be assessed quickly rather than first dividing it into parts. This technique also requires less work than others, saving time and resources while providing researchers with more reliable data sets to use in making research choices.
Conclusion
Quantitative and qualitative research uses sampling procedures, but it’s crucial to grasp the variations between them. Understanding the differences between quantitative vs qualitative research helps researchers use sampling procedures to assure accuracy and validity.
