The adapted Autobiographical interview: A systematic review and proposal for conduct and reporting

Miloyan, Beyon; McFarlane, Kimberley A.; Vásquez-Echeverría, Alejandro

doi:10.1016/j.bbr.2019.03.050

Cited by 16 publications

(10 citation statements)

References 22 publications

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“…The AI has been widely adopted to characterize the involvement of episodic memory during AM in healthy participants and patient populations (see https://levinelab.weebly.com/ai-testing.html; Miloyan et al, 2019 for review). Consistent with the original report, healthy younger adults provide more internal details and fewer external details than older adults when recalling past personal experiences (Addis et al, 2008; Addis et al, 2010; De Brigard et al, 2016; De Brigard et al, 2017; Ford et al, 2014; Gaesser et al, 2011; Madore et al, 2014; Peters et al, 2019; Robin & Moscovitch, 2017; Spreng et al, 2018; St Jacques & Levine, 2007; Vandermorris et al, 2013; Zavagnin et al, 2016).…”

mentioning

confidence: 99%

Taking stock of the past: A psychometric evaluation of the Autobiographical Interview

Lockrow

Setton

Spreng

et al. 2021

Preprint

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Autobiographical memory (AM) involves a rich phenomenological re-experiencing of a spatio-temporal event from the past, which is challenging to objectively quantify. The Autobiographical Interview (AI; Levine et al., 2002, Psychology & Aging) is a manualized performance-based assessment designed to quantify episodic (internal) and semantic (external) features of recalled and verbally conveyed prior experiences. The AI has been widely adopted yet has not undergone a comprehensive psychometric validation. We investigated the reliability, validity, association to individual differences measures, and factor structure in healthy younger and older adults (N=352). Evidence for the AI's reliability was strong: the subjective scoring protocol showed high inter-rater reliability and previously identified age effects were replicated. Internal consistency across timepoints was robust, suggesting stability in recollection. Central to our validation, internal AI scores were positively correlated with standard, performance-based measures of episodic memory, demonstrating convergent validity. The two-factor structure for the AI was not well-supported by confirmatory factor analysis. Adjusting internal and external detail scores for the number of words spoken (detail density) improved trait estimation of AM performance. Overall, the AI demonstrated sound psychometric properties for inquiry into the qualities of autobiographical remembering.

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confidence: 99%

Taking stock of the past: A psychometric evaluation of the Autobiographical Interview

Lockrow

Setton

Spreng

et al. 2021

Preprint

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“…We note that researchers differ in terms of whether they examine individual detail types or composite internal versus external scores. Moreover, depending on the goal of the study, the researcher may opt to control for verbal output by computing an internal-to-total ratio score (see Miloyan et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Semi-automated transcription and scoring of autobiographical memory narratives

et al. 2020

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Autobiographical memory studies conducted with narrative methods are onerous, requiring significant resources in time and labour. We have created a semi-automated process that allows autobiographical transcribing and scoring methods to be streamlined. Our paper focuses on the Autobiographical Interview (AI; Levine et al., 2002) but this method can be adapted for other narrative protocols. Specifically, here we lay out a procedure that guides researchers through the four main phases of the autobiographical narrative pipeline: (1) data collection, (2) transcribing,(3) scoring, and (4) analysis. First, we provide recommendations for incorporating transcription software to augment human transcribing. We then introduce an electronic scoring procedure for tagging narratives for scoring that incorporates the traditional AI scoring method with basic keyboard shortcuts in Microsoft Word. Finally, we provide a Python script that can be used to automate counting scored transcripts. This method accelerates the time it takes to conduct a narrative study and reduces opportunity for error in narrative quantification. Available open access on GitHub (https://github.com/cMadan/scoreAI), our pipeline makes narrative methods more accessible for future research.

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“…However, the integration of AI in qualitative research has not escaped criticism (Miloyan et al 2019, as cited in Christou, 2023a. The central concern within the scholarly community, as highlighted by Christou, revolves around the potential omission or intentional disregard in referencing its use.…”

Section: Table 1: Inclusion and Exclusion Criteria For The Bibliograp...mentioning

confidence: 99%

An analysis of the generative AI use as analyst in qualitative research in science education

Rodrigues dos Anjos,

De Souza,

Serrano de Andrade Neto

et al. 2024

Rev. Pesq. Qual.

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The article evaluates the effectiveness of generative artificial intelligence models, specifically ChatGPT 4.0 and Claude 2.0, in conducting qualitative research within the field of scientific education. By applying the Cognitive Networks Mediation Theory (CNMT) to analyze interviews from two students, it was found that Claude 2.0 surpassed ChatGPT 4.0 in recognizing cognitive mediations and distinguishing between pre- and post-test conditions. Although both models concurred on the concept of conceptual evolution, Claude 2.0 demonstrated a greater capacity for detail, notably by referencing specific interview excerpts to support its analyses upon request. In contrast, ChatGPT 4.0 exhibited difficulties in these areas, even when given additional prompts. The study concludes by acknowledging the utility of AI, particularly Claude 2.0, for qualitative research, while also emphasizing the critical role of human oversight in detecting potential deficiencies within these models.

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The adapted Autobiographical interview: A systematic review and proposal for conduct and reporting

Cited by 16 publications

References 22 publications

Taking stock of the past: A psychometric evaluation of the Autobiographical Interview

Taking stock of the past: A psychometric evaluation of the Autobiographical Interview

Semi-automated transcription and scoring of autobiographical memory narratives

An analysis of the generative AI use as analyst in qualitative research in science education

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