Page 1 of 3 A Note on Factor Analysis Applied in Medical Research KC Bhuyan* Department of Statistics, Jahangirnagar University, Bangladesh Mini Review The empirical analysis of the data observed in the field of medical science is based on some qualitative as well as quantitative character. But some of the characters are common with other characters of human being. For example, diabetes depends on age, height, weight, food habit, income, occupation, etc. In some studies, in both home and abroad, it was observed that body mass index (BMI) or in other words, higher level of obesity is one of the responsible factors for diabetes [1-6]. But BMI is a character which is measured using the character weight and height and level of obesity is decided using the level of BMI. Hence level of obesity is a common character among the characters height, weight, and BMI. Therefore, to study the impact of height and weight on diabetes, it is better to study the impact of BMI on diabetes. The statistical tool to select such a character among many characters is known as factor analysis. Factor analysis is a statistical tool used to describe variability among observed, correlated variables in terms of a potential lower number of unobserved variables called factors. The observed variables are modeled as linear combination of the potential factors, plus error terms. This technique of selection of factors is commonly used in biology, medical research, psychometrics, personality theories, educational statistics, etc. Let us consider that we have a set of observable random variables x 1 , x 2 , ……, x k with means M 1 , M 2 ..., M k . Let us also consider that there be some unknown constants a ij (i = 1, 2, …., k; j = 1, 2, ……, p) and some unobservable random variables F 1 , F 2 ….. F p . Then x i – M i = a i1 F 1 + a i2 F 2 + ………. + a ip F p + e i Here e i is the unobservable stochastic terms with zero means and finite variance which may not be same for all i. Here a ij is the factor weight of F j which indicates the importance of F j in explaining the total variation in the variables x 1 , x 2 …. x k . The factor F j is a common factor among the variables x 1 , x 2 , …… x k . Like level of obesity is a common factor among the variables BMI, height, and weight. The object of factor analysis is to identify the common factor F j which is important to study the variability in the data set. In practice, a common factor level of obesity is important to study the variability in the level of blood sugar (diabetes). *Corresponding author: KC Bhuyan, Professor of Statistics (Retired), Jahangirnagar University, Bangladesh. Received Date: April 20, 2019 Published Date: April 26, 2019 ISSN: 2687-8100 DOI: 10.33552/ABEB.2019.01.000516 Archives in Biomedical Engineering & Biotechnology Mini Review Copyright © All rights are reserved by KC Bhuyan This work is licensed under Creative Commons Attribution 4.0 License ABEB.MS.ID.000516. Table 1: Important results of factor analysis including Factor loadings in analyzing the data of prevalence of diabetes among adult people of Bangladesh. Variables Coefficient for Factor - 1 Coefficient for Factor- 2 Coefficient for Factor - 3 Coefficient for Factor –4 Coefficient for Factor - 5 Communality Residence -0.397 0.618 0.219 0.24 0.184 0.577 Age 0.252 -0.125 -0.169 -0.307 0.755 0.71 Gender 0.799 0.132 0.24 0.159 -0.133 0.742 Marital status -0.016 -0.212 0.384 0.661 0.489 0.502 Religion 0.118 -0.563 -0.134 0.493 -0.271 0.856 Education -0.694 0.435 0.066 0.1 0.024 0.7 Occupation 0.772 0.38 -0.025 0.177 -0.072 0.666 Type of work 0.612 0.147 0.432 0.111 0.228 0.489 Income -0.071 0.574-0.584 0.292 -0.059 0.524 Smoking habit 0..493 0.391 0.504 -0.23 -0.87 0.687