Reference 1

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Unlike a correlational analysis, which just looks for relation between variables, a regression looks at how much a change in one variable (say h-index) brings about a change in another variable (say odds of success). Because our dependent variable, success, is dichotomous, the regression needs to be logistic (as opposed to linear). We used multiple regression so that we could look at the effect of all the variables at once. Multiple regression looks for how much one variable brings about a change in another when all the other variables are held constant. For example how much does success rate change with changes in h-index when total number of citations is held constant. In doing so it allows us to determine the unique contribution each variable makes, something that is particularly important when the variables are likely to be correlated, as number of citations and h-index are likely to be. Finally a backwards regression is a form of multiple regression where all the variables are entered, if some do not independently explain a significant amount of the variance in the dependent variable then the one which is least significant is removed. The process is then repeated until all the variables significantly independently explain variance in the dependent variable.