Supplemental Material - Predicting Web Survey Breakoffs Using Machine Learning Models

Chen, Zeming; Cernat, Alexandru; Shlomo, Natalie

doi:10.25384/sage.20291722.v1

ScienceGate Book Chapters

JOURNAL ARTICLE

Supplemental Material - Predicting Web Survey Breakoffs Using Machine Learning Models

Chen, Zeming Cernat, Alexandru Shlomo, Natalie

Year: 2022 Journal: OPAL (Open@LaTrobe) (La Trobe University) Publisher: La Trobe University

DOI: 10.25384/sage.20291722.v1

Get Full-Text PDF Get Analytical Report

Abstract

Supplemental Material for Predicting Web Survey Breakoffs Using Machine Learning Models by Zeming Chen, Alexandru Cernat, and Natalie Shlomo in Social Science Computer Review

Keywords:

Predictive modelling The Internet Web application Deep learning Training set

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.52

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Topics

Survey Methodology and Nonresponse

Social Sciences → Social Sciences → Sociology and Political Science

Authorship Attribution and Profiling

Physical Sciences → Computer Science → Artificial Intelligence

Computational and Text Analysis Methods

Social Sciences → Social Sciences → General Social Sciences

Supplemental Material - Predicting Web Survey Breakoffs Using Machine Learning Models

Abstract

Metrics

Topics

Related Documents

Supplemental Material - Predicting Web Survey Breakoffs Using Machine Learning Models

Predicting Web Survey Breakoffs Using Machine Learning Models

Predicting Material Misstatements Using Machine Learning

Predicting Material Stability Using Machine Learning

Predicting Prosumption Survey Response using Machine Learning