1 / 16

Comparing Like with Like: The Role of Student and School Characteristics in Value-Added Models

Comparing Like with Like: The Role of Student and School Characteristics in Value-Added Models. Kilchan Choi Kyo Yamashiro Michael Seltzer Joan Herman CRESST Conference 2004 University of California, Los Angeles. Value - Added Models (VAM). Concerned with residual gains/growth

penda
Download Presentation

Comparing Like with Like: The Role of Student and School Characteristics in Value-Added Models

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. Comparing Like with Like: The Role of Student and School Characteristics in Value-Added Models Kilchan Choi Kyo Yamashiro Michael Seltzer Joan Herman CRESST Conference 2004University of California, Los Angeles

  2. Value-Added Models (VAM) • Concerned with residual gains/growth • In general, residuals are sensitive to controls and adjustments • Results are highly dependent on extent to which model adjusts for background characteristics

  3. Adjusting for Background Characteristics in VAM • School effectiveness literature still searching for appropriate ways to adjust for background characteristics • Type A: adjustment of student background (Sj), yet no adjustment of school-level contextual effects (Cj) and school policies and practices effects (Pj) • Type B: adjustment of student background (Sj) and contextual effects (Cj)

  4. Type A Effects • Type A effect: Aj = [j0– (0 + sSj)] = 1Pj + 2Cj = Uj • Includes effects of Pj & Cj, and wider social influences • Even if Pj and Cj aren’t observed, Uj captures Type A effect • Performance levels adjusted only for student background characteristics • Students and parents are particularly interested in this effect(shows parents how their child would do in a given school)

  5. Type B Effects • Type B effect: Bj=[j0 – (0+ sSj + 2Cj)] = 1Pj + Uj • Like Type A, adjusts for student background • Includes effects of Pj and excludes factors that lie outside their control (Cj) • Allows for comparison among schools which are similar in terms of their social class and wider social influences • Issue: If Pj and Cj are correlated, Type B effects are likely to be under- or over-estimated (Raudenbush, 2004) • Teachers and principals would be more interested in this effect(fairer comparison)

  6. Data Structure • Diverse urban school district in Pacific NW • 2,524 Students across 72 Schools • Average # students/school: 35 • Average % qualifying for FRPL: 36.2% • Average % Minority (African American, Native American, or Latino): 68.6% • 2 time-point ITBS reading scores (Grade 3 in 2001 & Grade 5 in 2003) • Standard Errors of Measurement (SE) on ITBS reading scores (Bryk et al., 1998)

  7. Latent Variable Hierarchical Gains Model (LVR-HM) • Level 1: Time Series within Student • Obtaining initial status and gain for each student i with standard errors • Level 2: Student Level • Gain for student i is modeled as function of his or her initial status (& SES) • Level 3: School Level • Gain for school j is modeled as function of school’s initial status (& SES)

  8. Models Compared • Mod 1: 3-level Unconditional Gains (no adj.) • Mod 2: + Student IS (initial status) • Mod 3:+ Student SES • Mod 4: + Student IS + Student SES • Mod 5: + Student IS + Student SES + School Mean IS • Mod 6: + Student IS + Student SES + School Mean IS + School Mean SES

  9. What adjustments make for a fair comparison? residual school

  10. Correlations between Model Results

  11. Ranking Schools by Model

  12. Correlations between Rank Order

  13. What adjustments make for a fair comparison of school effectiveness? • Lev 2: Student SES adds very little once you control for Student IS • Lev 3: Adding in School SES may be over-adjusting for contextual effects and may be taking out some of the school effects we are looking for (Raudenbush, 2004 in JEBS) • After adjusting for Student IS & SES, contextual effect of mean IS turns out to be insignificant, while contextual effect of mean SES is significant

  14. Philosophical Challenge • Should determinations of school effectiveness be based on absolute performance (AYP model) OR performance conditioned on things outside the school’s control (VAM, e.g., schoolwide background characteristics) and starting points? • Probably both, but need to be aware of potential over-adjustment

  15. School B: Highest Gain School C: Low IS, High Gain School A: Highest IS School D: Low IS, Better than exp gain School E: Med IS, Lower than exp gain School Initial Status vs. Gain

  16. Methodological/Measurement Challenge If Policy/Practice (P) and Contextual (C) effects are correlated, will part of the true school effect be removed when controlling for C? Need empirical information on: • Adequate measures of Policy & Practice • The degree to which these aspects (P&C) are correlated

More Related