Using the data set Assessment Project Data Set for Deaf Education and Hearing Sc
ID: 3371660 • Letter: U
Question
Using the data set Assessment Project Data Set for Deaf Education and Hearing Science, answer the questions below. The data set is provided as an Excel spreadsheet.
The data are not actual data, but are modeled after data collected by Trautwein and Ammerman (2013) for a study on the effects of cochlear implants (CI) on the auditory perception and speech production skills of 32 deaf children: 16 subjects with at least one cochlear implant (CI) and 16 subjects without an implant. (CI subjects were implanted at 13 or 14 months of age. So, no subjects had CIs at the time of the first data collection.) Auditory perception and speech production skills were measured when children were 11 months of age and at several points over a three-year period. Here, I include the data collected at 1 year of age and 3 years of age. The data set code book is provided after the questions.
The data set contains information of demographic variables and assessment variables. Here I briefly describe the assessments. If you wish to know more about the assessments, you can easily find additional information on the internet.
The Early Speech Perception (ESP) assessment contains single-word targets that differ in number of syllables and stress. This tool uses a series of subtests to assess pattern and word discrimination in a closed-set format. The results of this test allow the placement of children into four speech perception categories: 1=no pattern perception, 2=pattern perception, 3=some word identification, and 4=consistent word identification.
The Preschool Language Scale-5 LS-5 measures comprehensive language in children from birth through age 7.11. Scores include Auditory Comprehension (AC), Expressive Communication (EC), and a Total Language (TL) score. All scores have a mean of 100 with a standard deviation of 15. The TL score is typically the most representative of a child’s overall language functioning. The AC score probes aspects of comprehension whereas the EC score measures oral language expression.
The Clinical Evaluation of Language Fundamentals, Preschool Edition (CELF-P) evaluates a wide range of language skills in children aged 3.0 to 6.11. Scores include the Core Language Score (CLS), Receptive Language Index (RLI), Expressive Language Index (ELI), Language Content Index (LCI), and Language Structure Index (LSI). All scores have a mean of 100 with a standard deviation of 15. The CLS is typically the most representative of a child’s overall language functioning. The RLI probes aspects of comprehension whereas the ELI measures oral language expression.
Finally, all participants were assessed using the Goldman Fristoe Test of Articulation at the conclusion of the study.
The Goldman Fristoe has a mean of 100 and a standard deviation of 15.
3. What research question would you want to answer with this data set? What statistical test would you need to use and why?
5. Even though the Preschool Language Scale was standardized on hearing children, professionals in oral education of the deaf consider the mean of 100 to be the standard by which children with hearing loss should be judged as well. Given this reference point, how would you describe the findings for the two groups on receptive language (without applying a formal statistical test)? Be sure to describe what the data tell you about the groups’ central tendency, variability, and other notable characteristics of the distribution. Use non-statistical language in your descriptions. (Hint: It may be useful to graph the scores to depict the distributions visually.)
7. Trautwein and Ammerman are curious how the CI group compares to the norm on CLS at the end of the study.
a. What inferential test should the researchers use? Why?
b. What are the independent and dependent variables?
DEHS Data Set for Assessment Projects INTD 5064 Applied Statistics for Health Care Practitioners CI Subjects Subject ID Group Gender Mode of Communication ESP-Pre TL-Pre AC-Pre EC-Pre ESP Post CLS-Post RLI-Post ELI-Post LCI-Post LSI-Post GF-Post C01 1 1 1 1 86 89 95 3 95 96 99 90 88 101 C02 1 2 1 1 82 85 85 3 85 89 87 83 78 89 C03 1 1 1 1 85 78 80 3 90 92 88 88 84 85 C04 1 1 1 2 81 81 82 2 80 88 84 79 76 105 C05 1 2 1 1 92 95 100 4 100 100 95 98 104 82 C06 1 1 1 1 79 80 72 4 92 94 90 92 82 90 C07 1 1 1 1 78 80 74 3 84 88 79 86 80 80 C08 1 2 1 1 111 115 102 3 120 122 112 109 110 102 C09 1 1 1 2 87 89 79 4 88 92 87 87 82 82 C10 1 2 1 2 99 98 89 4 100 98 90 104 106 95 C11 1 1 1 1 67 69 68 3 79 84 82 70 78 77 C12 1 2 2 1 102 100 98 2 95 98 93 96 90 82 C13 1 1 2 1 74 74 70 2 70 72 70 71 68 74 C14 1 2 2 1 68 65 70 1 67 69 66 65 68 70 C15 1 1 2 1 95 90 101 2 90 92 88 85 95 87 C16 1 1 2 1 75 77 76 2 72 75 70 74 68 69 Non CI Subjects Subject ID Group Gender Mode of Communication ESP-Pre TL-Pre AC-Pre EC-Pre ESP Post CLS-Post RLI-Post ELI-Post LCI-Post LSI-Post GF-Post S01 2 2 2 1 95 89 100 2 93 89 85 100 92 82 S02 2 1 2 1 87 85 95 1 80 83 82 80 78 70 S03 2 2 2 2 76 78 80 1 78 72 70 77 80 65 S04 2 1 2 1 75 73 72 2 73 71 72 75 72 68 S05 2 1 2 1 79 78 80 1 80 70 72 85 81 63 S06 2 1 2 1 77 78 74 1 75 77 68 85 70 71 S07 2 1 2 1 78 80 72 2 72 75 70 72 69 60 S08 2 2 2 1 77 81 75 1 80 89 73 75 78 65 S09 2 1 2 1 87 88 84 1 80 84 80 75 80 68 S10 2 1 2 1 89 98 82 3 90 100 85 85 85 78 S11 2 2 2 1 90 94 92 1 85 90 82 79 87 72 S12 2 1 2 1 73 78 72 1 70 74 67 79 68 66 S13 2 2 2 1 74 74 75 1 70 72 69 67 75 67 S14 2 2 2 1 68 65 68 2 62 60 61 67 60 55 S15 2 1 2 1 75 74 72 1 71 77 67 75 71 70 S16 2 1 2 1 74 77 73 1 77 74 68 80 72 65Explanation / Answer
3) we would have to work with lot of assumptions. You can create a 2 sample paired t test , for each children create 2 samples each of the children who select the new subject and who did not select the new subject.so lets fomulate it for school1 and you can replicate the same for school 2
Randomly select 32 children from school 1 , measure there foreign credits before they were enrolled for the program.Then after they attend the program measure there foreign creditsafter they were enrolled for the program.
Then perform a paired t test to check the difference in the mean foreign credits before anf after the program. If the p value of the test is less than 0.05 , you can conclude that the new program increases/decreases the foreign credits and vice versa.
However, we can also conduct a 2 independent samples of children.
1 sample undergoes the program and the other doesn't undergo the program.
Perform a independent sample t test to check the average difference in the foreign credits for the 2 groups. If the p value is less than 0.05 , you can conclude that the children enrolled in new subject from 16 subjects have better foreign credits in comparison to other sample who did not undergo the new subject.
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