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SOCNET  November 2014

SOCNET November 2014

Subject:

Re: siena vs. relational event

From:

martina morris <[log in to unmask]>

Reply-To:

martina morris <[log in to unmask]>

Date:

Thu, 13 Nov 2014 11:18:32 -0800

Content-Type:

MULTIPART/MIXED

Parts/Attachments:

Parts/Attachments

TEXT/PLAIN (221 lines)

*****  To join INSNA, visit http://www.insna.org  *****

The other issue to note here is that, in the absence of a relationship 
with duration, there is no real mechanism for representing the factors 
that influence relationship dissolution.

best,
mm

On Thu, 13 Nov 2014, James Kitts wrote:

> ***** To join INSNA, visit http://www.insna.org *****
> 
> Ø2) The assumption, of what a tie is, is different in siena and relational event modeling. The former
> assumes it to be a state (enduring) whereas the latter an event (discrete). I believe that in the emergency
> care teams as network ties are measured as person A talking to person B, the assumption of a discrete event
> applies… So I have two chapters, which follow each other, with different assumptions for what a tie is…
> "When measuring information sharing by asking respondents to state 'how frequently they share information
> with person X over the past 3 months', the tie is operationalized as an enduring state. In this situation,
> it is assumed that - if everything else remains constant - the tie will continue to exists. However, when
> measuring information sharing by looking at every instant of interaction, the tie is not anymore enduring,
> but discrete." So my basic argument is that I measure the tie differently, and therefore have to use a
> different method. Is this convincing? Do I miss something?
>
> 
> 
> Katerina
> 
> Congratulations on an exciting dissertation that may engage a number of timely issues. In one chapter you
> have observer-coded information sharing events, with detail about the timing of those events. In another
> chapter you have self-reports of information sharing events, aggregated as frequencies over 3 months, so
> event timing is known only within an interval (along with other sources of error associated with
> self-reports). To me it appears that both chapters are trying to measure the same empirical animal (one or
> more ‘discrete’ information sharing events that occur at particular times), whether specified at time points
> or in time intervals. Some scholars observe relationships typically seen as essentially continuous in time
> (e.g. friendship, love, kinship, coauthorship, etc.) and there are of course grey areas that could be framed
> as events or temporally continuous states (e.g. conversation threads, reciprocal exchange), but it seems
> both your chapters here focus on recording discrete interaction events, in two different ways. This is fine.
> 
>
> 
> 
> It’s not hard to write a script to transform ‘events’ into presumed ‘states.’ Similarly, we can sometimes
> recover a set of approximately time-stamped events from sparse count data on sufficiently large number of
> short time intervals, as is practiced in applying event history analysis to event count data. But it’s
> important to think deeply about what that leap from event to state means, i.e. how you interpret those
> states. Given your interest in the dynamic interplay of information sharing and the timing of patient health
> status, your measurement of time-stamped information sharing events seems crucial. Aggregating those events
> into counts on time intervals would sacrifice timing and sequence information, but further binarizing that
> count into a ‘state’ makes us ignorant about even the frequency as well as the timing within the interval.
> Paradoxically, this binarizing step seems often to lead readers to interpret such an aggregated-event ‘tie’
> as something more than a set of events in time – something substantive and temporally continuous or
> ‘enduring’ as you say – even if all you have done is destroy timing and frequency information on underlying
> events. Are we tempted to think of an ‘open channel of communication’ state as something more than just ‘>k
> communication events at unknown times in this interval’? Importantly, does this also imply that
> below-threshold frequencies are temporally continuous ‘closed channels of communication’ on that interval? I
> suspect it would be hard to interpret zeros in a ‘communication channel’ state matrix for your small
> face-to-face emergency care teams over a three month period, especially if you set that threshold relatively
> high. Here, your coherent focus on frequency of sharing events seems to be an important feature of your
> lens, so I agree with Christian that it makes sense to avoid destroying any fine-grained information that
> you have.
>
> 
> 
> I recently published a review article focused largely on the correspondence of interaction events to other
> concepts of social ties (including role relations, sentiments, and opportunity structures), which may be
> useful to you as you think about your study. My angle is about theoretically grappling with the interplay
> between interaction-as-events and relationships-as-states, not about the choice to use SIENA vs relational
> event approach. In fact, I am more targeting conventional SNA lenses in light of this question.
>
> 
> 
> Best, James Kitts
> 
> ---------------------------------------
> 
> James A. Kitts
> 
> Associate Professor of Sociology
> 
> Director, Computational Social Science Institute
> 
> University of Massachusetts
> 
> Department of Sociology; 200 Hicks Way
> 
> Amherst, MA 01003-9277
> 
> http://www.jameskitts.com
>
> 
> 
> -----Original Message-----
> From: Social Networks Discussion Forum [mailto:[log in to unmask]] On Behalf Of Katerina Bohle Carbonell
> Sent: Thursday, November 06, 2014 7:45 AM
> To: [log in to unmask]
> Subject: [SOCNET] siena vs. relational event
>
> 
> 
> *****  To join INSNA, visit http://www.insna.org  *****
>
> 
> 
> Hello,
>
> 
> 
> I collected data in a simulation center (emergency care teams). The network
> 
> (information exchange) is coded from the videos. I have 33 group.
> 
> I want to look at how the interaction pattern changes with regard to changes in the
> 
> patients health, so the context plays a role. But for every group, the frequency and
> 
> sequence, with which the patient's health changes, is different, and the length of
> 
> time the patient is in good/regular/bad health is also different.
>
> 
> 
> I'm struggling with two points for the analysis:
>
> 
> 
> 1) I have been using siena in the past and I know that if I want to use the
> 
> multigroup option I need to have the same amount of waves for every group. I
> 
> also believe that every wave needs to last the same amount of time (e.g. 5
> 
> minutes). Am I correct?
>
> 
> 
> 2) The assumption, of what a tie is, is different in siena and relational event
> 
> modeling. The former assumes it to be a state (enduring) whereas the latter an
> 
> event (discrete). I believe that in the emergency care teams as network ties are
> 
> measured as person A talking to person B, the assumption of a discrete event
> 
> applies (similar to the radio communication example mentioned in Butts (2008).
> 
> The issue is that this study is part of my PhD and as I'm in the Netherlands, the
> 
> dissertation is in the form of a collection of articles. Another study, which is
> 
> included in the dissertation, uses siena. So I have two chapters, which follow each
> 
> other, with different assumptions for what a tie is. Therefore my arguments need
> 
> to be clear and convincing. In short this is what I argue: "When measuring
> 
> information sharing by asking respondents to state 'how frequently they share
> 
> information with person X over the past 3 months', the tie is operationalized as an
> 
> enduring state. In this situation, it is assumed that - if everything else remains
> 
> constant - the tie will continue to exists. However, when measuring information
> 
> sharing by looking at every instant of interaction, the tie is not anymore enduring,
> 
> but discrete." So my basic argument is that I measure the tie differently, and
> 
> therefore have to use a different method. Is this convincing? Do I miss something?
>
> 
> 
> Thank you for your help,
> 
> Katerina
>
> 
> 
> Researcher
> 
> Department of Educational Research and Development
> 
> Maastricht University
>
> 
> 
> _____________________________________________________________________
> 
> SOCNET is a service of INSNA, the professional association for social
> 
> network researchers (http://www.insna.org). To unsubscribe, send
> 
> an email message to [log in to unmask] containing the line
> 
> UNSUBSCRIBE SOCNET in the body of the message.
> 
> _____________________________________________________________________ SOCNET is a service of INSNA, the
> professional association for social network researchers (http://www.insna.org). To unsubscribe, send an
> email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.
>

****************************************************************
  Professor of Sociology and Statistics
  Director, UWCFAR Sociobehavioral and Prevention Research Core
  Box 354322
  University of Washington
  Seattle, WA 98195-4322

  Office:        (206) 685-3402
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http://faculty.washington.edu/morrism/

_____________________________________________________________________
SOCNET is a service of INSNA, the professional association for social
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