How to Calculate the Mean of a Variable in Stata
Use this interactive calculator to compute the arithmetic mean from raw values, preview Stata commands, and visualize how each observation compares with the average.
Mean Calculator for Stata-Style Analysis
Tip: In Stata, the mean is usually obtained with commands like summarize income or mean income. This calculator helps you verify the arithmetic before or after running Stata.
Observation Chart
The chart shows each entered observation and overlays the calculated mean as a reference line.
Expert Guide: How to Calculate the Mean of a Variable in Stata
Calculating the mean of a variable in Stata is one of the most common tasks in data analysis, whether you are working in economics, public health, education, political science, business analytics, or social research. The mean, also called the arithmetic average, summarizes the central tendency of a quantitative variable. In practical terms, it tells you the typical value in your dataset by adding all valid observations and dividing by the number of non-missing observations.
If you are asking how to calculate the mean of a variable in Stata, the good news is that Stata makes this very straightforward. The most common command is summarize, but analysts also use mean, tabstat, and conditional statements such as if or in to calculate subgroup means. Understanding which command to use matters because each one gives slightly different output and is best suited to different analytical goals.
What the mean represents
The mean is computed using the formula:
Mean = Sum of all valid values / Number of valid values
Suppose your variable is exam scores with values 70, 75, 80, 85, and 90. The sum is 400, the number of observations is 5, and the mean is 80. In Stata, if the variable is named score, you could calculate this with:
Stata would display the number of observations, mean, standard deviation, minimum, and maximum. For many analysts, this is the fastest way to check a variable’s average and spread at the same time.
The simplest way to calculate the mean in Stata
The easiest command is:
Replace variable_name with your actual variable. For example:
This command returns:
- The number of non-missing observations
- The mean
- The standard deviation
- The minimum value
- The maximum value
If your only goal is to know the average of a variable, summarize is often enough. It is fast, built into Stata, and widely used in academic and professional workflows.
Using the mean command
Another direct option is:
Example:
The mean command is helpful when you want more formal output, especially if you need confidence intervals around the estimated mean. This is useful in inferential statistics and reporting. Compared with summarize, the mean command is often better when you are preparing publication-quality statistical output or examining whether an estimated average differs across groups.
Using tabstat for flexible summary statistics
If you want the mean along with selected summary statistics in a cleaner table, use:
For example:
This is especially useful when you want a more customized display. Analysts often prefer tabstat when they need consistent output across multiple variables in descriptive sections of reports.
How Stata handles missing values
One of the most important things to understand is that Stata automatically excludes system missing values from mean calculations. That means if some observations are coded as missing with Stata’s internal missing notation, they are not included in the numerator or denominator.
However, many imported datasets use user-defined placeholders such as 99, 999, -9, or -99 to represent missing data. If you leave those values as ordinary numbers, Stata will treat them as real data, which can badly distort the mean. A common data-cleaning step is to recode such values to missing before calculating the average.
This is a crucial best practice. If your dataset contains placeholders for missing information, always recode them before interpreting the mean.
Calculating the mean for a subgroup
Very often, researchers do not want the overall mean. Instead, they want the mean for a subgroup, such as women, people older than 65, or households in a certain region. In Stata, this is easy with an if condition:
If your gender variable uses 1 for female and 0 for male, the first command calculates the mean income for women only. Conditions make Stata powerful because you can quickly generate group-specific averages without creating new datasets.
Calculating means by category
If you want the mean of a variable for each group in another variable, you can use bysort or table. A common pattern is:
This gives separate summary output for each region. If you want a compact table of means by group, another useful approach is:
This is ideal for comparing categories such as regions, school types, treatment groups, or years.
Weighted means in Stata
In survey research, labor statistics, and public-use microdata, analysts often need weighted means rather than simple arithmetic means. A weighted mean gives more influence to observations that represent more people or more sampling importance. In Stata, this can be done using weights:
You should only use weights that match the survey design and documentation for your data source. If you are working with official microdata, check the data guide carefully before applying probability, analytic, frequency, or importance weights.
Example with real-world style data
Imagine a health researcher has a variable called bmi for body mass index. They want to know the sample’s mean BMI. They can type:
If they want the mean BMI for adults aged 20 and above, they could type:
If they then want the mean BMI for each sex category, they could use:
This progression shows how Stata scales from simple descriptive summaries to more refined subgroup analysis without much extra syntax.
Comparison of common Stata commands for mean calculation
| Command | Main Use | Includes Mean | Also Shows | Best For |
|---|---|---|---|---|
| summarize income | Quick descriptive statistics | Yes | N, SD, min, max | Fast exploratory analysis |
| mean income | Mean estimation | Yes | Standard error, confidence interval | Formal reporting |
| tabstat income, statistics(mean sd n) | Custom summary table | Yes | User-selected statistics | Clean descriptive tables |
| table region, statistic(mean income) | Grouped output | Yes | Means by category | Subgroup comparison |
Illustrative numeric example
Suppose you have a small wage variable with the following hourly earnings in dollars:
18, 20, 22, 25, 25, 30
The total is 140 and the number of observations is 6, so the mean is 23.33. In Stata, if the variable is wage, the result would be equivalent to what summarize wage reports.
| Statistic | Value | Interpretation |
|---|---|---|
| Number of observations | 6 | Six valid wages are included |
| Sum | 140 | Total of all wage values |
| Mean | 23.33 | Average hourly wage |
| Minimum | 18 | Lowest recorded wage |
| Maximum | 30 | Highest recorded wage |
When the mean can mislead you
Although the mean is useful, it can be misleading when your data are highly skewed or contain outliers. For example, income data often have a long right tail because a small number of people earn much more than the rest. In those cases, the median may better represent the typical observation. Stata allows you to compare both with commands such as:
The detail option gives additional distributional information, including percentiles and the median. Good analysts do not interpret the mean in isolation. They compare it with the median, inspect the distribution, and check whether extreme values are affecting the average.
Step-by-step workflow for beginners
- Load your dataset into Stata.
- Identify the variable whose mean you want to calculate.
- Check for invalid codes such as 99 or 999 that actually represent missing values.
- Recode those placeholders to missing if needed.
- Run summarize variable_name for a quick mean.
- Use mean variable_name if you need confidence intervals.
- Use if or table to calculate subgroup means.
- Review standard deviation and range so the mean is interpreted in context.
Common mistakes to avoid
- Including user-coded missing values as if they were real observations.
- Reporting the mean without checking the sample size.
- Ignoring skewness or outliers when interpreting the average.
- Using unweighted means for survey data that require weights.
- Assuming subgroup means are comparable without checking definitions and coding.
How this calculator connects to Stata
The calculator above is a practical companion for Stata users. You can paste a list of values, exclude a custom missing code, and instantly compute the same arithmetic average that Stata would calculate for valid observations. It also generates a suggested Stata command using your selected command style. This is useful for teaching, quick verification, and checking small examples before you move to a full dataset in Stata.
Authoritative resources for statistical practice
If you want to deepen your understanding of means, descriptive statistics, and applied data analysis, these authoritative sources are excellent references:
- NIST Engineering Statistics Handbook
- UCLA Statistical Methods and Data Analytics Stata Resources
- CDC National Center for Health Statistics
Final takeaway
To calculate the mean of a variable in Stata, the standard answer is simple: use summarize variable_name. If you need a more formal estimate with confidence intervals, use mean variable_name. If you want a flexible table of summary measures, use tabstat. Whatever command you choose, the key is to make sure your variable is numeric, your missing values are handled correctly, and your result is interpreted in the context of the distribution.
In professional analysis, the mean is rarely just a single number. It is part of a broader descriptive story about a dataset. Good Stata users pair the mean with sample size, standard deviation, minimum and maximum values, subgroup comparisons, and careful data cleaning. When you use those habits consistently, your averages become accurate, transparent, and analytically meaningful.