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Mean Length of Utterance (MLU) A measure of language ability A Measure of Language Ability

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Mean Length of Utterance (MLU) MLU is a way to score a child’s language ability When scoring for MLU, researchers count the number of morphemes in the child’s utterances It has been found that as age increases, so does the average number of morphemes used by the child

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Morphemes Smallest element of meaning in speech E.g.: “Walk the dog” contains 3 morphemes “The dog walked” contains 4 morphemes -The suffix “-ed” adds meaning Remember: Morphemes and words are not the same thing

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Morphemes Free Morphemes: These are words on their own: E.g. “Dog”, “Walk”, “Sit”, “House” Bound Morphemes: Prefixes and Suffixes These are not words on their own: E.g. “re-”, “un-”, “pre-”, “-ed”, “-s”, “-ing”, “-ly”

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Using MLU to Assess Language Ability Shouldn’t be used as the only measure, but does correlate with other language measures Rice, Redmond, & Hoffman (2006) Showed MLU correlated positively with: Developmental Sentence Scoring (DSS) Index of Productive Syntax (IPSyn) MLU in Words (rather than Morphemes)

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MLU and Age Miller & Chapman (1981) Found a positive correlation of r=.88 between age and MLU Lots of research provides us with the average MLU for children at each age E.g. - At 30 months, you can expect an MLU of At 60 months, expect MLU of 5.36 Can be a diagnostic tool for Language Impairment, but researchers caution it shouldn’t be the only one

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Children’s Understanding of Morphemes Berko, 1958 e.g. Used nonsense words and pictures Found that children aged 4-7 correctly knew how to pluralize by adding an s or z sound. Correctly understood the use of the d sound for past tense Understood the use of -ing

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Scoring a Child for MLU Ideally: Observe and record interaction for mins where dialogue is likely, e.g. Playing with dolls with mom Pick out 100 utterances made by the child that are completely intelligible Transcribe the interaction (write out what was said) ->

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Scoring a Child for MLU What to count as a morpheme: Free Morphemes: (“truck” = 1) -s (used as plural- “girls” = 2) -ed (“jumped” = 2) -ing (“dancing” = 2) -s (used as possessive –”mom’s car” = 3) Contractions (“she’s” = 2) There are exceptions (see next slide)

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Scoring a Child for MLU Exceptions/What not to count as a morpheme: Unintentional repetitions (“He he is there”=3) Compound words (“doghouse” =1) “Does”, “Let’s”, “don’t”, “won’t” each = 1 Reduplications (“Choo, choo” =1) Proper Names (“Mickey Mouse” =1) Irregular plurals (“pants” =1) Catenatives (“gonna” =1) Fillers (“umm” =0)

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Example Utterances Some Examples: Mommy’s going downstairs” Mommy =1 -’s =1 go =1 -ing= 1 downstairs =1 Total Morphemes= 5

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Example Utterances “The truck, umm, the truck went vroom, vroom” The = 1 truck = 1 umm = 0 (filler) the = 0 (repetition) truck = 0 (repetition) went = 1 vroom = 1 vroom = 0 (reduplication) Total Morphemes = 4

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Scoring a Child for MLU Once the morphemes have been counted for each utterance: Add up all the morphemes Divide by the number of utterances Ideally 100 Now have the child’s Mean Length of Utterance score

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Example MLU Calculation 1) “Mommy’s going downstairs” = 5 Morphemes 2) “The truck, umm, the truck went vroom vroom” = 4 Morphemes Total = 9 Morphemes 9 Morphemes divided by 2 utterances = 4.5 MLU (in this very short transcript)= 4.5

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Part I: Data Collection As we watch the videos, try to write down everything the child says You will then be given transcripts of portions of the videos You will be given the last ten statements of her speech in each video

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Part II: Calculate MLU For each transcript: Count up the morphemes for each utterance Then, add them up, and divide by 10 This gives you an MLU score for each of the two transcripts

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Part III: Data Analysis Open the linked SPSS Data File Save to Desktop, Open SPSS, Open File Run a Dependent (paired-samples) t-test in SPSS to see if MLU scores changed significantly in participants from 30 mths(2.5 years old) to 36 mths (3 years old) Did the children’s language complexity increase in the space of 6 months? Each participant has been tested twice.

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Note about t-tests: Independent t-test: Compares two groups of different people E.g. Comparing the marks from one lab section to another Dependent t-test: Compares people to themselves at different times. E.g. Comparing each student’s 260 midterm mark to their exam mark. The t-test determines if two sets of scores are different from one another. When the Significance Level is less than 0.05, this tells us that there is only a 5% or less probability that the difference you found was not real. There is a 95% or more probability that this difference is real and would be found again and again. Reporting results: t(df)= insert t-value, p___0.05. The ‘p’ stands for “probability”. If it is less than 0.05, insert the “ ” if greater.

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