Optimizing random sampling of daylong audio

While naturalistic daylong audio recordings of children’s auditory environments have the potential to reveal key insights about the input children receive and inform our theories of language development, it also presents various methodological hurdles. In the present work, we used three fully transcribed daylong audio recordings to investigate the challenge of manually extrapolating aggregate statistics and quantify the kinds of sampling choices daylong researchers can make. We implemented a random sampling with replacement algorithm and investigated how sampling interval size and total time sampled impacts extrapolation on four linguistic features. Our findings highlight sampling choices that maximize sampling from the full distribution of the day and potential tradeoffs between human effort and obtaining accuracy.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Psychology

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2023

Author dc:creator

• Marasli, Zeynep Beyza

Contributors dc:contributor

• Montag, Jessica L

• Daylong Audio
• Language Development
• Manual Extrapolation
• Sampling

Statement dc:rights

• Copyright 2023 Zeynep Marasli

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/120460

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F120460