When is iq stable

Thus, the sampling design provided for a comparison of populations with starkly contrasting social conditions. The WISC-R is age-standardized and has a mean of and a standard deviation of 15 in the general population. Thus, a child whose IQ score remains the same from age 6 years to age 11 years does not show the same performance at both assessments. Instead, the child will exhibit gains in general knowledge, vocabulary, reasoning ability, and other domains.

What does not change is the child's score in comparison with his or her age-mates. Children were assessed individually under the same standardized laboratory conditions at both ages. Psychometricians were trained to a uniform standard, and all scoring was checked by a second tester. Assessments were conducted blindly with respect to low birth weight status. Psycho-metricians who conducted the assessment at age 11 were blind to the results obtained at age 6.

The correlation between full-scale IQ scores between ages 6 and 11 years was 0. We used multiple regression analysis, applying generalized estimating equations GEE 21 — 23 , to test and estimate the effects of urban versus suburban community, low birth weight, and family factors on IQ at ages 6 and 11 years.

The GEE approach offers advantages over other regression approaches used to measure change over time 24 , The GEE approach permits simultaneous modeling of the relation of specific risk factors to children's IQs at both age 6 years and age 11 years. Furthermore, the addition of interaction terms allowed us to examine whether the difference in mean IQ associated with a specific factor—for example, urban versus suburban community—was significantly greater at age 11 years than at age 6 years.

The coefficient for an interaction between a risk factor and age is equivalent to that produced in a standard regression model in which change in IQ over time is the response variable and the risk factor is entered as the predictor variable.

However, the GEE approach provides information on the relations of risk factors with IQ at each age, which is not available in a standard regression analysis of score change. In additional models, we evaluated other two- and three-way interactions between pairs of risk factors, e.

The GEE method estimates regression coefficients and their standard errors, taking the correlation between the children's IQ measures at ages 6 and 11 years into account.

This approach yields valid and robust estimates of variance, even when there is a known positive correlation between multiple outcome measures within subjects. The exchangeable correlation option was used as the working correlation in estimation of the GEE models. Mean values and standard deviations for descriptive data, including full-scale, verbal, and performance IQ scores by age, low birth weight versus normal birth weight, and urban versus suburban community, appear in table 2.

We focus here on full-scale IQ. Analyses of verbal and performance IQ data yielded similar results available from the authors. These data suggest a decline in IQ between ages 6 and 11 years in urban children but not in suburban children. Figure 1 displays the empirical cumulative distributions of IQ scores by age among urban and suburban children, according to birth weight status normal birth weight vs.

The cumulative distribution curves of urban children, both normal birth weight and low birth weight, fall to the left of the curves of suburban children, reflecting the IQ differences between urban and suburban children at both ages. In both birth weight groups, the IQ curves of suburban children at ages 6 and 11 years overlap closely, whereas the IQ curves of urban children show a downward shift between ages 6 and 11 years.

Empirical cumulative distributions of intelligence quotient IQ scores at ages 6 and 11 years among urban and suburban children, Detroit, Michigan, metropolitan area, — and — The curves show the percentage of each group falling at or below any given IQ score.

Top panel, normal birth weight children; bottom panel, low birth weight children. Table 3 displays results from two successive models used to test and estimate the effects of community urban vs. In model 1, we examined the effects of community and birth weight status. In model 2, we introduced the set of family covariates. In both models, we included only the single interaction that was found to be significant, i.

Other interactions—e. Results from model 1 show that urban children at age 6 scored Furthermore, from age 6 to age 11, the IQs of urban children, regardless of birth weight status, declined by 5.

A negligible change was detected among suburban children 0. From age 6 to age 11, the gap in mean IQ between urban and suburban children widened from Low birth weight children, both urban and suburban, scored 5.

The size of this difference changed little from age 6 to age 11 in either type of community. Regression estimates of children's scores on the Weschler Intelligence Scale for Children—Revised from successive generalized estimating equations models, with family variables maternal intelligence quotient IQ , education, and marital status added in model 2 to the basic model, Detroit, Michigan, metropolitan area, — and — Unstandardized partial regression coefficient representing difference in children's IQ scores associated with the independent variable.

Results from model 2 show that the addition of family factors to the GEE model attenuated markedly the observed urban-suburban difference in children's IQ at age 6, from Thus, the urban-suburban gap in children's IQ at the beginning of schooling was accounted for in large part by differences in family characteristics.

By far, the single most important family factor was maternal IQ: Adding only maternal IQ to model 1 reduced the observed urban-suburban difference in children's IQ at age 6 from In contrast, the decline in IQ at age 11 among urban children calculated in model 1 remained intact.

The urban-suburban IQ gap that was not accounted for by family variables increased from 4. The results of model 2 also show that maternal IQ was positively related to children's IQ, as was maternal educational level, and children born to single mothers scored lower than children born to married mothers. However, the interactions of these variables with age were near zero, indicating that they were unrelated to change in children's IQ.

To illustrate the implications of the IQ decline among urban children from age 6 years to age 11 years, we present in figure 2 the distributions of intraindividual changes in IQ scores in the two types of communities, combining low birth weight children and normal birth weight children.

The figure presents a smoothed plot line, using a cubic spline method with continuous second derivatives Although change in IQ score was pervasive in both communities, the net effect was different.

Thus, a surplus of A change of 10 WISC-R points falls well above conservative standards for separating change from fluctuation due to measurement error 1. Distributions of change in intelligence quotient IQ score age 11 years minus age 6 years among urban and suburban children, Detroit, Michigan, metropolitan area, — and — Vertical lines mark the median values. Additional GEE analyses were conducted in the subset of children who did not change residence between urban and suburban communities i.

The results of these analyses replicated closely the results shown in table 3. An analysis corresponding to model 1 in table 3 showed that the initial gap in mean IQ score between urban and suburban children in the residentially stable subset widened from The urban-suburban IQ gap that was not accounted for by maternal IQ, education, and marital status corresponding to model 2 in table 3 increased from 7.

Thus, the increments in the urban-suburban gap from ages 6 to 11 years, as estimated in these analyses, were approximately the same i. Our results suggest that growing up in the inner city might impose disadvantages that lead to a decline in children's IQ scores from age 6 years to age 11 years. On average, the IQs of urban children declined by more than 5 points.

A change of 5 points in an individual child might be judged by some as clinically nonsignificant. Nevertheless, a change of this size in a population's mean IQ, which reflects a downward shift in the distribution rather than a change in the shape of the distribution , means that the proportion of children scoring 1 standard deviation or more below the standardized IQ mean of would increase substantially.

In this study, the change from age 6 years to age 11 years increased the percentage of urban children scoring less than 85 on the WISC-R from The influence of urban versus suburban residence on IQ change contrasts with other important predictors of children's IQ, namely low birth weight, maternal IQ, maternal education, and single mother status.

Low birth weight was associated with an IQ deficit of approximately one third of a standard deviation in both disadvantaged inner-city children and middle-class suburban children, a deficit that was detected at age 6 and remained unchanged at age Low birth weight children neither fell further behind nor caught up with their normal birth weight age-mates in either community. IQs are increasing three points per decade. In fact, there was an point increase between and So the average IQ of a year-old in was lower than the average IQ of a year-old in An article in November in the journal Nature by Price and her colleagues is one example.

It had 33 adolescents, who were to years-old when the study started. Price and her team gave them IQ tests, tracked them for four years, and then gave them IQ tests again. The fluctuations in IQ were enormous. I'm not talking about a couple points, but plus IQ points, one way or another. These changes in IQ scores were not random — they tracked very nicely with structural and functional brain imaging. Suppose the adolescent's verbal IQ really went up during that time; it was verbal areas of the brain that changed.

There are quite a large number of other studies showing IQ can change. Many of the changes in IQ are correlated to changes in schooling. One way that school increases IQ is to teach children to "taxonimize," or group things systematically instead of thematically. This kind of thinking is rewarded on many IQ tests. There's also a number of studies showing that the brain changes after several kinds of regimen.

London Taxi drivers whose brains are scanned before and after they start driving, and learning to navigate London's maze of streets, show changes in the brain as they use more navigational skills.

Articles November 01 Braaten, PhD ; Ellen B. Braaten, PhD. This Site. Google Scholar. Pediatr Rev 27 11 : — Cite Icon Cite.

After completing this article, readers should be able to:. Describe the predictive validity of intelligence test scores. Discuss the factors that may influence performance on intelligence tests. You do not currently have access to this content.

This trend is particularly pronounced for girls and children who were diagnosed at a younger age, perhaps because those who have more severe symptoms tend to be diagnosed early. Longitudinal studies , which follow people across time, are generally expensive and time-consuming, and the participants often drop out. Still, these studies provide valuable information about how developmental disorders affect people as they age. They can also identify what childhood symptoms of autism predict outcomes later in life.

Among people with autism, those who have higher intelligence quotients IQs in their youth tend to have better life outcomes, such as living independently 1.

Patricia Howlin and her colleagues began assessing children with autism in London years ago, diagnosing 91 children with autism between and Most of the children received their diagnosis at age 6 and had a typical IQ of 70 or higher. The new study followed up with 60 of these individuals, now at an average age of The researchers found that childhood IQ is usually stable across life in people with autism.

The second long-term study, published 5 July in Frontiers in Human Neuroscience , also looked at intelligence over time 3.