## 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 […]

## Probit and Logit Models (Binary Outcome Models)

Probit and Logit models are binary outcome models used to predict the probability of an event occurring. The dependent variable in these models is a binary response, commonly coded as a 0 or 1 variable. The linear probability model is also discussed, but it has the clear drawback of not being able to capture the […]

## How to Do Propensity Score Matching

Propensity Score Matching in Stata using teffects Note: readers interested in this article should also be aware of King and Nielson’s 2019 paper Why Propensity Scores Should Not Be Used for Matching. For many years, the standard tool for propensity score matching in Stata has been the psmatch2 command, written by Edwin Leuven and Barbara Sianesi. However, Stata […]

## Propensity Score Matching – An Introduction

Propensity score matching is a non-experimental causal inference technique. It attempts to balance the treatment groups on confounding factors to make them comparable so that we can draw conclusions about the causal impact of a treatment on the outcome using observational data. This is important when AB testing is not feasible or simply not an option. In this […]

## Propensity score matching (PSM) – STATA

Propensity score matching (PSM) is a quasi-experimental method in which the researcher uses statistical techniques to construct an artificial control group by matching each treated unit with a non-treated unit of similar characteristics. Using these matches, the researcher can estimate the impact of an intervention. Matching is a useful method in data analysis for estimating the impact of a program […]

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