Instructor: Elena Erosheva C 14C, Padelford Hall elena at stat.washington.edu Computing help: Stephanie Lee syl3 at uw.edu 
· Questions by email are welcome. They will often be answered quite quickly, but this is not guaranteed. For example, I don't always check email over weekends. Please include course number 589 in the subject line. 
This
course will focus on multivariate analysis techniques that explore
relationship among several observed characteristics. Examples of research
questions include examining structure of work and parenthood styles of
dualearner couples, describing classes of heterogeneous service needs for
outpatient substanceuse disorder treatment, identifying structurally
different typical life course patterns, etc. Statistical methods introduced
in the course will include cluster analysis, multidimensional scaling,
principal component analysis, factor analysis for metrical and binary
variables, and latent class analysis. Timepermitting, we will also read,
discuss and critique published articles that make use of multivariate
analysis techniques. 
SOC
504505506 or equivalent. 
Analysis of Multivariate Social Science Data (2008) Bartholomew,D.J., Steele,F., Moustaki,I., and Galbraith, J.I. Other course materials on multivariate analysis: · Applied Multivariate Statistical Analysis (1998) Johnson, R.I., and Wichern, D.W. · Latent Variable Models and Factor Analysis (1999) Bartholomew,D.J. and Knott,M. 
· To gain statistical background necessary to understand multivariate analysis techniques. · To gain practical skills necessary to carry out analyses, interpret results, and present findings from a multivariate analysis study. · To become a critical reader of research papers that employ multivariate analysis techniques. 
Many software packages have capabilities for implementing
some or all of the multivariate analyses techniques that we will study in
this class (e.g., R, SAS, MPlus, Latent Gold). Analyses for all of the textbook examples are available on the web
in SPSS (for cluster analysis, multidimensional scaling, principal component
analysis and factor analysis for metrical variables) and Lami
(for factor analysis with binary variables and latent class analysis). I will
provide examples in R. For homework assignments, I encourage students to use R or Lami software (when available). You are welcome to use another software package, but it will be your responsibility to make sure that your package of choice provides results that are similar to those presented in textbook examples, and, in case of differences, explain any discrepancies in the results. If you are not sure what software to use for the course, please come see me. The course has no formal lab hour, but I will hold my office
hour on Wednesdays in SAV 121 (a.k.a., small CSSCR computer lab). You are
welcome to use that hour for going over examples provided in class or for
working on your homework. Software availability: Lami is a free software
package that you can use for chapters 811; download by clicking here. R
is also free; you can download it here.
Should you decide to work with SPSS, it is available in a number of computer
labs on campus, including and the Center for Social Science
Computation and Research, the School of Social Work Computer Lab, as well
as the CSDE terminal
servers. In addition, for general questions with
computing, consider using services of the Center for Social Science
Computation and Research that provides free computing consulting six days
a week during the academic year. 

If
you would like to request academic accommodations due to a disability, please
contact Disabled Student Services, 448 Schmitz, 5438924 (V/TTY). If
you have a letter from Disabled Student Services indicating you have a
disability that requires academic accommodations, please present the letter
to me so we can discuss the accommodations you might need for this class. 