Measurement Properties of Mean Length of Utterance in School-Age Children
Jill R Potratz1, Christina Gildersleeve-Neumann2, Melissa A Redford1
1Department of Linguistics, University of Oregon, Eugene.
Purpose:
Mean length of utterance (MLU) is one of the most widely reported measures of syntactic development in the developmental literature, but its responsiveness in young school-age children's language has been questioned, and it has been shown to correlate with nonsyntactic measures. This study tested the extent to which MLU shows measurement properties of responsiveness and construct validity when applied to language elicited from elementary school children.
Method:
Thirty-two typically developing children in two age groups (5 and 8 years) provided four short language samples each. Language samples were elicited in a question-answer context and a narrative context. MLU was calculated with both morpheme and word counts. Other established measures of syntactic complexity (clausal density [CD], developmental level [D-Level], mean length of clause [MLC]) and lexical diversity (lexical density, moving-average type-token ratio, number of different words) were also calculated.
Results:
Linear mixed-effects analyses revealed that MLU varied systematically with discourse context and children's age group. The syntactic measures, CD and MLC, were found to vary systematically with MLU. None of the lexical diversity measures varied systematically with MLU.
Conclusion:
Results suggest that MLU is a responsive and valid measure of children's syntactic development across age and discourse context during the early school-age years.
More Related Videos
09:09Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
Published on: September 27, 2024
08:05Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
Published on: June 30, 2020
Related Concept Videos
01:22Language Development
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
01:08Arithmetic Mean
When all the values in a data set are not unique, the sum in the numerator can be calculated by multiplying each distinct value by its frequency.
Sometimes, the arithmetic mean of a sample can be affected by a few data points...
01:13Mean Absolute Deviation
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
02:32Variation: Normal Distribution, Range, and Standard Deviation
01:21Testing a Claim about Mean: Unknown Population SD
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...
01:11Testing a Claim about Mean: Known Population SD
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...