Fixed or Random Effects?

New IZA Working Paper

The Choice Between Fixed and Random Effects Models: Some Considerations for Educational Research

Paul Clarke, Claire Crawford, Fiona Steele, Anna Vignoles

Abstract:
We discuss fixed and random effects models in the context of educational research and set out the assumptions behind the two approaches. To illustrate the issues, we analyse the determinants of pupil achievement in primary school, using data from the Avon Longitudinal Study of Parents and Children. We conclude that a fixed effects approach will be preferable in scenarios where the primary interest is in policy-relevant inference of the effects of individual characteristics, but the process through which pupils are selected into schools is poorly understood or the data are too limited to adjust for the effects of selection. In this context, the robustness of the fixed effects approach to the random effects assumption is attractive, and educational researchers should consider using it, even if only to assess the robustness of estimates obtained from random effects models. When the selection mechanism is fairly well understood and the researcher has access to rich data, the random effects model should be preferred because it can produce policy-relevant estimates while allowing a wider range of research questions to be addressed. Moreover, random effects estimators of regression coefficients and shrinkage estimators of school effects are more statistically efficient than those for fixed effects.

http://ftp.iza.org/dp5287.pdf

A Few More Links

1. Do happy people have fewer heart attacks?

2. Obama advisor Austan Goolsbee on the importance of "the first job after graduation": NYT 2006

3. This week's U.S. jobs measure: payroll tax exemption, and tax breaks for capital expenditure

4. CIRGE: the Center for Innovation and Research in Graduate Education

5. Panel Conditioning and Attrition in the AP-Yahoo! News Election Panel Study

6. Tomorrow's edition of The Economist runs with the following lead-story: "The Data Deluge - and How To Handle It"

7. Job Opportunities at Yahoo!: the tech giant recently advertised for Ph.D. graduates in Microeconomics

Turn off the TV

Dynamic Treatment Effect Analysis of TV Effects on Child Cognitive Development
Fali Huang & Myoung-jae Lee

We investigate whether TV watching at ages 6-7 and 8-9 affects cognitive development measured by math and reading scores at ages 8-9 using a rich childhood longitudinal sample from NLSY79. Dynamic panel data models are estimated to handle the unobserved child-specific factor, endogeneity of TV watching, and dynamic nature of the causal relation. A special emphasis is put on the last aspect where TV watching affects cognitive development which in turn affects the future TV watching. When this feedback occurs, it is not straightforward to identify and estimate the TV effect. We adopt estimation methods available in the biostatistics literature which can deal with the feedback feature; we also apply the standard econometric panel data IV approaches. Overall, for math score at ages 8-9, we find that watching TV for more than two hours per day during ages 6-9 has a negative total effect
http://d.repec.org/n?u=RePEc:eab:develo:1532&r=cbe

Classroom Peer Effects and Academic Achievement: Evidence from a Chinese Middle School

SCID Working Paper 366; 07/1/08

Author(s): Katherine Carman, Lei Zhang

This paper estimates peer effects on student achievement using a panel data set obtained from a middle school in China. Two unique features of the organization of Chinese middle schools (Grades 7 to 9) and the panel data allow us to identify peer effects at classroom level; in particular, we are able to overcome difficulties that have hindered the separation of peer effects from omitted individual factors due to self-selection and from common teacher effects. First, students are assigned to a class at entry of middle school (Grade 7) and stay with their classmates together for all subjects and for all grades in middle school. In other words, any self-selection into a class occurs before the interaction with classmates. Thus, individual fixed effects can capture all omitted student and family characteristics relevant for selection. Second, each teacher of Math, English, and Chinese teaches two classes and stay with the same two classes from Grade 7 to Grade 9. This panel nature allows us to use teacher by test fixed effects to capture the time-varying common teacher effect. We estimate peer effects for Math, English, Chinese, and overall test scores separately. In a linear-in-means model controlling for both individual and teacher-by-test fixed effects, peers are found to have a positive and significant effect on math and overall test scores and a positive but insignificant effect on Chinese test scores, but no effect on English test scores. Additionally, students at the middle of the ability distribution tend to benefit from better peers, whereas students at both ends do not.