Descriptive Statistics in SPSS: Mean, Median, Mode, and Standard Deviation

You’ve collected your data. The questionnaires are back from your respondents. You’ve spent weeks—maybe months—getting to this point. Now you open SPSS, stare at the blank spreadsheet, and a familiar feeling creeps in: What do I do now?

If this sounds familiar, you’re not alone. Thousands of Kenyan students face the same moment of panic when they first encounter SPSS. The good news is that descriptive statistics are the perfect place to start. They’re the foundation of any data analysis, and once you master them, the rest becomes much easier.

In this guide, I’ll walk you through everything you need to know about calculating mean, median, mode, and standard deviation in SPSS. No prior experience assumed. Just step-by-step instructions, clear explanations, and examples you can follow.

And if you get stuck? At Proposal Writers Kenya, our data analysis experts can run your entire SPSS analysis for you—from descriptive statistics to complex inferential tests. But first, let’s see how far you can go on your own.

What Are Descriptive Statistics?

In plain language, descriptive statistics are numbers that summarize your data. They tell you the story of your dataset without you having to look at every single response.

Think of it this way: If I gave you a list of 500 exam scores, you wouldn’t want to read each one. You’d want to know the average score, the highest score, the lowest score, and how spread out the scores were. That’s exactly what descriptive statistics provide.

Descriptive vs. Inferential Statistics

Descriptive statistics summarize your data (mean, median, standard deviation)
Inferential statistics help you draw conclusions beyond your data (t-tests, ANOVA, regression)

For your thesis proposal, descriptive statistics belong in Chapter 4 (Results/Findings). They are the first thing your supervisor will look for.

Understanding the Key Descriptive Statistics

Mean (The Average)

The mean is simply the sum of all values divided by the number of values. If five students scored 60, 65, 70, 75, and 80 on a test, the mean is (60+65+70+75+80) ÷ 5 = 70.

When to use it: Normally distributed data without extreme outliers (e.g., exam scores, height, weight)

Limitation: The mean is sensitive to outliers. If one student scored 100 and another scored 20, the mean gets pulled away from the typical value.

Median (The Middle Value)

The median is the middle value when you arrange your data in order. For 1, 3, 5, 7, 9 the median is 5. For an even number of values, it’s the average of the two middle values.

When to use it: Skewed data or when outliers are present (e.g., income, house prices, reaction times)

Example: If ten employees earn between 30,000 and 50,000 KES, but the CEO earns 5 million KES, the mean salary would be misleading. The median tells the real story.

Mode (The Most Frequent Value)

The mode is the value that appears most often. If most students in your survey are 22 years old, the mode is 22.

When to use it: Categorical data (e.g., most preferred payment method, most common gender, favorite social media platform)

Standard Deviation (The Spread)

Standard deviation tells you how spread out your data is around the mean. A small standard deviation means most responses are close to the average. A large standard deviation means responses are widely scattered.

Example: Two classes both have a mean exam score of 70%. Class A has a standard deviation of 5 (most scores between 65-75%). Class B has a standard deviation of 15 (scores between 55-85%). Class A is more consistent; Class B has more variation.

Preparing Your Data for SPSS Analysis

Before you run any analysis, your data must be correctly set up.

Variable View (Where you define your variables):

Name: Short, no spaces (e.g., Age, ExamScore, Gender)
Type: Numeric for numbers, String for text
Decimals: Usually 0 for whole numbers, 2 for continuous data
Label: A description (e.g., “Age of respondent in years”)
Values: Code categorical data (1=Male, 2=Female)
Measure: Scale for continuous data, Ordinal for ranked data, Nominal for categories

Data View (Where you enter your data):

Each row = one participant/respondent
Each column = one variable

For this tutorial, imagine you have 20 university students and their weekly study hours: 5, 8, 10, 10, 12, 12, 12, 14, 15, 15, 16, 16, 18, 18, 18, 20, 20, 22, 22, 24.

Step-by-Step: How to Calculate Descriptive Statistics in SPSS

Method 1: Using Frequencies (Best for Categorical Data)

Click Analyze > Descriptive Statistics > Frequencies
Move your variables to the “Variable(s)” box
Click the Statistics button
Select Mean, Median, Mode, Standard Deviation, Variance, Range
Click Continue then OK
Review your output table

Method 2: Using Descriptives (Best for Continuous Data)

Click Analyze > Descriptive Statistics > Descriptives
Move your continuous variables to the “Variable(s)” box
Click the Options button
Select Mean, Standard Deviation, Variance, Minimum, Maximum
Click Continue then OK

Note: The Descriptives method does not provide median or mode. Use Frequencies or Explore for those.

Method 3: Using Explore (Best for Detailed Statistics Including Median)

Click Analyze > Descriptive Statistics > Explore
Move your variables to the “Dependent List”
Click the Statistics button
Select Descriptives (this includes median and confidence intervals)
Click Continue then OK

Interpreting SPSS Output

From the Frequencies table:

Look at the “Statistics” table for mean, median, mode, and standard deviation
Look at the frequency distribution to see how many respondents chose each option

From the Descriptives table:

N: Number of valid responses (check this—missing data reduces N)
Minimum/Maximum: The range of your data
Mean: The average value
Std. Deviation: The spread around the mean

Example interpretation: “The average age of respondents was 24.5 years (SD = 3.2). The median age was 24.0 years, indicating a relatively symmetrical distribution. The youngest respondent was 19 years and the oldest was 32 years.”

How to Report Descriptive Statistics in Your Thesis (APA 7th Format)

Mean and Standard Deviation:

“Participants reported studying an average of 14.2 hours per week (SD = 4.8).”

Median and Range:

“The median age of respondents was 23.0 years (range = 18–45).”

Frequencies and Percentages:

“The sample consisted of 85 male (56.7%) and 65 female (43.3%) participants.”

Table format (for multiple variables):

Variable	N	Mean	SD	Min	Max
Age	150	24.5	3.2	19	32
Study Hours	150	14.2	4.8	5	24

Common Mistakes to Avoid

Mistake	Why It’s Wrong	How to Fix
Reporting mean for skewed data	Outliers distort the mean	Use median and report skewness
Forgetting to check for outliers	Outliers affect all statistics	Run boxplots first
Confusing SD with standard error	Different concepts entirely	SD for descriptive, SE for inferential
Not reporting N	Reader needs context	Always include sample size
Wrong decimal places	Too many or too few	Usually 2 decimals for means and SDs

Conclusion

Descriptive statistics are your first step toward making sense of your data. The mean gives you the center, the median handles skewed data, the mode shows what’s most common, and the standard deviation tells you how spread out everything is. With these four tools, you can summarize almost any dataset.

In SPSS, you have multiple ways to get these numbers—Frequencies, Descriptives, and Explore. Pick the method that works best for your data type, run the analysis, and interpret the output using the guidelines above.

But what if SPSS still feels overwhelming? Or you’re on a tight deadline and can’t afford to learn a new software package?

That’s where Proposal Writers Kenya comes in. Our data analysis experts can take your raw data and deliver complete, ready-to-use SPSS output with clear interpretations formatted perfectly for your thesis. We handle everything from descriptive statistics to t-tests, ANOVA, regression, and even advanced techniques like factor analysis.

Click here to get help with your data analysis and save yourself hours of frustration.