## SOCNET@LISTS.UFL.EDU

#### View:

 Message: [ First | Previous | Next | Last ] By Topic: [ First | Previous | Next | Last ] By Author: [ First | Previous | Next | Last ] Font: Proportional Font

Subject:

Re: Comparison of QAP and ERGMs

From:

Date:

Sun, 25 May 2008 17:50:32 -0400

Content-Type:

text/plain

Parts/Attachments:

 text/plain (93 lines)
 ```***** To join INSNA, visit http://www.insna.org ***** Param, good question. And, I will add that I think there is not a consensus on an appropriate answer. But, OK, I'll take a shot anyway. QAP was designed as a bivariate test (only two variables). Generally, QAP is perfectly fine for almost any bivariate network problem. ERGM or P* is really a multivariate procedure (if you consider all the terms that one usually includes in any ERGM analysis). Once you get into multiple independent variables, you are comparing ERGM to MRQAP (multiple regression quadratic assignment procedure), which is a bit more complicated. But, for multivariate cases, the quick and dirty answer is: If your dependent variable is continous or count data (like in a negative binomial case), MRQAP is best. If your dependent variable is binary, ERGM (P*) is best. The truth is, though, it is really not that simple. One can perform ERGM models on continuous dependent data (although I don't think this is implemented in Statnet as of yet). And, one can perform MRQAP on data that have a dichotomous dependent variable (basically, this is equivalent to using a linear probability model). The advantages and disadvantages of each are being actively explored as we speak, and I would hesitate to predict how all the constraints will play out. To this day, I am still surprised by cases where I thought MRQAP would work (or wouldn't work) and I am led to conclude the opposite through a set of carefully conducted monte carlo simulations. My personal experience is that both approaches "work" (provide reasonably unbiased tests) in many commonly found data sets. David Dekker and I presented a paper last Sunbelt in which we argued (again, with simulations) that the safe thing to do is simulate your own data conditions and test the test you want to use to make sure it is reasonably unbiased. But, I will admit this is asking a lot of the researcher and may not be practical in many cases. Finally, I will say that given you are at the University of Washington, you have one of the best concentrations of ERGM resources that exists anywhere. I would ask Mark Handcock or Martina Morris if I were there. -David ------------------ David Krackhardt, Professor of Organizations, Executive Editor of JoSS Heinz School of Public Policy and Management, and       The Tepper School of Business Carnegie Mellon University Pittsburgh, PA 15213 412-268-4758 website: www.andrew.cmu.edu/~krack     (Erdos#=2) Param Vir Singh wrote: > ***** To join INSNA, visit http://www.insna.org ***** > > Dear Socnetters, > I am trying to understand when one should use QAP (Quadratic Assignment > Problem) or ERGM (exponential random graph models) for explaining the > network structure. Is there any reference which explains the advantages of > one over the other? > > Thanks in Advance, > Param > > > -------------------------------------------- > Param Vir Singh > Doctoral Candidate (PhC) > Information Systems > Michael G Foster School of Business > University of Washington, Seattle > Phone: (206)-685-6419 Fax:(206)-543-3968 > > http://students.washington.edu/psidhu > > > > _____________________________________________________________________ > 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.```