Ackerman, P. L. & Cianciolo, A. T. (2000). Cognitive, perceptual-speed, and psychomotor determinants of individual differences during skill acquisition. Journal of Experimental Psychology: Applied, 6, 259–290.Google ScholarPubMed

American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (2014). Standards for educational and psychological testing. Washington, DC: American Educational Research Association.Google Scholar

American Psychological Association. (2017). Ethical principles of psychologists and code of conduct (2002, Amended June 1, 2010, and January 1, 2017). Retrieved from www.apa.org/ethics/code/index.aspx.Google Scholar

Arthur, W., Jr., Doverspike, D., & Bell, S. T. (2004). Information processing tests. In Hersen, M. & Thomas, J. C. (Eds.), Comprehensive handbook of psychological assessment: Volume 4, Industrial and organizational assessment (pp. 56–74). New York, NY: John Wiley & Sons, Inc.Google Scholar

Arthur, W., Jr., Doverspike, D., Kinney, T. B., & O’Connell, M. (2017). The impact of emerging technologies on selection models and research: Mobile devices and gamification as exemplars. In Farr, J. L., & Tippins, N. T (Eds.), Handbook of employee selection (2nd edn., pp. 967–986). New York, NY: Taylor & Francis/Psychology Press.CrossRefGoogle Scholar

Arthur, W., Jr., Doverspike, D., Muñoz, G. J., Taylor, J. E., & Carr, A. E. (2014). The use of mobile devices in high-stakes remotely delivered assessments and testing. International Journal of Selection and Assessment, 22, 113–123.CrossRefGoogle Scholar

Arthur, W., Jr., Edwards, B. D., & Barrett, G. V. (2002). Multiple-choice and constructed-response tests of ability: Race-based subgroup performance differences on alternative paper-and-pencil test formats. Personnel Psychology, 55, 985–1008.CrossRefGoogle Scholar

Arthur, W., Jr. & Glaze, R. M. (2011). Cheating and response distortion on remotely delivered assessments. In Tippins, N. T., & Adler, S. (Eds.), Technology-enhanced assessment of talent (pp. 99–152). San Francisco, CA: Jossey-Bass.CrossRefGoogle Scholar

Arthur, W., Jr., Keiser, N. L., & Doverspike, D. (2018). An information processing-based conceptual framework of the effects of unproctored internet-based testing devices on scores on employment-related assessments and tests. Human Performance, 31, 1–32.CrossRefGoogle Scholar

Arthur, W., Jr., Keiser, N. L., Hagen, E., & Traylor, Z. (2018). Unproctored internet-based testing device-type score differences: The role of working memory. Intelligence, 67, 67–75.CrossRefGoogle Scholar

Arthur, W. Jr. & Villado, A. J. (2008). The importance of distinguishing between constructs and methods when comparing predictors in personnel selection research and practice. Journal of Applied Psychology, 93, 435–442.CrossRefGoogle ScholarPubMed

Baddeley, A. (1992). Working memory. Science, 25, 181–185.Google Scholar

Bobko, P. & Roth, P. L. (2013). Reviewing, categorizing, and analyzing the literature on Black-White mean differences for predictors of job performance: Verifying some perceptions and updating/correcting others. Personnel Psychology, 66, 91–126.CrossRefGoogle Scholar

Brown, M. I. & Grossenbacher, M. A. (2017). Can you test me now? Equivalence of GMA tests on mobile and non-mobile devices. International Journal of Selection and Assessment, 25, 61–71.CrossRefGoogle Scholar

Brown, M. I., Grossenbacher, M., & Nguyen, D. (2016). Can you score me now? GMA testing using mobile devices. Paper presented at the presented at the 31st Annual Conference of the Society for Industrial and Organizational Psychology, Anaheim, CA.Google Scholar

Chang, L., Lawrence, A. D., O’Connell, M. S., & Kinney, T. B. (2016). Mobile vs. PC delivered simulations: Screen size matters. In T. D. McGlochin (Chair), Mobile equivalence: Expanding research across assessment methods, levels, and devices. Symposium presented at the 31st Annual Conference of the Society for Industrial and Organizational Psychology, Anaheim, CA.Google Scholar

Cleveland, J. N. & Murphy, K. R. (2016). Organizations want to abandon performance appraisal: Can they? Should they? In Stone, D. L. & Dulebohn, J. H. (Eds.), Human resource management theory and research on new employment relationships (pp. 15–46). Charlotte, NC: Information Age.Google Scholar

Colom, R., Martínez-Molina, A., Shih, P. C., & Santacreu, J. (2010). Intelligence, working memory, and multitasking performance. Intelligence, 38, 543–551.CrossRefGoogle Scholar

Cowan, N., Elliott, E. M., Saults, J. S., Morey, C. C., Mattox, S., Hismjatullina, A., & Conway, A. R. (2005). On the capacity of attention: Its estimation and its role in working memory and cognitive aptitudes. Cognitive Psychology, 51, 42–100.CrossRefGoogle ScholarPubMed

Dages, K. D., & Jones, J. W. (2015). Mobile device administration: Does length or level of assessment matter? In N. Morelli (Chair), Mobile devices in talent assessment: The next chapter. Symposium presented at the 30th Annual Conference of the Society for Industrial and Organizational Psychology, Philadelphia, PA.Google Scholar

Davies, S. A. & Wadlington, P. L. (2006). Factor and parameter invariance of a five factor personality test across proctored/unproctored computerized administration. Paper presented at the 21st Annual Conference of the Society for Industrial and Organizational Psychology, Dallas, TX.Google Scholar

Do, B., Shepherd, W., & Drasgow, F. (2005). Measurement equivalence across proctored and unproctored administration modes of web-based measures. Paper presented at the 20th Annual Conference of the Society for Industrial and Organizational Psychology, Los Angeles, CA.Google Scholar

Edwards, B. D. & Arthur, W., Jr. (2007). An examination of factors contributing to a reduction in subgroup differences on a constructed-response paper-and-pencil test of scholastic achievement. Journal of Applied Psychology, 92, 794–801.CrossRefGoogle ScholarPubMed

Edwards, B. D., Franco Watkins, A. M., McAbee, S. T., & Faura, L. (2017). The case for using working memory in practice. The Industrial-Organizational Psychologist, 55(1), 4–7.Google Scholar

Equal Employment Opportunity Commission, Civil Service Commission, Department of Labor, & Department of Justice. (1978). Adoption by four agencies of uniform guidelines on employee selection procedures. Federal Register, 43, 38290–38315.Google Scholar

Fursman, P. (2016). Warning message impact on assessment scores delivered on mobile devices. In T. D. McGlochin (Chair), Mobile equivalence: Expanding research across assessment methods, levels, and devices. Symposium presented at the 31st Annual Conference of the Society for Industrial and Organizational Psychology, Anaheim, CA.Google Scholar

Fursman, P., & Tuzinski, K. (2015). Reactions to mobile testing from the perspective of job applicants. Paper presented at the 30th Annual Conference of the Society for Industrial and Organizational Psychology, Philadelphia, PA.Google Scholar

Gray, C., Morelli, N. A., & McLane, W. (2015). Does use context affect selection assessments via mobile devices? In N. A. Morelli (Chair), Mobile devices in talent assessment: The next chapter. Symposium presented at the 30th Annual Conference of the Society for Industrial and Organizational Psychology, Philadelphia, PA.Google Scholar

Groves, R. M. & Heeringa, S. G. (2006). Responsive design for household surveys: Tools for actively controlling survey errors and costs. Journal of the Royal Statistical Society: Series A (Statistics in Society), 169, 439–457.CrossRefGoogle Scholar

Gutierrez, S. L. & Sanderson, K. (2015). Applicant reactions: What we know about testing on the go. In H. Payne (Chair), Future of assessment: Reactions to innovative formats and delivery methods. Symposium presented at the 30th Annual Conference of the Society for Industrial and Organizational Psychology, Philadelphia, PA.Google Scholar

Harari, G. M., Lane, N. D., Wang, R., Crosier, B. S., Campbell, A. T., & Gosling, S. D. (2016). Using smartphones to collect behavioral data in psychological science: Opportunities, practical considerations, and challenges. Perspectives on Psychological Science, 11, 838–854.CrossRefGoogle ScholarPubMed

Hornik, S., Johnson, R. D., & Wu, Y. (2007). When technology does not support learning: Conflicts between epistemological beliefs and technology support in virtual learning environments. Journal of Organizational and End User Computing, 19, 23–42.CrossRefGoogle Scholar

Illingworth, A. J., Moon, S. M., Morelli, N., McLance, W. L., Wilgus, S. J., Green, D. K., & Thompson, I. B. (2017). Criterion validity of assessments delivered on mobile and non-mobile devices. In N. A. Morelli (Chair), Mobile testing “in the wild”: Apps, reactions, images, and criterion-related validity. Symposium presented at the 32^nd Annual Conference of the Society for Industrial and Organizational Psychology, Orlando, FL.Google Scholar

Illingworth, A. J., Morelli, N. A., Scott, J. C., & Boyd, S. L. (2015). Internet-based, unproctored assessments on mobile and non-mobile devices: Usage, measurement equivalence, and outcomes. Journal of Business and Psychology, 30, 325–343.CrossRefGoogle Scholar

King, D. D., Ryan, A. M., Kantrowitz, T., Grelle, D., & Dainis, A. (2015). Mobile internet testing: An analysis of equivalence, individual differences, and reactions. International Journal of Selection and Assessment, 23, 382–394.CrossRefGoogle Scholar

Kinney, T., Besl, A., Lawrence, A., Moretti, D., & Chang, L. (2017). Demonstrating criterion-related validity equivalence with PC and mobile test-takers. In N. A. Morelli (Chair), Mobile testing “in the wild”: Apps, reactions, images, and criterion-related validity. Symposium presented at the 32nd Annual Conference of the Society for Industrial and Organizational Psychology, Orlando, FL.Google Scholar

Kirkpatrick, D. L. (1976). Evaluation of training. In Craig, R.L. (Ed.), Training and development handbook: A guide to human resource development (2nd edn., pp. 301–319). New York, NY: McGraw-Hill.Google Scholar

Kroft, P. & Wickens, C. D. (2002). Displaying multi-domain graphical database information: An evaluation of scanning, clutter, display size, and user activity. Information Design Journal, 11, 44–52.CrossRefGoogle Scholar

LaPort, K. (2016). Mobile assessment: Comparing traditional cognitive, cognitive reasoning, and non-cognitive performance. In T. D. McGlochin (Chair), Mobile equivalence: Expanding research across assessment methods, levels, and devices. Symposium presented at the 31st Annual Conference of the Society for Industrial and Organizational Psychology, Anaheim, CA.Google Scholar

Lawrence, A. D., Kinney, T. B., O’Connell, M. S., & Delgado, K. M. (2017). Stop interrupting me! Examining the relationship between interruptions, test performance and reactions. Personnel Assessment and Decisions, 3, 15–24.CrossRefGoogle Scholar

Lee, I., Kim, J., & Kim, J. (2005). Use contexts for the mobile internet: A longitudinal study monitoring actual use of mobile internet services. International Journal of Human-Computer Interaction, 18, 269–292.CrossRefGoogle Scholar

Levine, E. L., Spector, P. E., Menon, S., & Narayanan, L. (1996). Validity generalization for cognitive, psychomotor, and perceptual tests for craft jobs in the utility industry. Human Performance, 9, 1–22.CrossRefGoogle Scholar

McClure Johnson, T. K. & Boyce, A. S. (2015). Selection testing: An updated look at trends in mobile device usage. In N. Morelli (Chair), Mobile devices in talent assessment: The next chapter. Symposium presented at the 30th Annual Conference of the Society for Industrial and Organizational Psychology, Philadelphia, PA.Google Scholar

Morelli, N. A., Potosky, P., Arthur, W., Jr., & Tippins, N. T. (2017). A call for conceptual models of technology in I-O psychology: An example for technology-based talent assessment. Industrial and Organizational Psychology: Perspectives on Science and Practices, 10, 634–653.CrossRefGoogle Scholar

Nunnally, J. C. & Bernstein, I. H. (1994). Psychometric theory. New York, NY: McGraw-Hill.Google Scholar

O’Connell, M. S., Arthur, W. Jr., & Doverspike, D. (2015). Mobile device assessment: The horses have left the barn… now what? Invited workshop presented at the 30th Annual Conference of the Society for Industrial and Organizational Psychology, Philadelphia, PA.Google Scholar

O’Connell, M. S., Delgado, K., & Kung, M. C. (2012). Does proctoring impact measurement methods differently? An evaluation in a high stakes testing environment. Paper presented at the 26th Annual Conference of the Society for Industrial and Organizational Psychology, San Diego, CA.Google Scholar

O*NET (n.d). www.onetonline.org/find/descriptor/result/1.A.1.e.3 (accessed June 10, 2017).Google Scholar

Parker, B. N. & Meade, A. W. (2015). Smartphones in selection: Exploring measurement invariance using item response theory. In N. Morelli (Chair), Mobile devices in talent Assessment: The next chapter. Symposium presented at the 30th Annual Conference of the Society for Industrial and Organizational Psychology, Philadelphia, PA.Google Scholar

Payne, S. C., Mendoza, A. M., & Horner, M. T. (2018). Electronic performance management: Does altering the process improve the outcome? In Stone, D. L. & Dulebohn, J. H. (Eds.), Human resource management theory and research on eHRM (pp. 189–215). Charlotte, NC: Information Age.Google Scholar

Pearlman, K. (2009). Unproctored internet testing: Practical, legal, and ethical concerns. Industrial and Organizational Psychology: Perspectives on Science and Practice, 2, 14–19.CrossRefGoogle Scholar

Pew Research Center. (2015a). Technology device ownership: 2015. www.pewinternet.org/2015/10/29/technology-device-ownership-2015/ (accessed June 10, 2017).Google Scholar

Pew Research Center. (2015b). Device ownership. www.pewresearch.org/data-trend/media-and-technology/device-ownership/ (accessed June 10, 2017).Google Scholar

Pew Research Center. (2018). Mobile fact sheet. Retrieved from www.pewinternet.org/fact-sheet/mobile/ (accessed June 10, 2017).Google Scholar

Potosky, D. (2008). A conceptual framework for the role of the administration medium in the personnel assessment process. Academy of Management Review, 33, 629–48.CrossRefGoogle Scholar

Richardson, J. C. & Swan, K. (2003). Examining social presence in online courses in relation to students perceived learning and satisfaction. Journal of Asynchronous Learning Networks, 7, 68–88.Google Scholar

Rossini, J. (2016). Mobile device testing: A five-year look across job level. In T. D. McGlochlin (Chair), Mobile equivalence: Expanding research across assessment methods, levels, and devices. Symposium presented at the 31st Annual Conference of the Society for Industrial and Organizational Psychology, Anaheim, CA.Google Scholar

Sackett, P. R., Lievens, F., Van Iddekinge, C. H., & Kuncel, N. R. (2017). Individual differences and their measurement: A review of 100 years of research. Journal of Applied Psychology, 102, 254–273.CrossRefGoogle ScholarPubMed

Sanchez, C. A. & Goolsbee, J. Z. (2010). Character size and reading to remember from small displays. Computers & Education, 55, 1056–1062.CrossRefGoogle Scholar

Schmidt, F. L. & Hunter, J. E. (1998). The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings. Psychological Bulletin, 124, 262–274.CrossRefGoogle Scholar

Schmidt, F. L. & Hunter, J. (2004). General mental ability in the world of work: Occupational attainment and job performance. Journal of Personality and Social Psychology, 86, 162–173.CrossRefGoogle ScholarPubMed

Schroeders, U. & Wilhelm, O. (2010). Testing reasoning ability with handheld computers, notebooks, and paper and pencil. European Journal of Psychological Assessment, 26, 284–292.CrossRefGoogle Scholar

Siek, K. A., Rogers, Y., & Connelly, K. H. (2005). Fat finger worries: How older and younger users physically interact with PDAs. In Costabile, M. F., & Paternò, F. (Eds.), Human-Computer interaction – INTERACT 2005, Lecture notes in computer science (vol. 3585, pp. 267–280). Heidelberg, Germany: Springer Berlin Heidelberg.CrossRefGoogle Scholar

Sierra-Cedar (2016). 2016–2017 HR Systems Survey White Paper (19th annual edn.). Alpharetta, GA: Sierra-Cedar.Google Scholar

Society for Industrial and Organizational Psychology. (2003). Principles for the validation and use of personnel selection procedures (4th edn.). Bowling Green, OH: Society for Industrial and Organizational Psychology.Google Scholar

Society for Industrial and Organizational Psychology. (2015). Top 10 workplace trends for 2015. www.siop.org/siop_newsbriefs/2015/january/january/ (accessed June 10, 2017).Google Scholar

Society for Industrial and Organizational Psychology. (2017). Top 10 workplace trends for 2017. www.siop.org/article_view.aspx?article=1610 (accessed June 10, 2017).Google Scholar

Stone, D. L., Deadrick, D. L., Lukaszewski, K. M., & Johnson, R. (2015). The influence of technology on the future of human resource management. Human Resource Management Review, 25, 216–231.CrossRefGoogle Scholar

Tippins, N. T. (2015). Technology and assessment in selection. Annual Review of Organizational Psychology and Organizational Behavior, 2, 551–582.CrossRefGoogle Scholar

Tippins, N. T. & Alder, S. (Eds.), (2011). Technology-enhanced assessment of talent. San Francisco, CA: Jossey-Bass.CrossRefGoogle Scholar

Tippins, N. T., Beaty, J., Drasgow, F., Gibson, W. M., Pearlman, K., Segall, D. O., & Shepherd, W. (2006). Unproctored internet testing in employment settings. Personnel Psychology, 59, 189–225.CrossRefGoogle Scholar

Wood, E., Stephens, K., & Slither, K. (2015). Apples to oranges? Use and comparative scores for mobile and nonmobile selection assessments. Paper presented at the 2015 Annual Conference of the International Personnel Assessment Council, Atlanta, GA.Google Scholar