Data Analysis: Quantitative Data
Contrary to qualitative data analysis, quantitative data analysis follows a general approach with differences in how the data is summarized. This process can be broken down into three steps:
After collection, basic statistical information can be obtained by looking at [23]:​​​​​
Frequencies
Describing how many times something had occurred within a given interval.
- 2
- 4
- 3
- 1
Percentages
Utilized in comparing different groups by dividing the given number of units with the total number of units and multiplying it by 100.
- 33%
- 75%
Ratios
Looking at the numerical relationship between two groups.
Ratio is 4:3
Mean
Determining the average value of data.
1, 1, 2, 3, 3, 3, 3, 4, 5, 5, 6, 6, 6
(1+1+2+3+3+3+3+4+5+5+6+6+6) / 13 = 3.7
Median
Determining the midpoint value of the data, where half of the values are above and half are below this value.
1, 1, 2, 3, 3, 3, 3, 4, 5, 5, 6, 6, 6
Median is 3
Mode
Determining the most frequent value.
is the most frequent value
Standard Deviation (SD)
Measuring how close the data points are to the mean, where smaller values imply being closer to the mean.
2, 3, 4
SD of 1
10, 20, 30
SD of 10
Minimum and Maximum Values
The smallest and largest values in a data set.
1, 1, 2, 3, 3, 3, 3, 4, 5, 5, 6, 6, 6
Min Value: 1 and Max Value: 6
Next, the data can be reviewed and interpreted through:
Identifying patterns in the data.
Determining whether there are any extreme data (i.e., outliers) that may affect statistical analysis.
Finally, the data can be summarized through statistical analysis.
1.
These analyses allow for conclusions to be made on whether differences seen between two groups of data are significant from a mathematical standpoint.
2.
The analyses can be thought of as “What are the chances/probability that there was no difference between the groups we observed?”
3.
T-tests, ANOVAs (ANalysis Of VAriance), regressions, etc., can be utilized to do statistical analysis based on specific criteria for it.
We will go more in-depth on these statistical tests in later units though!
Pause and Check:
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Time to test your knowledge of data analysis methods! Match the method to its definition. ​
For your next experiment, don’t forget to consider how the purpose statement, data collection, and data analysis methods influence each other! Each research element should align with each other, telling the same research “story.”
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