Random Sampling: Definition, Types, Strengths and Weaknesses

Random sampling is a data collection method to determine the population in a phenomenon that occurs. Random sampling usually done randomly to avoid biased data results.

So, what exactly is meant by random sampling and what are the types, advantages and disadvantages? DailySocial.id has summarized the explanation for you.

What is that Random Sampling?

Random sampling is a type of random sampling. This is so that the representation of the recovered sample is unbiased from the total population.

Even though the sample is taken randomly, the method of taking it still uses the numbering or naming of the target population. The sample taken must also be close to or representative of the population so that it can be used as an unbiased representation of the total population.

If there is an error during the sampling process, it is necessary to re-sampling in order to get a sample that fits the population. Sampling is declared to be in error if the sample taken is not representative of an existing population. Therefore, random sampling must be carried out carefully and in detail to minimize errors.

Types Random Sampling

Random sampling There are several types of techniques that can be done. Here are some types random sampling, among others:

1. Simple Random Sample (Simple random sampling)

Simple random sampling This is the simplest and easiest sampling technique to implement. This technique is used for random sampling and comes from members of the existing population. Although random, each member of the population has the same opportunity to be selected as the sample.

The level of external validity in this type of technique is higher, because the sample size is quite large. Generally, technique random sampling this is done if the type of analysis being carried out is a simple descriptive form, such as to find out social status, gender, type of work, and so on.

2. Stratified Random Sample (Stratified Random Sampling)

Stratified random sampling is a sampling technique that is carried out in stages in a population. This technique is generally used in populations that have stratified or stratified elements.

The method used in this type of sampling is to divide the entire population into complete subgroups. So that each level of the population can be represented by the sample.

3. Random Sample by Area (Random Sampling Clusters)

Random sampling clusters is a sampling technique, in which the population used does not come from individuals, but groups or cluster. This technique is usually used in areas that have large populations and are geographically dispersed, such as urban areas or schools.

This sampling technique requires a lot of time and costs, because the target population is quite large and spread out. In addition, this technique also requires a high level of accuracy so that the samples obtained are correct and have external validity.

4. Systematic Random Sample (Systematic Random Sampling)

Final, systematic random sampling is a sampling technique from the first element of the population members only. To do so, the researcher must take a random sample systematically by observing each order from the enrolled members of the population.

This sampling technique uses a fairly representative sample of the total population. Therefore, this technique requires consideration of the sequence of the registered population so that the sample obtained can be ascertained to be valid.

Advantages of Random Sampling

1. Has a Slight Bias

Random sampling is used to reduce data bias. Because, the selection of samples in random sampling random so that every member of the population has an equal chance of being selected.

2. Easy to Do

Random sampling is the simplest and easiest method of data collection. Random sampling also does not require special skills to do so.

3. Doesn't Require a Lot of Theory

Apart from the two things above, random sampling nor does it require a lot of specific theory to use it.

Deficiency Random Sampling

Apart from the advantages above, random sampling also has some drawbacks. As for some drawbacks random sampling, among others:

1. It is difficult to access the entire population list

In random sampling, an accurate statistical measure of the population can only be obtained if there is a complete list of the population. Sadly, random sampling often encounter obstacles, namely the lack of accessibility to data on the entire population.

2. Requires a Long Time

Random sampling takes a long time to collect data. If there is an error in sampling, then random sampling must be done repeatedly, which will take even longer time.

3. Requires Fee

Apart from taking a long time, random sampling also requires a lot of money. Because, random sampling it is necessary to take a sample from a large enough population data.

Well, that's the explanation about random sampling that DailySocial.id has summarized for you. Of the many existing data collection methods, you can use random sampling by adjusting it to the needs of data collection in your research.