StudySmarter - The all-in-one study app.
4.8 • +11k Ratings
More than 3 Million Downloads
Free
Regardless of whether the researcher collects qualitative or quantitative data, raw data is continuously collected. This raw data is important in research because it is later organised, analysed, and interpreted to determine if the research findings support the hypothesis.
Raw data is converted through analysis into meaningful results, freepik.com/storyset
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 is data that has been collected from the researcher during research that has not yet been processed.
Raw data can be collected regardless of whether it is:
Quantitative – numerical data
Qualitative – non-numerical data
What is the difference between raw data and primary data?
Raw data is data collected during research that has not yet been processed. Primary data is data the researcher has collected themself from their experiment cited in a study. They are very similar.
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
Statistics, flaticon.com/premium-icon
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.
An example of coded data used in research is M for male participants and F for female participants.
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 when the data is recorded by tallying the number of responses of students who identified the specific colour as their favourite colour.
Red | Orange | Yellow | Pink | Green | Purple | Total |
4 | 2 | 1 | 6 | 2 | 5 | 20 |
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.
Red | Orange | Yellow | Pink | Green | Purple | Total |
4 | 2 | 1 | 6 | 2 | 5 | 20 |
4/20 = 0.2 | 2/20 = 0.1 | 1/20 = 0.05 | 6/20 = 0.3 | 2/20 = 0.1 | 5/20 = 0.25 | 1 |
Raw data may be converted from standard into decimal form when doing some calculations. For example, researchers should do this when constructing a pie chart but alter it to reflect 360°.
Researchers can also analyse raw data in their research. For example, they can use it to find the arithmetic means of the data.
The arithmetic mean (or simply just the mean) is a statistic used to find the average of a dataset. To calculate this value all of the values need to be added together and divided by the count of values added together.
The raw data table shown below shows participant responses to the question, 'Have you been experiencing more pain than last month?' after taking a drug to help any 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 given in two groups (drugs versus placebo). Below you can see how this can be calculated and interpreted:
Drug (experimental) group | Placebo (control) group |
1 | 7 |
1 | 5 |
3 | 6 |
5 | 5 |
2 | 8 |
2 | 8 |
1 | 4 |
3 | 6 |
2 | 6 |
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
This figure has been rounded down to two significant figures.
The average of the control group is: 7 + 5 + 6 + 5 + 8 + 8 + 4 + 6 + 6 = 55/ 9 = 6.11
This figure has been rounded down to two significant figures.
Interpreting results: from the arithmetic means, the researcher can interpret that, 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.
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:
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.
Calculating something such as 487 x 9876 would be an example of using raw data to estimate. The researcher may calculate 500 x 10,000.
By doing so, researchers roughly estimate what they can expect to find from the results.
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.
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 is data that has been collected from the researcher during research that has not yet been processed.
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.
Raw data is data collected during research that has not yet been processed. Primary data is data the researcher has collected themself from their experiment cited in a study. They are very similar.
Raw data in psychology research is important because it is later organised, analysed, and interpreted to determine if the research findings support the hypothesis.
A list of all the test scores students received on the last test is an example of raw data.
Be perfectly prepared on time with an individual plan.
Test your knowledge with gamified quizzes.
Create and find flashcards in record time.
Create beautiful notes faster than ever before.
Have all your study materials in one place.
Upload unlimited documents and save them online.
Identify your study strength and weaknesses.
Set individual study goals and earn points reaching them.
Stop procrastinating with our study reminders.
Earn points, unlock badges and level up while studying.
Create flashcards in notes completely automatically.
Create the most beautiful study materials using our templates.
Sign up to highlight and take notes. It’s 100% free.