How would you interpret coefficients on the independent variables?
A positive coefficient indicates that as the value of the independent variable increases, the mean of the dependent variable also tends to increase. A negative coefficient suggests that as the independent variable increases, the dependent variable tends to decrease.
Can an ordinal variable be an independent variable?
In ordinal regression analysis, the dependent variable is ordinal (statistically it is polytomous ordinal) and the independent variables are ordinal or continuous-level (ratio or interval). The independent variables are also called exogenous variables, predictor variables or regressors.
What are some examples of ordinal variables?
Examples of ordinal variables include: socio economic status (“low income”,”middle income”,”high income”), education level (“high school”,”BS”,”MS”,”PhD”), income level (“less than 50K”, “50K-100K”, “over 100K”), satisfaction rating (“extremely dislike”, “dislike”, “neutral”, “like”, “extremely like”).
How to interpret the coefficients in an ordinal logistic regression?
The interpretation of coefficients in an ordinal logistic regression varies by the software you use. In this FAQ page, we will focus on the interpretation of the coefficients in Stata and R, but the results generalize to SPSS and Mplus.
Is the coefficient of ordinal independent variable linear?
Yes. The coefficient reflects the change in log odds for each increment of change in the ordinal predictor. This (very common) model specification assumes the the predictor has a linear impact across its increments.
How to interpret the intercept of a logistic regression?
The coefficient of the intercept is β 0 = -1.93 and it should be interpreted assuming a value of 0 for all the predictors in the model. The intercept has an easy interpretation in terms of probability (instead of odds) if we calculate the inverse logit using the following formula: e β0 ÷ (1 + e β0) = e -1.93 ÷ (1 + e -1.93) = 0.13, so:
How to interpret parameter estimates from logistic regression?
This post describes how to interpret the coefficients, also known as parameter estimates, from logistic regression (aka binary logit and binary logistic regression). It does so using a simple worked example looking at the predictors of whether or not customers of a telecommunications company canceled their subscriptions (whether they churned).