Sampling and Probability in Research Quiz

Sampling and Probability in Research

• Drawing small samples from large populations with sampling without replacement is common due to minimal changes in probability.
• Probability of drawing any two particular names from a population of five names can be determined by listing all potential combinations of two names.
• A formula has been developed for determining the number of possible combinations of elements drawn at a time from a population of size n.
• The risk of generalizing from a sample to the population decreases as the sample size increases.
• Probability sampling provides more assurance that the sample is representative of the population as the number of elements sampled increases.
• A simple random sample gives each individual in the population an equal chance of being selected, making all combinations equally probable.
• Creating a sampling frame for a simple random sample can present challenges, especially in maintaining an accurate and complete list of population elements.
• Lists identifying a population may be biased, inaccurate, or non-existent, requiring careful construction and evaluation for accuracy.
• Various techniques, such as using random numbers tables or computer programs, can be employed to select a sample of the desired size from a population.
• Technology, such as the Research Randomizer and HotBits, offers alternatives for generating random samples, impacting research designs and sampling methods.
• The Research Randomizer provides pseudorandom numbers suitable for most research projects, while HotBits generates genuine random numbers from quantum mechanical processes.
• The impact of technology on research designs, sampling methods, and data collection is discussed in more detail in Chapter 12.

Sampling Techniques in Research

• Cluster sampling divides a population into subgroups of equal size, with each element having an equal chance of being selected as part of a cluster.
• In stratified sampling, strata are defined, and individual elements are sampled within each stratum.
• Cluster sampling is commonly used to study public opinion or voter preference in large, geographically dispersed populations.
• It may involve defining the population as a collection of city blocks or congressional districts, with each block or district serving as a cluster of citizens.
• Multistage sampling involves applying cluster sampling in successive stages or combining it with random sampling techniques.
• Multistage sampling can generate a sample of respondents located in specific districts, making it cost-effective and less labor-intensive.
• However, cluster sampling increases the risk of errors when generalizing from the sample to the population, as some clusters and elements may have no chance of being included in the final sample.
• Nonprobability sampling offers economy and convenience but lacks the ability to accurately estimate sampling error and evaluate biases.
• Accidental sampling, a type of nonprobability sample, involves individuals who accidentally come into contact with the researcher.
• Person-in-the-street interviews are typical of accidental samples but are influenced by biasing factors such as time and place.
• Nonprobability sampling includes accidental, quota, purposive, and snowball sampling, each with its own limitations and risks.
• Making statements about a population based on nonprobability sampling data is risky due to the inability to determine the probability of inclusion and the lack of inferential statistics.

Sampling Methods and Sample Size in Research

• Volunteer or convenience sampling is a nonprobability sampling method where participants contact the researcher to participate, with the researcher having no control over who participates.
• Snowball and purposive sampling methods work the same as in nonprobability quantitative research, with participants recommending others or the researcher intentionally choosing individuals with direct knowledge of the phenomenon.
• Theoretical sampling is used to generate a theory, commonly in grounded theory, by choosing individuals who best represent the constructs in the study.
• Theoretical sampling is complex and resource-intensive, often leading to researchers struggling to understand how to carry it out properly.
• The sample size in research should be large enough to permit data analysis within the researcher's time and budgetary limits.
• For quantitative research, the sample size should not be less than 30, with most researchers preferring a sample size closer to 100, and for extremely large populations, a sample size of up to 1,500 may be desirable.
• Probability sampling allows for determination of the sample size required to meet specified limits of sampling error.
• In qualitative research, the sample size may change as the research progresses; researchers must sample long enough to gather rich, detailed data.
• Data saturation in qualitative research occurs when no new information is obtained from interviews or observations, and it indicates the point at which the researcher should stop collecting data.
• Qualitative researchers often rely on judgment and experience to determine the point of saturation, and few share how they determined it, but they must convince the reader that the sample was appropriate and sufficient to justify the findings.
• The selection of the actual population to be sampled in research depends largely on the objectives of the researcher, and many research questions can be answered by selecting samples from different populations.
• The objective of any sampling plan, whether quantitative, qualitative, or mixed methods research, is to obtain a sample of the relevant population that is large enough to permit data analysis and can be studied within the researcher’s time and budgetary limits.