## Comparing CHAID, CARD, and QUEST Algorithms for Building Decision Trees

Decision tree is a popular machine learning technique that is used to solve classification and regression problems.  The three most popular algorithm choices that are available when you are running a decision tree are QUEST, CHAID, and CART. When you are working on a classification problem (dealing with categorical dependent variable), any of the three algorithms can be used. Though QUEST algorithm is generally faster compared to the other two […]

## Data Analysis Chapter of Your Research Work

Research Data Analysis: Significant Part of Your Research I am sure that as a researcher you will not deny the fact that your dissertation or thesis is the single most important document that you’ll ever create and it offers you a good placement opportunity, scope of good publication, and also widens your knowledge about your research area. However, writing a dissertation or thesis is not as straightforward as it seems […]

## Statistical Significance: The p-value

Lot of researchers don’t properly understand the concept of “Statistical Significance” or the p-value, as it is popularly called. In simple words, we can understand statistical significance as the probability that the observed relationship (between variables) or the observed difference (between means) in the sample data is purely by chance and the same does not exists if we consider the entire population. Alternately, in less technical terms, statistical significance of […]

## Structural Equation Modeling

Structural Equation Modeling Technique can be thought of as combination of factor analysis, path analysis, and regression analysis. It is a confirmatory cross-sectional linear statistical technique and it allows researchers to test and determine the validity of a certain model. There are many variables which cannot be measured directly and they are called ‘latent variables’. Latent variables are very common in behavioral science domain (such as psychology) and they represent […]

## Exploratory Factor Analysis

Exploratory Factor Analysis is a statistical technique that is primarily used to reduce a larger number of variables into smaller number of factors, and to identify and explore the underlying theoretical pattern of the concept. There are two common methods that are adopted in Exploratory Factor Analysis: Principal Component Method: This method is appropriate when we want to extract maximum variance from the original data using minimum number of factors. […]