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- In this explanation, you will get an introduction to how quantitative data is presented and displayed in psychology.
- Next, we will review the data presented in quantitative analysis.
- The explanation will focus on the visual representation of quantitative data and the graphical representation of quantitative data.
- Last, we will review a real example of how these visual and graphical data presentations are used and interpreted.
Presentation and Display of Quantitative Data: Psychology
When we conduct research, we usually collect masses of data, some useful and some not. So how do we present and display research that summarises the key points researchers want readers to take away from their publication?
One of the best ways to get a real feel for data is by putting it in tables and graphs. Researchers usually do this when they investigate descriptive statistics and want to aid the interpretation of the results obtained in the inferential analysis.
There are many different graphs, such as bar graphs, histograms, or scatterplots.
Data Presentation for Quantitative Analysis
Quantitative analysis refers to the type of analysis that includes quantitative data, which is, data expressed numerically.
Researchers use data presentation tools to visualise their findings in both the descriptive statistics and the statistical analysis of their studies.
Descriptive statistics are usually presented in summarising tables. These tables include a general overview of most of the variables under investigation.
Researchers may include a histogram to explore the data distribution, but these graphs do not necessarily have to be included in reports. They can be included in an appendix, for example.
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For the presentation of the inferential statistics analysis, different graphs are usually used. When plotting correlations graphically, scatterplots are typically employed. Bar graphs are suitable for visually representing the results when investigating differences between groups.
It is essential to keep in mind the levels of measurement when plotting variables; this is important because the type of data influences how the data should be displayed.
Nominal data, for example, cannot be plotted in a scatterplot. For this type of data, a bar graph is a better option.
| Level of Measurement | Definition | Variable Example |
| Nominal | Distinguishes there are differences, but there is no order to them, and we can't measure how much each differs. | Participants eye colour |
| Ordinal | Distinguishes differences, which have order, but we can't measure how much each differs. | Response on a Likert scale |
| Interval | There is an order, and the differences between figures are measurable. | Temperature |
| Ratio | The ratio is the same as the interval, with the difference that there is an absolute 0, meaning the values of the variable cannot go below 0. | Height |
It is essential to remember that graphs always accompany the numerical interpretation of the analysis, and graphs are never included as the sole method to present findings.
The purpose of tables and graphs is to illustrate what the numerical values say about the raw data.
Visual Presentation of Quantitative Data
As you learned earlier in this explanation, tables are visual presentations of data in columns and rows in which numbers are presented. Some of the most common tables are correlation, demographic, or regression.
Tables provide data in a summarised and concise manner, which makes the is easier for the data to be understood. Usually, tables include data such as mean and standard deviation.
According to the American Psychological Association, there are specific components that a table should include.
Each table should have a number. Thus, the first table in a report should be labelled "Table 1", while the seventh should be labelled "Table 7".
Every table should present a title briefly describing what the table includes and represents. For example, the table may be titled "Demographic characteristics of the sample".
The table must have every column and row labelled with the appropriate heading. These headings should resemble the name of the variables that the study investigated.
Examples of variable names would be age, gender, education, ethnicity or employment.
A table should include a note at the bottom with a short explanation of the contents that cannot be understood by looking at the table alone. This text can be used to explain abbreviations or asterisks.
| Table 1: Table summarising the mean age of male and female participants recruited in the study. | |
|---|---|
| Male | 25.78 |
| Female | 36.21 |
| *mean scores written to 2 d.p. | |
Graphical Presentations of Quantitative Data
Quantitative data can also be plotted in graphs, which present data based on two axes, the X and the Y axis. Examples of graphs include scatterplots and bar charts.
Scatterplots are widely used in correlational research. A scatterplot would present values on a given variable on axis X and values on a given variable on axis X. In this way, each pair of values represents a point in the scatterplot.
Scatterplots can show both interval and ratio types of data graphically.
In line with the APA standards for tables, scatterplots need to include headings with the variables' names and their measure. Graphs also need to include a short description of what the graph shows.
Bar graphs can plot data for nominal and ordinal data. Usually, these types of data are presented on the X-axis. Interval and ratio data are plotted on the Y-axis. The bars in a bar graph are not touching.
When a bar graph has its bard touching, this means the data is interval or ratio; this is continuous. And the plot would be a histogram.
Presentation of Data in Quantitative Research Example
Tables are a perfect way to find specific data in a fast way. Let's say you want to know how many participants had a semester grade below two points in the face-to-face learning condition. By looking at the table, it would take you a few seconds to realise it was 130 participants. Looking at raw data would be time-consuming and not as practical.
By looking at a scatterplot, you could easily spot that the correlation between age and brain volume in men is negative, meaning that as age increases, brain volume decreases. This is an interpretation that one could not do from raw data but from the correlation results.
Although reading the results from a correlation is helpful, researchers use visual presentations of the correlation because these provide a more comprehensive interpretation of the result.
Similarly, when it comes to the bar graph, at a single glance, you can spot that the highest error rate was for the condition in which the asynchrony took 8.3ms. If you only were provided with the percentage of errors for each condition, it would have taken you slightly longer to figure out which condition has the highest errors.
This is how graphs and tables are used and interpreted in psychological research.
Presentation of Quantitative Data - Key takeaways
- Graphs and tables provide summaries of descriptive statistics and when the inferential analyses are conducted.
- The presentation and display of quantitative data psychology are to allow readers to visualise and understand results better.
- Graphical presentation of quantitative data includes bar charts, histograms, tables and scatterplots.
- The type of visual presentation of quantitative data used depends on the type of data collected.
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Frequently Asked Questions about Presentation of Quantitative Data
How can quantitative data be presented?
Graphical presentation of quantitative data includes bar charts, histograms, tables and scatterplots.
How do you present quantitative analysis?
Quantitative analysis is presented graphically. Depending on the test, different graphs can be used. For a correlation, for example, a scatterplot would be used.
What are the two most commonly used quantitative data analysis methods?
The two main methods are descriptive statistics and inferential statistics. Descriptive statistics describe the data set, such as mean and standard deviation. Inferential statistics allow us to analyse the data to see if it supports our hypothesis.
Which are examples of quantitative data?
Quantitative data is to do with numbers and is measurable. For example, weight in kilograms or marks on a test.
What is quantitative data presentation and interpretation?
Quantitative data presentation is how findings are displayed, e.g. on tables or graphs, and interpretations are the inferences/ conclusions we can make from said results.
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