MB 876
Spring 2007 Schedule
Prof Steve Borgatti MB 813 Home

Legend

Assignments are due on the day they are listed (except for the very first class -- those readings should be read immediately after class). All items subject to change without notice. Please check the schedule frequently. Before printing articles, try reading them online. Is all that ink and toner really necessary?

Most of the links for journal articles require you to be logged in via your BC id in order to recognize you as legitimate members of the community. Click here for more information. The notation "LCG" is used to refer to the Lattin, Carroll and Green  text book. For example, "LCG 1,2" refers to chapters 1 and 2 of that book.

Shortcuts

16-Jan  Vectors
23-Jan  Matrices
30-Jan  More matrices
6-Feb  Measurement & Similarity
13-Feb  Visualizing proximities
20-Feb  ProFit & QAP
27-Feb  Factor Analysis 1 
6-Mar  Spring Break (no class)
13-Mar  Factor Analysis 2
20-Mar  Consensus analysis
27-Mar  Cluster analysis
3-Apr  Cluster analysis
10-Apr  Correspondence analysis
17-Apr  Presentations
 

 

16 Jan.  Vectors
Covered
in class:
  • Course overview
  • Introduction to vectors
  • Linear regression
  • Preview of similarity measures
  • Vector problem set
Reading:
Data:

 

23 Jan.  Matrices
Covered
in class:
  • Matrix notation and terminology
  • Ways and modes
  • Profile and proximity matrices
  • Operations on matrices
  • Lecture plan:  matrices
Data:
Reading:

 

30 Jan.  More Matrices
In class:
  • Working with Excel and UCINET
  • Matrix inverses, transposes, and products
  • Boolean matrix algebra
  • Lecture plan: More Matrices
Reading:
  • [optional] First four chapters of Kaw, Autar 2002. Introduction to Matrix Algebra. [pdf]
Data:

 

6 Feb  Measurement Theory & Similarity Measures
Covered
in class:
Homework due:
  • Quiz on vectors and matrices
Reading:
Optional:
  • Robinson, W.S. 1953. The Statistical Measurement of Agreement. American Sociological Review 22(1): 17-25 [pdf]
Data:

\

13 Feb. Visualizing Proximities
In class:
  • Multidimensional scaling (MDS)
  • Network representations of proximity data
  • Property Fitting (PROFIT)
  • MDS lecture plan
Reading:
  • LCG 7
  • Kruskal and Wish. Multidimensional Scaling.
  • Handout: MDS
  • Skim Weller and Romney. Metric Scaling.

  • DeJordy, Borgatti, Roussin and Halgin. Visualizing Proximity Data. Field Methods.[pdf][doc]  
Due:
  • Email everyone in the class two things: (a) One paragraph description of your research project, for my approval. (You may run this by ahead of time if you want to get started earlier in the semester), (b) Some data that we can MDS in class. Be sure to test it out first to ensure that it is doable and interesting.
Data:

 

20 Feb. PROFIT & QAP
In class:
  • PROperty FITting (PROFIT)
  • Correlating proximity matrices via QAP
Reading:
  • Borgatti. Property Fitting. [html]
  • Weller. 1984. Cross-Cultural Concepts of Illness: Variation and Validation. American Anthropologist, Vol. 86, No. 2. (Jun., 1984), pp. 341-351 [^pdf]
  • Borgatti, S. and Feld, S. 1994. How to test the strength of weak ties theory. [html]
  • Testing network hypotheses [pdf] {just look at material on permutation tests and QAP}
  • Borgatti, S.P. and Cross, R. 2003. A Relational View of Information Seeking and Learning in Social Networks. Management Science. 49(4): 432-445.[pdf]
  • PROFIT [pdf]
Optional:

Methods:

  • Smouse, Peter E., Jeffery C. Long, and Robert R. Sokal (1986), "Multiple Regression and Correlation Extensions of the Mantel Test of Matrix Correspondence," Systematic Zoology, 35 (4), 627-32.

  • Krackhardt, D. 1988. "Predicting with networks: Nonparametric multiple regression analysis of dyadic data." Social Networks. 10:359-381. [pdf]

  • Baker, F. and Hubert, L. 1981. The analysis of social interaction data. Sociological Methods & Research 9(3): 339-361.
  • Hubert, L. & Schultz, L. 1976. Quadratic assignment as a general data analysis strategy. British Journal of Mathematical & Statistical Psychology 29:190-241.

Applications:

  • Barley, S. 1990. The alignment of technology and structure through roles and networks. Administrative Science Quarterly 35: 61-103.
  • Burkhardt, M. 1994. Social interaction effects following a technological change: A longitudinal investigation. Academy of Management Journal 37(4): 869-898.
  • Boster, J. 1986. Exchange of Varieties and Information between Aguaruna manioc cultivators. American Anthropologist 88:428-436. [^pdf]

 

27 Feb.  Factor Analysis
In class:
  • Overview of factor analysis methodology
  • Principal components
  • Eigenvectors and eigenvalues
  • Principal component analysis and common factor analysis
  • Loadings & scores
  • Communalities
  • Rotation
Reading:
Data:
   

 

6 Mar.  Spring Break
In class:
  • No class

 

13 Mar.  Factor Analysis II
In class:
  • Common factor analysis
  • Scale Construction
Reading:
  • LCG 4
  • Building an additive scale [html]
  • Scales and Indices [html]
Due:
  • Email everyone in the class some data that we can factor analyze. Note: you should test this out yourself first and make sure it's doable and interesting.
Optional:
  • Green. Tools for Multivariate Analysis.

 

20 Mar. Consensus Analysis
In class:
  • Consensus analysis - theory and method
  • Measuring knowledge
Reading:
  • Borgatti and Carboni. Measuring knowledge. [doc]
  • Consensus analysis
  • Romney, A. Kimball,. Susan C. Weller and William H. Batchelder. 1986. Culture as Consensus: A Theory of Culture and Informant Accuracy. American Anthropologist 88:313 338 [^pdf]
  • Jaskyte, Kristina and William W. Dressler. (2004)  Studying culture as an integral aggregate variable: organizational culture and innovation in a group of nonprofit organizations.  Field Methods, Vol. 16, No. 3, 265-284 [^PDF]
Data:

 

27 Mar. Cluster Analysis I
In class:
  • Intro to cluster analysis
  • Johnson's hierarchical cluster analysis
Reading:
Homework due:
  •  

 

3 Apr.  Cluster Analysis 2
In class:
  • Combinatorial optimization
Reading:
Handouts:  
Optional:  

 

10 Apr.  Correspondence Analysis
In class:
  • Correspondence analysis
  • Singular value decomposition
  • 2-mode scaling
  • pls bring data to class so we can run Correspondence Analysis together. best data for correspondence analysis are frequencies -- as in a cross-tab.
Reading:
  • Weller & Romney. Metric Scaling.
  • StatSoft. "Correspondence Analysis" [html]
Handouts:
Optional:

  • Douglas Carroll, Paul Green & Catherine Shaefer. 1987. "Comparing Interpoint Distances in Correspondence Analysis: A Classification," with J.Douglas Carroll and Paul E. Green, Journal of Marketing Research, 24 (November),1987, 445-450.[skim]

  • Greenacre, M.J. (1989). The Carroll-Green-Schaffer scaling in correspondence analysis: A theoretical and empirical appraisal. Journal of Marketing Research, 26, 358-365.
  • Greenacre, M.J. & Hastie, T. (1987). The geometric interpretation of correspondence analysis. Journal of the American Statistical Association, 82, 437-447.
Due:
  • pls bring data to class so we can run Correspondence Analysis together. Best data for correspondence analysis are 2-mode frequencies -- as in a cross-tab of two variables.

 

17 Apr.  Wrap-Up.  (Extra long class)
In class:
  • Wrap-Up
  • In-class presentations
Due:
  • Final report (due 24 Apr via email; please cc everyone in the class on this email)
Optional:  

 

Copyright ©2005 Stephen P. Borgatti Revised: April 08, 2007 Home Page