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Original Article

Digital Footprints of Sensation Seeking

A Traditional Concept in the Big Data Era

Published Online:https://doi.org/10.1027/2151-2604/a000342

References

  • Alexander, D., Tropsha, A. & Winkler, D. A. (2015). Beware of r 2: Simple, unambiguous assessment of the prediction accuracy of qsar and qspr models. Journal of Chemical Information and Modeling, 55, 1316–1322. https://doi.org/10.1021/acs.jcim.5b00206 First citation in articleCrossrefGoogle Scholar

  • Aluja, A., Garcia, Ó. & Garcia, L. F. (2003). Psychometric properties of the Zuckerman–Kuhlman personality questionnaire (ZKPQ-III-R): A study of a shortened form. Personality and Individual Differences, 34, 1083–1097. https://doi.org/10.1016/S0191-8869(02)00097-1 First citation in articleCrossrefGoogle Scholar

  • Andone, I., Błaszkiewicz, K., Eibes, M., Trendafilov, B., Montag, C. & Markowetz, A. (2016). How age and gender affect smartphone usage. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (pp. 9–12). New York, NY: ACM. https://doi.org/10.1145/2968219.2971451 First citation in articleCrossrefGoogle Scholar

  • Arendasy, M., Sommer, M. & Feldhammer, M. (2011). Manual Big-Five Structure Inventory BFSI. Mödling, Austria: Schuhfried. First citation in articleGoogle Scholar

  • Baumeister, R. F., Vohs, K. D. & Funder, D. C. (2007). Psychology as the science of self-reports and finger movements: Whatever happened to actual behavior? Perspectives on Psychological Science, 2, 396–403. https://doi.org/10.1111/j.1745-6916.2007.00051.x First citation in articleCrossrefGoogle Scholar

  • Beauducel, A., Strobel, A. & Brocke, B. (2003). Psychometrische Eigenschaften und Normen einer deutschsprachigen Fassung der Sensation Seeking-Skalen, Form V [Psychometric properties and norms of a German version of the Sensation Seeking Scales, Form V]. Diagnostica, 49, 61–72. https://doi.org/10.1026//0012-1924.49.2.61 First citation in articleLinkGoogle Scholar

  • Benson, M. J. & Campbell, J. P. (2007). To be, or not to be, linear: An expanded representation of personality and its relationship to leadership performance. International Journal of Selection and Assessment, 15, 232–249. https://doi.org/10.1111/j.1468-2389.2007.00384.x First citation in articleCrossrefGoogle Scholar

  • Bischl, B., Lang, M., Kotthoff, L., Schiffner, J., Richter, J., Studerus, E., … Jones, Z. M. (2016). mlr: Machine learning in r. Journal of Machine Learning Research, 17, 1–5.Retrieved from http://jmlr.org/papers/v17/15-066.html First citation in articleGoogle Scholar

  • Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324 First citation in articleCrossrefGoogle Scholar

  • Brinkman, W.-P. & Fine, N. (2005). Towards customized emotional design: An explorative study of user personality and user interface skin preferences. Proceedings of the 2005 Annual Conference of the European Association of Cognitive Ergonomics (pp. 107–114). Athens, Greece: University of Athens. First citation in articleGoogle Scholar

  • Canzian, L. & Musolesi, M. (2015). Trajectories of depression: Unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 1293–1304). New York, NY: ACM. https://doi.org/10.1145/2750858.2805845 First citation in articleCrossrefGoogle Scholar

  • Chai, T. & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7, 1247–1250. https://doi.org/10.5194/gmd-7-1247-2014 First citation in articleCrossrefGoogle Scholar

  • Chen, E. E. & Wojcik, S. P. (2016). A practical guide to big data research in psychology. Psychological Methods, 21, 458–474. https://doi.org/10.1037/met0000111 First citation in articleCrossrefGoogle Scholar

  • Chen, T., He, T. & Benesty, M. (2015). Xgboost: Extreme gradient boosting, R Package Version 0.4-2., 1–4. Retrieved from https://cran.r-project.org/web/packages/xgboost First citation in articleGoogle Scholar

  • Cheung, M. W.-L. & Jak, S. (2016). Analyzing big data in psychology: A split/analyze/meta-analyze approach. Frontiers in Psychology, 7, 738. https://doi.org/10.3389/fpsyg.2016.00738 First citation in articleCrossrefGoogle Scholar

  • Chittaranjan, G., Blom, J. & Gatica-Perez, D. (2011). Who’s who with Big-Five: Analyzing and classifying personality traits with smartphones. 15th Annual International Symposium on Wearable Computers (ISWC) (pp. 29–36). Los Alamitos, CA: IEEE Computer Society. https://doi.org/10.1109/ISWC.2011.29 First citation in articleCrossrefGoogle Scholar

  • Chittaranjan, G., Blom, J. & Gatica-Perez, D. (2013). Mining large-scale smartphone data for personality studies. Personal and Ubiquitous Computing, 17, 433–450. https://doi.org/10.1007/s00779-011-0490-1 First citation in articleCrossrefGoogle Scholar

  • Dahlen, E. R., Martin, R. C., Ragan, K. & Kuhlman, M. M. (2005). Driving anger, sensation seeking, impulsiveness, and boredom proneness in the prediction of unsafe driving. Accident Analysis & Prevention, 37, 341–348. https://doi.org/10.1016/j.aap.2004.10.006 First citation in articleCrossrefGoogle Scholar

  • Danner, D., Rammstedt, B., Bluemke, M., Treiber, L., Berres, S., Soto, C. & John, O. (2016). Die deutsche Version des Big Five Inventory 2 (BFI-2) [German version of the Big Five Inventory (BFI-2)]. Zusammenstellung sozialwissenschaftlicher Items und Skalen. Advance online publication. https://doi.org/10.6102/zis247 First citation in articleGoogle Scholar

  • de Montjoye, Y. A., Quoidbach, J., Robic, F. & Pentland, A. S. (2013). Predicting personality using novel mobile phone-based metrics. International conference on social computing, behavioral-cultural modeling, and prediction (pp. 48–55). Berlin, Germany: Springer. First citation in articleCrossrefGoogle Scholar

  • Fox, J. & Weisberg, S. (2011). An R companion to applied regression (2nd ed.). Thousand Oaks, CA: Sage. Retrieved from http://socserv.socsci.mcmaster.ca/jfox/Books/Companion First citation in articleGoogle Scholar

  • Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29, 1189–1232. https://doi.org/10.1214/aos/1013203451 First citation in articleCrossrefGoogle Scholar

  • Friedman, J., Hastie, T. & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33, 1–22. First citation in articleCrossrefGoogle Scholar

  • Glicksohn, J. & Abulafia, J. (1998). Embedding sensation seeking within the big three. Personality and Individual Differences, 25, 1085–1099. https://doi.org/10.1016/S0191-8869(98)00096-8 First citation in articleCrossrefGoogle Scholar

  • Guszkowska, M. & Bołdak, A. (2010). Sensation seeking in males involved in recreational high risk sports. Biology of Sport, 27, 157–162. https://doi.org/10.5604/20831862.919331 First citation in articleCrossrefGoogle 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. https://doi.org/10.1177/1745691616650285 First citation in articleCrossrefGoogle Scholar

  • Jack, S. & Ronan, K. R. (1998). Sensation seeking among high-and low-risk sports participants. Personality and Individual Differences, 25, 1063–1083. https://doi.org/10.1016/S0191-8869(98)00081-6 First citation in articleCrossrefGoogle Scholar

  • James, G., Witten, D., Hastie, T. & Tibshirani, R. (2013). An introduction to statistical learning, Vol. 112, New York, NY: Springer. https://doi.org/10.1007/978-1-4614-7138-7 First citation in articleCrossrefGoogle Scholar

  • Kafadar, K. (2003). John Tukey and robustness. Statistical Science, 18, 319–331. https://doi.org/10.1214/ss/1076102419 First citation in articleCrossrefGoogle Scholar

  • Karatzoglou, A., Smola, A., Hornik, K. & Zeileis, A. (2004). Kernlab – an S4 package for Kernel methods in R. Journal of Statistical Software, 11, 1–20. https://doi.org/10.18637/jss.v011.i09 First citation in articleCrossrefGoogle Scholar

  • 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, 5802–5805. https://doi.org/10.1073/pnas.1218772110 First citation in articleCrossrefGoogle Scholar

  • Kuhn, M. & Johnson, K. (2013). Applied predictive modeling, Vol. 26, New York, NY: Springer. https://doi.org/10.1007/978-1-4614-6849-3 First citation in articleCrossrefGoogle Scholar

  • Kwon, M., Lee, J.-Y., Won, W.-Y., Park, J.-W., Min, J.-A., Hahn, C., … Kim, D.-J. (2013). Development and validation of a Smartphone Addiction Scale (SAS). PLoS One, 8, e56936. https://doi.org/10.1371/journal.pone.0056936 First citation in articleCrossrefGoogle Scholar

  • Lepp, A. & Gibson, H. (2008). Sensation seeking and tourism: Tourist role, perception of risk and destination choice. Tourism Management, 29, 740–750. https://doi.org/10.1016/j.tourman.2007.08.002 First citation in articleCrossrefGoogle Scholar

  • Leung, L. (2008). Leisure boredom, sensation seeking, self-esteem, addiction: Symptoms and patterns of cell phone use. In E. A. KonijnM. A. TanisS. UtzA. LindenEds., Mediated interpersonal communications (pp. 359–381). Mahwah, NJ: Erlbaum. First citation in articleGoogle Scholar

  • Mayer-Schönberger, V. & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Boston, MA: Houghton Mifflin Harcourt. First citation in articleGoogle Scholar

  • McDaniel, S. R. (2002). Investigating the roles of gambling interest and impulsive sensation seeking on consumer enjoyment of promotional games. Social Behavior and Personality: An International Journal, 30, 53–64. https://doi.org/10.2224/sbp.2002.30.1.53 First citation in articleCrossrefGoogle Scholar

  • Montag, C., Błaszkiewicz, K., Sariyska, R., Lachmann, B., Andone, I., Trendafilov, B., … Markowetz, A. (2015). Smartphone usage in the 21st century: Who is active on Whatsapp? BMC Research Notes, 8, 331. https://doi.org/10.1186/s13104-015-1280-z First citation in articleCrossrefGoogle Scholar

  • R Core Team. (2017). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/ First citation in articleGoogle Scholar

  • Revelle, W. (2017). Psych: Procedures for psychological, psychometric, and personality research. Evanston, IL: Northwestern University. Retrieved from https://CRAN.R-project.org/package=psych First citation in articleGoogle Scholar

  • Roberti, J. W. (2004). A review of behavioral and biological correlates of sensation seeking. Journal of Research in Personality, 38, 256–279. https://doi.org/10.1016/S0092-6566(03)00067-9 First citation in articleCrossrefGoogle Scholar

  • Rousseeuw, P. J. & Croux, C. (1993). Alternatives to the median absolute deviation. Journal of the American Statistical Association, 88, 1273–1283. https://doi.org/10.2307/2291267 First citation in articleCrossrefGoogle Scholar

  • Saeb, S., Zhang, M., Karr, C. J., Schueller, S. M., Corden, M. E., Kording, K. P. & Mohr, D. C. (2015). Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: An exploratory study. Journal of Medical Internet Research, 17, e175. https://doi.org/10.2196/jmir.4273 First citation in articleCrossrefGoogle Scholar

  • Schiffner, J., Bischl, B., Lang, M., Richter, J., Jones, Z. M., Probst, P., … Kotthoff, L. (2016). Mlr tutorial. Retrieved from https://arxiv.org/pdf/1609.06146.pdf First citation in articleGoogle Scholar

  • Schoedel, R., Au, Q., Völkel, S., Bühner, M. & Stachl, C. (2018, March). Digital footprints of sensation seeking: A traditional concept in the big data era. Open Science Framework. Retrieved from https://osf.io/wg38b/ First citation in articleGoogle Scholar

  • Stachl, C., Hilbert, S., Au, J.-Q., Buschek, D., De Luca, A., Bischl, B., … Bühner, M. (2017). Personality traits predict smartphone usage. European Journal of Personality, 31, 701–722. https://doi.org/10.1002/per.2113 First citation in articleCrossrefGoogle Scholar

  • Stachl, C., Schoedel, R., Au, Q., Völkel, S., Buschek, D., Hussmann, H., … Bühner, M. (2018, March). The phonestudy project. Open Science Framework. https://doi.org/10.17605/osf.io/ut42y First citation in articleGoogle Scholar

  • Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T. & Zeileis, A. (2008). Conditional variable importance for random forests. BMC Bioinformatics, 9, 307. https://doi.org/10.1186/1471-2105-9-307 First citation in articleCrossrefGoogle Scholar

  • Tkalcic, M. & Chen, L. (2015). Personality and Recommender Systems. In F. RicciL. RokachB. ShapiraEds., Recommender systems handbook (pp. 715–739). Boston, MA: Springer. First citation in articleGoogle Scholar

  • Tonetti, L., Fabbri, M. & Natale, V. (2009). Relationship between circadian typology and big five personality domains. Chronobiology International, 26, 337–347. https://doi.org/10.1080/07420520902750995 First citation in articleCrossrefGoogle Scholar

  • Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10, 988–999. https://doi.org/10.1109/72.788640 First citation in articleCrossrefGoogle Scholar

  • Weisskirch, R. S. & Murphy, L. C. (2004). Friends, porn, and punk: Sensation seeking in personal relationships, internet activities, and music preference among college students. Adolescence, 39, 189–201. First citation in articleGoogle Scholar

  • Wickham, H., Francois, R., Henry, L. & Müller, K. (2017). Dplyr: A grammar of data manipulation. Retrieved from https://CRAN.R-project.org/package=dplyr First citation in articleGoogle Scholar

  • Williams, M. J., Whitaker, R. M. & Allen, S. M. (2012). Measuring individual regularity in human visiting patterns. 2012 International Conference on Privacy, Security, Risk and Trust (PASSAT) and 2012 International Conference on Social Computing (SocialCom) (pp. 117–122). IEEE. https://doi.org/10.1109/SocialCom-PASSAT.2012.93 First citation in articleCrossrefGoogle Scholar

  • Wing, M. K. C. from, Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., … Hunt, T. (2017). Caret: Classification and regression training. Retrieved from https://CRAN.R-project.org/package=caret First citation in articleGoogle Scholar

  • Wolpert, D. H. & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1, 67–82. https://doi.org/1089-778X(97)03422-X First citation in articleCrossrefGoogle Scholar

  • Wright, M. N. & Ziegler, A. (2017). ranger: A fast implementation of random forests for high dimensional data in C++ and R. Journal of Statistical Software, 77, 1–17. https://doi.org/10.18637/jss.v077.i01 First citation in articleCrossrefGoogle Scholar

  • Yarkoni, T. (2010). Personality in 100,000 words: A large-scale analysis of personality and word use among bloggers. Journal of Research in Personality, 44, 363–373. https://doi.org/10.1016/j.jrp.2010.04.001 First citation in articleCrossrefGoogle Scholar

  • Yarkoni, T. & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12, 1100–1122. https://doi.org/10.1177/1745691617693393 First citation in articleCrossrefGoogle Scholar

  • Youyou, W., Kosinski, M. & Stillwell, D. (2015). Computer-based personality judgments are more accurate than those made by humans. Proceedings of the National Academy of Sciences, 112, 1036–1040. https://doi.org/10.1073/pnas.1418680112 First citation in articleCrossrefGoogle Scholar

  • Zabel, K. L., Christopher, A. N., Marek, P., Wieth, M. B. & Carlson, J. J. (2009). Mediational effects of sensation seeking on the age and financial risk-taking relationship. Personality and Individual Differences, 47, 917–921. https://doi.org/10.1016/j.paid.2009.07.016 First citation in articleCrossrefGoogle Scholar

  • Ziegler, M. & Buehner, M. (2009). Modeling socially desirable responding and its effects. Educational and Psychological Measurement, 69, 548–565. https://doi.org/10.1177/0013164408324469 First citation in articleCrossrefGoogle Scholar

  • Zou, H. & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67, 301–320. https://doi.org/1369-7412/05/67301 First citation in articleCrossrefGoogle Scholar

  • Zuckerman, M. (1994). Behavioral expressions and biosocial bases of sensation seeking. Cambridge, UK: Cambridge University Press. First citation in articleGoogle Scholar

  • Zuckerman, M. (2002). Zuckerman-Kuhlman Personality Questionnaire (ZKPQ): An alternative five-factorial model. In B. De RaadM. PeruginiEds., Big Five Assessment (pp. 377–396). Seattle, WA: Hogrefe & Huber. First citation in articleGoogle Scholar

  • Zuckerman, M. & Aluja, A. (2015). Measures of sensation seeking. In G.J. BoyleD.H. SaklofskeG. MatthewsEds., Measures of personality and social psychological constructs (pp. 352–380). San Diego, CA: Elsevier. First citation in articleGoogle Scholar

  • Zuckerman, M., Eysenck, S. B. & Eysenck, H. J. (1978). Sensation seeking in England and America: Cross-cultural, age, and sex comparisons. Journal of Consulting and Clinical Psychology, 46, 139. https://doi.org/10.1037/0022-006X.46.1.139 First citation in articleCrossrefGoogle Scholar