Can ordinal variables be treated as continuous?

Can ordinal variables be treated as continuous?

An often posed question by applied researchers is about the most favorable approach for factor analysis in the presence of ordinal variables. First, ordinal variables could be treated as in the case of continuous variables, and the same estimation method would be used.

When can an ordinal variable be continuous?

In some cases, the measurement scale for data is ordinal, but the variable is treated as continuous. For example, a Likert scale that contains five values – strongly agree, agree, neither agree nor disagree, disagree, and strongly disagree – is ordinal.

How are ordinal predictors treated in regression models?

In most cases, ordinal predictors are treated as either nominal (unordered) variables or metric (continuous) variables in regression models, which is theoretically and/or computationally undesirable. In this paper, we discuss the benefit of taking a smoothing spline approach to the modeling of ordinal predictors.

Is it common to treat ordinal variables as continuous?

In the social sciences I have encountered that it is common to treat ordinal variables as continuous, for example variables originating from rating or Likert scales (strongly disagree, disagree, agree, strongly agree).

Can you use ordinal predictors in a GLM?

However, when it comes to including ordinal variables as predictors in a GLM (or GzLM), the choices are slim. In nearly all cases, ordinal predictors are treated as either nominal (unordered) or continuous variables in regression models, which can lead to convoluted and possibly misleading results.

How is the ordinal smoothing spline used in regression?

Our results reveal that the ordinal smoothing spline offers a flexible approach for incorporating ordered predictors in regression models, and has the benefit of being invariant to any monotonic transformation of the predictor scores. 1. Introduction 1.1. Motivation