Describe and apply descriptive statistics and data-analysis techniques to psychological data.

What this dot point is asking

Collecting data is only half of research. This part of Module 1 is about turning raw numbers into a clear, defensible conclusion, and it is regularly examined alongside research methods.

Types of data

• Quantitative data are numerical (reaction time in milliseconds, number of words recalled). They are easy to analyse statistically and compare.
• Qualitative data are descriptive (interview transcripts, open responses). They are rich and detailed but harder to summarise objectively.

Quantitative data are further split into levels of measurement: nominal (categories, such as favourite colour), ordinal (ranked, such as finishing position) and interval or ratio (equal intervals, such as temperature or time). The level you have limits which summary is appropriate.

Measures of central tendency

• Mean: the arithmetic average; uses every score but is distorted by extreme outliers.
• Median: the middle score when ordered; resistant to outliers, useful for skewed data.
• Mode: the most frequent score; the only average usable for nominal data.

Measures of spread

The range is the highest score minus the lowest; quick but based on only two values. The standard deviation measures the average distance of scores from the mean: a small standard deviation means scores cluster tightly, a large one means they are widely spread. Reporting spread alongside an average is essential, because two groups can share a mean yet differ greatly in consistency.

Distributions

In a normal distribution, scores form a symmetrical bell curve where the mean, median and mode coincide and most scores fall near the centre. Data can also be skewed: a positive skew has a tail of high scores pulling the mean above the median, while a negative skew has a tail of low scores. Recognising skew tells you that the median may describe the typical score better than the mean.

Graphing data

Choosing the right display matters. A bar graph compares separate categories (nominal or ordinal data). A histogram shows the frequency of a continuous variable with no gaps between bars. A line graph shows change over a continuous scale, such as time. A scatterplot shows the relationship between two variables and is the basis for correlation.

Interpreting correlation

A correlation coefficient (r) summarises the strength and direction of a relationship between two variables, running from minus 1 to plus 1. A value near plus 1 is a strong positive relationship (both rise together), near minus 1 is a strong negative relationship (one rises as the other falls), and near 0 means little or no linear relationship.

Putting it together

When handed data in the exam, first identify the type and level of measurement, then pick the appropriate average and measure of spread, describe the shape of the distribution, and only then interpret what the numbers mean for the hypothesis. For relationships, describe the direction and strength of the correlation and resist any causal claim. This disciplined sequence is exactly what earns full marks on data-handling questions.