Logistic Regression
A regression method for binary outcomes that models the probability of an event, reporting results as odds ratios for each predictor.
Logistic regression models a categorical outcome, most often binary, such as pass or fail, adopt or not adopt. Instead of predicting the outcome directly, it models the log odds of the event as a function of one or more predictors, which may be continuous or categorical. Coefficients are usually reported as odds ratios: values above 1 mean the predictor raises the odds of the event, values below 1 mean it lowers them, holding other predictors constant.
Extensions handle outcomes with more than two categories or with ordered categories. The model requires independent observations and enough cases of the outcome relative to the number of predictors.
In a thesis, logistic regression is the standard choice whenever your dependent variable is a yes-or-no state, letting you test hypotheses while controlling for confounding variables. Report odds ratios with confidence intervals and interpret them in plain language in the discussion chapter.
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