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Sampling Methods and Statistical Analysis in Survey Research

In conducting research, the primary objective is to analyze a phenomenon and find effective solutions to problems. The process of research involves collecting facts, evidence or results to develop, test or enhance knowledge about natural and social phenomena. Research plays a significant role in advancing science, and it is essential that research findings are made accessible to all.

The research process is based on two main methods: experimental research and survey research. This discussion will focus on survey research, which is commonly used to study populations and determine the incidence, distribution, or correlation of variables under investigation.

In survey research, researchers can either use census techniques or sampling techniques. While census techniques can be used for limited populations, sampling techniques are often employed for large populations due to their cost-effectiveness and efficiency in terms of time management.

Sampling techniques

Sampling techniques can be classified into two categories, namely probability sampling and non-probability sampling. Probability sampling allows each element of the population to have an equal chance of being selected as a sample, while non-probability sampling does not offer equal opportunity. Probability sampling techniques include simple random sampling, stratified random sampling, cluster sampling, and stratified cluster sampling.

Simple random sampling is a technique where each element in the population has an equal chance of being selected as a sample, assuming that the population is homogeneous and a sample frame is available. Stratified random sampling, on the other hand, is a method where the population is divided into homogeneous subgroups or strata, and random samples are drawn from each stratum.

Cluster sampling is a technique where the population is divided into clusters or groups, and a random sample of clusters is selected. For example, in surveying universities in a region, universities may be randomly selected as the primary cluster, and a secondary cluster could be students in each university, where a sample of students is taken randomly.

Lastly, stratified cluster sampling is a technique where the population is stratified into subgroups or strata, and clusters are selected from each stratum. For example, universities may be stratified based on their size and then randomized to take universities from each stratum. Further, students within each university can be stratified based on their academic year and then randomized to select a sample from each group.

On the other hand, non-probability sampling methods include purposive sampling, quota sampling, and accidental sampling. Purposive sampling selects individuals based on specific criteria, while quota sampling selects samples based on a predetermined quota.

Accidental sampling is a spontaneous sampling technique that is determined by the researcher. It is crucial to ensure that the sampling method used is precise and accurate to ensure the success of a research study.

Statistical Analysis in Survey Research

In academic research, a phenomenon or event is analyzed scientifically using measurable methodologies and procedures to obtain a generalization of the object under study. Research plays a crucial role in decision-making across various fields, and university students are required to complete a final assignment in the form of a thesis or paper as a graduation requirement.

To produce high-quality research, students are trained to become researchers through guidance from experienced supervisors. This involves preparing research plans, developing methodologies, conducting research in the field or laboratory, verifying and analyzing data, interpreting data, writing results and discussions, and publishing scientific articles in reputable journals or presenting them at national and international scientific forums.

Data analysis is a critical component of research and must be carried out correctly to ensure accurate results. Incorrect conclusions can negatively impact the researcher and those interested in the research topic. As a result, data analysis instruments must be created using a research methodology that adheres to scientific principles.

Distinguishing Parametric and Nonparametric Statistical Analysis

In survey research, there are two methods of data analysis, namely parametric statistical analysis and nonparametric statistics. These two methods of data analysis can often pose difficulties in analyzing research data, and it is important to know when to use them.

The main difference between the two methods is that parametric analysis requires the data to meet assumptions of normal distribution, while nonparametric analysis does not require such normal distribution assumption.

Data scales are used to identify whether data from survey research, inputted into a worksheet, should be analyzed using parametric or nonparametric methods. Nominal and ordinal data are included in nonparametric methods, while interval and ratio data are included in parametric methods.

Nominal and ordinal data are typically found in social studies, such as studies on farmers’ perceptions, extension worker performance, and farmer satisfaction. On the other hand, interval and ratio data that use parametric analysis are usually related to data on costs, income, and production.

It is important to avoid forcing data to be analyzed using parametric analysis when nonparametric analysis is more appropriate. Parametric analysis has specific assumptions that need to be met, such as normal distribution of data.

Therefore, it is important to pay attention to the terms and conditions and adjust the methodology in data analysis, especially when dealing with survey research data. This information can be helpful for those who will conduct data analysis.



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