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A Jackson Stenner

Publications and source records attributed to A Jackson Stenner.

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How accurate are lexile text measures?

The Lexile Framework for Reading models comprehension as the difference between a reader measure and a text measure. Uncertainty in comprehension rates results from unreliability in reader measures and inaccuracy in text readability measures. Whole-text processing eliminates sampling error in text measures. However, Lexile text measures are imperfect due to misspecification of the Lexile theory. The standard deviation component associated with theory misspecification is estimated at 64L for a standard-length passage (approximately 125 words). A consequence is that standard errors for longer texts (2,500 to 150,000 words) are measured on the Lexile scale with uncertainties in the single digits. Uncertainties in expected comprehension rates are largely due to imprecision in reader ability and not inaccuracies in text readabilities.

Adolescent↗

Assessment issues in the testing of children at school entry.

The authors introduce readers to the research documenting racial and ethnic gaps in school readiness. They describe the key tests, including the Peabody Picture Vocabulary Test (PPVT), the Early Childhood Longitudinal Study (ECLS), and several intelligence tests, and describe how they have been administered to several important national samples of children. Next, the authors review the different estimates of the gaps and discuss how to interpret these differences. In interpreting test results, researchers use the statistical term "standard deviation" to compare scores across the tests. On average, the tests find a gap of about 1 standard deviation. The ECLS-K estimate is the lowest, about half a standard deviation. The PPVT estimate is the highest, sometimes more than 1 standard deviation. When researchers adjust those gaps statistically to take into account different outside factors that might affect children's test scores, such as family income or home environment, the gap narrows but does not disappear. Why such different estimates of the gap? The authors consider explanations such as differences in the samples, racial or ethnic bias in the tests, and whether the tests reflect different aspects of school "readiness," and conclude that none is likely to explain the varying estimates. Another possible explanation is the Spearman Hypothesis-that all tests are imperfect measures of a general ability construct, g; the more highly a given test correlates with g, the larger the gap will be. But the Spearman Hypothesis, too, leaves questions to be investigated. A gap of 1 standard deviation may not seem large, but the authors show clearly how it results in striking disparities in the performance of black and white students and why it should be of serious concern to policymakers.

Bias↗