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Data Analysis
Introduction
Research Process
Qualitative Analysis
Quantitative Analysis
What It Is
Issues of Analysis
Types of Data
Statistical Hypothesis Testing
The p-value
Statistical Power
Validity and Reliability
Descriptive or Summary Statistics
Measures of Central Tendency
Measures of Dispersion
Inferential Statistics
Chi-Squared (Difference)
Mann-Whitney (Difference)
Wilcoxon (Difference)
Independent T-Test (Difference)
Paired T-Test (Difference)
Chi-Squared (Association)
Spearman (Association)
Pearson (Association)
Further Reading
Critical Appraisal
Further Resources
Glossary
  Descriptive or Summary Statistics

Quantitative research may well generate masses of data. For example, a comparatively small study that distributes 200 questionnaires with maybe 20 items on each can generate potentially 4000 items of raw data.

To make sense of this data it needs to be summarised in some way, so that the reader has an idea of the typical values in the data, and how these vary. To do this researchers use descriptive or summary statistics: they describe or summarise the data, so that the reader can construct a mental picture of the data and the people, events or objects they relate to.

Types of descriptive statistics
All quantitative studies will have some descriptive statistics, as well as frequency tables. For example, sample size, maximum and minimum values, averages and measures of variation of the data about the average. In many studies this is a first step, prior to more complex inferential analysis.

The two main types of descriptive statistics encountered in research papers are measures of central tendency, (averages) and measures of dispersion.

Note: The choice of which particular descriptive statistics to report will affect the “picture” that is presented of the data, and there is the potential to mislead.