I’m a curious person, and I wanted to analyze survey data coming from the European Social Survey. Especially, as I’m based in Germany, I was interested to see how Germany was rated compared to the rest of Europe. The European Social Survey (ESS) is an academically driven cross-national survey that has been conducted across Europe since its establishment in 2001. Every two years, face-to-face interviews are conducted with newly selected, cross-sectional samples. The survey measures the attitudes, beliefs and behavior patterns of a diverse population in more than 30 nations.
The survey began gathering data in 2002 and continued every two years until 2018. The data ultimately was compiled into nine different data sets. The ESS questionnaire consists of a collection of questions that can be classified into two main parts: a core section that focuses on a range of different themes that are largely the same in each round and a rotating section that is dedicated to specific themes and can differ from round to round. The ESS data is available free of charge for non-commercial use and can be downloaded from this website after a short registration.
These data sets can be downloaded either as SAS, SPSS or STATA. I’ve chosen SPSS. JMP is very versatile in opening data coming from different sources, and SPSS is one of them. After concatenating and cleaning the data, including recoding and removing nearly empty columns, I got to the fun part: analyzing the data.
Since I was able to save the different data preparation steps in the script coming from the Action Recorder in the log view, which is new in JMP 16 (see Data Cleaning.jsl), it wouldn’t take much time if I needed to repeat the data preparation steps.
I first had a look at how many responses were obtained in each country during the different years. Graph Builder is the perfect platform to execute many different types of graphs. The graph below shows that Germany completed its survey every year with close to 3,000 respondents.
In each country, sets of questions were asked on politics, education, religion and life satisfaction. An interesting outcome was that a happiness trend in the different countries of Europe was revealed by asking such questions as “How happy are you?” and “How satisfied are you with life as a whole?” To answer these questions, I used different analysis techniques in Graph Builder and in multiple correspondence analysis.
Using geographic mapping of the countries involved in the survey and by coloring the question “How happy are you?” it was very clear to me that the happiest and most satisfied people live in Scandinavian countries and Switzerland (in dark red). The highest rated country was Iceland, with 8.27. In contrast, countries with the lowest scores are in Eastern European countries and Portugal (blue to dark blue). The lowest rated country was Ukraine with 5.79. Germany had an overall rating of 7.43, which is good!
By plotting survey question scores on the Y axis and Years on the X axis, and then changing the graph display to a mosaic plot, it was easy to see the trend of happiness year over year in the different countries. Using the Column Switcher, I could easily switch from one question to another and see the percentage of the scores, as seen below with Germany as the example. Interestingly, the trend over the years is an increase in happiness and life satisfaction as a whole, again which is good!!
How happy are you? 2002-2018
How satisfied are you with life as a whole? 2002-2018
Next, I wanted to verify my results using a multiple correspondence analysis (MCA) in JMP. MCA is an analysis technique for nominal categorical data, used to detect and represent underlying structures in a data set. It takes multiple categorical variables and seeks to identify any association between the levels of those variables. MCA is frequently used in survey analysis to identify question agreement. Therefore, this type of analysis was a perfect fit for my purposes. I used the question “How happy are you?” as my response and “Country” as my factor. The results are below. We can see the scores nicely distributed on the horizontal axes, from Extremely Happy on the left to Extremely Unhappy on the right. Again, it is no surprise to see the Scandinavian countries on left part of the graph and Eastern European countries on the right part. Germany was positioned on the left and scored between 7 and 8, which is the overall score spanning 16 years.
But is there a positive happiness trend for Germany over the years, as was observed in the mosaic plot? To check this, I recreated the multiple correspondence analysis from above but now used Year as a by variable so that I could split my analysis into the corresponding years. I saved the coordinates for X and Y from all nine years. I concatenated the different X and Y coordinates and used the final data set in a bubble plot to see the trend for each country year by year, and specifically, for Germany. I colored the dots: red dots for scores 7, 8, 9 (extremely happy) and the blue dot for Germany. The gray dots are for European countries other than Germany. When animating the bubble plot, the dots begin to move. The trend for Germany was particularly interesting. In 2002, the dot was close to 7, but year after year, the dot got closer to 8 and 9, which again is a good sign.
My findings were verified using different techniques in JMP. Many other questions could have been analyzed for different countries using these different techniques. All the graphs and results in this blog have been published on JMP Public: How happy are we here in Europe? | JMP Public
