In psychological research, we collect information to understand human behavior and mental processes. This information, or data, isn't all the same. The first step in making sense of it is to understand its type. The most basic split is between categorical and numerical data.

Categorical data sorts information into groups or categories. Think of it as putting things into labeled boxes. Examples include a person's marital status (single, married, divorced), their favorite color, or whether they answered "yes" or "no" to a question. The data points are labels, not amounts.

Numerical data, on the other hand, represents quantities. These are measurements or counts, like a person's age, their score on a personality test, or the number of times they check their phone in an hour. This data tells you "how much" or "how many."

To get more specific, we can classify data into four levels of measurement. These levels, or scales, tell us how much information our data holds. They determine what we can say about the data and what kinds of statistical analyses are appropriate. The scales are nominal, ordinal, interval, and ratio.

The nominal scale is the simplest level. It's just for labeling. For example, if we're studying personality types, we might categorize people as "Introvert" or "Extrovert." These are just labels. One isn't mathematically greater than the other.

The ordinal scale adds a crucial piece of information: order. Think about finishing a race. We know that 1st place came before 2nd, and 2nd before 3rd. There is a clear ranking. Other examples include levels of education (e.g., high school diploma, bachelor's degree, master's degree) or customer satisfaction ratings (e.g., very dissatisfied, neutral, very satisfied).

However, we don't know the distance between these ranks. The difference in skill between the 1st and 2nd place runners might be tiny, while the gap between the 2nd and 3rd could be huge. The scale only tells us the sequence.

The interval scale steps things up again. Here, the order matters and the differences between values are equal and meaningful. The classic example is temperature measured in Celsius or Fahrenheit. The difference between 10°C and 20°C is the same as the difference between 20°C and 30°C. Many psychological tests, like IQ tests, are treated as interval data. The difference between a score of 100 and 110 is considered the same as between 110 and 120.

The key feature of interval scales is the lack of a "true zero." A true zero means the complete absence of the thing being measured. On the Celsius scale, 0° doesn't mean there is no heat. Because of this, you can't make ratio comparisons. For instance, 20°C is not "twice as hot" as 10°C.

The ratio scale is the most informative. It has everything the interval scale has—order and equal intervals—plus a true zero. A true zero allows for meaningful ratio statements. For example, height is measured on a ratio scale. A height of 0 cm means no height. This allows us to say that someone who is 180 cm tall is twice as tall as someone who is 90 cm tall.

In psychology, many physical or count-based measures are ratio data, such as reaction time in milliseconds, the number of words recalled in a memory test, or age in years. A value of zero in these cases means the complete absence of that attribute (e.g., zero words recalled).

Understanding these four levels of measurement is essential because it guides how we analyze and interpret our data. You can't calculate a meaningful average of nominal data like "dog," "cat," and "fish." Similarly, the statistical tests you use for ordinal data are different from those for ratio data.

Here’s a simple table to summarize the properties of each scale:

Knowing these distinctions is the foundation for conducting sound psychological research and drawing accurate conclusions from your findings.