[PART 3] TEKNIK SAMPLING NON PROBABILITAS

Summary

This video provides an in-depth explanation of non-probability sampling techniques used in research. It covers four main methods: accidental sampling, purposive sampling, quota sampling, and snowball sampling. These techniques are employed when researchers cannot use probability sampling due to various constraints. The video emphasizes how these methods help researchers select samples based on specific criteria or chance, ensuring the sample is relevant to the research. The goal is to minimize generalization errors and achieve a representative sample, especially when dealing with large or hard-to-reach populations.

Takeaways

• 😀 Non-probability sampling techniques are used when researchers cannot offer equal opportunities to all population members for selection.
• 😀 In non-probability sampling, researchers directly select members based on specific criteria or convenience, without random selection.
• 😀 Accidental sampling occurs when a researcher selects a sample based on accidental encounters, such as meeting a relevant participant by chance.
• 😀 Purposive sampling involves selecting participants intentionally based on specific attributes or characteristics that align with the research goals.
• 😀 In quota sampling, researchers set a target number of participants and then select them based on convenience, ensuring they meet specific quotas.
• 😀 Snowball sampling starts with a small number of initial participants, who then refer other participants, allowing the sample size to expand over time.
• 😀 Non-probability sampling techniques are often used in qualitative research or when researchers face limitations in conducting probability sampling.
• 😀 The key advantage of non-probability sampling is its practicality in situations where random sampling is not feasible or appropriate.
• 😀 Combining non-probability sampling with probability sampling can help improve the representativeness of the sample and minimize generalization errors.
• 😀 A researcher should always aim for a sample that represents the population well, minimizing errors in generalizing the findings to the entire population.
• 😀 The larger the sample size relative to the population, the smaller the chance of generalization errors, ensuring more accurate results in the research.

Q & A

What is the main difference between probabilistic and non-probabilistic sampling techniques?

In probabilistic sampling, every member of the population has an equal chance of being selected as part of the sample. In non-probabilistic sampling, researchers do not give every member of the population an equal chance to be chosen. Instead, samples are selected based on specific criteria or convenience.

What is Accidental Sampling, and how does it work?

Accidental sampling occurs when a researcher selects a sample based on an unplanned encounter with a member of the population who fits the criteria of the study. The researcher chooses individuals who are conveniently available or meet the study’s goals, but not everyone in the population has an equal chance of selection.

Can you explain the concept of Purposive Sampling with an example?

Purposive sampling involves selecting individuals who meet specific criteria that align with the research objectives. For instance, a researcher studying Islamic banking might select professors who teach relevant courses, as they have the knowledge required for the study, rather than randomly choosing any professor.

How does Quota Sampling differ from other non-probabilistic sampling methods?

Quota sampling requires researchers to determine the number of samples they need to select from each subgroup in the population, often using a convenience sampling approach. Unlike other non-probabilistic methods, quota sampling ensures that certain subgroups are adequately represented in the sample.

What is Snowball Sampling, and how does it grow the sample size?

Snowball sampling starts with a small initial sample, and those participants are asked to recommend other participants, who in turn recommend more, expanding the sample. This method is particularly useful when the population is hard to reach or if participants are scattered or hard to identify initially.

Why might a researcher use non-probabilistic sampling methods?

Non-probabilistic sampling methods are used when there are limitations, such as time, cost, or difficulty in accessing a random sample. These methods can still provide valuable insights, especially in exploratory or qualitative research, though the results may not be generalizable to the entire population.

What does it mean for a sample to be 'representative' in non-probabilistic sampling?

A representative sample in non-probabilistic sampling refers to a group of individuals that adequately reflects the characteristics of the population being studied, even though the selection process was not random. Researchers aim for this representation to minimize bias and ensure the sample aligns with the research objectives.

What is the main advantage of combining probabilistic and non-probabilistic sampling methods?

Combining probabilistic and non-probabilistic sampling can provide the benefits of both approaches. The probabilistic method ensures random selection, which supports generalization, while the non-probabilistic method offers flexibility and control over the sample, particularly in cases with specific research requirements.

How does the size of the sample affect the accuracy of generalization in research?

The larger the sample size relative to the population, the smaller the potential error in generalizing the results. A sample size that is too small may lead to higher generalization errors, while a larger sample gives more reliable data that better represents the population.

What is the primary challenge when conducting research with non-probabilistic sampling methods?

The primary challenge with non-probabilistic sampling is that the results may not be as generalizable to the broader population because not all members had an equal chance of being selected. Researchers must be cautious about making sweeping conclusions based on non-random samples.