Wake Forest Alcohol Imagery Set: Development and Validation of a Large Standardized Alcohol Imagery Dataset

Peterson, Hope; Simpson, Sean L.; Laurienti, Paul J.

doi:10.1111/acer.14214

Cited by 8 publications

(7 citation statements)

References 20 publications

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“…Furthermore, researchers should report stimulus validation procedures routinely. Where possible, researchers can make use of existing validated stimulus sets [44,[66][67][68][69], and in situations where this is inappropriate (e.g. cultural differences in drinking preferences, brand familiarity) available guidelines (see [44]) should be utilized to develop new sets.…”

Section: Raising the Bar: Recommendations For Researchersmentioning

confidence: 99%

Raising the bar: improving methodological rigour in cognitive alcohol research

et al. 2021

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Background and Aims A range of experimental paradigms claim to measure the cognitive processes underpinning alcohol use, suggesting that heightened attentional bias, greater approach tendencies and reduced cue‐specific inhibitory control are important drivers of consumption. This paper identifies methodological shortcomings within this broad domain of research and exemplifies them in studies focused specifically on alcohol‐related attentional bias. Argument and analysis We highlight five main methodological issues: (i) the use of inappropriately matched control stimuli; (ii) opacity of stimulus selection and validation procedures; (iii) a credence in noisy measures; (iv) a reliance on unreliable tasks; and (v) variability in design and analysis. This is evidenced through a review of alcohol‐related attentional bias (64 empirical articles, 68 tasks), which reveals the following: only 53% of tasks use appropriately matched control stimuli; as few as 38% report their stimulus selection and 19% their validation procedures; less than 28% used indices capable of disambiguating attentional processes; 22% assess reliability; and under 2% of studies were pre‐registered. Conclusions Well‐matched and validated experimental stimuli, the development of reliable cognitive tasks and explicit assessment of their psychometric properties, and careful consideration of behavioural indices and their analysis will improve the methodological rigour of cognitive alcohol research. Open science principles can facilitate replication and reproducibility in alcohol research.

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Section: Raising the Bar: Recommendations For Researchersmentioning

confidence: 99%

Raising the bar: improving methodological rigour in cognitive alcohol research

et al. 2021

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“…We propose the development of a web-based application, incorporating in-built quality assurance methods and validated inventories. Our review revealed that a large proportion of studies assessed psychiatric symptoms in total or relative isolation (Cunningham et al, 2017a;Cunningham et al, 2017b;Halkjelsvik, 2021;Moody et al, 2017;Peterson et al, 2019;Strickland and Stoops, 2018;Tilden et al, 2020). It is long-established that psychiatric symptoms and diagnoses interact and overlap (Gillan and Daw, 2016;Zimmerman and Mattia, 2001).…”

Section: Discussionmentioning

confidence: 91%

Measuring psychiatric symptoms online: A systematic review of the use of inventories on Amazon Mechanical Turk (mTurk)

Kolc¹,

Tan²,

Lo³

et al. 2023

Preprint

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Symptom measurement in psychiatric research increasingly uses digitized self-report inventories and is turning to crowdsourcing platforms for recruitment, e.g., Amazon Mechanical Turk (mTurk). The impact of digitizing pencil-and-paper inventories on the psychometric properties is underexplored. Against this background, numerous studies report high prevalence estimates of psychiatric symptoms in mTurk samples. Here we develop a framework to evaluate the online implementation of psychiatric symptom inventories relative to two domains, that is, the adherence to (i) validated scoring and (ii) standardized administration. We apply this new framework to the online use of the Patient Health Questionnaire (PHQ-9), Generalised Anxiety Disorder-7 (GAD-7), and Alcohol Use Disorder Identification Test (AUDIT). Our systematic review of the literature identified 36 implementations of these three inventories on mTurk across 27 publications. We also evaluated methodological approaches to enhance data quality, e.g., the use of bot detection and attention check items. Of the 36 implementations, 23 reported the applied diagnostic scoring criteria and only 18 reported the specified symptom timeframe. None of the 36 implementations reported adaptations made in their digitization of the inventories. While recent reports attribute higher rates of mood, anxiety, and alcohol use disorders on mTurk to data quality, our findings indicate that this inflation may also relate to the assessment methods. We provide recommendations to enhance both data quality and fidelity to validated administration and scoring methods.

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Section: Discussionmentioning

confidence: 98%

“…For example, Clarke et al (2021) used one criterion of attention-check items and excluded 0.2% of their sample. Peterson et al (2019) used two criteria of attention-check and bot detection, resulting in an exclusion of 28.3% of their sample. Ophir et al (2020) applied three attention-check criteria of infrequency items, reading speed, and participant internal consistency, and filtered suspicious IP addresses, excluding 31.4% of their sample.…”

Section: Discussionmentioning

confidence: 99%