Automated quantitative analysis of open-ended survey responses for transportation planning

Severin, Karl; Gokhale, Swapna S.; Konduri, Karthik C.

doi:10.1109/uic-atc.2017.8397567

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Many statistical learning algorithms are now available in statistical software like R and Python, and it is not possible to give a complete overview here (see e.g., Hao and Ho, 2019 , for a Python overview). However, we do want to point to some of the most popular choices that have been applied to classifying answers to open-ended questions: these include tree-based methods like random forests and boosting (Schonlau and Couper, 2016 ; Kern et al, 2019 ; Schierholz and Schonlau, 2021 ), support vector machines (SVM) (Joachims, 2001 ; Bullington et al, 2007 ; He and Schonlau, 2020 , 2021 ; Khanday et al, 2021 ), multinomial regression (Schierholz and Schonlau, 2021 ) and naïve Bayes classifiers (Severin et al, 2017 ; Paudel et al, 2018 ).…”

Section: Survey Motivation In the Gesis Panelmentioning

confidence: 99%

The semi-automatic classification of an open-ended question on panel survey motivation and its application in attrition analysis

Haensch¹,

Weiß²,

Steins³

et al. 2022

Front. Big Data

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In this study, we demonstrate how supervised learning can extract interpretable survey motivation measurements from a large number of responses to an open-ended question. We manually coded a subsample of 5,000 responses to an open-ended question on survey motivation from the GESIS Panel (25,000 responses in total); we utilized supervised machine learning to classify the remaining responses. We can demonstrate that the responses on survey motivation in the GESIS Panel are particularly well suited for automated classification, since they are mostly one-dimensional. The evaluation of the test set also indicates very good overall performance. We present the pre-processing steps and methods we used for our data, and by discussing other popular options that might be more suitable in other cases, we also generalize beyond our use case. We also discuss various minor problems, such as a necessary spelling correction. Finally, we can showcase the analytic potential of the resulting categorization of panelists' motivation through an event history analysis of panel dropout. The analytical results allow a close look at respondents' motivations: they span a wide range, from the urge to help to interest in questions or the incentive and the wish to influence those in power through their participation. We conclude our paper by discussing the re-usability of the hand-coded responses for other surveys, including similar open questions to the GESIS Panel question.

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Section: Survey Motivation In the Gesis Panelmentioning

confidence: 99%

The semi-automatic classification of an open-ended question on panel survey motivation and its application in attrition analysis

Haensch¹,

Weiß²,

Steins³

et al. 2022

Front. Big Data

View full text Add to dashboard Cite

show abstract

“…Many statistical learning algorithms are now available in statistical software like R and Python, and it is not possible to give a complete overview here (see e.g., Hao and Ho, 2019, for a Python overview). However, we do want to point to some of the most popular choices that have been applied to classifying answers to open-ended questions: these include treebased methods like random forests and boosting (Schonlau and Couper, 2016;Kern et al, 2019;Schierholz and Schonlau, 2021), support vector machines (SVM) (Joachims, 2001;Bullington et al, 2007;Schonlau, 2020, 2021;Khanday et al, 2021), multinomial regression (Schierholz and Schonlau, 2021) and naïve Bayes classifiers (Severin et al, 2017;Paudel et al, 2018).…”

Section: Semi-automated Coding: Svm and Other Optionsmentioning

confidence: 99%

Data_Sheet_1.pdf

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Coding Text Answers to Open-ended Questions: Human Coders and Statistical Learning Algorithms Make Similar Mistakes

Schonlau

2020

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Automated quantitative analysis of open-ended survey responses for transportation planning

Cited by 4 publications

References 5 publications

The semi-automatic classification of an open-ended question on panel survey motivation and its application in attrition analysis

The semi-automatic classification of an open-ended question on panel survey motivation and its application in attrition analysis

Data_Sheet_1.pdf

Coding Text Answers to Open-ended Questions: Human Coders and Statistical Learning Algorithms Make Similar Mistakes

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