Simple and simple random sample with multiple attributes
Simple and simple random sample with multiple attributes

This article" Simple and simple random sample with multiple attributes " will explain the idea of multiple attributes sampling.

Simple and simple random sample with multiple attributes

In such studies, multiple attributes are generally considered. Suppose you have 10 attributes and you need to conduct a study on 30 pairs of students namely, one male student and one female student. There are two methods for conducting the research. Either you go through all the attributes of students individually and select the best one for the study, or you can consider all the attributes and select the most suitable one individually. Thus the selection of a sample for use across different attributes is called multi-attribute sampling or MASS. Generally in researches the choice of the attributes can change. If the attribute is different among the students, then the researcher has to make other decisions like selecting few attributes from others or choosing only one attribute among others. This can make the paper difficult to read; however, multi-attribute sampling is one of the important approaches in statistics.

Multi-Attribute Sampling is a broad topic in statistical modeling. It involves the process of creating many identical subsets of the total population.

Multi-attribute sampling vs Ordinary Sampling:

Ordinary sampling refers to the process of randomly selecting every unit in our sample. An example of ordinary sampling is when we divide the sample size by the total number of samples that were required.

Multi-attribute sampling is similar, but it differs in terms of sample size in the sense that the sample size increase as number of attributes in that attribute increase. An advantage of this approach is that it tends to be less expensive as compared to the ordinary sampling approach.

Multi-attribute sampling is beneficial because it makes it easier to analyze and evaluate results of experiments, and it is also beneficial for obtaining meaningful answers to questions about human behavior.

Multi-attribute sampling is often implemented across various social science researches. When multi-attribute sampling was introduced in 1960, the term ‘multi-attribute’ was coined in reference to multi-related topics of interest such as health, development, crime and criminal justice etc. In 2002, the name multi-attribute sampling was applied to include socio-demographic, educational, career, and demographic factors. The aim was that multi-attribute variables are represented in combinations of at least two or more attributes, like gender of someone. The application of this approach is widespread in a wide variety of studies such as criminology, gender, education, immigration, religion, and criminal activity.