There are two main types of variables: categorical and continuous. Categorical variables are those that have discrete categories or levels. Categorical variables can be further defined as nominal, dichotomous, or ordinal. Nominal variables describe categories that do not have a specific order to them. What type of data is ordinalRead More →
Dichotomous variables are categorical variables with two levels. These could include yes/no, high/low, or male/female. To remember this, think di = two. Ordinal variables have two are more categories that can be ordered or ranked. Can a binary variable be ordinal? Binary data is discrete data that can be inRead More →
Ordinal is categorical with ranking where as scale is quantitative. Are scales ordinal or continuous? Categorical and dichotomous usually mean that a scale is nominal. “Continuous” variables are usually those that are ordinal or better. Are rating scales ordinal or nominal? Nominal data collection does not include rating scales, whichRead More →
Ordinal data is used to carry out surveys or questionnaires due to its “ordered” nature. Statistical analysis is applied to collected responses in order to place respondents into different categories, according to their responses. Why are ordinal numbers important? The purpose of using ordinal numbers is to indicate position, orRead More →
Logistic regression is applied to predict the categorical dependent variable. In other words, it’s used when the prediction is categorical, for example, yes or no, true or false, 0 or 1. The predicted probability or output of logistic regression can be either one of them, and there’s no middle ground.Read More →