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From |
Steven Raemaekers <s.raemaekers@sig.eu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Nested design problem |

Date |
Fri, 16 Nov 2012 13:32:59 +0100 |

Hello David, Thanks for your response! You can view the connections between people as a directed graph, with arbitrary arrows between people when they have a connection. There is not a value for each possible pair of people, a lot of connections are missing and are then considered irrelevant, but I'm only interested in the subjects which have connections to other people so this should not be a problem. Maybe it is indeed an analysis problem because I have already collected the data and I already know my research question. Then the question becomes more how to choose a statistical model that does most justice to reality. Regards, Steven On 16 nov. 2012, at 13:13, David Hoaglin wrote: > Hi, Steven. > > The problem you describe is interesting. I don't recall seeing > anything like it in the statistical literature. I view it as an > analysis problem, rather than a design problem (you have already > collected the data). Some of the work in sociology on networks may be > relevant. > > I think it would be more appropriate to consider closeness as the > dependent variable. > > Do you have a value of closeness for each of the possible pairs of > people? If not, what is the structure of the subset of people for > whom you do have closeness? > > David Hoaglin > > On Thu, Nov 15, 2012 at 4:44 PM, Steven Raemaekers <s.raemaekers@sig.eu> wrote: >> Hello, >> >> I do not know where else to post this problem so I hope somebody can help me here. I have a statistical design issue which is as follows. >> As an example, let's say I have a table with information on people in the following format: >> >> Id IQ Introvertness … >> person1 120 80 … >> person2 110 70 … >> person3 130 40 … >> >> This table contains a number of properties of certain persons, each person is a unique entry in this table. I also have a table which contains relationships between those persons: >> >> Id1 Id2 Closeness >> person1 person2 80 >> person1 person3 70 >> >> "Closeness" is a property of the relationship between person1 and person2. The numbers do not make sense but are just for illustration. >> >> Now I want to test whether people that are in relationships "closer" to each other are more intelligent. I also possibly want to take into account other properties of people. >> In regression terms: I want to regress the dependent variable IQ on the independent variable closeness and introvertness. How can I do this? >> >> The problem is, persons can appear multiple time in the list so applying normal linear regression or correlation on this table produces incorrect results. >> Additionally, there are two sides to the relationship, so there is a connection between the IQ of person1, the closeness of their relationship and the IQ of person2. >> I believe the thing I need is called a nested hierarchical model/mixed model, but I have no idea how to design this. >> >> My questions are therefore the following: >> 1) What statistical test do I need to test my hypothesis on this data? >> 2) What assumptions does this test make and how can I check these assumptions? >> 3) How should I format my data? >> 4) What are the commands in Stata to execute this test? >> >> Thanks very much! >> >> Regards, >> >> Steven Raemaekers >> PhD student >> Software Improvement Group/TU Delft > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/statalist-faq/ > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Nested design problem***From:*David Hoaglin <dchoaglin@gmail.com>

**Re: st: Nested design problem***From:*Marcello Pagano <pagano@hsph.harvard.edu>

**References**:**st: Nested design problem***From:*Steven Raemaekers <s.raemaekers@sig.eu>

**Re: st: Nested design problem***From:*David Hoaglin <dchoaglin@gmail.com>

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