Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-26T16:00:16.406Z Has data issue: false hasContentIssue false

Chapter 6 - Mobile Sensing around the Globe

Considerations for Cross-Cultural Research

from Part II - Global Perspectives on Key Methods/Topics

Published online by Cambridge University Press:  08 November 2023

By
Le Vy Phan ,
Nick Modersitzki ,
Kim K. Gloystein  and
Sandrine R. Müller
Edited by
Louis Tay ,
Sang Eun Woo  and
Tara Behrend
Louis Tay
Affiliation:
Purdue University, Indiana
Sang Eun Woo
Affiliation:
Purdue University, Indiana
Tara Behrend
Affiliation:
Purdue University, Indiana
Get access

Summary

The ubiquity of mobile devices allows researchers to assess people’s real-life behaviors objectively, unobtrusively, and with high temporal resolution. As a result, psychological mobile sensing research has grown rapidly. However, only very few cross-cultural mobile sensing studies have been conducted to date. In addition, existing multi-country studies often fail to acknowledge or examine possible cross-cultural differences. In this chapter, we illustrate biases that can occur when conducting cross-cultural mobile sensing studies. Such biases can relate to measurement, construct, sample, device type, user practices, and environmental factors. We also propose mitigation strategies to minimize these biases, such as the use of informants with expertise in local culture, the development of cross-culturally comparable instruments, the use of culture-specific recruiting strategies and incentives, and rigorous reporting standards regarding the generalizability of research findings. We hope to inspire rigorous comparative research to establish and refine mobile sensing methodologies for cross-cultural psychology.

Keywords

mobile sensingcross-cultural researchmeasurement biasmeasurement invariancemitigation strategies
Type
Chapter
Information
Technology and Measurement around the Globe , pp. 176 - 210
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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, 285–291. 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, 787–792. 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. 10–18). 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. 124–130). 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), 613–622. 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), 507–526. 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. 1087–1098). 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. 101–129). Cambridge University Press.Google Scholar
Borsboom, D. (2006). When does measurement invariance matter? Medical Care, 44(11), S176–S181. 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), 75–98. 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), 199–231. 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), 3–31. 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, 120–132. 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), 2361–2408. 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), 1–26. 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, 129–138. 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), 838–854. 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, 83–90. 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. 299–311). 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), 204–228. 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. 763–790). 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), 649–669. 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), 1–19. 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), 111–135. 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), 61–83. 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), 1037–1059. 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), 521–551. 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), 57–83. 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), 402–427. 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), 1–24. https://doi.org/10.1145/3351246CrossRefGoogle Scholar
Killingsworth, M. A., & Gilbert, D. T. (2010). A wandering mind is an unhappy mind. Science, 330(6006), 932–932. 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), 1–15. 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), 149–167. 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), 22–33. 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), 1–10. 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), 14–21. 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), 532–565. 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. 142–147). 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), 525–543. 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), 23–47. 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, 1–10. 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), 714–732. 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), 1126–1137. 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, 1–10. 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), 49–117. 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), 1–17. 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, 71–90. 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), 11401–11405. 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), 142–160. 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), 92–108. 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. 427–522). 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. 435–443). 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), 85–98. 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, 96–101. 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), 733–752. 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), 1123–1128. 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), 17680–17687. 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), 613–631. 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. 127–140). 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), 3–46. 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), 1–39. 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), S39–S49. 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. 65–92). 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), 119–135. 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), 273–291. 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), 1545–1558. 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. 3–14). 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), 1–21. 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), 1–13. 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. 59–85). 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), 1100–1122. 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), 1–30. https://doi.org/10.1145/3161414Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×