How to Run Descriptive Statistics in SPSS (Mean, Median, SD, Frequencies)

Descriptive statistics are the starting point of every data analysis project. Before you run any hypothesis test, you need to understand your data: what the averages look like, how spread out the values are, and whether the distribution makes sense. SPSS makes this straightforward with built-in tools that generate all the key descriptive statistics in seconds.

What Are Descriptive Statistics?

Descriptive statistics summarize your dataset using numbers and visuals. They do not test hypotheses or make predictions — they describe what is in front of you. The most common descriptive statistics are:

Mean — The arithmetic average
Median — The middle value when data is sorted
Mode — The most frequently occurring value
Standard Deviation (SD) — How spread out values are from the mean
Range — The difference between the highest and lowest values
Skewness — Whether the distribution leans left or right
Kurtosis — How peaked or flat the distribution is

For categorical variables, you use frequency tables and percentages instead.

Method 1: Descriptive Statistics for Continuous Variables

This is for variables like age, test scores, income, or any measured number.

Step-by-Step

Open your data file in SPSS
Go to Analyze > Descriptive Statistics > Descriptives
Move your continuous variables into the "Variable(s)" box
Click Options and check the statistics you want:
- Mean
- Std. deviation
- Minimum
- Maximum
- Skewness
- Kurtosis
Click Continue, then OK

Reading the Output

SPSS produces a "Descriptive Statistics" table with one row per variable. Here is an example:

| Variable | N | Minimum | Maximum | Mean | Std. Deviation | Skewness | Kurtosis | |----------|---|---------|---------|------|----------------|----------|----------| | Test_Score | 60 | 45 | 98 | 74.23 | 12.81 | -0.34 | -0.67 | | Study_Hours | 60 | 2 | 35 | 15.47 | 8.22 | 0.58 | -0.42 |

What this tells you:

The average test score is 74.23 with a standard deviation of 12.81
Scores range from 45 to 98
Skewness of -0.34 means a slight left skew (most scores are on the higher end)
Kurtosis of -0.67 indicates a slightly flatter distribution than normal

Rule of thumb for normality: Skewness and kurtosis values between -2 and +2 are generally considered acceptable for parametric tests.

Method 2: Using Explore for More Detail

If you need more detailed descriptive output including percentiles and normality tests:

Go to Analyze > Descriptive Statistics > Explore
Move your variable to the "Dependent List" box
Optionally move a grouping variable to "Factor List" (to get descriptives per group)
Click Statistics and check "Descriptives" and "Percentiles"
Click Plots and check "Histogram" and "Normality plots with tests"
Click Continue, then OK

The Explore output gives you everything from Method 1 plus:

5% Trimmed Mean — Mean after removing top and bottom 5% (useful for checking outlier impact)
Interquartile Range (IQR) — The range of the middle 50% of values
Percentiles (25th, 50th, 75th)
Shapiro-Wilk normality test
Histograms and Q-Q plots

Method 3: Frequency Tables for Categorical Variables

For variables like gender, education level, or satisfaction rating:

Go to Analyze > Descriptive Statistics > Frequencies
Move your categorical variables into the "Variable(s)" box
Click Charts and select "Bar charts" if you want visuals
Click Continue, then OK

Reading the Frequency Table

| Category | Frequency | Percent | Valid Percent | Cumulative Percent | |----------|-----------|---------|---------------|--------------------| | Male | 32 | 53.3 | 53.3 | 53.3 | | Female | 28 | 46.7 | 46.7 | 100.0 | | Total | 60 | 100.0 | 100.0 | |

Frequency — Count of cases in each category
Percent — Percentage of total (including missing)
Valid Percent — Percentage of non-missing cases
Cumulative Percent — Running total of percentages

If you have missing data, the difference between Percent and Valid Percent becomes important. Always report Valid Percent in your results.

Getting Descriptive Statistics by Group

Often you need descriptives broken down by groups (e.g., test scores by gender):

Go to Analyze > Compare Means > Means
Move your continuous variable to "Dependent List"
Move your grouping variable to "Independent List"
Click Options and select the statistics you want per group
Click Continue, then OK

Alternatively, use Explore with a Factor variable (described in Method 2) for a more complete group-level analysis.

Reporting Descriptive Statistics in APA Format

For continuous variables in text:

Participants scored an average of M = 74.23 (SD = 12.81) on the knowledge test, with scores ranging from 45 to 98.

For group comparisons:

Male participants (M = 76.41, SD = 11.23) scored slightly higher than female participants (M = 71.74, SD = 14.12) on the knowledge test.

In a table:

When reporting multiple variables, use a descriptive statistics table:

| Variable | M | SD | Min | Max | |----------|-----|------|-----|-----| | Test Score | 74.23 | 12.81 | 45 | 98 | | Study Hours | 15.47 | 8.22 | 2 | 35 | | GPA | 3.12 | 0.54 | 1.80 | 4.00 |

Note the APA formatting: M and SD are italicized, and values are rounded to two decimal places.

Common Mistakes to Avoid

Using mean for skewed data — If your data is heavily skewed, the median is a better measure of central tendency. Report both and explain why you chose one.
Ignoring the standard deviation — A mean without SD is incomplete. The spread matters as much as the center.
Running descriptives on coded categorical variables — If gender is coded as 1 and 2, SPSS will happily calculate a mean of 1.47. That number is meaningless. Use Frequencies for categorical data.
Not checking for impossible values — Descriptive statistics are your first chance to catch data entry errors. An age of 350 or a Likert score of 8 on a 5-point scale should jump out in the min/max values.
Forgetting to check normality — Descriptive statistics inform your choice of inferential tests. If skewness or kurtosis falls outside the acceptable range, you may need nonparametric alternatives.

Quick Reference: Which SPSS Menu to Use

| What you need | SPSS menu path | |---------------|---------------| | Mean, SD, min, max | Analyze > Descriptive Statistics > Descriptives | | Detailed stats + normality | Analyze > Descriptive Statistics > Explore | | Frequency counts for categories | Analyze > Descriptive Statistics > Frequencies | | Descriptives per group | Analyze > Compare Means > Means |

Next Steps

Once you understand your data through descriptive statistics, you are ready to move to inferential tests. If your data is normally distributed, proceed with parametric tests like t-tests or ANOVA. If not, consider nonparametric alternatives.

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