Ordinal variables are variables that have categories with a specific order or ranking to them, but the distances between the categories are not known or consistent (Babbie et al., 2007). Examples include rating scales like “low”, “medium”, and “high” or education levels such as “elementary”, “high school”, and “college”. The key feature of ordinal variables […]

Categorical variables are a kind of statistical data type, also known as qualitative variables, that divide data into various categories or groups based on certain features, characteristics or labels (Lewis-Beck, Bryman & Liao, 2004). These sorts of variables are commonly used in cross-sectional studies such as a population census and on likert scale questionnaires. Examples

Nominal variables are variables that represent categories without any inherent order or ranking. They are simply used to distinguish different groups or categories without assigning any form of hierarchy or sequence to them (Babbie et al., 2007). “Gender”, “marital status”, “nationality”, and “types of occupation” are typical nominal variables examples. These sorts of variables are

Qualitative variables, also known as categorical variables, classify observations into defined, non-numerical groups (Babbie, Halley, & Zaino, 2007). Distinct from numerical variables that characterize data along a numeric continuum, qualitative variables capture important attributes in non-numeric ways. They provide the means to discern and categorize data according to specific properties, styles, or characteristics that hold

A discrete variable is any variable that can only take on a certain number of distinct values, typically represented by whole numbers (Norman & Streiner, 2008). These variables can be subdivided into dichtomous types, which only take two values, and polytomous types, taking three or more. Discrete variables are categorized as true or arbitrary based

Continuous variables are numerical variables that can take on an infinite number of values within a given range. They often include fractions and decimals. Examples may include height, weight, and time where values exist along a continuum with infinite possibilities behind the decimal point, within a specified range. Continuous variables are distinct in that they

Quantitative variables, also known as numerical variables, quantify observations and can be counted or measured (Creswell & Creswell, 2018). Quantitative variables contrast sharply with qualitative variables, the latter of which classify data into predefined groups without quantities or measures attached to them. Quantitative variables involve a numerical output that can be analyzed with various statistical

Control variables, sometimes called “controlled” variables or “constant” variables, are elements within a study that researchers deliberately keep constant. In a research study, it is often required to determine the possible impact of one or more independent variables on a dependent variable. To maintain the validity of the results, scientists keep certain variables in check,

Dichotomous variables are just what they sound like: variables with two, and only two, distinct categories or options. Distinct primarily in the world of statistics, these variables provide binary alternatives, such as yes or no, present or absent, success or failure (Hunter & Schmidt, 2004). In quantitative research, they serve an instrumental role in a

Confounding variables are variables that ‘confound’ (meaning to confuse) the data in a study. In scholarly terms, we say that they are extraneous variables that correlate (positively or negatively) with both the dependent variable and the independent variable (Scharrer & Ramasubramanian, 2021). These variables present a challenge in research as they can obscure the potential