In statistics, there are three common ways to measure where the “center” of a dataset is located:
- Mean: The average value.
- Median: The middle value, located at the 50th percentile.
- Mode: The most commonly occurring value.
These are known as measures of central tendency.
The easiest way to calculate the mean, median and mode in SPSS is to use Analyze > Descriptive Statistics > Frequencies.
The following example shows how to do so in practice.
Example: How to Calculate Mean, Median and Mode in SPSS
Suppose we have the following dataset in SPSS that shows the exam scores received by various students in some class:
Suppose that we would like to calculate the mean, median and mode of the exam scores.
To do so, click the Analyze tab, then click Descriptive Statistics, then click Frequencies:
In the new window that appears, drag Exam_Score to the Variables panel:
Then click the Statistics button.
In the new window that appears, check the boxes next to Mean, Median and Mode under the Central Tendency group:
Then click Continue. Then click OK.
The following output will appear:
The first table in the output titled Statistics contains the values of the mean, median and mode of exam scores.
From this table we can see:
- Mean exam score: 89.67
- Median exam score: 92
- Mode exam score: 97
The second table then shows the frequency of each unique exam score.
From this table we can see that the value 97 occurs 3 times, which is the most frequently occurring value and thus the mode.
Related: Real Life Examples of Using Mean, Median, & Mode
Additional Resources
The following tutorials explain how to perform other common operations in SPSS:
How to Calculate Interquartile Range in SPSS
How to Create a Frequency Table in SPSS
How to Calculate Percentiles in SPSS
Iwant simpl way to analyze Health data like diseases and injuries in UAE
Hi Sumia…If you want a **simple way to analyze health data** (such as diseases and injuries) in the UAE, follow these steps:
### **Step 1: Collect Data**
– **Sources:** Government websites (e.g., UAE Ministry of Health), WHO, hospitals, and research studies.
– **Format:** Data may be in Excel, CSV, or online reports.
– **Example Data:** Columns like “Disease Name,” “Cases Reported,” “Age Group,” “Gender,” “City,” “Year.”
### **Step 2: Use Excel or Google Sheets for Basic Analysis**
– **Summarize Data:** Use pivot tables to find total cases per disease or region.
– **Trends:** Create line charts to see if cases are increasing or decreasing over time.
– **Comparison:** Use bar charts to compare different diseases or injury types.
### **Step 3: Use Simple Python Tools (Optional for More Insights)**
If you’re comfortable with Python, use:
– `pandas` – to clean and summarize data.
– `matplotlib` & `seaborn` – to create visualizations.
– `scikit-learn` – for predictive models (optional).
Example Python Code:
“`python
import pandas as pd
import matplotlib.pyplot as plt
# Load the data
df = pd.read_csv(“health_data_uae.csv”)
# Summarize total cases by disease
disease_counts = df[‘Disease Name’].value_counts()
# Plot the top 10 diseases
disease_counts[:10].plot(kind=’bar’, color=’skyblue’)
plt.xlabel(“Disease”)
plt.ylabel(“Number of Cases”)
plt.title(“Top 10 Reported Diseases in UAE”)
plt.show()
“`
### **Step 4: Use Online Dashboards**
– Websites like **Tableau Public** or **Power BI** can help visualize data without coding.
– Example: Upload your dataset, create a dashboard, and filter by **disease, city, or year**.
### **Step 5: Generate Insights**
Ask questions like:
– Which diseases are most common in the UAE?
– Are injuries more frequent in certain cities?
– How does health data change over years?