Utilizing content-knowledge questionnaires to assess study eligibility and detect deceptive responding

References

• Devine EG, Waters ME, Putnam M, Surprise C, O’Malley K, Richambault C, Fishman RL, Knapp CM, Patterson EH, Sarid-Segal O, et al. Concealment and fabrication by experienced research subjects. Clin Trials. 2013;10:935–48.
   PubMed Web of Science ®Google Scholar
• Apseloff G, Ashton HM, Friedman H, Gerber N. The importance of measuring cotinine levels to identify smokers in clinical trials. Clin Pharm Ther. 1994;56:460–62.doi:10.1038/clpt.1994.161.
   PubMed Web of Science ®Google Scholar
• SC R, Lewine RJ, Jewart RD, Eccard MB, McDaniel JS, Risby ED. Ensuring the normalcy of” normal” volunteers. Am J Psychiatry. 1990;147:682–83.doi:10.1176/ajp.147.5.682-a.
   PubMed Web of Science ®Google Scholar
• McCaul ME, Wand GS. Detecting deception in our research participants: are your participants who you think they are? Alcohol Clin Exp Res. 2018;42:230–37.doi:10.1111/acer.2018.42.issue-2.
   PubMed Web of Science ®Google Scholar
• Chandler J, Shapiro D. Conducting clinical research using crowdsourced convenience samples. Annu Rev Clin Psychol. 2016;12:53–81.doi:10.1146/annurev-clinpsy-021815-093623.
   PubMed Web of Science ®Google Scholar
• Follmer DJ, Sperling RA, Suen HK. The role of mTurk in education research: advantages, issues, and future directions. Educ Res. 2017;46:329–34.doi:10.3102/0013189X17725519.
   Web of Science ®Google Scholar
• Miller JD, Crowe M, Weiss B, Maples-Keller JL, Lynam DR. Using online, crowdsourcing platforms for data collection in personality disorder research: the example of Amazon’s Mechanical Turk. Pers Disord. 2017;8:26–34.doi:10.1037/per0000191.
   PubMedGoogle Scholar
• Strickland JC, Stoops WW. The use of crowdsourcing in addiction science research: amazon mechanical turk. Exp Clin Psychopharmacol. 2019;27:1–18.doi:10.1037/pha0000235.
   PubMed Web of Science ®Google Scholar
• Boynton MH, Richman LS. An online daily diary study of alcohol use using Amazon’s Mechanical Turk. Drug Alcohol Rev. 2014;33:456–61.doi:10.1111/dar.12163.
   PubMed Web of Science ®Google Scholar
• Huhn AS, Garcia-Romeu AP, Dunn KE. Opioid overdose education for individuals prescribed opioids for pain management: randomized comparison of two computer-based interventions. Front Psychiatry. 2018;9:e34.doi:10.3389/fpsyt.2018.00034.
   PubMed Web of Science ®Google Scholar
• Lanaj K, Johnson RE, Barnes CM. Beginning the workday yet already depleted? Consequences of late-night smartphone use and sleep. Organ Behav Hum Decis Process. 2014;124:11–23.doi:10.1016/j.obhdp.2014.01.001.
   Web of Science ®Google Scholar
• Kuerbis AN, Hail L, Moore AA, Muench FJ. A pilot study of online feedback for adult drinkers 50 and older: feasibility, efficacy, and preferences for intervention. J Subst Abuse Treat. 2017;77:126–32.doi:10.1016/j.jsat.2017.04.004.
   PubMed Web of Science ®Google Scholar
• Strickland JC, Stoops WW. Feasibility, acceptability, and validity of crowdsourcing for collecting longitudinal alcohol use data. J Exp Anal Behav. 2018;110:136–53.doi:10.1002/jeab.445.
   PubMed Web of Science ®Google Scholar
• Strickland JC, Hill JC, Stoops WW, Rush CR. Feasibility, acceptability, and initial efficacy of delivering alcohol use cognitive interventions via crowdsourcing. Alcohol Exp Clin Res. 2019;43:888–99.doi:10.1111/acer.13987.
   PubMed Web of Science ®Google Scholar
• Dunn KE, Barrett FS, Herrmann ES, Plebani JG, Sigmon SC, Johnson MW. Behavioral risk assessment for infectious diseases (BRAID): self-report instrument to assess injection and noninjection risk behaviors in substance users. Drug Alcohol Depend. 2016;168:69–75.doi:10.1016/j.drugalcdep.2016.07.032.
   PubMed Web of Science ®Google Scholar
• Dunn KE, Barrett FS, Yepez-Laubach C, Meyer AC, Hruska BJ, Sigmon SC, Fingerhood M, Bigelow GE. Brief Opioid Overdose Knowledge (BOOK): a questionnaire to assess overdose knowledge in individuals who use illicit or prescribed opioids. J Addict Med. 2016;10:314–23.
   PubMed Web of Science ®Google Scholar
• Gearhardt AN, Corbin WR, Brownell KD. Development of the yale food addiction scale version 2.0. Psychol Addict Behav. 2016;30:113–21.doi:10.1037/adb0000136.
   PubMed Web of Science ®Google Scholar
• Lac A, Berger DE. Development and validation of the alcohol myopia scale. Psychol Assess. 2013;25:738–47.doi:10.1037/a0032535.
   PubMed Web of Science ®Google Scholar
• Strickland JC, Lile JA, Stoops WW. Evaluating non-medical prescription opioid demand using commodity purchase tasks: test-retest reliability and incremental validity. Psychopharmacol. 2019;236:2641–52.doi:10.1007/s00213-019-05234-y.
   Web of Science ®Google Scholar
• Hydock C. Assessing and overcoming participant dishonesty in online data collection. Behav Res Methods. 2018;50:1563–67.doi:10.3758/s13428-017-0984-5.
   PubMed Web of Science ®Google Scholar
• Kan IP, Drummey AB. Do imposters threaten data quality? An examination of worker misrepresentation and downstream consequences in Amazon’s Mechanical Turk workforce. Comput Hum Behav. 2018;83:243–53.doi:10.1016/j.chb.2018.02.005.
   Web of Science ®Google Scholar
• Wessling KS, Huber J, Netzer O, Dahl D, Fischer E, Johar G, Morwitz V. MTurk character misrepresentation: assessment and solutions. J Consumer Res. 2017;44:211–30.doi:10.1093/jcr/ucx053.
   Web of Science ®Google Scholar
• Harrison L, Hughes A. Introduction–the validity of self-reported drug use: improving the accuracy of survey estimates. NIDA Res Monogr. 1997;167:1–16.
   PubMedGoogle Scholar
• Turner CF, Ku L, Rogers SM, Lindberg LD, Pleck JH, Sonenstein FL. Adolescent sexual behavior, drug use, and violence: increased reporting with computer survey technology. Sci. 1998;280:867–73.doi:10.1126/science.280.5365.867.
   PubMed Web of Science ®Google Scholar
• Kim HS, Hodgins DC. Reliability and validity of data obtained from alcohol, cannabis, and gambling populations on Amazon’s Mechanical Turk. Psychol Addict Behav. 2017;31:85–94.doi:10.1037/adb0000219.
   PubMed Web of Science ®Google Scholar
• Necka EA, Cacioppo S, Norman GJ, Cacioppo JT, Wicherts JM. Measuring the prevalence of problematic respondent behaviors among mTurk, campus, and community participants. PLoS One. 2016;11:e0157732.doi:10.1371/journal.pone.0157732.
   PubMed Web of Science ®Google Scholar
• Chandler JJ, Paolacci G. Lie for a dime: when most prescreening responses are honest but most study participants are impostors. Soc Psychol Pers Sci. 2017;8:500–08.doi:10.1177/1948550617698203.
   Web of Science ®Google Scholar
• Fischbacher U, Föllmi-Heusi F. Lies in disguise—an experimental study on cheating. J Eur Econ Assoc. 2013;11:525–47.doi:10.1111/jeea.12014.
   Web of Science ®Google Scholar
• Pruckner GJ, Sausgruber R. Honesty on the streets: a field study on newspaper purchasing. J Eur Econ Assoc. 2013;11:661–79.doi:10.1111/jeea.12016.
   Web of Science ®Google Scholar
• Mazar N, Amir O, Ariely D. The dishonesty of honest people: a theory of self-concept maintenance. J Mark Res. 2008;45:633–44.doi:10.1509/jmkr.45.6.633.
   Web of Science ®Google Scholar
• Cuttler C, Spradlin A, Ito E. Measuring cannabis consumption: psychometric properties of the daily sessions, frequency, age of onset, and quantity of cannabis use inventory (DFAQ-CU). PLoS One. 2017;12:e0178194.doi:10.1371/journal.pone.0178194.
   PubMed Web of Science ®Google Scholar
• Bondy SJ, Victor JC, Diemert LM. Origin and use of the 100 cigarette criterion in tobacco surveys. Tob Control. 2009;18:317–23.doi:10.1136/tc.2008.027276.
   PubMed Web of Science ®Google Scholar
• Lee CP, Holmes T, Neri E, Kushida CA. Deception in clinical trials and its impact on recruitment and adherence of study participants. Contemp Clin Trials. 2018;72:146–57.doi:10.1016/j.cct.2018.08.002.
   PubMed Web of Science ®Google Scholar
• Blank MD, Breland AB, Cobb CO, Spindle T, Ramôa C, Eissenberg T. Clinical laboratory evaluation of electronic cigarettes: methodological challenges. Tob Reg Sci. 2016;2:426–39.doi:10.18001/TRS.2.4.12.
   PubMedGoogle Scholar
• Dunn KE, Barrett FS, Bigelow GE. Naloxone formulation for overdose reversal preference among patients receiving opioids for pain management. Addict Behav. 2018;86:56–60.doi:10.1016/j.addbeh.2018.03.011.
   PubMed Web of Science ®Google Scholar
• Johnson MW, Johnson PS, Rass O, Pacek LR. Behavioral economic substitutability of e-cigarettes, tobacco cigarettes, and nicotine gum. J Psychopharmacol. 2017;31:851–60.doi:10.1177/0269881117711921.
   PubMed Web of Science ®Google Scholar
• Salazar CA, Tomko RL, Akbar SA, Squeglia LM, McClure EA. Medical cannabis use among adults in the southeastern United States. Cannabis. 2019;2:53–65.doi:10.26828/cannabis.
   PubMedGoogle Scholar
• Carlini BH. Potency increase, product development and marijuana marketing in times of legalization. Addic. 2017;112:2178–79.doi:10.1111/add.13945.
   PubMed Web of Science ®Google Scholar
• Krauss MJ, Sowles SJ, Sehi A, Spitznagel EL, Berg CJ, Bierut LJ, Cavazos-Rehg PA. Marijuana advertising exposure among current marijuana users in the US. Drug Alcohol Depend. 2017;174:192–200.
   PubMed Web of Science ®Google Scholar