Course
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 2021-2022. Please note that changes may occur.
Semesters
Facts
Course code
DUH165
Credits (ECTS)
5
Semester tution start
Spring
Language of instruction
English
Number of semesters
1
Exam semester
Spring
Content
In the seminar, we will introduce Confirmative Factor Analysis (CFA) and Structural Equations Modelling (SEM) and see how Latent Growth Curve Models (LGCMs) 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 LGCMs (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 streamed as a webinar and is open for a wider audience (no credits are awarded for the DSEM-webinar).
Course leader: Ulrich Dettweiler (UiS)
Instructors: Ulrich Dettweiler (UiS), Knud Knudsen (UiS), Thormod Idsøe (NUBU, UiS), Lars-Erik Malmberg (Oxford University)
Learning outcome
By completion of this course, the PhD candidate will have gained the following:
Knowledge
- of measurement theory
- a good understanding of multiple regression and factor analysis
- a good understanding of hierarchical structures in data, and how to address them in analysis
- a good understanding of SEM and LGC in complex survey data
Skills:
- running SEM and LGC analyses in MPlus and R (optional)
- preparing results of such analyses for publication
General competences:
- being able to choose and apply the right analyses for the given data
- developing advanced strategies for further research
Required prerequisite knowledge
Recommended prerequisites
Exam
| Form of assessment | Weight | Duration | Marks | Aid | Exam system | Withdrawal deadline | Exam date |
|---|---|---|---|---|---|---|---|
| 1/1 | 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.
Coursework requirements
Course teacher(s)
Course teacher:
Thormod IdsøeCourse teacher:
Knud KnudsenCourse coordinator:
Lene VestadCourse teacher:
Njål FoldnesCourse teacher:
Ulrich DettweilerStudy Program Director:
Hein BerdinesenCourse teacher:
Simona Carla Silvia CaravitaOpen for
Course assessment
Literature
Book
Mplus User’s Guide Muthén, L.K.; Muthén, B.O. , Los Angeles, CA, Muthén & Muthén, 1998-2017, https://www.statmodel.com/ugexcerpts.shtmlView online
Book
Latent curve models : a structural equation perspective Bollen, Kenneth A., Curran, Patrick J., Hoboken, N.J, Wiley-Interscience, Wiley series in probability and statistics, 2006, isbn:9780471455929,
Book
Principles and practice of structural equation modeling Kline, Rex B., New York, The Guilford Press, XVII, 534 sider, [2016], isbn:9781462523344; 9781462523351,
Book
Data analysis using regression and multilevel/hierarchical models Gelman, Andrew; Hill, Jennifer, New York ; Cambridge, Cambridge University Press, Analytical methods for social research, 2007, xxii, 625 p.isbn:9780521867061 (hbk.),
Book
Growth modeling : structural equation and multilevel modeling approaches Grimm, Kevin J.,, Ram, Nilam,; Estabrook, Ryne,, New York :, The Guilford Press, XVII, 535 s., Methodology in the social sciences, [2017]., xvii, 537 pagesisbn:9781462526062 (hbk.) :,
Book Chapter
Bayesianism vs. Frequentism in Statistical Inference Sprenger, Jan, Hájek, Alan Hitchcock, Christopher, Bayesianism vs. Frequentism in Statistical Inference, Oxford, Oxford University Press, 2016, 382-405,
Article
A gentle introduction to bayesian analysis: applications to developmental research van de Schoot, R.; Kaplan, D.; Denissen, J.; Asendorpf, J. B.; Neyer, F. J.; van Aken, M. A., Child Dev, 3, 85, 2014,May-Jun, 842-60, https://www.ncbi.nlm.nih.gov/pubmed/24116396View online
Article
Bayesian analyses: where to start and what to report van den Schoot, Rens; Depaoli, Sarah, The European Health Psychologist, 2, 16, 2014, 75-84,
Article
Intraindividual structural equation models for learning experiences Malmberg, Lars-Erik, International Journal of Research & Method in Education, 4, 43, 2020, 413-430,
Article
A primer on two-level dynamic structural equation models for intensive longitudinal data in Mplus McNeish, D.; Hamaker, E. L., Psychol Methods, 5, 25, 2020,Oct, 610-635, https://www.ncbi.nlm.nih.gov/pubmed/31855015View online