300068 Linear Causal Modeling with Missing Data (BS) (WiSe 2011/2012)

The seminar will give a basic introduction into the techniques of causal modeling and show how a theoretical model containing causal relationships can be represented by a so-called path diagram and translated into a structural equation model. Different specifications of so-called measurement models are tested with confirmatory factor analysis. We will then deal with
measurement models relating single or multiple indicators to latent variables. The usefulness of such models for measuring attitudinal and behavioral aspects will be emphasized. We will expand these models to allow measurement and structural relations in the model simultaneously. Techniques of model estimation will be shown by the program Mplus (http.//www.statmodel.com).
Data of several cross-sectional as well as longitudinal studies will be used for the examples. Participants are encouraged to bring their own data for the possibility to test their own models.
Participants should have some basic knowledge in statistical data analysis with programs like SPSS and STATA which are required by Master and Diploma degrees.

We will also discuss missing data. Background information will be given on what problems may arise when analyzing incomplete datasets and what solutions there are to overcome these problems. Participants will furthermore learn in a hands-on session, how to create plausible multiple imputations of missing data using the R environment for statistical computing, and how to read these data into other statistical packages like Mplus for further analysis. Participants should bring their own computers and have R already installed (available from www.r-project.org).

Literaturangaben:

Bollen, K. A., & Curran, P. J. (2006). Latent curve models: A
structural equation perspective New Jersey: Wiley.

Backhaus, K., Erichson, B., Plinke, W., & Weiber, R. (2006).
Multivariate Analysemethoden - Eine anwendungsorientierte
Einführung (11. Aufl.). Berlin: Springer.

Bollen, K. A. (1989). Structural equations with latent variables
New York: Wiley.

Bollen, K. A., & Long, J. S. (Eds.). (1993). Testing structural
equation models Newbury Park: Sage.

Bortz, J. (1999). Statistik für Sozialwissenschaftler (5. Aufl.).
Berlin: Springer.

Duncan, T. E., Duncan, S. C., Strycker, L. A., Li, F., & Alpert, A.
(2006). An introduction to latent variable growth curve modeling: Concepts, issues, and applications Mahwah: Lawrence Erlbaum.

Engel, U., & Reinecke, J. (1994). Panelanalyse: Grundlagen -
Techniken - Beispiele Berlin: DeGruyter.

Diamantopoulos, A., & Siguaw, J. A. (2000). Introducing
LISREL Thousand Oaks: Sage.

Hancock, G. R., & Mueller, R. O. (2006) (Eds.). Structural
equation modeling: A second course Greenwich, CT:
Information Age Publishing.

Hox, J. J. (2010). Multilevel analysis. Techniques and
applications, 2nd. ed., Mahwah: Lawrence Erlbaum.

Kaplan, D. (2004) (Ed.). The Sage handbook of quantitative
methodology for the social sciences Thousand Oaks: Sage.

Kline, R. B. (1998). Principles and practice of structural
equation modeling 2nd. Edition, New York: Guilford Press.

Mulaik, S. A. (2009). Linear causal modeling with structural
equations. Chapman Hall/CRC.

Muthén, L., & Muthén, B. O. (2006). Mplus users guide (4rd
ed.) Los Angeles: Muthén & Muthén.

Raykov, T., & Marcoulides, G. A. (2006). A first course in
structural equation modeling Mahwah: Lawrence Erlbaum.

Reinecke, J. (2005). Strukturgleichungsmodelle in den
Sozialwissenschaften München: Oldenbourg.

Schumacker, R. E., & Lomax, R. G. (2010). A beginners guide
to structural equation modeling (3. ed.) Mahwah: Lawrence
Erlbaum.

Skrondal, A. & Rabe-Hesketh, S. (2004). Generalized latent
variable modeling: Multilevel, longitudinal, and structural
equation models Bora Raton: Chapman & Hall.