PROCESS<\/strong><\/p>\n\n\n\nMy goal was to find and analyze the varying degrees of relationships across the Germany boys\u2019 class based on the attributes measured by Delitsch (i.e. handicapped, class ranking, sweetsgiver, and repeater).<\/p>\n\n\n\n
For clarification, \u201csweetsgiver\u201d refers to a student who \u2018buys into\u2019 friendships with sweets and money. \u201cRepeater\u201d refers to a student who is repeating the class\/grade.<\/em><\/p>\n\n\n\nNodes<\/strong>Figure 1<\/em> shows the visualization without any other attributes. Originally, I used the total number of relationships as unit for scale, but I also created visualizations using “in-degree relationships” for scale (in-degree meaning a specific, singular direction of friendship a.k.a. one student seeing another student as his friend). After I noticed the nodes were better dispersed yet more clustered, I used the in-degree values for sizing the node scale since it revealed the dynamic of the relationships more clearly. <\/p>\n\n\n\nThis made sense especially since the in-degree value refers to the quantity of incoming relationships a student has, it can be likened more to popularity than the total number of relationships one has. A student could have a large number of relationships, but if only one of them is incoming (another student considers them a friend) then it does not adequately signify popularity. Notice the huge flip in node sizing when I adjust the scale using only the number of outgoing (out-degree) relationships (Figure 3<\/em>).<\/p>\n\n\n\nFigure 1<\/strong> – Color: none \/ Scale: total relationships<\/figcaption><\/figure><\/li> Figure 2<\/strong> – Color: handicapped \/ Scale: in-degree relationships<\/em> <\/figcaption><\/figure><\/li> Figure 3<\/strong> – Color: handicapped \/ Scale: out-degree relationships <\/em> <\/figcaption><\/figure><\/li><\/ul><\/figure>\n\n\n\nIn order to focus on distinct attributes, I decided on making small multiples of the networks. I used various node colors in order to convey the four recorded attributes, then node size as a rough reference for the amount of relationships each student had.<\/p>\n\n\n\n Figure 4<\/strong> – Color: ranking \/ Scale: in-degree relationships<\/em> <\/figcaption><\/figure><\/li> Fig. 5 <\/strong>– Color: repeater \/ Scale: in-degree relationships<\/em> <\/figcaption><\/figure><\/li><\/ul><\/figure>\n\n\n\nFig. 6<\/strong> – Color: sweetsgiver \/ Scale: in-degree relationships<\/figcaption><\/figure><\/li>Fig. 7<\/strong> – Color: handicapped \/ Scale: in-degree relationships<\/figcaption><\/figure><\/li><\/ul><\/figure>\n\n\n\nEdges<\/strong><\/p>\n\n\n\n
INTERPRETATION OF RESULTS<\/strong><\/p>\n\n\n\nPopularity<\/strong>Pfeil and Vetter are both repeater students, but are not the highest ranked in the class. Whereas Schlegel is ranked number 1, but his node size is noticeably smaller than theirs, the other repeaters, and Lasch. The majority of highly ranked students have low quantities of in-degree relationships, with the exception of Schlegel and Wolf . It would be interesting to dive in further as to why these two are outliers in that area. Lasch’s node is quite large in scale despite not being very highly ranked or one of the repeaters. In Figure 6<\/em> we can see that probable cause is his sweetgiving habit, which is mentioned in Delitsch’s study. “Delitsch labels him the \u2018sweets giver\u2019, because he generously \u2018buys into\u2019 friendships with sweet and money from his grandmother, who sells sweets at fairs” (Heidler et al., 2014). Unfortunately, many of the handicapped students are extremely small in scale, with one student having 0 in-degree relationships (can be found, with much difficulty, at the very bottom left of the visualization and connected to Meinhold). Although, it’s quite interesting to see the stark contrast in node size between the number of in-degree (Figure 2<\/em>) vs. out-degree (Figure 3<\/em>) relationships these handicapped students have.<\/p>\n\n\n\nCliques<\/strong>Figure 4<\/em> and 5<\/em> reveals that highly ranked students tend to mingle together, as well as the repeaters amongst themselves.<\/p>\n\n\n\n<\/p>\n\n\n\n
REFLECTION<\/strong><\/p>\n\n\n\n As I mentioned in my Methods<\/strong>, the arrows were difficult to see and I\u2019m curious if a radial diagram would have better articulated the networks between students. I imagine reciprocity might be harder to discern, but that wasn’t something I focused on and connections could be more easily seen\/less convoluted by the nodes and labels overlapping each other. There was also the option to make edge colors the same color as the parent node, however these varying colors became distracting are hard to extract information from. This was a result of the node\/edges arrangement, whereas in a radial diagram, all the edges would be located on the perimeter. <\/p>\n\n\n\nI realized at the end of my project that I could have added a column into the dataset combining handicapped, sweetsgiver, and repeater attributes since they do not apply to all 53 students and never overlap. This would have removed the need for a series of small multiples.<\/p>\n\n\n\n
Additionally, the color choice I used for denoting class ranking can be confusing. The idea was to go from bright blue to dark blue to gray. I chose gray to signify the lowest ranked students so their nodes wouldn\u2019t show up as clearly as the higher ranked students, however the bright blue doesn\u2019t automatically make me think the nodes are of higher rank than the darker blue or blue-gray nodes. The varying node size also makes it difficult to distinguish certain colors from one another (or see them at all in the case of the handicapped visualization). You have to zoom into the visualizations to better evaluate the colors. This could potentially be alleviated by including a legend on the ranking visualization.<\/p>\n\n\n\n
BIBLIOGRAPHY<\/strong><\/p>\n\n\n\nHeidler, R., Gamper, M., Herz, A., & E\u00dfer, F. (2014). Relationship patterns in the 19th century: The friendship network in a German boys\u2019 school class from 1880 to 1881 revisited. Social Networks<\/em>, 37<\/em>, 1\u201313. https:\/\/doi.org\/10.1016\/j.socnet.2013.11.001<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"One of the earliest studies in the field of social network analysis was in the year of 1880-1881, where German teacher, Johannes Delitsch, collected the socio-relational data of a class of 53 students. This study is particularly significant for providing \u201cinsight into the process of friendship formation in school classes during an interesting time where…<\/p>\n","protected":false},"author":562,"featured_media":17427,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[342,1],"tags":[],"coauthors":[455],"class_list":["post-17356","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-networks","category-visualization"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/studentwork.prattsi.org\/infovis\/wp-content\/uploads\/sites\/3\/2019\/11\/indegreesize_ranking-4.jpg?fit=2479%2C3508&ssl=1","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/paBdcV-4vW","_links":{"self":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/17356","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/users\/562"}],"replies":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/comments?post=17356"}],"version-history":[{"count":4,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/17356\/revisions"}],"predecessor-version":[{"id":19335,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/17356\/revisions\/19335"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/media\/17427"}],"wp:attachment":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/media?parent=17356"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/categories?post=17356"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/tags?post=17356"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/coauthors?post=17356"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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