Cluster Random Sampling: Pengertian, Tahapan, dan Contoh Perhitungan Lengkap

Summary

This educational lecture on Cluster Random Sampling explains its definition, when to use it, and how it differs from other sampling methods. It focuses on selecting entire groups or clusters at random, instead of individuals, to obtain a representative sample. The lecturer highlights practical applications, including estimating average household gas consumption and per capita income in a city. Key concepts such as error estimation, confidence intervals, and sample size determination are covered, with real-life examples and statistical formulas to help students understand the method's practical use in data collection and estimation.

Takeaways

• 😀 Cluster random sampling is a method where entire clusters, rather than individual elements, are selected randomly for the study.
• 😀 This method is ideal when a complete list of the population elements is unavailable or when it's difficult to gather data from individual elements.
• 😀 In cluster random sampling, groups (or clusters) are homogeneous within, but heterogeneous between, making the method suitable for large populations.
• 😀 The clusters chosen are observed in full once selected, meaning that everyone in the chosen cluster is included in the sample.
• 😀 Cluster sampling is more cost-effective in geographically dispersed populations, as it reduces travel or data collection costs.
• 😀 The choice of the clusters is based on practical considerations such as geographical proximity or manageable group size, for instance, neighborhoods or RTs in Indonesia.
• 😀 When deciding on the size and number of clusters, the researcher must balance practicality and representativeness based on the population characteristics.
• 😀 For calculating population averages or totals, formulas such as Y bar (mean) and confidence intervals (using t-statistics) are applied.
• 😀 In the example provided, researchers used city blocks as clusters to estimate per capita income in a small city, and 25 blocks were randomly selected.
• 😀 The sample's error of estimation is influenced by the number of clusters chosen; increasing the number of clusters helps reduce error margins.
• 😀 For estimating proportions, the researcher uses the ratio of observed categories (e.g., rented vs owned homes) and calculates confidence intervals to gauge the accuracy of estimates.

Q & A

What is Cluster Random Sampling and how does it differ from other sampling methods?

Cluster Random Sampling is a method where entire clusters (e.g., neighborhoods, schools) are randomly selected, and all elements within these clusters are studied. Unlike simple random sampling where individual members are chosen, cluster sampling groups individuals into clusters and selects whole clusters at random. This method is especially useful when a complete list of individuals is not available.

When is Cluster Random Sampling typically used?

Cluster Random Sampling is typically used when the sampling frame is unavailable, or it's too costly or logistically challenging to access every individual in a population. It's also helpful when the population is geographically spread out, or the cost of obtaining individual data is too high.

How are clusters chosen in Cluster Random Sampling?

Clusters are chosen randomly from the population. For example, if a district is being studied, neighborhoods (RT) or villages could be used as clusters. Once a cluster is selected, all members within that cluster are surveyed.

What are the main advantages of using Cluster Random Sampling?

The main advantages of Cluster Random Sampling include reduced costs, logistical simplicity, and practicality when dealing with large, dispersed populations or when individual sampling is difficult or expensive.

What is the difference between Cluster Random Sampling and Stratified Random Sampling?

In Stratified Random Sampling, the population is divided into subgroups (strata) based on certain characteristics, and then samples are drawn from each subgroup. In contrast, Cluster Random Sampling involves grouping individuals into clusters and selecting entire clusters randomly. Stratified sampling aims to ensure representation from all subgroups, while cluster sampling focuses on representing clusters as a whole.

What is meant by 'M' in the context of Cluster Random Sampling?

'M' refers to the number of elements within each selected cluster. For example, if 'RT' (neighborhoods) are the clusters, 'M' would represent the number of households within a selected neighborhood.

How is the sample size determined in Cluster Random Sampling?

The sample size is typically determined by the desired level of precision (margin of error). The optimal sample size depends on factors like the population size, the number of clusters, and the variance within clusters. A formula is used to calculate the minimum number of clusters to be sampled to achieve reliable results.

What is error of estimation in Cluster Random Sampling, and how is it calculated?

Error of estimation represents the uncertainty or potential error in estimating a population parameter (like mean or proportion). It is calculated based on the variability in the sample and the sample size. The smaller the error, the more precise the estimate. Confidence intervals are used to quantify this error.

Why might a researcher choose to use a few large clusters versus many small ones in Cluster Random Sampling?

The decision depends on the specific research context. Using fewer large clusters might be more feasible or practical, while many smaller clusters can offer a better representation of population diversity. The choice balances cost, ease of handling, and ensuring the sample remains representative of the population.

What is the role of confidence intervals in Cluster Random Sampling?

Confidence intervals provide a range within which the true population parameter is likely to fall. In Cluster Random Sampling, these intervals help quantify the precision of estimates (like the mean or proportion) and the potential error in the results.