Kaisa_2012_3_photo by Veikko Somerpuro

Enrol
13.12.2017 at 09:00 - 19.1.2018 at 23:59

Timetable

Here is the course’s teaching schedule. Check the description for possible other schedules.

DateTimeLocation
Fri 26.1.2018
12:15 - 14:45
Fri 2.2.2018
12:15 - 14:45
Fri 9.2.2018
12:15 - 14:45
Fri 16.2.2018
12:15 - 14:45
Fri 23.2.2018
12:15 - 14:45
Fri 2.3.2018
12:15 - 14:45
Fri 9.3.2018
12:15 - 14:45
Fri 16.3.2018
12:15 - 14:45
Fri 23.3.2018
12:15 - 14:45
Fri 6.4.2018
12:15 - 14:45
Fri 13.4.2018
12:15 - 14:45
Fri 20.4.2018
12:15 - 14:45
Fri 27.4.2018
12:15 - 14:45
Fri 4.5.2018
12:15 - 14:45

Description

Up to 20 doctoral students; suitable for all doctoral students in the Faculty of Educational Sciences regardless of the research field, basic understanding of quantitative methods and structural equation modelling techniques required

Learning to implement the most important applications of structural equation modelling in educational research using own research data.

Opintojen alkuvaiheessa

The course is acceptably completed when all the homework (listed below) is done and – instead of a final exam – the participant has written a scientifically sound description of an analysis performed during the course using own research data. Preferably, this final report forms the methods and the results sections of the participants’ next manuscript.

Prior to the first workshop: Reading an article about the basics of SEM.

Lei, P.-W. & Wu, Q. (2007). Introduction to structural equation modeling: Issues and practical considerations. Educational Measurement: Issues and Practice, 26 (3), 33-43.

The course begins by a discussion based on the article.

Workshop 1

The basics of SEM; transforming a simple rehearse dataset Mplus compatible; factor analysis using Mplus

Homework:

  1. Selecting research questions and hypotheses relevant to own research, writing them down and making an initial analysis plan. Listing personal wishes about the contents of the course. Submitting these to the coordinator of the course shortly after the first workshop.
  2. Transforming own research data into a text file required by Mplus. Bringing the file to the next workshop.

Workshop 2

A review of the participants’ research designs and needs related to analytical approaches. An introduction to scientific reporting and publication of results analysed by SEM. Dividing participants to small groups (if necessary). Individual guidance (for the groups) by a visiting lecturer about methodological choices.

Homework:

  1. Writing a short analysis plan based on the guidance. This serves as a basis for the final report submitted at the end of the course. Reading literature suggested by the visiting lecturer for writing the final report.
  2. Reading Byrne & Stewart (2006) for discussing it in the next workshop:

Byrne, B.M & Stewart, S.M. (2006) TEACHER'S CORNER: The MACS approach to testing for multigroup invariance of a second-order structure: A walk through the process. Structural Equation Modeling: A Multidisciplinary Journal, 13 (2), 287-321. DOI: 10.1207/s15328007sem1302_7

Workshop 3

Measurement invariance between time points and groups; group comparisons over time; measurement invariance with categorical variables; other measurement invariance related applications

Homework:

  1. Reading the first article of Berlin, Williams & Parra (2014) for discussing it in the next workshop:

Berlin, K. S., Williams, N. A., & Parra, G. R. (2014). An introduction to latent variable mixture modeling (part 1): Overview and cross-sectional latent class and latent profile analyses. Journal of Pediatric Psychology, 39, 174−187. http://dx.doi.org/10.1093/jpepsy/jst084

You may also be interested in (not obligatory):

Muthén, B. (2008). Latent variable hybrids: Overview of old and new models. In Hancock, G. R., & Samuelsen, K. M. (Eds.), Advances in latent variable mixture models, pp. 1−24. Charlotte, NC: Information Age Publishing. Retrieved from http://statmodel.com/download/Maryland%20keynote%20V21.pdf

Workshop 4 Friday 17.3 from 12 to 15

Mixture modelling + latent profile analysis

Homework:

  1. Reading the second article of Berlin, Parra & Williams for discussing it in the next workshop:

Berlin, K. S., Parra, G. R., & Williams, N. A. (2014). An introduction to latent variable mixture modeling (part 2): Longitudinal latent class growth analysis and growth mixture models. Journal of Pediatric Psychology, 39, 188−203. http://dx.doi.org/10.1093/jpepsy/jst085

The participants reading Finnish may also be interested in (not obligatory, available through ELEKTRA service of the university library or from the coordinator of the course):

Aunola, K. (2005). Latentin kasvukäyräanalyysin avulla muutoksen mallintamisesta kehityksellisten ilmiöiden parempaan ymmärtämiseen. Psykologia 5−6, 502–514.

Workshop 5

Latent growth curve modelling

Homework:

  1. Reading an article about multilevel SEM provided at the beginning of the course for discussing it in the next workshop.

Workshop 6

Principles of multilevel structural equation modelling

Submission of the first version of the final report shortly after this workshop. The coordinator of the course writes a peer review of the report by the next workshop.

Workshop 7

A review of the participants’ first course assignment drafts. Dividing participants to small groups. Individual guidance (for the groups) by a visiting lecturer about reporting the results.

Workshop 8

Working on the peer review and the visiting lecturer’s feedback, making a revision plan, review of the course contents, a look into the future.

Submission of the revised final report two weeks after the last workshop.