Collecting & Recording Data

Navigate the knowledge tree: 🌿 Skills ➡ Life Processes

Learning Intentions

By the end of this topic, you should be able to:

Italics

Glossary

Key terms and definitions.

qualitative data: Definition

quantitative data: Definition

word: Definition

What is raw data?

Raw data is the first and original form of data collected directly from experiments or surveys, before calculations are performed. It is recorded exactly as it is observed or measured — without any changes, calculations, or interpretations. This means no averaging, no graphing, no editing, and definitely no deleting results you don’t like!

Raw data represents the true evidence collected directly from the experiment. Scientists always keep their raw data because it proves their results are real, reliable, and not altered to fit a prediction.

If the data are collected directly by the user, these are called primary data. If the data are obtained indirectly from other sources reporting raw or processed data (such as books, articles or from the internet), these are secondary data.

Recording your raw data is important because it:

Shows real variation in your results
Allows others to check reliability (because big differences between your trials may signal errors)
Provides proof of your findings
Helps track patterns emerging in your data before analysis begins
Ensures the investigation is transparent and trustworthy

You must be able to show how you arrived at your conclusions, and your recorded raw data is the foundation for that.

What are the different types of data we can collect?

All data can be categorised as either quantitative data or qualitative data.

Quantitative data (Numerical data)

As its name suggests, quantitative data relate to a quantity (an amount). These values are not just numbers - qualitative data values consist of a number and a scientific measuring unit.

For example, if the height of a student is 155 cm, then the value consists of the number '155' and the unit 'cm' or centimetres. Any calculation done to that value (155 cm) must be done on the number and the unit. Handling units in calculations is very important part of Science.

🌿Jump to: Science Skills > Getting Started in the Laboratory > Math Skills

We can collect quantitative data by measuring or counting.

Quantitative (numerical) data can be further subdivided into:

Discrete data which are always integers (whole numbers) and can be counted. E.g. The number of children in a family (0, 1, 2, 3).
Continuous data which can be measured and take any possible value in a given range. Continuous data could be in fractions or in decimals. E.g. The height of children in a family (1.5 m, 0.8 m).

Furthermore, continuous quantitative data can be further subdivided into:

Interval data, where 0 has no physical meaning and negative values are possible. E.g. temperature in °C or °F.
Ratio data, where 0 has a physical meaning and negative values are impossible. E.g. temperature in K, height.

Qualitative data (Categorical data)

By contrast, qualitative data are non-numerical and descriptive. For example, the eye colour of students represents qualitative data as there are no numbers involved.

However if the numbers of students with each eye colour are counted, then each eye colour becomes a category with an associated numerical value. So, quantitative data can be generated from qualitative data.

The eye colour of a student is a quality / category and therefore an example of qualitative data
The number of students with brown eyes is a variable consisting of quantitative data.

So even if it is non-numerical, you can collect qualitative data by counting the number of occurrence of each category (in a tally chart).

Qualitative (categorical) data can be further subdivided into:

Binary data, where only two categories exist. E.g. pass or fail categories.
Nominal data, where there are three or more categories with no natural order to them. E.g. regions in NZ, days of the week, colours.
Ordinal data, where there are three or more categories with a natural order to them, but with irregular differences between them. E.g. spice level - mild, medium, hot. Here, the data are ranked in order, but the intervals between the orders may not be equal. There might be a small difference between the spice level between 'mild' and 'medium', but a huge difference between 'medium' and 'hot'.

It is important to consider the type of data that will be collected. Where possible, it is best to collect quantitative or numerical data, because it is easier to analyse it objectively (without bias).

This is an excellent video that clearly distinguishes between the different types of data and which graphs to choose to represent different types of data.

How to collect data

Collecting raw data is a key part of any scientific investigation because it gives you the evidence you need to support or reject your hypothesis. Raw data collection should begin as soon as you start working with your organism, equipment, or environment — not just when the main experiment starts.

Scientists collect two main types of raw data:

Observations — what you see or notice (e.g. A snail is more active when lights are dim.)
Measurements — numerical data (e.g. the plant height increases by 2.3 cm after three days.)

In some cases, even conditions such as temperature, light level, or humidity during set up are data because they can influence results. These measurements of the non-living environment are called abiotic factors.

Good raw data collection requires:

Consistency — collect data using the same method each time
Detail — include date, time, units, and conditions
Completeness — record every result, not just the best ones

All raw data should be written directly into your logbook. Remember, if you didn’t write it down, it didn’t happen in science!

How to record data

Recording raw data clearly is just as important as collecting it. If you have recorded your results accurately and in an organised way, it makes analysing and understanding your data easier. All raw data (observations and measurements) must be recorded in your physical or digital logbook.

Log books

A lot book records your ideas and results throughout your scientific investigation. It also provides proof that you have carried out the work.

An A4 lined exercise book is a good choice for a log book. It gives enough space to write ideas and record results and provides space to glue in photos or extra materials such as printouts.

Each entry in your log book must have the date recorded. Also make sure that you can read what you write at a later date. A log book entry is meaningless if it is incomplete a or cannot be read.

Dataloggers

A datalogger (also called a data recorder) is an electronic device that automatically records data over time. They have a variety of sensors to measure different physical properties. Common sensors include ight, temperature, pH, conductivity and humidity. Dataloggers can be used in both field or laboratory experiments, and can be left to collect data without the experimenter being present. Information collected by the datalogger can be downloaded to a computer so that the data can be accessed and analysed.

Data table

If you are not using a datalogger, a table is often a good way to record and present your results as you collect them. This is because tables allow a large amount of information to be condensed systematically. They help to organise information clearly, making it easier to identify relationships and trends, compare results, and decide what data may need to be graphed or analysed further. Finally, recording data in a table from the very beginning of your experiment allows you to identify any trends early on and change experimental conditions if necessary.

Data tables are the most common way to record raw data in school Science classrooms.

Features of a good data table

A good scientific table must include several key features to ensure it is clear and professional. Every table needs:

A full and informative title that shows the relationship being investigated, such as 'Effect of Light Intensity on Plant Growth', so that the reader knows exactly what the table is about.
To be ruled into rows and columns.

To place the independent variable in the first column on the left-hand side, under a heading that includes the name of the variable and its units — for example, 'Light intensity (lux)'. Note that the units are enclosed in brackets.
To place the dependent variable in the next column or columns, also with headings that include its name and units, such as 'Plant height (cm)'. Note that the units are enclosed in brackets. Like the title, clear row and column headings ensure the reader knows exactly what information each row and column contains.
Additional columns should be included for calculations such as rates and summary statistics (e.g. mean or standard deviation). For example, if an investigation involves multiple trials, a final column could show the average (mean) of all the trials to make it easier to see overall trends.

🌿Related to this knowledge area: Analysing & Interpreting Data

There are also several important conventions that make data tables accurate and easy to read.

Each column has correct headings and units
Units are stated once in the headings and not repeated after each value.
Numbers are written using the same number of decimal places or significant figures
Decimal points must line up vertically, for clarity
If there were control experiments, their results should go at the top of the table
Tables should be fully boxed in with neat ruled lines
If multiple tables are used in a report, they should be numbered (Table 1, Table 2, …) with clear titles for each.