Doctoral Dissertation Research: Memory Bookmarking: An Approach to Improve Recall in Surveys

博士论文研究：记忆书签：提高调查回忆的方法

• 批准号: 1659776
• 负责人: Frederick Conrad
• 金额: $ 1.6万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1659776&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Memory; Bookmarking

This research project will advance survey methods for time-use measurement. Social scientists widely use retrospective survey questions (questions about events that occurred in the past) to gather information about respondents' recent autobiographical events. Recall of even recent events is burdensome for respondents, however, and it often leads to uneven data quality due to recall error. This project will experimentally evaluate a new approach called Memory Bookmarking (MB) to address recall error in surveys. The MB approach collects information about respondents' experiences while they are underway. This information is later fed back to respondents as memory cues or bookmarks. Depending on the experimental condition, respondents will be asked to provide bookmarks about everyday events, in the form of text messages, photographs, or locations. The approach should not only improve memory for the cued events but also help respondents remember other events that are temporally adjacent and semantically related to the cued event -- much like an actual bookmark allows readers to see what is on the previous and next pages. The results of this project will advance current thinking about autobiographical memory, especially about the formats (verbal, visual, and spatial) in which people primarily represent the events of their lives. The project will use techniques from data science to analyze unstructured data and thus contribute to ongoing efforts to integrate data science techniques into survey methodology. As a Doctoral Dissertation Research Improvement award, support is provided to enable a promising student to establish a strong, independent research career.The MB approach will be evaluated in a factorial experiment in which respondents are asked to reconstruct the full sequence of events they have experienced over the prior forty-eight hours. MB respondents are "pinged" on their smartphone at designated times and asked to provide information via their smartphones about the event that is in progress at that moment. This information is then presented to the respondents as memory cues to help them reconstruct all their activities, not just the pinged events, during the prior forty-eight hours. Respondents' recall will be compared between the experimental conditions created by crossing the number of pings with the bookmark formats, and between the bookmark conditions and a no-bookmark control condition. The research will address the following questions: Does the MB approach lead to more accurate and complete recall than when no cues are provided? How effective are the three formats of memory cues (verbal, visual, and spatial) for different types of events? Will respondents reply to pings in a timely manner during the forty-eight hours, especially as the number of bookmarks increases?

该研究项目将推进时间使用测量的调查方法。 社会科学家广泛使用回顾性调查问题（关于过去发生的事件的问题）来收集关于受访者最近自传事件的信息。然而，即使是最近发生的事件的回忆对受访者来说也是沉重的负担，而且由于回忆错误，它往往导致数据质量不均匀。这个项目将实验性地评估一种名为记忆书签（MB）的新方法，以解决调查中的回忆错误。监测和评价方法收集关于调查对象在调查过程中的经历的信息。这些信息随后会作为记忆线索或书签反馈给受访者。根据实验条件，受访者将被要求提供有关日常事件的书签，以短信，照片或位置的形式。这种方法不仅可以提高对线索事件的记忆，还可以帮助受访者记住与线索事件在时间上相邻且在语义上相关的其他事件--就像一个真正的书签可以让读者看到上一页和下一页上的内容一样。这个项目的结果将推进当前对自传体记忆的思考，特别是关于人们主要代表他们生活中事件的格式（口头，视觉和空间）。该项目将使用数据科学技术来分析非结构化数据，从而有助于将数据科学技术整合到调查方法中。作为博士论文研究改进奖，提供支持，使有前途的学生建立一个强大的，独立的研究生涯。MB方法将在析因实验中进行评估，受访者被要求重建他们在过去48小时内经历的事件的完整序列。 MB受访者在指定的时间在他们的智能手机上被“ping”，并被要求通过他们的智能手机提供有关当时正在进行的事件的信息。然后，这些信息作为记忆线索呈现给受访者，帮助他们重建之前48小时内的所有活动，而不仅仅是被ping到的事件。受访者的回忆将在实验条件之间进行比较，实验条件是通过将ping的数量与书签格式交叉，以及在书签条件和无书签控制条件之间。该研究将解决以下问题：MB方法是否比没有提供线索时更准确和完整的回忆？对于不同类型的事件，记忆线索的三种形式（语言、视觉和空间）的有效性如何？ 受访者是否会在这48小时内及时回复ping，特别是书签数量增加时？