Factor Analysis vs Path Analysis – The Difference
Factor analysis and path analysis are both statistical techniques used in the field of multivariate analysis, particularly in the context of structural equation modeling (SEM). While they share some similarities, they serve different purposes and have distinct methodologies. Factor Analysis: Purpose: Factor analysis is primarily used to identify underlying factors or latent variables that explain […]
How to do Mediation Analysis Using Multiple Regression Analysis
How to Testing Mediation Analysis with Regression Analysis Mediation analysis is a hypothesized causal chain in which one variable affects a second variable that, in turn,affects a third variable. The intervening variable, M, is the mediator. It “mediates” the relationship between a predictor, X, and an outcome. Graphically, mediation can be depicted in the following […]
Linear vs. Logistic Probability Models: Which is Better, and When?
Interpretability Let’s start by comparing the two models explicitly. If the outcome Y is a dichotomy with values 1 and 0, define p = E(Y|X), which is just the probability that Y is 1, given some value of the regressors X. Then the linear and logistic probability models are: p = a0 + a1X1 + a2X2 + … + akXk (linear) ln[p/(1-p)] = b0 + b1X1 + b2X2 + … + bkXk (logistic) […]
The Difference Between the Bernoulli and Binomial Distributions
You might already be familiar with the binomial distribution. It describes the scenario where the result of an observation is binary—it can be one of two outcomes. You might label the outcomes as “success” and “failure” (or not!). Or, if you want to get mathematical about it, you might label them “1” and “0.” You […]
Logistic Regression Analysis: Understanding Odds and Probability
Probability and odds measure the same thing: the likelihood or propensity or possibility of a specific outcome. People use the terms odds and probability interchangeably in casual usage, but that is unfortunate. It just creates confusion because they are not equivalent. How Odds and Probability Differ They measure the same thing on different scales. Imagine how confusing it would be […]
How to Conduct Probit and Logit Models (Binary Outcome Models)
Probit and Logit Models (Binary Outcome Models) Do you want to understand the factors that influence binary outcomes? Then you’ve come to the right place. In this article, we’ll delve into the world of Probit and Logit models, which are commonly used in statistical analysis to predict binary outcomes. Whether you’re a researcher, […]
Logistic Regression Analysis: Understanding Odds and Probability
Probability and odds measure the same thing: the likelihood or propensity or possibility of a specific outcome. People use the terms odds and probability interchangeably in casual usage, but that is unfortunate. It just creates confusion because they are not equivalent. How Odds and Probability Differ They measure the same thing on different scales. Imagine how confusing it would be […]
The Difference between Logistic and Probit Regression
Both are types of generalized linear models. This means they have this form: Both can be used for modeling the relationship between one or more numerical or categorical predictor variables and a categorical outcome. Both have versions for binary, ordinal, or multinomial categorical outcomes. And each of these requires specific coding of the outcome. For example, in both logistic and […]
Retention methods in exploratory factor analysis (EFA)
Retention methods in exploratory factor analysis (EFA) involve determining how many factors to retain from the initial factor extraction. The goal is to identify a meaningful and interpretable number of factors that adequately represent the underlying structure of the observed variables. Several common retention methods are used for this purpose: Kaiser’s Criterion: Proposed by Kaiser, […]
How to perform Heteroscedasticity test in STATA for time series data
Heteroskedastic means “differing variance” which comes from the Greek word “hetero” (‘different’) and “skedasis” (‘dispersion’). It refers to the variance of the error terms in a regression model in an independent variable. If heteroscedasticity is present in the data, the variance differs across the values of the explanatory variables and violates the assumption. This will […]
