No abstract

Hierarchical Linear Models (HLM) have been used extensively for value-added analysis, adjusting for important student and school-level covariates such as socioeconomic status. A recently proposed alternative, the Layered Mixed Effects Model (LMEM) also analyzes learning gains, but ignores sociodemographic factors. Other features of LMEM, such as its ability to apportion credit for learning gains among multiple schools and its utilization of incomplete observations, make it appealing. A third model that is appealing due to its simplicity is the Simple Fixed Effects Models (SFEM). Statistical and computing specifications are given for each of these models. The models were fitted to obtain value-added measures of school performance by grade and subject area, using a common data set with two years of test scores. We investigate the practical impact of Downloaded from differences among these models by comparing their value-added measures. The value-added measures obtained from the SFEM were highly correlated with those from the LMEM. Thus, due to its simplicity, the SFEM is recommended over LMEM. Results of comparisons of SFEM with HLM were equivocal. Inclusion of student level variables such as minority status and poverty leads to results that differ from those of the SFEM. The question of whether to adjust for such variables is, perhaps, the most important issue faced when developing a school accountability system. Either inclusion or exclusion of them is likely to lead to a biased system. Which bias is most tolerable may depend on whether the system is to be a high-stakes one.

We consider the problem of record linkage in the situation where we have only non-unique identifiers, like names, sex, race etc., as common identifiers in databases to be linked. For such situations much work on probabilistic methods of record linkage can be found in the statistical literature. However, although many groups undoubtedly still use deterministic procedures, not much literature is available on deterministic strategies. Furthermore, there appears to exist almost no documentation on the comparison of results for the two strategies. In this work we compare a stepwise deterministic linkage strategy with a probabilistic strategy, as implemented in AUTOMATCH, for a situation in which the truth is known. The comparison was carried out on a linkage between medical records from the Regional Perinatal Intensive Care Centers database and educational records from the Florida Department of Education. Social security numbers, available in both databases, were used to decide the true status of each record pair after matching. Match rates and error rates for the two strategies are compared and a discussion of their similarities and differences, strengths and weaknesses is presented.

ABSTRACT. Objective. To assess the relative effects and the impact of perinatal and sociodemographic risk factors on long-term morbidity within a total birth population in Florida.Methods. School records for 339 171 children entering kindergarten in Florida public schools in the 1992-1993, 1993-1994, or 1994 -1995 academic years were matched with Florida birth records from 1985 to 1990. Effects on long-term morbidity were assessed through a multivariate analysis of an educational outcome variable, defined as placement into 9 mutually exclusive categories in kindergarten. Of those categories, 7 were special education (SE) classifications determined by statewide standardized eligibility criteria, 1 was academic problems, and the reference category was regular classroom. Generalized logistic regression was used to simultaneously estimate the odds of placement in SE and academic problems. The impact of all risk factors was assessed via estimated attributable excess/deficit numbers, based on the multivariate analysis.Results. Educational outcome was significantly influenced by both perinatal and sociodemographic factors. Perinatal factors had greater adverse effects on the most severe SE types, with birth weight <1000 g having the greatest effect. Sociodemographic predictors had greater effects on the mild educational disabilities. Because of their greater prevalence, the impact attributable to each of the factors (poverty, male gender, low maternal education, or non-white race) was between 5 and 10 times greater than that of low birth weight and >10 times greater than that of very low birth weight, presence of a congenital anomaly, or prenatal care.Conclusions. Results are consistent with the hypothesis that adverse perinatal conditions result in severe educational disabilities, whereas less severe outcomes are influenced by sociodemographic factors. Overall, sociodemographic factors have a greater total impact on adverse educational outcomes than perinatal factors. Pediatrics 1999;104(6). URL: http://www.pediatrics.org/ cgi/content/full/104/6/e74; birth weight, child development, special education, educational status, morbidity, infant, low birth weight, risk factors, socioeconomic factors, logistic models, Florida.

Multiple births have increased risks of birth defects compared to singletons.