by    in Data, Data Science, Opinion Graph

A Ranker Opinion Graph of Important Life Goals

What does it mean to be successful, and what life goals should we be setting in order to get there? Is spending time with family most important? What about your career?  We asked people to rank their life goals in order of importance on Ranker, and using a layout algorithm (force atlas in Gephi), we were able to determine goal categories and organized these goals into a layout which placed goals most closely related nearer to each other.

The connecting lines in the graph represent significant correlations or relationships between different life goals, with thicker lines indicating stronger relationships.  The colors in the graph differentiate between unique groups that emerged from a cluster analysis.  Click on the below graph to expand it.

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The classification algorithm produced 5 main life goal clusters:
(1) Religion/Spirituality (e.g., Christian values, achieving Religion & Spirituality),
(2) Achievement and Material Goods (e.g., being a leader, avoiding failure, having money/wealth),
(3) Interpersonal Involvement/Moral Values (e.g., sharing life, doing the right thing, being inspiring),
(4) Personal Growth (e.g., achieving wisdom & serenity, pursuing ideals and passions, peace of mind), and
(5) Emotional/Physical Well-Being (e.g., being healthy, enjoying life, being happy).

These clusters are well matched to those identified by Robert Emmon’s (1999) psychological research on goal pursuit and well-being. Emmon’s found that life goals form 4 primary categories: work and achievement, relationships and intimacy, religion and spirituality, and generativity (leaving legacy/contributing to society).

However, not all goals are created equal.  While success related goals may be able to help us get ahead in life, they also have downsides.   People who focus on zero-sum goals such as work and achievement tend to report less happiness and life satisfaction compared to people who pursue goals. Our data also show a large divide between Well-being and Work/Achievement goals with relatively no overlap between these two groups.

Other interesting relationships in our graph:

  • Goals related to moral values (e.g., doing the right thing) were clustered with (and therefore more closely related to) interpersonal goals than they were to religious goals.
  • Sexuality was related to goals from opposite ends of the space in unique ways. Well-being goals were related to sexual intimacy whereas Achievement goals were related to promiscuity.
  • While most goal clusters were primarily made up of goals for pursuing positive outcomes, the Achievement/Material Goods goal cluster also included the most goals related to avoiding negative consequences (e.g., avoiding failure, avoiding effort, never going to jail).
  • Our Personal Growth goal cluster is unique from many of the traditional goal taxonomies in the psychological literature, and our data did not find the typical goal cluster related to Generativity. This may show a shift in goal striving from community growth to personal growth.

– Kate Johnson

Citation: Emmons, R. A. (1999). The psychology of ultimate concerns: Motivation and spirituality in personality. New York: Guilford Press.

 

An Opinion Graph of the World’s Beers

One of the strengths of Ranker‘s data is that we collect such a wide variety of opinions from users that we can put opinions about a wide variety of subjects into a graph format.  Graphs are useful as they let you go beyond the individual relationships between items and see overall patterns.  In anticipation of Cinco de Mayo, I produced the below opinion graph of beers, based on votes on lists such as our Best World Beers list.  Connections in this graph represent significant correlations between sentiment towards connected beers, which vary in terms of strength.  A layout algorithm (force atlas in Gephi) placed beers that were more related closer to each other and beers that had fewer/weaker connections further apart.  I also ran a classification algorithm that clustered beers according to preference and colored the graph according to these clusters.  Click on the below graph to expand it.

Ranker's Beer Opinion Graph

One of the fun things about graphs is that different people will see different patterns.  Among the things I learned from this exercise are:

  • •The opposite of light beer, from a taste perspective, isn’t dark beer.  Rather, light beers like Miller Lite are most opposite craft beers like Stone IPA and Chimay.
  • •Coors light is the light beer that is closest to the mainstream cluster.  Stella Artois, Corona, and Heineken are also reasonable bridge beers between the main cluster and the light beer world.
  • •The classification algorithm revealed six main taste/opinion clusters, which I would label: Really Light Beers (e.g. Natural Light), Lighter Mainstream Beers (e.g. Blue Moon), Stout Beers (e.g. Guinness), Craft Beers (e.g. Stone IPA), Darker European Beers (e.g. Chimay), and Lighter European Beers (e.g. Leffe Blonde).  The interesting parts about the classifications are the cases on the edge, such as how Newcastle Brown Ale appeals to both Guinness and Heineken drinkers.
  • •Seeing beers graphed according to opinions made me wonder if companies consciously position their beers accordingly.  Is Pyramid Hefeweizen successfully appealing to the Sam Adams drinker who wants a bit of European flavor?  Is Anchor Steam supposed to appeal to both the Guinness drinker and the craft beer drinker?  I’m not sure if I know enough about the marketing of beers to know the answer to this, but I’d be curious if beer companies place their beers in the same space that this opinion graph does.

These are just a few observations based on my own limited beer drinking experience.  I tend to be more of a whiskey drinker, and hope more of you will vote on our Best Tasting Whiskey list, so I can graph that next.  I’d love to hear comments about other observations that you might make from this graph.

– Ravi Iyer