Alyssa Everett, AuD, CCC-A
University of Arizona
Department of Speech, Language, and Hearing Sciences
Predicting hearing aid success and determining which microphone mode to use prior to the fitting is an area of research that needs further investigation.
The current study examines possible predictive factors of hearing aid directional benefit. Specifically, we aim to answer the following questions,
1) Does an unaided speech-in-noise test or unaided acceptance of noise test predict a significant amount of variance in hearing aid directional benefit?
2) Do self-reported measures predict a significant amount of variance in the results on a speech-in-noise test, an acceptance of noise test, or directional benefit?
- Need to determine the mean differences between collocated and spatially separated conditions (4 different signal-to-noise ratios) for each measure (omni-directional and directional) [within each measure first Omni co vs sep and Directional co vs sep].
- THEN I need to know the mean differences between groups (omni and directional). So, I need to compare the mean differences of 0, +4, +8, and +12 for each speaker orientation across omni-directional and directional groups.
- For the above two analyses, refer to Table 2, I need to know if they are significantly different from one another.
- From here, I need the Directional Benefit/Spatial Advantage which is just the spatially separated conditions minus the collocated condition (Table 3 it is already calculated).
- Need to determine if the directional group is significantly more beneficial than the omnidirectional group.
- Then I need to perform predictions using probably linear regression.
- Specific Aim 1: Can ANL scores or QuickSIN scores predict the Directional Benefit/Spatial Advantage for any of the SNRs (ANL and QuickSIN in Table 2, Spatial Advantage shown in Table 3)
- Specific Aim 2: This is more of an exploratory analysis so I am not highly concerned with my low n and lots of measures. Can any subjective measure (APHAB unaided, APHAB aided, APHAB benefit, SSQ Speech, SSQ Spatial, SSQ Qualities, or SIG) predict Spatial Advantage (shown in Table 3), QuickSIN, or ANL (shown in Table 2).
- To note: I would like to know the r values to know the effect sizes
I attached my Excel Document for the data as well. I am also attaching my R Code for my attempts. The coding equations may or may not be correct but I do not understand the results either.