American Time Survey data
The American Time Survey data is included within Watson Analytics as a sample data set called American Time Use Survey.csv
Imagine you’re a university student thinking about going to graduate school and wondering what the impact would be on your income and how this affects your free time over the long term.
The American Time Use Survey data set contains data about the amount of time people spend doing various activities, such as paid work, volunteering, childcare, and socializing. This demographic data is about a subset of Americans but can be applied more widely.
It all starts with your Data!
- Select American Time Use Survey.csv, scroll down and then click the Import button.
The data set appears as a tile in your Personal data folder.
Watson Analytics analyzes the data and metadata when uploading the csv file to provide smarter data discovery and analysis. In this process, Watson Analytics identifies field names and concepts, possible measurements and hierarchies in your data and captures metadata including data quality, data distributions, skewness and missing values.
Let’s ask our first question.
Does higher education lead to higher earnings?
- Tap the American Time Use Survey data set tile.
You are taken into a new Discovery set. This is where you start interacting with the data. That single tap gave you a list of Starting points, which are different ways to launch yourself into data analysis and visualizations.
- Let’s enter our question: does higher education lead to higher earnings, and then press Enter.
You now see different Starting points based on your question and these are ranked by relevance. The most relevant inquiries bubble to the top of the list.
- Select the Starting point: What is the breakdown of Weekly Earnings by Education Level?
The results are shown in a treemap visualization. The size of each rectangle below indicates the relative size of weekly earnings by education level. The largest rectangles are for those with advanced degrees.
This visualization is for all ages. Let’s see how weekly earnings by education level breaks down when ages are added in.
At the very bottom of the window is the Data Tray showing all the column headings in the data set.
- Add Age Range to the visualization. Just drag it from the data tray (the grey strip on the bottom) and drop it anywhere on the visualization. Note: you can also drop it on the Data Slot beside the drop down for Education Level on the bottom left just below the visualization.
There’s a lot more detail in the visualization now, perhaps too much.
- Let’s focus in on people with college or university degrees. Below the visualization, you can modify what is displayed. Select Education Level and tap the items listed from 9th grade down to Some College to remove them from the visualization.
You may need to scroll down in the box to complete this.
Some of the smaller rectangles are for age groups that aren’t really relevant to the question that we’re exploring. People aged 0-19 have generally not completed university or college, and those aged 70 and older have generally retired from paid work.
- Let’s filter out these groups:
Tap Age Range at the bottom of the Visualization
Select 0-19, 70-79, and 80+ to remove them.
Then tap Done or outside the Age Range list to close it.
Try a different visualization type
Different visualization types communicate information about data in different ways. Let’s see what else we can learn by using a different visualization type.
- Tap to the left of your visualization to see what Watson Analytics recommends. You can, of course, pick any type you want.
Tap the first recommended visualization: the Bar chart.
You see that earnings peak when people are in their 30s and 40s, regardless of education level.
But what about work-life balance?
Earnings is one way to look at it. However, life is about more than how much money you earn. Does someone with more education work longer hours? Do they have time to spend with their families and friends?
- Lets add to this discovery set with a simple click on the plus button circled below and then ask the question “How do weekly hours worked compare by education level?”
By clicking on the insight tile circled above you will see the treemap. We can see that people with more advanced education level spending more time working.
- In the previous inquiry on weekly hours worked by education level, I see that there are other questions we could ask that are more predictive in nature. Similar to Step 8 lets add to the Discovery Set and determine “What drives Weekly Earnings?” Select the circled insight tile.
It may take a few minutes for this insight to process as it is going through many predictive models to determine what drives weekly earnings. Once it evaluates thousands of models, it will present us with a short list of predictive relationships.
- Not surprising -based on what we have already seen that weekly hours worked and education level have relatively strong relationship with weekly earnings with a predictive strength of 45%. If you wanted to see more drivers, you can tap the link for “Show more drivers”. If we tap the button to the right of the driver we can see more details on the driver.
As we mouse over the blue blocks in this heatmap, which show the key elements of the relationship, the cell values for weekly income (shown as color intensity) are generally higher earnings as you move your cursor up and to the right.
What did we learn?
These findings show us that working hard to get good marks in school to attain a higher education does not stop there. We will need to keep working after we have attained our advanced degree to continue in building up the weekly earnings. This of course affects our free time.
Don’t stop there – Try this type of analysis with your own data set!