1 / 20

Multilevel modeling of educational longitudinal data with crossed random effects

Multilevel modeling of educational longitudinal data with crossed random effects . Minjeong Jeon Sophia Rabe-Hesketh University of California, Berkeley. 2008 Fall North American Stata Users Group meeting Nov. 13. 2008. Motivation: How to model this data?.

ikia
Download Presentation

Multilevel modeling of educational longitudinal data with crossed random effects

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Multilevel modeling of educational longitudinal data with crossed random effects Minjeong Jeon Sophia Rabe-Hesketh University of California, Berkeley 2008 Fall North American Stata Users Group meeting Nov. 13. 2008

  2. Motivation: How to model this data?  Longitudinal cross-classified data • Longitudinal data • Repeated observations within students • Promotion to high school • First two years in middle school • Last two years in high school

  3. Diagram: Longitudinal cross-classified data <1> <2> <3> Rasbash et al. (2005; 2008) Jeon & Rabe-Hesketh T1,..T4: Time(wave), Stu: students MS: middle school , HS: high school

  4. Purpose of the study Propose three modeling strategies • Estimate crossed random effects of middle school (MS) and high school (HS) • By xtmixed in Stata ★Key point ! • Impacts of MS and HS random effects change over time

  5. Data  Source: The Korea Youth Panel Survey (KYPS) (http://www.nypi.re.kr/panel/index.asp) • Prospective panel survey: (2003-2006 year) • Middle school 2nd(8th graders), Age(m) =13 • Sample design: Stratified multi-year cluster sampling

  6. More about the data • Summary statistics: • Number of schools & students

  7. Data: Crossed structure • Cross-classification between MS and HS MS id HS id

  8. More about the crossed structure • Number of high schools within middle school Number of HS per MS: 2~17 Number of MS per HS: 1~5

  9. School area information • 15 Areas that students do not “cross” when moving from MS to HS Maximum number of MS per area = 21 Maximum number of HS per area = 175

  10. Study variables

  11. Self esteem: within-student, within-school variation N=31 N=24 N=20 N=7

  12. Model specification: Model1  Trick 1

  13. Model specification: Model1  Trick 2

  14. Using a trick? • Exactly same results! (from model1)

  15. Modeling strategies

  16. Stata commands

  17. Results: Random effects • Random intercept model

  18. Fixedeffects (From model 1) • Increase over time • Decrease in the increase

  19. Discussion  Use a trick for computational efficiency  Need an easy way to handle random slopes in cross-classified model  Future work: Find weights empirically

  20. Thank you very much! Contact Minjeong Jeon (mjj@berkeley.edu) Sophia Rabe-Hesketh(sophiarh@berkeley.edu) Graduate School of Education University of California, Berkeley

More Related