# Sampling. Part 2 of 2 on Surveys and Sampling.

Graham R Gibbs2012-10-24

research#survey#social survey#sampling#probability sampling#simple sampling#systematic sampling#stratification#multi-stage#cluster sampling#non-probability sampling#quota sampling#opportunity sampling#snowball sampllng#theoretical sampling#research design#qualitative#quantitative#how to#social research

41K views|11 years ago

ðŸ’« Short Summary

Sampling methods, including probability and nonprobability, are crucial in research to collect representative data efficiently. Techniques like cluster sampling and systematic approaches help minimize bias and errors in sample selection. Stratification divides populations into subgroups for accurate representation, while quota sampling uses specific quotas like gender and age. Theoretical sampling selects individuals based on important differences to support underlying theories. Choosing the right sampling method ensures timely and relevant data collection for effective decision-making in research studies.

âœ¨ Highlights

ðŸ“Š Transcript

âœ¦

Importance of Sampling in Research.

00:59Sampling is crucial in research to efficiently target subgroups within large populations.

Different sampling methods, such as cluster sampling, are used to ensure cost-effectiveness and timeliness.

Quicker data collection through sampling is essential for opinion polls before elections to provide timely results for decision-making.

Choosing a sample over the entire population makes data collection easier and faster, ensuring relevant and up-to-date information for effective decision-making.

âœ¦

Importance of Sampling in Obtaining a Representative Sample.

03:23Techniques like random selection are used to ensure typicality and avoid bias in sampling.

The size of the sample in relation to the population is crucial, with less than ten percent generally considered adequate for large populations.

For populations in the hundreds of thousands or millions, even a sample of a quarter million can be sufficient.

It is important to be aware of the representativeness of the sample and choose appropriate sampling techniques to minimize bias.

âœ¦

Sampling errors can occur when taking a sample from a population, leading to variations in results.

06:05The size of the sample needed to minimize errors depends on the variation within the population.

Generally, samples of around 2000 individuals from a large population are considered acceptable.

Properly designed studies by organizations often use sample sizes in the thousands for surveys.

Student projects may require smaller sample sizes due to resource limitations.

âœ¦

Probability sampling is the gold standard for ensuring a representative sample using random number techniques.

09:43Every person in the sample has an unknown chance of being included, allowing for statistical inferences for population predictions.

Policymakers benefit from the solid information provided by probability sampling.

Constraints such as time, population size, and information limitations may prevent the use of probability sampling.

Despite the limitations, probability sampling remains the most reliable method for data collection.

âœ¦

Limitations of Nonprobability Sampling

12:11Nonprobability sampling relies on human judgment for participant selection, potentially leading to non-representative samples.

Techniques can still yield good samples, but generalizing results may be limited.

A sampling frame is necessary for accurate sample selection, but inaccuracies such as missing categories can impact representativeness.

Errors in lists, such as duplicate entries or missing individuals, can also affect the quality of the sample.

âœ¦

Importance of using the right units in sampling for accurate data collection.

14:58Incorrect allocation of units and mortality can impact the reliability of sampling lists.

Different probability sampling methods, such as simple random sampling and cluster sampling, are discussed.

The systematic approach is highlighted as an effective method for taking samples.

Combining sampling stages and frames for subpopulations can improve the sampling process.

âœ¦

Modern methods of generating random numbers involve using computer-generated numbers like Excel's function.

17:40The process includes creating a sampling frame, numbering individuals, and selecting a sample.

For large populations, a systematic or quasi-random approach is preferred to avoid manual selection.

By starting from a certain position in the list and picking every nth person, a representative sample can be efficiently obtained.

This method is ethically sound and reduces variation in samples from large populations.

âœ¦

Importance of Systematic and Simple Random Sampling

21:55Avoiding bias in sample selection is crucial for accurate representation.

Issues with structured lists can lead to non-representative samples.

Stratification involves dividing the population into strata based on characteristics.

Stratification helps reduce sampling errors and ensures a more accurate representation of the population.

âœ¦

Importance of representative sample groups in research studies.

23:10Stratifying the sample based on factors like age, gender, and specific illnesses is crucial for accuracy.

Utilizing previous census data can help create a sample that aligns with population proportions.

The chosen stratification factor must be relevant to the research issue, such as gender or diseases.

Internal homogeneity within the sample group is essential for accurate research outcomes.

âœ¦

Importance of reducing sampling errors by adjusting proportions in survey samples.

25:52Disproportionate sampling can help ensure a more representative sample, with examples of adjusting gender proportions in surveys.

Multi-stage sampling is highlighted as a practical approach for large-scale national surveys, improving geographical concentration and convenience for face-to-face interviews.

Dividing the sampling frame into large groups and selecting specific counties as examples of the multi-stage approach is explained.

âœ¦

Selection process for surveys involves choosing subsets at each stage without needing a full list of individuals in the country.

29:04Cluster sampling is utilized in large-scale surveys, with the final stage involving taking everyone in a selected cluster.

This method allows for practical contact with a large number of people, despite potential error introduced.

Non-probability sampling does not use random methods to select samples, in contrast to probability sampling techniques.

âœ¦

Quota sampling as a stratification approach involves selecting individuals based on specific quotas such as gender and age, commonly used in opinion polls.

31:07Human choice is involved in selecting participants for quota sampling, providing results similar to random sampling when properly conducted.

Other non-random sampling methods mentioned include convenient sampling, which involves bias from selecting friends and neighbors.

Snowball sampling is another non-random method discussed, along with purposive sampling for collecting specific data.

âœ¦

Theoretical sampling in qualitative research involves selecting participants based on important differences to capture unique behaviors.

34:15This method ensures the inclusion of individuals with varying characteristics, such as physical abilities or demographics.

The process aligns with theoretical principles to support the research's underlying theories.

Theoretical sampling is commonly used to gather diverse perspectives and enhance the depth of analysis in qualitative research.

ðŸŽ¥ Related Videos

ðŸ”¥ Recently Summarized Examples