Raw data

When researchers collect data for their studies, they have to start from somewhere. And that somewhere is raw data. When carrying out research, collecting raw data is inevitable, which can be either qualitative or quantitative. Raw data is important in research because it is later organised, analysed, and interpreted to determine if the research findings support the hypothesis.

• We will start by looking at how raw data in research is used and the raw data meaning.
• Moving on, we will apply some raw data examples to identify uses of raw data, such as how researchers may convert raw data from standard form to decimal form and how they may be used to find arithmetic means.
• And to finish off, we will look at a sample raw data example to learn how raw data can sometimes be used to make estimations.

Raw data is converted through analysis into meaningful results, freepik.com/storyset

Raw Data in Research

Raw data is essential in psychology research. Researchers use raw data to calculate descriptive statistics, which describe and summarise the data, helping the researcher and reader visualise the collected raw data and present it more clearly.

Raw Data Meaning

Raw data can be collected regardless of whether it is:

• Quantitative – numerical data

• Qualitative – non-numerical data

Uses of Raw Data in Research

There are many uses of raw data in research. Some of these include:

• Organising data that can be later interpreted and analysed.

• Making estimations of what the researcher can expect to find from the results.

• Comparing data values between conditions/groups in experiments to identify notable differences.

• Constructing tables, graphs or charts, such as;

  • Frequency tables, bar charts, histograms and/or pie charts.

Raw Data in Research

When collecting data for research, raw data is always collected. This data needs to be organised to be later analysed and interpreted by recording the data on a table. When designing raw data recording tables, the researcher needs to keep in mind several important things:

• All of the data collected needs to be somehow recorded onto the table.

• The researcher needs to consider how to record the data. For example, the data may be coded or tallied. The purpose of this is to make it easier to analyse data later.

Raw Data Examples – Standard Form to Decimal Form

Below you can see an example data table. This table summarises the favourite colours of students. The raw data is written in standard form. The standard form is data recorded by tallying the number of responses of students who identified the specific colour as their favourite colour.

The researcher may use this data to convert it from standard form into decimal form. To change raw data from standard to decimal form, the researcher must divide each category's frequency by the total number of responses, as shown in the example below.

Raw Data Examples – Finding Arithmetic Means

Researchers can also analyse raw data in their research. For example, they can use it to find the arithmetic means of the data.

The raw data table below shows participant responses to the question, 'Have you been experiencing more pain than last month?' after taking a drug to help relieve pain. The response was based on a 1–10 Likert scale; 1 represents less pain, and 10 represents more pain. Researchers recorded the participants' responses in the raw data table below.

The researcher wanted to measure the average responses of the two groups (drugs versus placebo). Below you can see how this can be calculated and interpreted:

The average of the experimental group is: 1 + 1 + 3 + 5 + 2 + 2 + 1 + 3 + 2 = 20. We then divide this by 9 = 2.22

The average of the control group is: 7 + 5 + 6 + 5 + 8 + 8 + 4 + 6 + 6 = 55/ 9 = 6.11

The results can be interpreted as, on average, the experimental group experienced less pain than the control group. Researchers can then use further statistical tests to measure the significance of these results etc.

Raw Data – Sample Raw Data

When collecting raw data in psychology research, it is good to round the data values to two significant figures. There should not be more than two numerical values after a decimal point in data figures. Whether the figure should be rounded up or down determines these numbers. The numbers should be:

• Rounded up if the third digit after the decimal point is above 5.
• Rounded down if the third digit after the decimal point is below 5.

Researchers sometimes use raw data to make estimations. This is occasionally used when a psychologist wants to make a quick approximation/estimation of the data that has been collected.

By making estimations, the researchers can roughly estimate what they can expect to find from their results.