https://doi.org/10.1016/j.biopsych.2022.09.034

Identifying Transdiagnostic Mechanisms in Mental Health Using Computational Factor Modeling

http://manuscript.elsevier.com/S009174352200086X/pdf/S009174352200086X.pdf

COVID-19 vaccine behaviors and intentions among a national sample of United States adults ages 18–45

Fraudulent participation is an escalating concern for online clinical trials and research studies and can have a significant negative impact on findings. We aim to shed light on the risk and to provide practical recommendations for detecting and managing such instances. 

Management of fraudulent participants in online research: Practical recommendations from a randomized controlled feasibility trial.

Background The use of web-based methods to collect population-based health behavior data has burgeoned over the past two decades. Researchers have used web-based platforms and research panels to study a myriad of topics. Data cleaning prior to statistical analysis of web-based survey data is an important step for data integrity. However, the data cleaning processes used by research teams are often not reported. Objective The objectives of this manuscript are to describe the use of a systematic approach to clean the data collected via a web-based platform from panelists and to share lessons learned with other research teams to promote high-quality data cleaning process improvements. Methods Data for this web-based survey study were collected from a research panel that is available for scientific and marketing research. Participants (N=4000) were panelists recruited either directly or through verified partners of the research panel, were aged 18 to 45 years, were living in the United States, had proficiency in the English language, and had access to the internet. Eligible participants completed a health behavior survey via Qualtrics. Informed by recommendations from the literature, our interdisciplinary research team developed and implemented a systematic and sequential plan to inform data cleaning processes. This included the following: (1) reviewing survey completion speed, (2) identifying consecutive responses, (3) identifying cases with contradictory responses, and (4) assessing the quality of open-ended responses. Implementation of these strategies is described in detail, and the Checklist for E-Survey Data Integrity is offered as a tool for other investigators. Results Data cleaning procedures resulted in the removal of 1278 out of 4000 (31.95%) response records, which failed one or more data quality checks. First, approximately one-sixth of records (n=648, 16.20%) were removed because respondents completed the survey unrealistically quickly (ie, <10 minutes). Next, 7.30% (n=292) of records were removed because they contained evidence of consecutive responses. A total of 4.68% (n=187) of records were subsequently removed due to instances of conflicting responses. Finally, a total of 3.78% (n=151) of records were removed due to poor-quality open-ended responses. Thus, after these data cleaning steps, the final sample contained 2722 responses, representing 68.05% of the original sample. Conclusions Examining data integrity and promoting transparency of data cleaning reporting is imperative for web-based survey research. Ensuring a high quality of data both prior to and following data collection is important. Our systematic approach helped eliminate records flagged as being of questionable quality. Data cleaning and management procedures should be reported more frequently, and systematic approaches should be adopted as standards of good practice in this type of research.

/pdf/strategies-and-lessons-learned-during-cleaning-of-data-from-3fmfhnp8.pdf

Strategies and Lessons Learned During Cleaning of Data From Research Panel Participants: Cross-sectional Web-Based Health Behavior Survey Study

Twinks, Jocks, and Bears, Oh My! Differing subcultural appearance identifications among gay men and their associated eating disorder psychopathology.

OBJECTIVE Our original aim was to validate and norm common eating disorder (ED) symptom measures in a large, representative community sample of transgender adults in the United States. We recruited via Amazon Mechanical Turk (MTurk), a popular online recruitment and data collection platform both within and outside of the ED field. We present an overview of our experience using MTurk. METHOD Recruitment began in Spring 2020; our original target N was 2,250 transgender adults stratified evenly across the United States. Measures included a demographics questionnaire, the Eating Disorder Examination-Questionnaire, and the Eating Attitudes Test-26. Consistent with current literature recommendations, we implemented a comprehensive set of attention and validity measures to reduce and identify bot responding, data farming, and participant misrepresentation. RESULTS Recommended validity and attention checks failed to identify the majority of likely invalid responses. Our collection of two similar ED measures, thorough weight history assessment, and gender identity experiences allowed us to examine response concordance and identify impossible and improbable responses, which revealed glaring discrepancies and invalid data. Furthermore, qualitative data (e.g., emails received from MTurk workers) raised concerns about economic conditions facing MTurk workers that could compel misrepresentation. DISCUSSION Our results strongly suggest most of our data were invalid, and call into question results of recently published MTurk studies. We assert that caution and rigor must be applied when using MTurk as a recruitment tool for ED research, and offer several suggestions for ED researchers to mitigate and identify invalid data.

Concerns and recommendations for using Amazon MTurk for eating disorder research

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Concerns and recommendations for using Amazon MTurk for eating disorder research