Applied Statistics for Educational Researchers DUH165
This course is organized in several workshops and will introduce in structural equations models with growth curve modeling and autoregressive dynamic structural equations models as special applications. We will use the software packages Mplus and R.
Course description for study year 2022-2023. Please note that changes may occur.
By completion of this course, the PhD candidate will have gained the following:
of measurement theorya good understanding of multiple regression and factor analysisa good understanding of hierarchical structures in data, and how to address them in analysisa good understanding of SEM and LGC in complex survey data
running SEM and LGC analyses in MPlus and R (optional)preparing results of such analyses for publication
being able to choose and apply the right analyses for the given datadeveloping advanced strategies for further research
Required prerequisite knowledge
The students are expected to
know the data structure of their projects and the research questions. and get themselves acquainted with MPlus and/or R prior to the course so that they master to prepare the data for import in Mplus,
have some datasets prepared in the right format (.dat) for MPlus or .csv in R.
Form of assessment
Passed / Not Passed
Evaluation will be based on the active participation and analyses performed in group work, presented in a brief paper.Coursework requirements: Active participation in lectures and seminars at the workshop. Self-study. The students’ workload will be approximately 150 hours of work.
80 % attendance
At least 80 % attendance in lectures and seminares.
Seminar: In the seminar, we will introduce CFA and SEM and see how Latent Growth Curve modelling can be understood as an extension of SEM with intercepts and/or slopes being modelled as latent variables, first as an unconditional latent curve model. We will then look at conditional Latent Growth Curve models (including mediation models, cross-lagged models, hierarchical/multilevel models), and comparison of (latent) groups with different approaches of testing measurement invariance, also in the Bayesian SEM framework. This will be extended to Dynamic Structural Equation Models with autoregressive slopes (DSEM), i.e. accounting for the influence of the respective previous time points on the outcome variable (time-lagging). Those models are important to analyze intensive longitudinal data, where many observations are nested in individuals. In contrast to latent growth modelling, Borrowing logic from time-series analyses DSEM is interested in the dynamics over time, in terms of autoregressive associations between the same variable at Times T (concurrent time-point) and T-1 (the previous time-point), and cross-lagged associations between variables between T and T-1. Such models are possible to implement using Maximum Likelihood with user specified lagged variables. More complex models (e.g., multiple random slopes) are possible to implement using the Bayesian estimator in Mplus.
The working format is a blending of lectures, group discussions, and hand-on analyses in Mplus/R. The last session on DSEM will be additionally available as a webinar and open for a wider audience (no credits awarded for DSEM-webinar).
International and local students enrolled in a doctoral program. Max. 25 participants. WNGER II students will be prioritized up to a quote of 10.
A dialogue with the students to gain information for similar courses in the future. Final discussion with the students and concluding report from the course leader. The course will be included in the evaluation procedure of the PhD programs at the faculty.
Advanced Statistics for Educational Researchers: Analyzing Structural Equation Models and Latent Growth Curves w/ MPlus (DSP165)