SPSS Fundamentals and Boxplot (Box-and-Whisker Plot): A Complete Tutorial

This tutorial is designed for psychology, behavioral science, and social science students and researchers. It provides a structured introduction to SPSS fundamentals and a comprehensive explanation of boxplots, covering statistical principles, SPSS workflow, and academic interpretation. The content is suitable for teaching materials, experiment methods sections, and data analysis training.

1. What Is SPSS and What Is It Used For?

SPSS (Statistical Package for the Social Sciences) is a GUI-based statistical analysis software widely used in:

  • Psychology and cognitive science research
  • Education, sociology, and management studies
  • Medical and public health research

Key advantages of SPSS:

  • No programming background required
  • Standardized and reproducible statistical workflows
  • Publication-ready output for reports and academic papers

2. SPSS Interface and Core Concepts

2.1 Data View vs. Variable View

Data View

  • Each row represents one participant or observation
  • Each column represents one variable

Variable View

Each variable must be properly defined before analysis:

  • Name: Variable name (English, no spaces)
  • Label: Descriptive name (can be long or multilingual)
  • Type: Numeric / String
  • Values: Value labels (e.g., 1 = Male, 2 = Female)
  • Measure:
    • Nominal
    • Ordinal
    • Scale

Boxplots require Scale (continuous) variables.

3. Descriptive Statistics: Why Boxplots Matter

Our free Boxplot Calculator builds a boxplot from your own data in seconds, so you can practice reading one before opening SPSS.

Before conducting inferential statistics, researchers should address three core questions:

  1. What is the shape of the data distribution?
  2. Are there outliers?
  3. Do different groups show visibly different distributions?

Boxplots are designed to answer all three questions simultaneously.

4. Statistical Foundations of Boxplots

4.1 The Five-Number Summary

A standard boxplot visualizes five key statistics:

  • Minimum
  • First quartile (Q1, 25th percentile)
  • Median (Q2, 50th percentile)
  • Third quartile (Q3, 75th percentile)
  • Maximum

The box spans from Q1 to Q3, with a line marking the median.

4.2 Interquartile Range (IQR)

[ IQR = Q3 - Q1 ]

The IQR represents the middle 50% of the data and forms the basis for outlier detection.

5. Identifying Skewness Using Boxplots

5.1 Median Position and Whisker Length

  • Right-skewed (positive skew):

    • Median closer to Q1
    • Longer upper whisker

  • Left-skewed (negative skew):

    • Median closer to Q3
    • Longer lower whisker

  • Symmetrical distribution:

    • Median near the center of the box
    • Whiskers of approximately equal length

5.2 Common Examples in Psychology

  • Reaction time, income → typically right-skewed
  • Age at death → typically left-skewed
  • Height, IQ → approximately symmetric

6. Creating Boxplots in SPSS (Step-by-Step)

Method 1: Simple Boxplot (Single Variable)

  1. Go to Graphs → Chart Builder
  2. Select Boxplot
  3. Drag Simple Boxplot into the preview area
  4. Drag the continuous variable to the Y-axis
  5. Click OK

Best for:

  • Inspecting the distribution of one variable

Method 2: Grouped Boxplot (Most Common in Experiments)

  1. Open Graphs → Chart Builder
  2. Choose Boxplot → Clustered Boxplot
  3. Y-axis: dependent variable (e.g., reaction time)
  4. X-axis: grouping variable (e.g., condition)
  5. Click OK

Best for:

  • Comparing experimental and control groups
  • Multi-condition experimental designs

7. Outliers in SPSS Boxplots

7.1 SPSS Symbols

  • ○ : Mild outliers (outside 1.5 × IQR)
    • : Extreme outliers (outside 3 × IQR)

7.2 Best Practices in Psychological Research

  • Do not remove outliers automatically
  • Always consider:
    • Experimental logs
    • Participant behavior
    • Theoretical plausibility

Common approaches:

  • Report and retain
  • Winsorization
  • Data transformation (e.g., log transform for RT data)

8. Boxplots and Normality Assessment

Boxplots are not formal normality tests, but they are critical for:

  • Visual inspection before inferential analysis
  • Deciding whether parametric or non-parametric tests are appropriate

Recommended combinations:

  • Boxplot + Q–Q plot
  • Boxplot + Shapiro–Wilk test

9. Standard Academic Reporting Examples

“Visual inspection of boxplots revealed a positively skewed distribution of reaction times, with several mild outliers beyond 1.5 IQR.”

“Boxplots indicated no substantial deviation from symmetry across experimental conditions.”

These statements are suitable for Methods or Results sections.

10. Common Mistakes and Teaching Notes

  • Using boxplots for nominal variables
  • Deleting outliers without justification
  • Treating boxplots as inferential statistics

Teaching recommendations:

  • Introduce boxplots before t-tests or ANOVA
  • Emphasize the distinction between description and inference

11. Summary

Boxplots are:

  • A core tool in descriptive statistics
  • Essential for understanding data distributions
  • A conceptual bridge between raw data and statistical inference

In SPSS-based psychology education and research, boxplots are methodologically foundational, not optional.

Last updated: 2026-10-20

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