An introduction to descriptive statistics
Descriptive statistics summarize and organize characteristics of a data set. A data set is a collection of responses or observations from a sample or entire population.
In quantitative research, after collecting data, the first step of data analysis is to describe characteristics of the responses, such as the average of one variable (e.g., age), or the relation between two variables (e.g., age and creativity).
The next step is inferential statistics, which help you decide whether your data confirms or refutes your hypothesis and whether it is generalizable to a larger population.
Types of descriptive statistics
There are 3 main types of descriptive statistics:
- The distribution concerns the frequency of each value.
- The central tendency concerns the averages of the values.
- The variability or dispersion concerns how spread out the values are.
You can apply these to assess only one variable at a time, in univariate analysis, or to compare two or more, in bivariate and multivariate analysis.
A data set is made up of a distribution of values, or scores. In tables or graphs, you can summarize the frequency of every possible value of a variable in numbers or percentages.
From this table, you can see that more women than men took part in the study.
|Library visits in the past year||Percent|
From this table, you can see that most people visited the library between 5 and 16 times in the past year.
Measures of central tendency
Here we will demonstrate how to calculate the mean, median, and mode using the first 6 responses of our survey.
|Data set||15, 3, 12, 0, 24, 3|
|Sum of all values||15 + 3 + 12 + 0 + 24 + 3 = 57|
|Total number of responses||N = 6|
|Mean||Divide the sum of values by N to find M: 57/6 = 9.5|
|Ordered data set||0, 3, 3, 12, 15, 24|
|Middle numbers||3, 12|
|Median||Find the mean of the two middle numbers: (3 + 12)/2 = 7.5|
|Ordered data set||0, 3, 3, 12, 15, 24|
|Mode||Find the most frequently occurring response: 3|
Measures of variability
Measures of variability give you a sense of how spread out the response values are. The range, standard deviation and variance each reflect different aspects of spread.
The range gives you an idea of how far apart the most extreme response scores are. To find the range, simply subtract the lowest value from the highest value.
The standard deviation (s) is the average amount of variability in your dataset. It tells you, on average, how far each score lies from the mean. The larger the standard deviation, the more variable the data set is.
There are six steps for finding the standard deviation:
- List each score and find their mean.
- Subtract the mean from each score to get the deviation from the mean.
- Square each of these deviations.
- Add up all of the squared deviations.
- Divide the sum of the squared deviations by N – 1.
- Find the square root of the number you found.
|Raw data||Deviation from mean||Squared deviation|
|15||15 – 9.5 = 5.5||30.25|
|3||3 – 9.5 = -6.5||42.25|
|12||12 – 9.5 = 2.5||6.25|
|0||0 – 9.5 = -9.5||90.25|
|24||24 – 9.5 = 14.5||210.25|
|3||3 – 9.5 = -6.5||42.25|
|M = 9.5||Sum = 0||Sum of squares = 421.5|
Step 5: 421.5/5 = 84.3
Step 6: √84.3 = 9.18
From learning that s = 9.18, you can say that on average, each score deviates from the mean by 9.18 points.
The variance is the average of squared deviations from the mean. Variance reflects the degree of spread in the data set. The more spread the data, the larger the variance is in relation to the mean.
To find the variance, simply square the standard deviation. The symbol for variance is s2.
Univariate descriptive statistics
Univariate descriptive statistics focus on only one variable at a time. It’s important to examine data from each variable separately using multiple measures of distribution, central tendency and spread. Programs like SPSS and Excel can be used to easily calculate these.
|Visits to the library|
If you were to only consider the mean as a measure of central tendency, your impression of the “middle” of the data set can be skewed by outliers, unlike the median or mode.
Likewise, while the range is sensitive to extreme values, you should also consider the standard deviation and variance to get easily comparable measures of spread.
Bivariate descriptive statistics
If you’ve collected data on more than one variable, you can use bivariate or multivariate descriptive statistics to explore whether there are relationships between them.
In bivariate analysis, you simultaneously study the frequency and variability of two variables to see if they vary together. You can also compare the central tendency of the two variables before performing further statistical tests.
Multivariate analysis is the same as bivariate analysis but with more than two variables.
In a contingency table, each cell represents the intersection of two variables. Usually, an independent variable (e.g., gender) appears along the vertical axis and a dependent one appears along the horizontal axis (e.g., activities). You read “across” the table to see how the independent and dependent variables relate to each other.
|Number of visits to the library in the past year|
Interpreting a contingency table is easier when the raw data is converted to percentages. Percentages make each row comparable to the other by making it seem as if each group had only 100 observations or participants. When creating a percentage-based contingency table, you add the N for each independent variable on the end.
|Visits to the library in the past year (Percentages)|
From this table, it is more clear that similar proportions of men and women go to the library over 17 times a year. Additionally, men most commonly went to the library between 5 and 8 times, while for women, this number was between 13 and 16.
A scatter plot is a chart that shows you the relationship between two or three variables. It’s a visual representation of the strength of a relationship.
In a scatter plot, you plot one variable along the x-axis and another one along the y-axis. Each data point is represented by a point in the chart.
Frequently asked questions about descriptive statistics
- What’s the difference between descriptive and inferential statistics?
- What are the 3 main types of descriptive statistics?
The 3 main types of descriptive statistics concern the frequency distribution, central tendency, and variability of a dataset.
- What’s the difference between univariate, bivariate and multivariate descriptive statistics?
- Univariate statistics summarize only one variable at a time.
- Bivariate statistics compare two variables.
- Multivariate statistics compare more than two variables.