Yes, gender is a categorical variable. It places people into distinct groups rather than measuring them on a numerical scale, which makes it one of the most common examples of categorical data in statistics, surveys, and research.
Why Gender Is Categorical
Variables in statistics fall into two broad types: categorical and numerical. Categorical variables sort observations into groups or labels. Numerical variables measure quantities on a scale, like height in centimeters or blood pressure in millimeters of mercury. Gender fits squarely into the categorical side because responses like “male,” “female,” and “nonbinary” are labels, not measurements. You can’t average them, and the distance between categories has no mathematical meaning.
Within categorical variables, there’s a further distinction. Nominal variables have categories with no natural order. Ordinal variables have categories that follow a meaningful sequence, like pain rated as mild, moderate, or severe. Gender is nominal. There is no logical way to rank “male” above “female” or vice versa. The Mayo Clinic uses sex as a textbook example of a nominal variable, distinguishing it from ordinal variables like cancer staging where the order matters.
How Gender Differs From Sex in Data
Researchers increasingly treat sex and gender as separate variables. Sex refers to biological characteristics like chromosomes, hormones, and reproductive organs. Gender refers to social roles, identities, and norms associated with being a man, woman, or another identity. The World Health Organization defines gender as interacting with, but distinct from, biological sex. A report from the National Academies of Sciences, Engineering, and Medicine recommends that the National Institutes of Health collect gender data by default and stop conflating it with sex as a biological variable.
Both sex and gender are categorical, but they capture different information. In clinical research, sex might matter for drug metabolism, while gender might matter for health behaviors or access to care. A 2024 review in BMJ Open found that statistical analyses in acute care trials rarely addressed how sex and gender separately influenced treatment effects and adverse events, even though these differences can be clinically significant.
How Many Categories Does Gender Have?
Historically, gender was collected as a binary variable with two options: male and female. Many surveys and datasets still use this format. However, current data collection standards from major health institutions recommend expanding beyond two categories to capture gender identity more accurately. The National Academies report calls for standardized survey questions that distinguish between sex assigned at birth and current gender identity, with options beyond the binary.
The number of categories you encounter depends entirely on who designed the survey or dataset. Some forms offer three options (man, woman, nonbinary), others offer five or more, and some include a write-in field. Regardless of how many categories appear, the variable remains categorical and nominal. Adding more groups doesn’t change the fundamental type.
How Gender Gets Converted to Numbers
Even though gender is categorical, statistical software and machine learning models need numerical input. The standard solution is dummy coding, sometimes called one-hot encoding. Each category gets its own column filled with 0s and 1s. If a dataset includes “male” and “female,” the software creates two columns. A male observation gets a 1 in the male column and a 0 in the female column, and the reverse for female observations.
When you use these dummy variables in a regression model that includes an intercept term, you typically drop one column to avoid redundancy. The dropped category becomes the reference group. So if “female” is the reference, the model’s coefficient for “male” tells you how being male shifts the outcome compared to being female. For a gender variable with three categories, you’d create two dummy columns and use the third as the reference. This pattern scales to any number of categories: a variable with five gender options would need four dummy columns.
Effects coding is an alternative approach that uses 1, 0, and negative 1 instead of just 1 and 0. Rather than comparing each group to a single reference, effects coding compares each group to the overall average. The choice between dummy coding and effects coding depends on the research question, but both start from the same premise: gender is categorical and needs to be translated into numbers before a model can use it.
Statistical Tests for Gender Data
Because gender is categorical, it calls for a specific set of statistical tools. The most common is the chi-square test, which checks whether the distribution of one categorical variable differs across groups of another. If you want to know whether treatment preference varies by gender, you’d build a contingency table and run a chi-square test to see if the pattern is statistically meaningful.
When both variables are categorical and each has only two levels (like gender as male/female and an outcome as yes/no), Fisher’s exact test is an alternative that works well with small sample sizes. For paired data, where the same people are measured at two time points, McNemar’s test compares the proportions. And when gender is one of several categorical predictors in a model, logistic regression or log-linear analysis can handle the complexity of multiple dimensions.
One thing to watch for: because gender categories have no inherent order, tests designed for ordinal data (like the chi-square test for trend) don’t apply. The lack of ranking is what makes gender nominal, and your choice of statistical test should reflect that.
When Gender Appears as a Covariate
In clinical trials and observational studies, gender frequently shows up as a covariate, a variable included in the analysis to account for its potential influence on results. If a study is testing whether a new therapy improves recovery time, the researchers might include gender as a categorical covariate to ensure that any differences between gender groups don’t distort the main finding.
This is straightforward when gender has two categories, requiring just one dummy variable. It becomes slightly more complex with additional categories, but the logic stays the same. The model estimates a separate effect for each gender group relative to the reference, allowing researchers to isolate the treatment effect from gender-related variation. In practice, this is one of the most routine uses of categorical variables in any field that collects demographic data.