2026-09-19T16:06:15Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/120460 2023-09-05 col_2142_5131 col_2142_11614 com_2142_5130 com_2142_11613 com_2142_8903

Montag, Jessica L 2023-05 application/pdf en text Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01 The student, Zeynep Marasli, accepted the attached license on 2023-05-03 at 15:39. The student, Zeynep Marasli, submitted this Thesis for approval on 2023-05-03 at 15:54. This Thesis was approved for publication on 2023-05-04 at 13:45. DSpace SAF Submission Ingestion Package generated from Vireo submission #19324 on 2023-09-01 at 17:16:00 Optimizing random sampling of daylong audio Marasli, Zeynep Beyza 2023-05-04 Daylong Audio Language Development Manual Extrapolation Sampling 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. Text eng https://hdl.handle.net/2142/120460 Copyright 2023 Zeynep Marasli M.S. Thesis Psychology University of Illinois at Urbana-Champaign Psychology