Visualizing

Visualization


Dynamic network charts (Westaby, 2012), which we also now refer to as "network goal graphs" (Westaby & Shon, 2017) or "network goal analysis" (Westaby & Parr, forthcoming), are used to visualize how social networks influence goal pursuits. We use this terminology to help distinguish the approach from traditional sociograms/social network analysis (SNA), which does not incorporate goal nodes within network structures. In the traditional approach, the social structure itself has been the focus, which is understandable, justified, and incredibly important in many systems as discussed in Westaby et al. (2014).  However, by inserting goal nodes into network goal analysis, a helpful new way to understand complex human behavior can be attained, since goals, wishes, and desires drive much human action as well as the formation of social structures to help (or hinder) that action.  It's presumed to be the heart of human behavior.

Using goal nodes in network goal analysis is also helpful because it shows how the network is influencing the goal independently (e.g., individual striving) or inter-dependently (e.g., a person distally helping someone else's goal pursuit). To see how this differs from traditional social network analysis and organizational charts, click here.  

There are various ways to create and visualize these network goal systems, such as through computer visualization, our main focus, and manual approaches, which are useful to quickly portray human dynamics of myriad types.

Network Goal Analysis


Implications in this simple example


  • As one can see from the statistics, the goal is very central to this system along with some entities playing a more central social network role to the goal than others.  This means that the goal (and its information about the task) is connecting people in the system through those trying to achieve it independently (G) or getting feedback from the processes associated with it (FB).
  • Without a network goal analysis, these insights about complex systems would be more easily hidden.  A DNT perspective makes this examination very explicit and allows us to visualize the system at multiple levels and different motivational angles. 

 

Visualizing Massive Online Behavior


Copyright James D. Westaby (C).  All rights reserved. 

Comparing Approaches


The following illustrates some basic differences between organizational charts (no goals), traditional social network analysis (no goals), and network goal analysis (with goals), which we also referred to as dynamic network charts in the past.  The new terminology may help create better differentiation from traditional approaches.

 

-->see Westaby et al. (2014)

However, it is important to keep in mind that network goal analysis supplements traditional social network analysis, it is not meant to replace it.  There are many investigations that can rightfully focus on structural relations alone with considerable importance, such as examining information flow and friendship networks.  Network goal analysis becomes of interest if those analyses are also interested in examining the multiple goals being targeted in those systems or why those linkages may be occurring. 

 

Computerized Contrasts

The following illustrations use our computer visualizations, which are automatically generated from our network goal surveys.  The R statistical program is used heavily in our analytic and visualization work.   This example illustrates the modeling of a simple supervisory example. 

--Traditional Social Network Analysis (no goal node)

  • System support linkages shown (S) 

 

 

--Network Goal Analysis (goal node inserted)

  •  "Network motivation" sub-network shown with goal striving (G), feedback (FB), and system support (S), which we have also referred to as "goal pursuit links".


 

Why Network Goal Analysis Can Add Critical Value Above

In the above visual, you can see that the focal entity, "You", is independently working on the goal very intensely with the Coworker providing some independent work, but not as strongly.  Moreover, you can see that the Supervisor is not working directly on the project, but instead serving a more inter-dependent support function.  Network goal analysis allows us to see all the underlying dynamics, including who gets feedback from the goal pursuit itself (e.g., information from task instruments), a critical motivational factor in psychological science.

 

Other Major Differences

-->see Westaby & Shon (2017)

 

 

Copyright James D. Westaby (C).  All rights reserved. 

The manual approach can be helpful to quickly describe a situation of interest and has been used many times in education, practice, and intervention.  Little technology is needed and it can be drawn by hand as well, which is especially helpful to describe basic dynamics of human interaction.  One simply needs to have basic knowledge about the theory, its social network roles, and the simple graphing techniques, such as illustrated in the Appendix of Westaby (2012) or some of the basic ideas demonstrated on this website.  If referring to this original theory book, please keep in mind that we are now generalizing system reactance to represent constructive linkages during conflicts. 

To note, though, the manual approach can be laborious since all summary calculations need to be done manually and it's harder to fit a full analysis in one page by hand when the system gets larger. It's also harder when trying to examine different motivational parts of our systems, such as contrasting system support to goal prevention sub-networks.  For centrality metrics, computer automation is helpful and almost always a necessity as well, since it can be difficult for non-statisticians to generate such metrics in larger systems.  Our network goal surveys and network goal graphs are generated automatically by computer, simplifying the process immensely in research projects. 

Nonetheless, a simple system is drawn by hand of Person 1 striving for Goal X.  This person has two direct supporters (Person 2 and Person 3) and one of those supporters (Person 2) is also getting help from another distal supporter (Person 4), who is also indirectly helping in the system.  Hence, this qualitative narrative can be visualized in quantitative chart form below.  In other words, the story can be transposed into graph form by hand or by our survey tools, thus making it quantitative (i.e., the simple links shown below could be scored 1 and non-links/structural holes could be scored 0 between given dyads). 

Example Manually Drawn by Hand 

Steps to Create Manual Network Goal Graphs (aka Dynamic Network Charts)

See Appendix in Westaby (2012) for more detailed illustrations, but keep in mind, again, that we have now re-formulated system negation (N) to more broadly represent entities being "upset" with one another in relation to the focal goal and system reactance (R) to represent constructive reactance to such conflicts.

Computerized Tools


Using R statistical programming, our lab takes the results from survey participants and automatically transforms them into striking network goal graphs (also previously known as dynamic network charts) that participants and researchers can visualize and use online. This allows users to deeply understand their dynamic network system (aka network goal system) and then start planning for positive change as needed.  One of our objectives with such visualization is to promote users' deep learning about their complex systems at multiple levels and angles to advance positive change.  

The computerized reports allow participants to visually zoom in on key relations impacting their pursuits or lives, ranging from supportive and loving ones to those generating conflict and despair.  Click here to see an example of such sub-network analysis.  These visualizations also allow our lab to bridge the gap between complexity and parsimony, something that social, organizational, and international scholars have struggled with in the past.

We believe that parsimonious explanations, grounded in dynamic network theory, underlie many of the complexities we see in everyday life at individual, group, organizational and even international levels.  Practically, our computer visualizations give survey participants or research partners unique insight into improving goal achievement, performance, climates, and system well-being.

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