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
statistical 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.
Table of contents
Types of descriptive statistics
Frequency distribution
Measures of central tendency
Measures of variability
Univariate descriptive statistics
Bivariate descriptive statistics
Frequently asked questions about descriptive statistics
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.
Research exampleYou want to study the popularity of different leisure activities by gender. You distribute a
survey and ask participants how many times they did each of the following in the past year:Go to a library
Watch a movie at a
theaterVisit a national park
Your data set is the collection of responses to the survey. Now you can use descriptive statistics to find out the overall frequency of each activity (distribution), the averages for each activity (central tendency), and the spread of responses for each activity (variability).
Frequency distribution
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. This is called a
frequency distribution.
Simple frequency distribution table
Grouped frequency distribution table
For the variable of gender, you list all possible answers on the left hand column. You count the number or percentage of responses for each answer and display it on the right hand column.GenderNumber
Male182
Female235
Other27
From this table, you can see that more women than men or people with another gender identity took part in the study.
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See an exampleMeasures of central tendency
Measures of central tendency estimate the center, or average, of a data set. The mean, median and mode are 3 ways of finding the average.
Here we will demonstrate how to calculate the mean, median, and mode using the first 6 responses of our survey.
Mean
Median
Mode
The
mean, or M, is the most commonly used method for finding the average.
To find the mean, simply add up all response values and divide the sum by the total number of responses. The total number of responses or observations is called N.
Mean number of library visitsData set15, 3, 12, 0, 24, 3
Sum of all values15 + 3 + 12 + 0 + 24 + 3 = 57
Total number of responsesN = 6
MeanDivide the sum of values by N to find M: 57/6 = 9.5
The
median is the value that’s exactly in the middle of a data set.
To find the median, order each response value from the smallest to the biggest.
Then, the median is the number in the middle. If there are two numbers in the middle, find their mean.
Median number of library visitsOrdered data set0, 3, 3, 12, 15, 24
Middle numbers3, 12
MedianFind the mean of the two middle numbers: (3 + 12)/2 = 7.5
The
mode is the simply the most popular or most frequent response value. A data set can have no mode, one mode, or more than one mode.
To find the mode, order your data set from lowest to highest and find the response that occurs most frequently.
Mode number of library visitsOrdered data set0, 3, 3, 12, 15, 24
ModeFind 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.
Range
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.
Range of visits to the library in the past yearOrdered data set: 0, 3, 3, 12, 15, 24
Range: 24 – 0 = 24
Standard deviation
The
standard deviation (s or SD) 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.
Standard deviations of visits to the library in the past yearIn the table below, you complete Steps 1 through 4.Raw dataDeviation from meanSquared deviation
1515 – 9.5 = 5.530.25
33 – 9.5 = -6.542.25
1212 – 9.5 = 2.56.25
00 – 9.5 = -9.590.25
2424 – 9.5 = 14.5210.25
33 – 9.5 = -6.542.25
M = 9.5Sum = 0Sum 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.
Variance
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.
Variance of visits to the library in the past yearData set: 15, 3, 12, 0, 24, 3
s = 9.18
s2 = 84.3
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
N6
Mean9.5
Median7.5
Mode3
Standard deviation9.18
Variance84.3
Range24
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
outliers, 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.
Contingency table
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
Group0–45–89–1213–1617+
Children3268372322
Adults3648438325
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)
Group0–45–89–1213–1617+N
Children18%37%20%13%12%182
Adults15%20%18%35%11%235
From this table, it is more clear that similar proportions of children and adults go to the library over 17 times a year. Additionally, children most commonly went to the library between 5 and 8 times, while for adults, this number was between 13 and 16.
Scatter plots
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.
Scatter plot example: Library visits and movie theater visitsYou investigate whether people who visit the library more tend to watch a movie at a theater less. You plot the number of times participants watched movies at a theater along the x-axis and visits to the library along the y-axis.
From your scatter plot, you see that as the number of movies seen at movie theaters increases, the number of visits to the library decreases. Based on your visual assessment of a possible linear relationship, you perform further tests of correlation and regression.
Frequently asked questions about descriptive statistics
What’s the difference between descriptive and inferential statistics? What are the 3 main types of descriptive statistics? What’s the difference between univariate, bivariate and multivariate descriptive statistics? Cite this Scribbr article
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