Optimizing random sampling of daylong audio

Marasli, Zeynep Beyza

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

Description

Title

Optimizing random sampling of daylong audio

Author(s)

Marasli, Zeynep Beyza

Issue Date

2023-05-04

Director of Research (if dissertation) or Advisor (if thesis)

Montag, Jessica L

Department of Study

Psychology

Discipline

Psychology

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• daylong audio
• language development
• manual extrapolation
• sampling

Abstract

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.

Graduation Semester

2023-05

Type of Resource

Thesis

Handle URL

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

Copyright and License Information

Copyright 2023 Zeynep Marasli

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