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Part II - Global Perspectives on Key Methods/Topics

Published online by Cambridge University Press:  08 November 2023

Louis Tay
Affiliation:
Purdue University, Indiana
Sang Eun Woo
Affiliation:
Purdue University, Indiana
Tara Behrend
Affiliation:
Purdue University, Indiana
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Print publication year: 2023

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References

Agarwal, A., Singh, R., & Toshniwal, D. (2018). Geospatial sentiment analysis using Twitter data for UK-EU referendum. Journal of Information and Optimization Sciences, 39(1), 303317. https://doi.org/10.1080/02522667.2017.1374735CrossRefGoogle Scholar
Alpaydin, E. (2020). Introduction to machine learning. MIT Press.Google Scholar
An, X., Ganguly, A., Fang, Y., Scyphers, S. B., Hunter, A. M., & Dy, J. G. (2014). Tracking climate change opinions from Twitter data (Workshop on Data Science for Social Good, pp. 16).Google Scholar
Arnoux, P.-H., Xu, A., Boyette, N., Mahmud, J., Akkiraju, R., & Sinha, V. (2017). 25 tweets to know you: A new model to predict personality with social media. Proceedings of the International AAAI Conference on Web and Social Media, 11, 472475.CrossRefGoogle Scholar
Ashton, M. C., & Lee, K. (2007). Empirical, theoretical, and practical advantages of the HEXACO model of personality structure. Personality and Social Psychology Review, 11(2), 150166. https://doi.org/10.1177/1088868306294907CrossRefGoogle ScholarPubMed
Bachrach, Y., Kosinski, M., Graepel, T., Kohli, P., & Stillwell, D. (2012). Personality and patterns of Facebook usage. Proceedings of the 4th Annual ACM Web Science Conference, 24–32.CrossRefGoogle Scholar
Batson, C. D., Shaw, L. L., & Oleson, K. C. (1992). Differentiating affect, mood, and emotion: Toward functionally based conceptual distinctions. In Clark, M. S. (Ed.), Emotion (pp. 294326). Sage Publications, Inc.Google Scholar
Bond, C. S., Ahmed, O. H., Hind, M., Thomas, B., & Hewitt-Taylor, J. (2013). The conceptual and practical ethical dilemmas of using health discussion board posts as research data. Journal of Medical Internet Research, 15(6), e112. https://doi.org/10.2196/jmir.2435CrossRefGoogle ScholarPubMed
Cameron, M. P., Barrett, P., & Stewardson, B. (2016). Can social media predict election results? Evidence from New Zealand. Journal of Political Marketing, 15(4), 416432. https://doi.org/10.1080/15377857.2014.959690CrossRefGoogle Scholar
Carr, C. T., & Hayes, R. A. (2015). Social media: Defining, developing, and divining. Atlantic Journal of Communication, 23(1), 4665. https://doi.org/10.1080/15456870.2015.972282CrossRefGoogle Scholar
Celli, F., Bruni, E., & Lepri, B. (2014). Automatic personality and interaction style recognition from Facebook profile pictures. Proceedings of the 22nd ACM International Conference on Multimedia, 1101–1104. https://doi.org/10.1145/2647868.2654977CrossRefGoogle Scholar
Ceron, A., Curini, L., Iacus, S. M., & Porro, G. (2014). Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France. New Media & Society, 16(2), 340358. https://doi.org/10.1177/1461444813480466CrossRefGoogle Scholar
Chang, K.-C., Chiang, C.-F., & Lin, M.-J. (2021). Using Facebook data to predict the 2016 U.S. presidential election. PLoS ONE, 16(12), e0253560. https://doi.org/10.1371/journal.pone.0253560CrossRefGoogle ScholarPubMed
Chen, L., & Tsoi, H. K. (2011). Privacy concern and trust in using social network sites: A comparison between French and Chinese users. In Campos, P., Graham, N., Jorge, J., Nunes, N., Palanque, P., & Winckler, M. (Eds.), Human-computer interaction – INTERACT 2011 (Vol. 6948, pp. 234241). Springer. https://doi.org/10.1007/978-3-642-23765-2_16CrossRefGoogle Scholar
Chen, S. L., Hall, G. J., & Johns, M. D. (2004). Research paparazzi in cyberspace: The voices of the researched. In Johns, M. D., Chen, S. L., & Hall, G. J. (Eds.), Online social research: Methods, issues, and ethics (pp. 157175). Peter Lang.Google Scholar
Collins, S., Sun, Y., Kosinski, M., Stillwell, D., & Markuzon, N. (2015). Are you satisfied with life?: Predicting satisfaction with life from Facebook. In Agarwal, N., Xu, K., & Osgood, N. (Eds.), Social computing, behavioral-cultural modeling, and prediction (Vol. 9021, pp. 2433). Springer International Publishing. https://doi.org/10.1007/978-3-319-16268-3_3CrossRefGoogle Scholar
Conway, M., & O’Connor, D. (2016). Social media, big data, and mental health: Current advances and ethical implications. Current Opinion in Psychology, 9, 7782. https://doi.org/10.1016/j.copsyc.2016.01.004CrossRefGoogle ScholarPubMed
De Choudhury, M., & Counts, S. (2013). Understanding affect in the workplace via social media. Proceedings of the 2013 Conference on Computer Supported Cooperative Work – CSCW’13, 303. https://doi.org/10.1145/2441776.2441812CrossRefGoogle Scholar
De Choudhury, M., Counts, S., & Horvitz, E. (2013). Social media as a measurement tool of depression in populations. Proceedings of the 5th Annual ACM Web Science Conference – WebSci’13, 47–56. https://doi.org/10.1145/2464464.2464480CrossRefGoogle Scholar
Dean, B. (2022, January 5). How many people use Twitter in 2022? BacklinkO. https://backlinko.com/twitter-usersGoogle Scholar
Ekkekakis, P. (2012). Affect, mood, and emotion. In Tenenbaum, G., Eklund, R. C., & Kamata, A. (Eds.), Measurement in sport and exercise psychology (pp. 321332). Human Kinetics. https://doi.org/10.5040/9781492596332.ch-028CrossRefGoogle Scholar
Ekman, P. (1992). An argument for basic emotions. Cognition and Emotion, 6(3–4), 169200. https://doi.org/10.1080/02699939208411068CrossRefGoogle Scholar
Fischer, A. H., Rodriguez Mosquera, P. M., van Vianen, A. E. M., & Manstead, A. S. R. (2004). Gender and culture differences in emotion. Emotion, 4(1), 8794. https://doi.org/10.1037/1528-3542.4.1.87CrossRefGoogle ScholarPubMed
Gao, Q., Abel, F., Houben, G.-J., & Yu, Y. (2012). A comparative study of users’ microblogging behavior on Sina Weibo and Twitter. In Masthoff, J., Mobasher, B., Desmarais, M. C., & Nkambou, R. (Eds.), User modeling, adaptation, and personalization (Vol. 7379, pp. 88101). Springer. https://doi.org/10.1007/978-3-642-31454-4_8CrossRefGoogle Scholar
Gao, R., Hao, B., Bai, S., Li, L., Li, A., & Zhu, T. (2013). Improving user profile with personality traits predicted from social media content. Proceedings of the 7th ACM Conference on Recommender Systems, 355–358. https://doi.org/10.1145/2507157.2507219CrossRefGoogle Scholar
Golbeck, J., Robles, C., Edmondson, M., & Turner, K. (2011). Predicting personality from Twitter. 2011 IEEE Third Int’l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int’l Conference on Social Computing, 149–156. https://doi.org/10.1109/PASSAT/SocialCom.2011.33CrossRefGoogle Scholar
Golder, S., Ahmed, S., Norman, G., & Booth, A. (2017). Attitudes toward the ethics of research using social media: A systematic review. Journal of Medical Internet Research, 19(6), e195. https://doi.org/10.2196/jmir.7082CrossRefGoogle ScholarPubMed
Gosling, S. D., Augustine, A. A., Vazire, S., Holtzman, N., & Gaddis, S. (2011). Manifestations of personality in online social networks: Self-reported Facebook-related behaviors and observable profile information. Cyberpsychology, Behavior, and Social Networking, 14(9), 483488. https://doi.org/10.1089/cyber.2010.0087CrossRefGoogle ScholarPubMed
Guo, F., Gallagher, C. M., Sun, T., Tavoosi, S., & Min, H. (2021). Smarter people analytics with organizational text data: Demonstrations using classic and advanced NLP models. Human Resource Management Journal, 1–16. https://doi.org/10.1111/1748-8583.12426CrossRefGoogle Scholar
Hickman, L., Thapa, S., Tay, L., Cao, M., & Srinivasan, P. (2022). Text preprocessing for text mining in organizational research: Review and recommendations. Organizational Research Methods, 25(1), 114146. https://doi.org/10.1177/1094428120971683CrossRefGoogle Scholar
Hopkins, D. J., & King, G. (2010). A method of automated nonparametric content analysis for social science. American Journal of Political Science, 54(1), 229247. https://doi.org/10.1111/j.1540-5907.2009.00428.xCrossRefGoogle Scholar
Howard, P. N., & Parks, M. R. (2012). Social media and political change: Capacity, constraint, and consequence. Journal of Communication, 62(2), 359362.CrossRefGoogle Scholar
Iqbal, M. (2022, January 11). Twitter revenue and usage statistics (2022). Business of Apps. https://www.businessofapps.com/data/twitter-statistics/Google Scholar
Kent, M. L. (2010). Directions in social media for professionals and scholars. In Heath, R. L. (Ed.), The SAGE handbook of public relations (pp. 643656). SAGE.Google Scholar
Khan, Md. S. S., Rafa, S. R., Abir, A. E. H., & Das, A. K. (2021). Sentiment analysis on Bengali Facebook comments to predict fan’s emotions towards a celebrity. Journal of Engineering Advancements, 2(3), 118124. https://doi.org/10.38032/jea.2021.03.001CrossRefGoogle Scholar
Kim, S. M., Valitutti, A., & Calvo, R. A. (2010). Evaluation of unsupervised emotion models to textual affect recognition. Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, 62–70.Google Scholar
Kirelli, Y., & Arslankaya, S. (2020). Sentiment analysis of shared tweets on global warming on Twitter with data mining methods: A case study on Turkish language. Computational Intelligence and Neuroscience, 2020, 19. https://doi.org/10.1155/2020/1904172CrossRefGoogle ScholarPubMed
Kleanthous, S., Herodotou, C., Samaras, G., & Germanakos, P. (2016). Detecting personality traces in users’ social activity. In Meiselwitz, G. (Ed.), Social computing and social media (pp. 287297). Springer International Publishing.CrossRefGoogle Scholar
Kosicki, G. (2020). Survey methods, traditional, and public opinion polling. In Bulck, J. V. d. (Ed.), The international encyclopedia of media psychology (pp. 15). John Wiley & Sons, Inc. https://doi.org/10.1002/978111901107.iemp.0045Google Scholar
Kosinski, M., Bachrach, Y., Kohli, P., Stillwell, D., & Graepel, T. (2014). Manifestations of user personality in website choice and behaviour on online social networks. Machine Learning, 95(3), 357380. https://doi.org/10.1007/s10994–013-5415-yCrossRefGoogle Scholar
Kosinski, M., Matz, S. C., Gosling, S. D., Popov, V., & Stillwell, D. (2015). Facebook as a research tool for the social sciences: Opportunities, challenges, ethical considerations, and practical guidelines. American Psychologist, 70(6), 543556. https://doi.org/10.1037/a0039210CrossRefGoogle ScholarPubMed
Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110(15), 58025805. https://doi.org/10.1073/pnas.1218772110CrossRefGoogle ScholarPubMed
Krum, R. (2010). 2010 Facebook vs. Twitter social demographics. Cool Infographics. https://coolinfographics.com/blog/2011/2/10/2010-facebook-vs-twitter-social-demographics.htmlGoogle Scholar
Li, M., Hickman, L., Tay, L., Ungar, L., & Guntuku, S. C. (2020). Studying politeness across cultures using English Twitter and Mandarin Weibo. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW2), 115. https://doi.org/10.48550/arXiv.2008.02449Google Scholar
Liu, L., Preotiuc-Pietro, D., Samani, Z. R., Moghaddam, M. E., & Ungar, L. (2016, March). Analyzing personality through social media profile picture choice. Tenth International AAAI Conference on Web and Social Media.Google Scholar
Makazhanov, A., Rafiei, D., & Waqar, M. (2014). Predicting political preference of Twitter users. Social Network Analysis and Mining, 4(1), 193. https://doi.org/10.1007/s13278–014-0193-5CrossRefGoogle Scholar
Mandl, T. (2009). Comparing Chinese and German blogs. Proceedings of the 20th ACM Conference on Hypertext and Hypermedia – HT’09, 299. https://doi.org/10.1145/1557914.1557964CrossRefGoogle Scholar
McKee, R. (2013). Ethical issues in using social media for health and health care research. Health Policy, 110(2), 298301. https://doi.org/10.1016/j.healthpol.2013.02.006CrossRefGoogle ScholarPubMed
Michaelidou, N., Moraes, C., & Micevski, M. (2016). A scale for measuring consumers? Ethical perceptions of social media research. In Petit, O., Merunka, D., & Oullier, O. (Eds.), Let’s get engaged! Crossing the threshold of marketing’s engagement era (pp. 97100). Springer.CrossRefGoogle Scholar
Mikal, J., Hurst, S., & Conway, M. (2016). Ethical issues in using Twitter for population-level depression monitoring: A qualitative study. BMC Medical Ethics, 17(1), Article 22. https://doi.org/10.1186/s12910–016-0105-5CrossRefGoogle ScholarPubMed
Min, H., Peng, Y., Shoss, M., & Yang, B. (2021). Using machine learning to investigate the public’s emotional responses to work from home during the COVID-19 pandemic. Journal of Applied Psychology, 106(2), 214229. https://doi.org/10.1037/apl0000886CrossRefGoogle ScholarPubMed
Mittal, A., & Goel, A. (2012). Stock prediction using Twitter sentiment analysis. Stanford University.Google Scholar
Monti, C., Zignani, M., Rozza, A., Arvidsson, A., Zappella, G., & Colleoni, E. (2013). Modelling political disaffection from Twitter data. Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining – WISDOM’13, 1–9. https://doi.org/10.1145/2502069.2502072CrossRefGoogle Scholar
Moreno, M. A., Goniu, N., Moreno, P. S., & Diekema, D. (2013). Ethics of social media research: Common concerns and practical considerations. Cyberpsychology, Behavior, and Social Networking, 16(9), 708713. https://doi.org/10.1089/cyber.2012.0334CrossRefGoogle ScholarPubMed
Norman, J. (2018, May 4). Young Americans still wary of investing in stocks. Gallup. https://news.gallup.com/poll/233699/young-americans-wary-investing-stocks.aspxGoogle Scholar
Park, G., Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Kosinski, M., Stillwell, D. J., Ungar, L. H., & Seligman, M. E. P. (2015). Automatic personality assessment through social media language. Journal of Personality and Social Psychology, 108(6), 934952. https://doi.org/10.1037/pspp0000020CrossRefGoogle ScholarPubMed
Pervin, L. A., Cervone, D., & John, O. P. (2005). Theories of personality. In Pervin, L. A. , Cervone, D., & John, O. P. (Ed.), Personality: Theory and research (pp. 365386). Wiley.Google Scholar
Putka, D. J., Beatty, A. S., & Reeder, M. C. (2018). Modern prediction methods: New perspectives on a common problem. Organizational Research Methods, 21(3), 689732.CrossRefGoogle Scholar
Qiu, L., Lin, H., Ramsay, J., & Yang, F. (2012). You are what you tweet: Personality expression and perception on Twitter. Journal of Research in Personality, 46(6), 710718. https://doi.org/10.1016/j.jrp.2012.08.008CrossRefGoogle Scholar
Quercia, D., Kosinski, M., Stillwell, D. J., & Crowcroft, J. (2011). Our Twitter profiles, our selves: Predicting personality with twitter. 2011 IEEE Third International Conference on Social Computing, 180–185.CrossRefGoogle Scholar
Rhee, L., Bayer, J. B., Lee, D. S., & Kuru, O. (2021). Social by definition: How users define social platforms and why it matters. Telematics and Informatics, 59, 101538. https://doi.org/10.1016/j.tele.2020.101538CrossRefGoogle Scholar
Roccas, S., Sagiv, L., Schwartz, S. H., & Knafo, A. (2002). The big five personality factors and personal values. Personality and Social Psychology Bulletin, 28(6), 789801. https://doi.org/10.1177/0146167202289008CrossRefGoogle Scholar
Romero, D. M., Meeder, B., & Kleinberg, J. (2011). Differences in the mechanics of information diffusion across topics: Idioms, political hashtags, and complex contagion on twitter. Proceedings of the 20th International Conference on World Wide Web, 695–704.CrossRefGoogle Scholar
Russell, J. A. (1991). Culture and the categorization of emotions. Psychological Bulletin, 110(3), 426450.CrossRefGoogle ScholarPubMed
Salmons, J., & Woodfield, K. (2013). Social media, social science & research ethics. Social Media in Social Research Conference: Ethics of Social Media Research, 1–24.Google Scholar
Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., … & Ungar, L. H. (2013). Personality, gender, and age in the language of social media: The open-vocabulary approach. PLoS ONE, 8(9), e73791. https://doi.org/10.1371/journal.pone.0073791CrossRefGoogle ScholarPubMed
Seder, J. P., & Oishi, S. (2012). Intensity of smiling in Facebook photos predicts future life satisfaction. Social Psychological and Personality Science, 3(4), 407413. https://doi.org/10.1177/1948550611424968CrossRefGoogle Scholar
Smith, M., Szongott, C., Henne, B., & von Voigt, G. (2012). Big data privacy issues in public social media. 2012 6th IEEE International Conference on Digital Ecosystems and Technologies (DEST), 1–6. https://doi.org/10.1109/DEST.2012.6227909CrossRefGoogle Scholar
Thilakaratne, M., Weerasinghe, R., & Perera, S. (2016, October). Knowledge-driven approach to predict personality traits by leveraging social media data. In 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) (pp. 288295). IEEE.CrossRefGoogle Scholar
Townsend, L., & Wallace, C. (2016). Social media research: A guide to ethics. University of Aberdeen.Google Scholar
Twitter Development. (n.d.). Counting characters when composing tweets. Twitter Developer Platform. https://developer.twitter.com/en/docs/counting-charactersGoogle Scholar
Vitalis, I. (2019, October 25). US demographics and the stock market. Tradimo News. https://news.tradimo.com/us-demographics-and-the-stock-market/Google Scholar
Wang, Y., & Pal, A. (2015, June). Detecting emotions in social media: A constrained optimization approach. Twenty-Fourth International Joint Conference on Artificial Intelligence.Google Scholar
Williams, M. (2015). Towards an ethical framework for using social media data in social research. Social Data Lab. http://socialdatalab.net/wp-content/uploads/2016/08/EthicsSM-SRA-Workshop.pdfGoogle Scholar
Zheng, W., Yuan, C.-H., Chang, W.-H., & Wu, Y.-C. J. (2016). Profile pictures on social media: Gender and regional differences. Computers in Human Behavior, 63, 891898. https://doi.org/10.1016/j.chb.2016.06.041CrossRefGoogle Scholar

References

Armstrong, M. B., Ferrell, J., Collmus, A. B., & Landers, R. N. (2016). Correcting misconceptions about gamification of assessment: More than SJTs and badges. Industrial and Organizational Psychology, 9(3), 671677. https://doi.org/10.1017/iop.2016.69CrossRefGoogle Scholar
Auer, E. M., Mersy, G., Marin, S., Blaik, J., & Landers, R. N. (2022). Using machine learning to model trace behavioral data from a game-based assessment. International Journal of Selection and Assessment, 30(1), 82102. https://doi.org/10.1111/ijsa.12363CrossRefGoogle Scholar
Bhatia, S., & Ryan, A. M. (2018). Hiring for the win: Game-based assessment in employee selection. In Dulebohn, J. H. & Stone, D. L. (Eds.), The brave new world of eHRM 2.0 (pp. 81110). IAP Information Age Publishing.Google Scholar
Bina, S., Mullins, J. K., & Petter, S. (2021). Examining game-based approaches in human resources recruitment and selection: A literature review and research agenda. In Proceedings of the 54th Hawaii International Conference on System Sciences (pp. 1325–1334).CrossRefGoogle Scholar
Bjogvinsson, E., Ehn, P., & Hillgren, P.-A. (2012). Design things and design thinking: Contemporary participatory design challenges. Design Issues, 28(3), 101116. https://doi.org/10.1162/DESI_a_00165CrossRefGoogle Scholar
Caillois, R. (2011). Man, play, and games. University of Illinois Press.Google Scholar
Cubrich, M., King, R., Mracek, D., Strong, J., Hassenkamp, K., Vaughn, D., & Dudley, N. (2021). Examining the criterion-related validity evidence of LinkedIn profile elements in an applied sample. Computers in Human Behavior, 120, 106742. https://doi.org/10.1016/j.chb.2021.106742CrossRefGoogle Scholar
Deterding, S., Sicart, M., Nacke, L., O’Hara, K., & Dixon, D. (2011). Gamification: Using game-design elements in non-gaming contexts. In CHI’11 extended abstracts on human factors in computing systems (pp. 24252428). ACM Press.Google Scholar
Grelle, D. M., & Gutierrez, S. L. (2019). Developing device-equivalent and effective measures of complex thinking with an information processing framework and mobile first design principles. Personnel Assessment and Decisions, 5(3). https://doi.org/10.25035/pad.2019.03.004CrossRefGoogle Scholar
Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world?. The Behavioral and Brain Sciences, 33(2–3), 61135. https://doi.org/10.1017/S0140525X0999152XCrossRefGoogle ScholarPubMed
Howison, J., Wiggins, A., & Crowston, K. (2011). Validity issues in the use of social network analysis with digital trace data. Journal of the Association for Information Systems, 12(12). https://doi.org/10.17705/1jais.00282CrossRefGoogle Scholar
Hunicke, R., LeBlanc, M., & Zubek, R. (2004). MDA: A formal approach to game design and game research. Proceedings of the AAAI Workshop on Challenges in Game AI, 4(1), 17221726.Google Scholar
Jackson, G. T., Grace, L., Inglese, P., Wain, J., & Hone, R. (2018). Awkward Annie: Game-based assessment of English pragmatic skills. In Cheok, A., Inami, M., & Romão, T. (Eds.), Advances in computer entertainment technology. ACE 2017. Lecture Notes in Computer Science, Vol. 10714. Springer. https://doi.org/10.1007/978-3-319-76270-8_54Google Scholar
Jones, S. E. (2008). The meaning of video games: Gaming and textual strategies. Routledge.CrossRefGoogle Scholar
Kapp, K. M., Valtchanov, D., & Pastore, R. (2020). Enhancing motivation in workplace training with casual games: A twelve month field study of retail employees. Educational Technology Research and Development, 68(5), 22632284. https://doi.org/10.1007/s11423-020-09769-2CrossRefGoogle Scholar
Landers, R. N., Armstrong, M. B., Collmus, A. B., Mujcic, S., & Blaik, J. (2021). Theory-driven game-based assessment of general cognitive ability: Design theory, measurement, prediction of performance, and test fairness. Journal of Applied Psychology, 107(10). https://doi.org/10.1037/apl0000954Google ScholarPubMed
Landers, R. N., Auer, E. M., Collmus, A. B., & Armstrong, M. B. (2018). Gamification science, its history and future: Definitions and a research agenda. Simulation & Gaming, 49(3), 315337. https://doi.org/10.1177/1046878118774385CrossRefGoogle Scholar
Landers, R. N., & Behrend, T. S. (2023). Auditing the AI auditors: A framework for evaluating fairness and bias in high stakes AI predictive models. American Psychologist, 78(1), 3649. https://doi.org/10.1037/amp0000972CrossRefGoogle ScholarPubMed
Landers, R. N., & Sanchez, D. R.(2022). Game-based, gamified, and gamefully design assessments for employee selection: Definitions, distinctions, design, and validation. International Journal of Selection and Assessment, 30(1), 113. https://doi.org/10.1111/ijsa.12376CrossRefGoogle Scholar
Landers, R. N., Tondello, G. F., Kappen, D. L., Collmus, A. B., Mekler, E. D., & Nacke, L. (2019). Defining gameful experience as a psychological state caused by gameplay: Replacing the term ‘gamefulness’ with three distinct constructs. International Journal of Human Computer Studies, 127, 8194. https://doi.org/10.1016/j.ijhcs.2018.08.003CrossRefGoogle Scholar
Lejuez, C. W., Read, J. P., Kahler, C. W., Richards, J. B., Ramsey, S. E., Stuart, G. L., Strong, D. R., & Brown, R. A. (2002). Evaluation of a behavioral measure of risk taking: The balloon analogue risk task (BART). Journal of Experimental Psychology: Applied, 8(2), 7584. https://doi.org/10.1037//1076-898x.8.2.75Google ScholarPubMed
Michael, D. R., & Chen, S. L. (2005). Serious games: Games that educate, train, and inform. Thomson Course Technology.Google Scholar
Mislevy, R. J., Oranje, A., Bauer, M. I., von Davier, A., Hao, J., Corrigan, S., Hoffman, E., DiCerbo, K., & John, M. (2014). Psychometric considerations in game-based assessment. GlassLab. https://www.envisionexperience.com/~/media/files/blog/glasslab- psychometrics.pdf?la=enGoogle Scholar
Mollick, E. R., & Rothbard, N. (2014). Mandatory fun: Consent, gamification and the impact of games at work. The Wharton School Research Paper Series. https://ssrn.com/abstract=2277103Google Scholar
Myors, B., Lievens, F., Schollaert, E., Van Hoye, G., Cronshaw, S. F., Mladinic, A., … Sackett, P. R. (2008). International perspectives on the legal environment for selection. Industrial and Organizational Psychology, 1(2), 206246. https://doi.org/10.1111/j.1754-9434.2008.00040.xCrossRefGoogle Scholar
Orji, R., Mandryk, R. L., & Vassileva, J. (2017). Improving the efficacy of games for change using personalization models. ACM Transactions on Computer-Human Interaction (TOCHI), 24(5), 122. https://doi.org/10.1145/3119929CrossRefGoogle Scholar
Pitoyo, M. D., Sumardi, S., & Asib, A. (2019). Gamification based assessment: A test anxiety reduction through game elements in Quizizz platform. International Online Journal of Education and Teaching (IOJET), 6(3), 456471. http://iojet.org/index.php/IOJET/article/view/626Google Scholar
Plattner, H., Meinel, C., & Leifer, L. (2011). Design thinking: Understand, improve, apply. Springer-Verlag.Google Scholar
Rowe, P. (1986). Design thinking. The MIT Press.Google Scholar
Shen, W., Sackett, P. R., Lievens, F., Schollaert, E., Van Hoye, G., Steiner, D. D., … Cook, M. (2017). Updated perspectives on the international legal environment for selection. In Farr, J. L., Tippins, N. T., Borman, W. C., Chan, D., Coovert, M. D., Jacobs, R., … & Schneider, B. (Eds.), Handbook of employee selection (2nd ed., pp. 659677). Routledge. https://doi.org/10.4324/9781315690193-29CrossRefGoogle Scholar
Tippins, N. T. (2015). Technology and assessment in selection. Annual Review of Organizational Psychology and Organizational Behavior, 2(1), 551582. https://doi.org/10.1146/annurev-orgpsych-031413-091317CrossRefGoogle Scholar

References

Ai, P., Liu, Y., & Zhao, X. (2019). Big Five personality traits predict daily spatial behavior: Evidence from smartphone data. Personality and Individual Differences, 147, 285291. https://doi.org/10.1016/j.paid.2019.04.027CrossRefGoogle Scholar
Åkerberg, A., Lindén, M., & Folke, M. (2012). How accurate are pedometer cell phone applications? Procedia Technology, 5, 787792. https://doi.org/10.1016/j.protcy.2012.09.087CrossRefGoogle Scholar
Altini, M., Vullers, R., Van Hoof, C., van Dort, M., & Amft, O. (2014, March). Self-calibration of walking speed estimations using smartphone sensors. In 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS) (pp. 1018). IEEE. https://doi.org/10.1109/PerComW.2014.6815158CrossRefGoogle Scholar
Bai, Y., Xu, B., Ma, Y., Sun, G., & Zhao, Y. (2012). Will you have a good sleep tonight? Sleep quality prediction with mobile phone. In Balasingham, I. (Ed.), Proceedings of the 7th International Conference on Body Area Networks (pp. 124130). Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering. https://doi.org/10.4108/icst.bodynets.2012.250091Google Scholar
Bastos, A. S., & Hasegawa, H. (2013). Behavior of GPS signal interruption probability under tree canopies in different forest conditions. European Journal of Remote Sensing, 46(1), 613622. https://doi.org/10.5721/eujrs20134636CrossRefGoogle Scholar
Bauer, D. J. (2017). A more general model for testing measurement invariance and differential item functioning. Psychological Methods, 22(3), 507526. https://doi.org/10.1037/met0000077CrossRefGoogle ScholarPubMed
Blunck, H., Bouvin, N. O., Franke, T., Grønbæk, K., Kjaergaard, M. B., Lukowicz, P., & Wüstenberg, M. (2013). On heterogeneity in mobile sensing applications aiming at representative data collection. In Mattern, F. (Ed.), Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication (pp. 10871098). Association for Computing Machinery. https://doi.org/10.1145/2494091.2499576CrossRefGoogle Scholar
Boehnke, K., Lietz, P., Schreier, M., & Wilhelm, A. (2011). Sampling: The selection of cases for culturally comparative psychological research. In Matsumoto, D. & van de Vijver, F. J. R. (Eds.), Cross-cultural research methods in psychology (pp. 101129). Cambridge University Press.Google Scholar
Borsboom, D. (2006). When does measurement invariance matter? Medical Care, 44(11), S176S181. https://doi.org/10.1097/01.mlr.0000245143.08679.ccCrossRefGoogle ScholarPubMed
Borsboom, D., Romeijn, J. W., & Wicherts, J. M. (2008). Measurement invariance versus selection invariance: Is fair selection possible? Psychological Methods, 13(2), 7598. https://doi.org/10.1037/1082-989X.13.2.75CrossRefGoogle ScholarPubMed
Breiman, L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical Science, 16(3), 199231. https://doi.org/10.1214/ss/1009213726CrossRefGoogle Scholar
Brunswik, E. (1956). Perception and the representative design of psychological experiments (2nd ed.). University of California Press.CrossRefGoogle Scholar
Chaffin, D., Heidl, R., Hollenbeck, J. R., Howe, M., Yu, A., Voorhees, C., & Calantone, R. (2017). The promise and perils of wearable sensors in organizational research. Organizational Research Methods, 20(1), 331. https://doi.org/10.1177/1094428115617004CrossRefGoogle Scholar
Cornet, V. P., & Holden, R. J. (2018). Systematic review of smartphone-based passive sensing for health and wellbeing. Journal of Biomedical Informatics, 77, 120132. https://doi.org/10.1016/j.jbi.2017.12.008CrossRefGoogle ScholarPubMed
Counterpoint. (2021, September 13). Top 5 smartphone model share for 8 countries. https://www.counterpointresearch.com/top-5-smartphone-model-share-8-countries/Google Scholar
de Vries, L. P., Baselmans, B. M., & Bartels, M. (2021). Smartphone-based ecological momentary assessment of well-being: A systematic review and recommendations for future studies. Journal of Happiness Studies, 22(5), 23612408. https://doi.org/10.1007/s10902–020-00324-7CrossRefGoogle ScholarPubMed
Deffner, D., Rohrer, J. M., & McElreath, R. (2021). A causal framework for cross-cultural generalizability. PsyAirXiv. https://doi.org/10.31234/osf.io/fqukpCrossRefGoogle Scholar
Delaporte, A., Bahia, K., Carboni, I., Cruz, G., Jeffrie, N., Sibthorpe, C., Suardi, S., & Groenestege, M. T. (2021). The state of mobile internet connectivity 2021. GSM Association. https://www.gsma.com/r/wp-content/uploads/2021/09/The-State-of-Mobile-Internet-Connectivity-Report-2021.pdfGoogle Scholar
Götz, F. M., Stieger, S., & Reips, U. D. (2017). Users of the main smartphone operating systems (iOS, Android) differ only little in personality. PLoS ONE, 12(5), e0176921. https://doi.org/10.1371/journal.pone.0176921CrossRefGoogle ScholarPubMed
Grammenos, A., Mascolo, C., & Crowcroft, J. (2018). You are sensing, but are you biased? Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(1), 126. https://doi.org/10.1145/3191743CrossRefGoogle Scholar
Harari, G. M., Gosling, S. D., Wang, R., Chen, F., Chen, Z., & Campbell, A. T. (2017). Patterns of behavior change in students over an academic term: A preliminary study of activity and sociability behaviors using smartphone sensing methods. Computers in Human Behavior, 67, 129138. https://doi.org/10.1016/j.chb.2016.10.027CrossRefGoogle 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. Perspectives on Psychological Science, 11(6), 838854. https://doi.org/10.1177/1745691616650285CrossRefGoogle ScholarPubMed
Harari, G. M., Müller, S. R., Aung, M. S. H., & Rentfrow, P. J. (2017). Smartphone sensing methods for studying behavior in everyday life. Current Opinion in Behavioral Sciences, 18, 8390. https://doi.org/10.1016/j.cobeha.2017.07.018CrossRefGoogle Scholar
Harari, G. M., Müller, S. R., & Gosling, S. D. (2020). Naturalistic assessment of situations using mobile sensing methods. In Rauthmann, J. F., Sherman, R. A., & Funder, D. C. (Eds.), The Oxford handbook of psychological situations (pp. 299311). Oxford University Press.Google Scholar
Harari, G. M., Müller, S. R., Stachl, C., Wang, R., Wang, W., Bühner, M., Rentfrow, P. J., Campbell, A. T., & Gosling, S. D. (2020). Sensing sociability: Individual differences in young adults’ conversation, calling, texting, and app use behaviors in daily life. Journal of Personality and Social Psychology, 119(1), 204228. https://doi.org/10.1037/pspp0000245CrossRefGoogle ScholarPubMed
Harari, G. M., Stachl, C., Müller, S. R., & Gosling, S. D. (2021). Mobile sensing for studying personality dynamics in daily life. In Rauthmann, J. F. (Ed.), The handbook of personality dynamics and processes (pp. 763790). Academic Press. https://doi.org/10.1016/B978–0-12-813995-0.00029-7CrossRefGoogle Scholar
Harari, G. M., Vaid, S. S., Müller, S. R., Stachl, C., Marrero, Z., Schoedel, R., Bühner, M., & Gosling, S. D. (2020). Personality sensing for theory development and assessment in the digital age. European Journal of Personality, 34(5), 649669. https://doi.org/10.1002/per.2273CrossRefGoogle Scholar
He, J., & van de Vijver, F. (2012). Bias and equivalence in cross-cultural research. Online Readings in Psychology and Culture, 2(2), 119. https://doi.org/10.9707/2307-0919.1111CrossRefGoogle Scholar
Henrich, J., Heine, S. J., & Norenzayan, A. (2010a). Beyond weird: Towards a broad-based behavioral science. Behavioral and Brain Sciences, 33(2–3), 111135. https://doi.org/10.1017/s0140525x10000725CrossRefGoogle Scholar
Henrich, J., Heine, S. J., & Norenzayan, A. (2010b). The weirdest people in the world? Behavioral and Brain Sciences, 33(2–3), 6183. https://doi.org/10.1017/S0140525X0999152XCrossRefGoogle ScholarPubMed
Horstmann, K. T., & Ziegler, M. (2020). Assessing personality states: What to consider when constructing personality state measures. European Journal of Personality, 34(6), 10371059. https://doi.org/10.1002/per.2266CrossRefGoogle Scholar
John, O. P., & Robins, R. W. (1993). Determinants of interjudge agreement on personality traits: The Big Five domains, observability, evaluativeness, and the unique perspective of the self. Journal of Personality, 61(4), 521551. https://doi.org/10.1111/j.1467-6494.1993.tb00781.xCrossRefGoogle ScholarPubMed
Kayhan, V. O., Chen, Z., French, K. A., Allen, T. D., Salomon, K., & Watkins, A. (2018). How honest are the signals? A protocol for validating wearable sensors. Behavior Research Methods, 50(1), 5783. https://doi.org/10.3758/s13428–017-1005-4CrossRefGoogle ScholarPubMed
Khan, W. Z., Xiang, Y., Aalsalem, M. Y., & Arshad, Q. (2013). Mobile phone sensing systems: A survey. IEEE Communications Surveys & Tutorials, 15(1), 402427. https://doi.org/10.1109/SURV.2012.031412.00077CrossRefGoogle Scholar
Khwaja, M., Vaid, S. S., Zannone, S., Harari, G. M., Faisal, A. A., & Matic, A. (2019). Modeling personality vs. modeling personalidad: In-the-wild mobile data analysis in five countries suggests cultural impact on personality models. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3(3), 124. https://doi.org/10.1145/3351246CrossRefGoogle Scholar
Killingsworth, M. A., & Gilbert, D. T. (2010). A wandering mind is an unhappy mind. Science, 330(6006), 932932. https://doi.org/10.1126/science.1192439CrossRefGoogle ScholarPubMed
Kos, A., Tomažič, S., & Umek, A. (2016). Evaluation of smartphone inertial sensor performance for cross-platform mobile applications. Sensors, 16(4), 115. https://doi.org/10.3390/s16040477CrossRefGoogle ScholarPubMed
Krendl, A. C., & Pescosolido, B. A. (2020). Countries and cultural differences in the stigma of mental illness: The East–West divide. Journal of Cross-Cultural Psychology, 51(2), 149167. https://doi.org/10.1177/0022022119901297CrossRefGoogle Scholar
Kuhlmann, T., Garaizar, P., & Reips, U.-D. (2021). Smartphone sensor accuracy varies from device to device in mobile research: The case of spatial orientation. Behavior Research Methods, 53(1), 2233. https://doi.org/10.3758/s13428–020-01404-5CrossRefGoogle ScholarPubMed
Lane, N. D., Mohammod, M., Lin, M., Yang, X., Lu, H., Ali, S., … & Campbell, A. (2011, May). Bewell: A smartphone application to monitor, model and promote wellbeing. In 5th International ICST Conference on Pervasive Computing Technologies for Healthcare (pp. 23–26).CrossRefGoogle Scholar
Lantz, B. (2019). Machine learning with R: Expert techniques for predictive modeling (3rd ed.). Packt Publishing.Google Scholar
Lee, H., Ahn, H., Choi, S., & Choi, W. (2014). The SAMS: Smartphone addiction management system and verification. Journal of Medical Systems, 38(1), 110. https://doi.org/10.1007/s10916–013-0001-1CrossRefGoogle ScholarPubMed
Leong, J. Y., & Wong, J. E. (2016). Accuracy of three Android-based pedometer applications in laboratory and free-living settings. Journal of Sports Sciences, 35(1), 1421. https://doi.org/10.1080/02640414.2016.1154592CrossRefGoogle ScholarPubMed
Lundberg, I., Johnson, R., & Stewart, B. M. (2021). What is your estimand? Defining the target quantity connects statistical evidence to theory. American Sociological Review, 86(3), 532565. https://doi.org/10.1177/00031224211004187CrossRefGoogle Scholar
Ma, L., Zhang, C., Wang, Y., Peng, G., Chen, C., Zhao, J., & Wang, J. (2020). Estimating urban road GPS environment friendliness with bus trajectories: A city-scale approach. Sensors, 20(6), 1580. https://doi.org/10.3390/s20061580CrossRefGoogle ScholarPubMed
Ma, Y., Xu, B., Bai, Y., Sun, G., & Zhu, R. (2012). Daily mood assessment based on mobile phone sensing. In Yang, G.-Z. (Ed.), 2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks (pp. 142147). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/bsn.2012.3CrossRefGoogle Scholar
Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58(4), 525543. https://doi.org/10.1007/BF02294825CrossRefGoogle Scholar
Mohr, D. C., Zhang, M., & Schueller, S. M. (2017). Personal sensing: Understanding mental health using ubiquitous sensors and machine learning. Annual Review of Clinical Psychology, 13(1), 2347. https://doi.org/10.1146/annurev-clinpsy-032816-044949CrossRefGoogle ScholarPubMed
Müller, S. R., Bayer, J. B., Ross, M. Q., Mount, J., Stachl, C., Harari, G. M., Chang, Y.-J., & Le, H. T. K. (2022). Analyzing GPS Data for Psychological Research: A Tutorial. Advances in Methods and Practices in Psychological Science, 5(2). https://doi.org/10.1177/25152459221082680CrossRefGoogle Scholar
Müller, S. R., Chen, X. L., Peters, H., Chaintreau, A., & Matz, S. C. (2021). Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples. Scientific Reports, 11, 110. https://doi.org/10.1038/s41598–021-93087-xCrossRefGoogle Scholar
Müller, S. R., Peters, H., Matz, S. C., Wang, W., & Harari, G. M. (2020). Investigating the relationships between mobility behaviours and indicators of subjective well‐being using smartphone‐based experience sampling and GPS tracking. European Journal of Personality, 34(5), 714732. https://doi.org/10.1002%2Fper.2262CrossRefGoogle Scholar
Oort, F. J., Visser, M. R., & Sprangers, M. A. (2009). Formal definitions of measurement bias and explanation bias clarify measurement and conceptual perspectives on response shift. Journal of Clinical Epidemiology, 62(11), 11261137. https://doi.org/10.1016/j.jclinepi.2009.03.013CrossRefGoogle ScholarPubMed
Orrù, G., Monaro, M., Conversano, C., Gemignani, A., & Sartori, G. (2020). Machine learning in psychometrics and psychological research. Frontiers in Psychology, 10, 110. https://doi.org/10.3389/fpsyg.2019.02970CrossRefGoogle ScholarPubMed
Pernot-Leplay, E. (2020). China’s approach on data privacy law: A third way between the US and the EU? Penn State Journal of Law & International Affairs, 8(1), 49117. https://elibrary.law.psu.edu/jlia/vol8/iss1/6Google Scholar
Phan, L. V., & Rauthmann, J. F. (2021). Personality computing: New frontiers in personality assessment. Social and Personality Psychology Compass, 15(7), 117. https://doi.org/10.1111/spc3.12624CrossRefGoogle Scholar
Putnick, D. L., & Bornstein, M. H. (2016). Measurement invariance conventions and reporting: The state of the art and future directions for psychological research. Developmental Review, 41, 7190. https://doi.org/10.1016/j.dr.2016.06.004CrossRefGoogle ScholarPubMed
Rabbi, M., Ali, S., Choudhury, T., & Berke, E. (2011, September 17–21). Passive and in-situ assessment of mental and physical well-being using mobile sensors. In J. Landay & Y. Shi (Chairs), How healthy? [Symposium]. Proceedings of the 13th International Conference on Ubiquitous Computing, Beijing, China. https://doi.org/10.1145/2030112.2030164CrossRefGoogle Scholar
Rad, M. S., Martingano, A. J., & Ginges, J. (2018). Toward a psychology of homo sapiens: Making psychological science more representative of the human population. Proceedings of the National Academy of Sciences, 115(45), 1140111405. https://doi.org/10.1073/pnas.1721165115CrossRefGoogle Scholar
Ram, N., Conroy, D. E., Pincus, A. L., Lorek, A., Rebar, A., Roche, M. J., Coccia, M., Morack, J., Feldman, J., & Gerstorf, D. (2014). Examining the interplay of processes across multiple time-scales: Illustration with the intraindividual study of affect, health, and interpersonal behavior (iSAHIB). Research in Human Development, 11(2), 142160. https://doi.org/10.1080/15427609.2014.906739CrossRefGoogle ScholarPubMed
Rauthmann, J. F. (2016). Motivational factors in the perception of psychological situation characteristics. Social and Personality Psychology Compass, 10(2), 92108. https://doi.org/10.1111/spc3.12239CrossRefGoogle Scholar
Rauthmann, J. F. (2021). Capturing interactions, correlations, fits, and transactions: A person-environment relations model. In Rauthmann, J. F. (Ed.), The handbook of personality dynamics and processes (pp. 427522). Academic Press. https://doi.org/10.1016/b978-0-12-813995-0.00018-2CrossRefGoogle Scholar
R Core Team. (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/Google Scholar
Sailhan, F., Issarny, V., & Tavares-Nascimiento, O. (2017, October). Opportunistic multiparty calibration for robust participatory sensing. In 2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS) (pp. 435443). IEEE. https://doi.org/10.1109/MASS.2017.56CrossRefGoogle Scholar
Sanchez, W., Martinez, A., Campos, W., Estrada, H., & Pelechano, V. (2015). Inferring loneliness levels in older adults from smartphones. Journal of Ambient Intelligence and Smart Environments, 7(1), 8598. https://doi.org/10.3233/ais-140297CrossRefGoogle Scholar
Sandstrom, G. M., Lathia, N., Mascolo, C., & Rentfrow, P. J. (2017). Putting mood in context: Using smartphones to examine how people feel in different locations. Journal of Research in Personality, 69, 96101. https://doi.org/10.1016/j.jrp.2016.06.004CrossRefGoogle Scholar
Schoedel, R., Pargent, F., Au, Q., Völkel, S. T., Schuwerk, T., Bühner, M., & Stachl, C. (2020). To challenge the morning lark and the night owl: Using smartphone sensing data to investigate day–night behaviour patterns. European Journal of Personality, 34(5), 733752. https://doi.org/10.1002/per.2258CrossRefGoogle Scholar
Silver, L. (2019, February 5). Smartphone ownership is growing rapidly around the world, but not always equally. Pew Research Center. https://www.pewresearch.org/global/2019/02/05/smartphone-ownership-is-growing-rapidly-around-the-world-but-not-always-equally/Google Scholar
Simons, D. J., Shoda, Y., & Lindsay, D. S. (2017). Constraints on Generality (COG): A proposed addition to all empirical papers. Perspectives on Psychological Science, 12(6), 11231128. https://doi.org/10.1177/1745691617708630CrossRefGoogle ScholarPubMed
Stachl, C., Au, Q., Schoedel, R., Gosling, S. D., Harari, G. M., Buschek, D., Völkel, S. T., Schuwerk, T., Oldemeier, M., Ullmann, T., Hussmann, H., Bischl, B., & Bühner, M. (2020). Predicting personality from patterns of behavior collected with smartphones. Proceedings of the National Academy of Sciences of the United States of America, 117(30), 1768017687. https://doi.org/10.1073/pnas.1920484117CrossRefGoogle ScholarPubMed
Stachl, C., Pargent, F., Hilbert, S., Harari, G. M., Schoedel, R., Vaid, S., Gosling, S. D., & Bühner, M. (2020). Personality research and assessment in the era of machine learning. European Journal of Personality, 34(5), 613631. https://doi.org/10.1002/per.2257CrossRefGoogle Scholar
Statista. (2020, August 20). Number of smartphone users from 2016 to 2021. https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/Google Scholar
Stieger, S., Götz, F. M., & Gehrig, F. (2015). Soccer results affect subjective well-being, but only briefly: A smartphone study during the 2014 FIFA World Cup. Frontiers in Psychology, 6. https://doi.org/10.3389/fpsyg.2015.00497CrossRefGoogle ScholarPubMed
Stisen, A., Blunck, H., Bhattacharya, S., Prentow, T. S., Kjærgaard, M. B., Dey, A., Sonne, T., & Jensen, M. M. (2015). Smart devices are different: Assessing and mitigating mobile sensing heterogeneities for activity recognition. In Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems (pp. 127140). Association for Computing Machinery. https://doi.org/10.1145/2809695.2809718CrossRefGoogle Scholar
Tay, L., Meade, A. W., & Cao, M. (2015). An overview and practical guide to IRT measurement equivalence analysis. Organizational Research Methods, 18(1), 346. https://doi.org/10.1177/1094428114553062CrossRefGoogle Scholar
Tay, L., Woo, S. E., Hickman, L., Booth, B. M., & D’Mello, S. (2021). A conceptual framework for investigating and mitigating Machine Learning Measurement Bias (MLMB) in psychological assessment. PsyArXiv. https://doi.org/10.31234/osf.io/mjph3CrossRefGoogle Scholar
Tennekes, M. (2018). Tmap: Thematic maps in R. Journal of Statistical Software, 84(6), 139. https://doi.org/10.18637/jss.v084.i06CrossRefGoogle Scholar
Teresi, J. A. (2006). Overview of quantitative measurement methods: Equivalence, invariance, and differential item functioning in health applications. Medical Care, 44(11), S39S49. https://doi.org/10.1097/01.mlr.0000245452.48613.45CrossRefGoogle ScholarPubMed
Vaid, S., & Harari, G. M. (2019). Smartphones in personal informatics: A framework for self-tracking research with mobile sensing. In Baumeister, H. & Montag, C. (Eds.), Digital phenotyping and mobile sensing (pp. 6592). Springer. https://doi.org/10.1007/978-3-030-31620-4_5CrossRefGoogle Scholar
van de Vijver, F., & Leung, K. (2021). Methods and data analysis for cross-cultural research (2nd ed.). Cambridge University Press.CrossRefGoogle Scholar
van de Vijver, F., & Tanzer, N. K. (2004). Bias and equivalence in cross-cultural assessment: An overview. European Review of Applied Psychology, 54(2), 119135. https://doi.org/10.1016/j.erap.2003.12.004CrossRefGoogle Scholar
Vinciarelli, A., & Mohammadi, G. (2014). A survey of personality computing. IEEE Transactions on Affective Computing, 5(3), 273291. https://doi.org/10.1109/TAFFC.2014.2330816CrossRefGoogle Scholar
von Stumm, S. (2018). Feeling low, thinking slow? Associations between situational cues, mood and cognitive function. Cognition and Emotion, 32(8), 15451558. https://doi.org/10.1080/02699931.2017.1420632CrossRefGoogle ScholarPubMed
Wahl, D. R., Villinger, K., König, L. M., Ziesemer, K., Schupp, H. T., & Renner, B. (2017). Healthy food choices are happy food choices: Evidence from a real life sample using smartphone based assessments. Scientific Reports, 7(1), 17069. https://doi.org/10.1038/s41598–017-17262-9CrossRefGoogle ScholarPubMed
Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., Zhou, X., Ben-Zeev, D., &. Campbell, A. T. (2014, September 13–17). StudentLife: Assessing mental health, academic performance and behavioral trends of college students using smartphones. In Brush, A. J (Ed.), Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 314). Association for Computing Machinery. https://doi.org/10.1145/2632048.2632054CrossRefGoogle Scholar
Wang, W., Harari, G. M., Wang, R., Müller, S. R., Mirjafari, S., Masaba, K., & Campbell, A. T. (2018). Sensing behavioral change over time: Using within-person variability features from mobile sensing to predict personality traits. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(3), 121. https://doi.org/10.1145/3264951Google Scholar
Wiernik, B. M., Ones, D. S., Marlin, B. M., Giordano, C., Dilchert, S., Mercado, B. K., Stanek, K. C., Birkland, A., Wang, Y., Ellis, B., Yazar, Y., Kostal, J. W., Kumar, S., Hnat, T., Ertin, E., Sano, A., Ganesan, D. K., Choudhoury, T., & Al’Absi, M. (2020). Using mobile sensors to study personality dynamics. European Journal of Psychological Assessment, 36(6), 113. https://doi.org/10.1027/1015‐5759/a000576CrossRefGoogle Scholar
Woo, S. E., Tay, L., Jebb, A. T., Ford, M. T., & Kern, M. L. (2020). Big data for enhancing measurement quality. In Woo, S. E., Tay, L., & Proctor, R. W. (Eds.), Big data in psychological research (pp. 5985). American Psychological Association. https://doi.org/10.1037/0000193-004CrossRefGoogle Scholar
Yan, Z., & Chakraborty, D. (2014). Semantics in mobile sensing. Morgan & Claypool. https://doi.org/10.2200/S00577ED1V01Y201404WBE008CrossRefGoogle Scholar
Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 11001122. https://doi.org/10.1177/1745691617693393CrossRefGoogle ScholarPubMed
Zhang, X., Li, W., Chen, X., & Lu, S. (2018). MoodExplorer: Towards compound emotion detection via smartphone sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(4), 130. https://doi.org/10.1145/3161414Google Scholar

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