ABSTRACT
A number of studies have examined the e~ectiveness of biomedical risk conditio^, SES index measures, and measures of the q u ~ t y of the home e n ~ ~ n m e n t as predictors of performance on cognitive and intelligence tests. any studies have focused on children characterized by very low
erinatal compromise, and/or preterm births. A variety of les have been used to predict performance on intelligence
tests using scores obtained from normal and “at risk”’ child re^. Crisaf& Drisco~, Rey, and Adler (1987) studied 144 children characterized by very low birthwei~t (< 1,500 grams). They found that asphyxia at birth, SES, and sex of infant (male infants are at greater risk) combined to account for 32% of the variance in predicting %year Bayley Mental Development Index ( M ~ ~ scores. Siege1 (1982a) reported results of a study with 42 children who had been characterized as preterm and very low birthwei~t (<
S). She found a variety of biomedical and social risk conditions order, m a t e ~ a l smoking, respiratory distress,
e predictive of 3-year Stanford-Binet and Reyn scores, accounting for from 22% to 35% of the variance. ~ ~ s m a n , F k and Rosen (1987) predicted scores of 3-yeardds on the Merrill Palmer Test. The scores of 39 children who had k e n exposed to methadone as infants were predicted by a combination of neonatal comp~cations and family social organization, with 32% of the variance accounted for. Hack and Breslau (1986) found that neonatal risk conditions, b ~ h w e i ~ t , head c~umference, measures of neurologic i m ~ a ~ e n t ob-
inet sco~es of
§core§ was able to cl
Fowler, 1992; ~ v ~ n u ~ & of cor~lations we
a § t ~ i n ~ point for CO nce scores and with that o~tained v a ~ a ~ l e § should include the rneas
nt are the basic m e ~ u e. There are other could be added, For e , Geci ( 1 ~ ~ ~ ) pre-
e ~ o u n t of t h e a child spends in sc nce on both intelligence and achievement tests. ce gains made during the school year are at leas
vacations, with bo resumption of schoo e gains made due to
schools administer ring. One factor tha in predictive models
to extend this model to inte gence tests also are influen
s h o ~ n g that change
searcher have reported just such eEects for age at school e n t ~ n c
1. INTELLIGENCE AND ACHIEVEMENT 7
administered yield different effects, with length of schooling havi greater influence on scores than maturation. This difference can be t by examining data from children who are grouped according to grade in school (e.g., first grade, second grade). Those children whose bi~hdates are just before the cutoff date for school entry are younger when they enter each grade than children who just miss the cutoff date and who must enter school the following year. For example, first grade is composed of children who have reached age 6 by a specific cutoff date (e.g., a c o ~ o n cutoff date is September l)) children who have birthdates &er the cutoff date who enter first grade when they have reached age 7, and by children whose school entrance is delayed due to parentlteacher decisions. Thus, first-grade classes are composed of children who range fkorn older. By comparing predictive models in which children grade, it is possible to examine pe~ormance scores for the effects of maturity (chronological age in months) at the time when the tests are administered. By including children who have been administered intelligence and achievement tests at different times in the school year, it is possible to examine the separate effects of length of time in school on performance.
