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Quantitative data excel in areas where knowledge is being developed, whereas qualitative tends to excel in unknown territories.
- First, we are going to provide a quantitative data definition.
- Then, we will discuss the quantitative data types, provide quantitative data examples, and show how to interpret and display quantitative data.
- Finally, we will evaluate the interpretation and display of qualitative data, highlighting the advantages and disadvantages of quantitative data.
Quantitative data is fast, scientific, and easier to analyse, freepik.com/pch.vector
The Quantitative Data Definition
Quantitative data is essentially numerical data that is used in research to summarise, describe, evaluate and analyse datasets.
There are many reasons psychologists use quantitative data in research. Some of these reasons are:
- Researchers can easily identify patterns and trends in data
- Researchers can use inferential statistics (this is a quantitative data example) to identify if results are significant (not due to chance) and identify if the data supports/ rejects the hypothesis proposed
- Researchers can easily compare results from other researchers
- Researchers can use quantitative data to describe the data that was collected.
Interpretation and display of quantitative data
In psychology, research data needs to be interpreted for it to be of use. When this is reported researchers usually use tables, graphs and charts to illustrate what the data is showing.
Some quantitative data examples of charts and graphs used to illustrate data are:
- frequency tables
- bar charts
- histograms
- scatter plots
The chart or graph that a researcher uses is determined by what the researcher wishes to describe and illustrate regarding the data. This will be explained in further detail with illustrative examples!
Quantitative data examples - frequency table
Frequency tables are used by researchers to show the frequency of a group. This table is usually used in psychology research to describe the characteristics of a data set.
The frequency table below shows the number of people who completed a test in each age group.
| Age | Frequency | Interpretation |
| 20 | 40 | 40 people who completed the test were aged 20 |
| 21 | 27 | 27 people who completed the test were aged 21 |
| 22 | 33 | 33 people who completed the test were aged 22 |
| 23 | 35 | 35 people who completed the test were aged 23 |
| 24 | 52 | 52 people who completed the test were aged 24 |
| 25 | 49 | 49 people who completed the test were aged 25 |
| 26 | 30 | 30 people who completed the test were aged 26 |
| 27 | 26 | 26 people who completed the test were aged 27 |
| 28 | 30 | 30 people who completed the test were aged 28 |
| 29 | 50 | 50 people who completed the test were aged 29 |
| 30 | 56 | 56 people who completed the test were aged 30 |
Quantitative data types - bar charts and histograms
Bar charts and histograms are both forms of a graph that is used to illustrate frequency tables. This can make it easier for readers to visualise and understand the characteristics of the data.
The data from the quantitative data table example has been used to plot onto a bar chart. This is shown in the graph below.
A bar chart can reveal information about data immediately, Sharon Thind, StudySmarter Originals
The bar chart illustrates that the most people who completed the test were aged 30 and the least were aged 27. When displaying quantitative data in a bar chart and any other graph, it is important that the researcher in their report:
- Titles the chart/graph
- Labels the axis
- Interprets what the chart/graph shows
The same data has been used to create a histogram. This is shown in the graph below. Histograms display continuous data.
Histograms are used for continuous data, Sharon Thind, StudySmarter Originals
This shows that 26-31 has the highest frequency density and both 38-43 and 44-49 have the lowest.
Bar charts and histograms essentially have the same purpose of illustrating data from frequency tables. However, the type that the researcher may use is determined by the number of frequencies that the data is measuring.
Data that is measuring the frequency of many variables are likely to use histograms to illustrate this. This is because it groups the data. A bar chart would be inappropriate as there would be too many bars and it may make it difficult to interpret the graph.
Quantitative data forms - scatter plot
Scatter plots are illustrative quantitive data examples. A common inferential statistic used in psychology is correlations. Researchers use scatter plots to illustrate the findings from a correlational test. How a scatter plot looks can be used to:
- Identify the relationship between two variables (positive, negative or, none)
- Identify the strength of the relationship (strong, weak or, moderate)
In a hypothetical psychology report, the psychologist added a scatterplot to illustrate whether there is an association between the age of participants and the number of people who completed the test. This is shown in the example below (the frequency table data was used to plot this).
Scatterplots are great for seeing correlations, Sharon Thind, StudySmarter Originals
From the scatter plot, the quantitative data can be interpreted as there is no relationship/association between the frequency of reporting and the age of participants.
Evaluation of quantitative data
Let's now discuss the advantages and disadvantages of quantitative data!
The advantages of quantitative data
The advantages of quantitative data are:
- Quantitative data can make it easier to accept or reject a hypothesis than qualitative data
- Visualising data may make it easier for the reader to understand the data
- It is considered an objective and scientific way to assess hypotheses, the majority of the psychology community believes that research should be scientific
- It is easier to analyse the data of a large sample
- Researchers can identify if the results are significant, this can assess the validity of the results.
The disadvantages of quantitative data
The disadvantages of quantitative data are:
- It does not provide detailed information about the data, unlike qualitative data
- It is difficult to establish cause and effect from quantitative data. Quantitative data has more of a descriptive nature so, it is difficult to use to explain a phenomenon
- You can miss important information in data as it is usually used to describe and analyse the average of data
- For the researcher to find significant data, the larger the sample, the better.
Quantitative Data - Key takeaways
- The quantitative data definition is essentially numerical data that is used in research to summarise, describe, evaluate and analyse datasets.
- Some quantitative data examples are frequency tables, bar charts, histograms, and scatterplots.
- Some of the advantages of quantitative data are visualising data may make it easier for the reader to understand the data, and quantitative data allows researchers to identify patterns/ trends and analysis of data. From this, the researcher can accept/ reject the hypothesis. Quantitative data is considered an objective and scientific way to assess hypotheses
- Some of the disadvantages of quantitative data are it does not provide detailed information about the data unlike qualitative data, you can miss important information in data as it is usually used to describe and analyse the average of data, and for the data to find significant data, a large sample ideally needs to be used.
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Frequently Asked Questions about Quantitative Data
How is quantitative data not holistic?
Qualitative data is considered by many as holistic whereas, quantitative is not often considered holistic. This is because qualitative data provides detailed information which allows researchers to get an in-depth understanding of phenomenons.
How much quantitative data is needed to make a conclusion?
When carrying out research on quantitative data, the data needs to be carried out on a large enough sample to make a conclusion. In addition to this, statistical tests need to be carried out on the data to make a conclusion.
Is a list qualitative data or quantitative data?
If a list consists of numerical data then it is quantitative however, if it is non-numerical data then it would be classed as qualitative data.
Is data collection quantitative?
Data collection can be quantitative or qualitative. Quantitative data examples of data collection are questionnaires consisting of close-ended questions, experiments, and fixed-response surveys. However, questionnaires consisting of open-ended questions are qualitative.
What are the key differences between qualitative and quantitative data?
Qualitative data is detailed, non-numerical data. Whereas quantitative data is the opposite, numerical data that usually provide a description/ analysis of the summary of results.
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