Effective Sampling Techniques for Better Data Analysis

Sampling has lots of importance in the research. With the help of sampling, you can determine the accuracy of the research. If there is anything wrong in the sample, it will be reflected in the final results with the help of sampling. Therefore, we have to use the best sampling technique to analyze the data. We should select the best sampling technique based on needs and requirements. For the data analysis, we divide all the sampling techniques into two categories. These two categories are probability sampling and non-probability sampling. Here, experts of PhD dissertation writing services will discuss effective sampling techniques for better data analysis.

Probability Sampling Methods:

In the probability sampling method, there are equal chances of selection for each member of the population. We have to use this kind of sampling technique while conducting quantitative research. With the help of this sampling technique, you can produce such results that can represent the whole population. The most important types of probability sampling methods are given below;

• Simple Random Sampling: In simple random sampling, each member of the population gets an equal chance of selection. In the sampling frame of simple random sampling, we have to include the whole population. You can use various tools to conduct this kind of sampling. The best tools that you can use to conduct this kind of sampling are random number generators.
• Systematic Sampling: It is similar to simple random sampling. Anyhow, you can conduct it easily. To use this kind of sampling technique, you will have to list all the members of the population with the numbers. To conduct this kind of sampling technique, you don’t need to use random number generators. Instead of using random number generators, the members are chosen after regular intervals.

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• Stratified Sampling: In this kind of sampling technique, we have to divide the population into subpopulations. After dividing the population into the subpopulations, we have to draw precise conclusions of these populations. Anyhow, we have to represent each subgroup in the sample. While dividing the population, you will have to keep in mind the relevant characteristics of the subgroups.
• Cluster Sampling: In cluster sampling, we have to divide the population into subpopulations. Now, we have to prepare these subgroups based on the characteristics of the whole sample. It means that you don’t need to divide the individuals based on their characteristics. It means that you will have to select the members randomly from the group. After making the clusters, if you will feel that these clusters are large, you should divide these clusters into subgroups. This is the best sampling technique to deal with large groups of populations.

Non Probability Sampling Methods:

If we have to select the members of a population-based on the non-random criteria, we have to use non-probability sampling methods. In these kinds of sampling methods, all the members of the population don’t get the chance of selection. The most important sampling techniques for better data analysis are given below;

• Convenience Sampling: It is an easy and inexpensive way to analyze the data. You can use this sampling technique only if your sample is representative of the population. You can use it to produce the most specific results. It is not the best sampling technique to create the generalizable results of the data.

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• Voluntary Response Sampling: Just like convenience sampling, it is also the best technique to analyze the data easily. By using this kind of sampling technique, the researchers don’t need to contact with the participants. The population members contact with the researchers vulnerary. This is also the best way to do an unbiased analysis of the data. On the other hand, if you will analyze the data by selecting participants, there are some chances of biasing.
• Purposive Sampling: In this kind of sampling, the researcher has to show his judgment to select a sample. This kind of sampling technique will increase the purpose of the research. We usually use this kind of sampling technique while conducting qualitative research. With the help of this sampling technique, the researcher tries to get detailed knowledge about a specific phenomenon.
• Snowball Sampling: If it is hard for you to access the population, you should use this kind of sampling technique. With the help of the snowball sampling, we have to select the participants with the help of the other participants. These participants remain in contact with other people. This kind of sampling technique requires the participants to access the snowballs. We use this kind of sampling technique by using exploratory research.