TEKNIK SAMPLING DAN DISTRIBUSI SAMPLING

Summary

The lecture covers the concepts of data collection and sampling methods in research. It explains the difference between primary and secondary data and highlights the importance of sampling in gathering representative information. The speaker outlines various sampling techniques, such as probability and non-probability sampling, and their applications. Additionally, the lecture discusses statistical concepts like distribution, sampling error, and population parameters. Real-world examples are provided to demonstrate how to calculate probabilities and analyze data accurately using these methods.

Takeaways

• 📊 Data is divided into two types: primary data (collected directly) and secondary data (obtained from reliable sources).
• 🔍 Primary data can be collected using four methods: qualitative analysis, observation, experiments, and census.
• 📋 A census involves collecting data from the entire population, while a survey gathers information from a sample of the population.
• 📉 Sampling is used to collect data when it's impractical to collect data from the entire population due to time or cost constraints.
• 📈 Probability sampling ensures every unit in the population has an equal chance of being selected, while non-probability sampling doesn't offer equal selection chances.
• 🎯 Simple random sampling, stratified sampling, systematic sampling, cluster sampling, and multistage sampling are the main types of probability sampling.
• 📐 Sampling accuracy depends on factors like population size, geographic distribution, and available resources.
• 📊 In sampling distribution, the sample mean converges to the population mean as the sample size increases.
• 📉 The standard error of the sample mean decreases as the sample size increases, improving the accuracy of estimates.
• 📏 If the sample size is small, less than 30, the t-distribution is used instead of the normal distribution to account for variability in the sample.

Q & A

What are the two main types of data discussed in the script?

The script discusses two main types of primary data and secondary data. Primary data is collected directly by the researcher, while secondary data is obtained from valid sources.

What are the four methods of collecting primary data?

The four methods of collecting primary data mentioned in the script are qualitative methods, observation, experimentation, and conducting a census.

What is the difference between a census and a survey in data collection?

A census involves collecting data from the entire population, while a survey gathers data from a subset of the population (a sample).

Why is sampling used instead of collecting data from the entire population?

Sampling is used because it is often more practical, less expensive, and less time-consuming. It can also be more effective in getting detailed information from a smaller group rather than superficial data from a larger population.

What is the goal of obtaining a representative sample?

The goal of obtaining a representative sample is to ensure that the sample accurately reflects the characteristics of the larger population being studied.

What are probability and non-probability sampling methods?

Probability sampling gives each unit in the population an equal chance of being selected, making the results more accurate and generalizable. Non-probability sampling does not ensure that each unit has an equal chance, and the selection may be biased.

What are some types of probability sampling methods?

The script lists several probability sampling methods: simple random sampling, stratified sampling, systematic random sampling, cluster sampling, and multistage sampling.

What is sampling error, and why is it important?

Sampling error refers to the difference between the sample results and the actual population values. It’s important because it affects the accuracy and reliability of the data collected.

How can you calculate the probability of a certain value in a normal distribution?

To calculate the probability of a certain value in a normal distribution, you use a z-score, which measures how far a value is from the mean. This is followed by consulting a z-table or using statistical software to find the probability.

What happens when the sample size is small, and how is it addressed?

When the sample size is small (less than 30), the sample’s variance fluctuates more, and the data may not follow a normal distribution. In such cases, the t-distribution is used to account for the increased variability.