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How to Analyse Quantitative Data

From describing your data to testing relationships with the right statistics.

8 min read · Written by UK academic writers

Quick Answer

Analysing quantitative data means preparing and cleaning it, then summarising it with descriptive statistics and testing relationships or differences with inferential statistics. Choosing the right test depends on your data type and research questions, and tools such as SPSS or Excel are commonly used.

Quantitative analysis can feel intimidating, but it follows a clear sequence. The key is choosing the right statistics for your data and questions. This guide gives you the overview you need.

Prepare and Clean Your Data

Before analysis, organise your data, check for errors and handle missing values. Clean data is essential, as mistakes here distort everything that follows.

Use Descriptive Statistics

Descriptive statistics summarise your data, including measures such as the mean, median, mode and standard deviation, plus frequencies and distributions. They give an overall picture before any testing.

Choose the Right Inferential Test

Inferential statistics let you test hypotheses and relationships. The correct test depends on your data type, number of groups and research question.

  • t-tests compare two group means
  • ANOVA compares more than two groups
  • Correlation measures relationships between variables
  • Regression predicts one variable from others
  • Chi-square tests categorical associations

Use the Right Tools

Software such as SPSS, R or Excel performs the calculations. Understanding what each test does and how to interpret its output matters more than the software itself.

Report Results Clearly

Present statistics accurately, report whether results are statistically significant, and explain what they mean for your research questions, saving deeper interpretation for the discussion.

Key Takeaways
  • Clean and prepare your data first
  • Summarise with descriptive statistics
  • Choose inferential tests to match your data and questions
  • Use tools such as SPSS, R or Excel
  • Report results clearly and accurately

Frequently Asked Questions

What is the difference between descriptive and inferential statistics?

Descriptive statistics summarise your data; inferential statistics test hypotheses and draw conclusions about a wider population.

How do I choose the right statistical test?

It depends on your data type, the number of groups, and whether you are comparing, correlating or predicting.

What does statistical significance mean?

It indicates that a result is unlikely to be due to chance, usually judged against a threshold such as p < 0.05.

What software should I use?

SPSS, R and Excel are common. Choose one you can access and learn to interpret its output.

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