For practical reasons, surveys that aim for a large number of respondents tend to restrict themselves to closed-ended responses. Despite potentially bringing richer insights, the use of open-ended questions poses great challenges in terms of extracting useful information while significantly increasing the analysis time. Nevertheless, automatic text analysis techniques speed up the analysis of open-ended responses. In this research, we explore the potential to use techniques in topic modelling [Latent Dirichlet Allocation (LDA) and Supervised LDA (sLDA)] to extract information from open-ended responses. This is compared to the information obtained from closed-ended responses, accomplished using a questionnaire that measures the intention to use shared autonomous vehicles (SAVs). Two versions of the questionnaire- Ver_OE and Ver_Lk were used, with open-ended and Likert scales measuring the same attitudes in the alternative versions. Factors were extracted for closed-ended questions. For questions common to both versions of the questionnaire, respondents answering Ver_OE had a higher positive attitude towards autonomous vehicles. These attitudinal questions were placed after the open-ended questions. When evaluating the performance of the models that predict the intention to use SAVs, models estimated using Ver_OE performed better. This increased further with the inclusion of the information extracted from the open-ended responses using both, the unsupervised (LDA) and supervised (sLDA) methods. No improvement was observed in the model for Ver_Lk. These indicate the potential for the use of open-ended questions to measure attitudes and topic modelling to extract information from these responses.

中文翻译：

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开放式与封闭式响应：使用主题建模和因子分析的比较研究

出于实际原因，针对大量受访者的调查倾向于将自己局限于封闭式回答。尽管可能带来更丰富的见解，但开放性问题的使用在提取有用信息同时显着增加分析时间的同时也带来了巨大挑战。但是，自动文本分析技术可以加快开放式响应的分析速度。在这项研究中，我们探索了在主题建模中使用技术的潜力[潜在狄利克雷分配（LDA）和受监督的LDA（sLDA）]，可以从开放式响应中提取信息。将其与从封闭式响应中获得的信息进行比较，该信息是通过使用问卷调查来完成的，该问卷测量了使用共享自动驾驶汽车（SAV）的意图。使用了两个版本的问卷-Ver_OE和Ver_Lk，开放式和李克特量表在替代版本中的测量态度相同。提取封闭式问题的因素。对于两个版本的问卷都有共同的问题，回答Ver_OE的受访者对自动驾驶汽车持较高的积极态度。这些态度问题放在不限成员名额的问题之后。在评估预测使用SAV意图的模型的性能时，使用Ver_OE估计的模型表现更好。随着包括使用无监督（LDA）和无监督（sLDA）方法从开放式响应中提取的信息，这一点进一步增加。在Ver_Lk的模型中未观察到任何改进。