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
×
Home
Hostname: page-component-55597f9d44-t4qhp Total loading time: 1.911 Render date: 2022-08-14T07:08:54.665Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "useNewApi": true } hasContentIssue true
The Cambridge Handbook of Technology and Employee Behavior The Cambridge Handbook of Technology and Employee Behavior
Buy print or eBook[Opens in a new window]

Book contents

Part VI - Technology in Statistics and Research Methods

Published online by Cambridge University Press:  18 February 2019

Edited by
Richard N. Landers
Richard N. Landers
Affiliation:
University of Minnesota
Get access

Summary

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
The Cambridge Handbook of Technology and Employee Behavior , pp. 743 - 846
Publisher: Cambridge University Press
Print publication year: 2019

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.)

Purchase

Buy Book

Buy print or eBook[Opens in a new window]

References

Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete. Wired Magazine 16 (7). http://archive.wired.com/science/discoveries/magazine/16-07/pb_theory/.Google Scholar
Baker, C. A., Bosco, F. A., Uggerslev, K. L., & Steel, P. (2016). metaBUS: An open search engine of I-O research findings. The Industrial-Organizational Psychologist. 54(1). http://www.siop.org/tip/july16.aspx.Google Scholar
Bhatia, E. N. (2014). Optical character recognition techniques: A review. International Journal of Advanced Research in Computer Science and Software Engineering, 4(5), 1219–1223.Google Scholar
Bono, J. E., Glomb, T. M., Shen, W., Kim, E., & Koch, A. J. (2013). Building positive resources: Effects of positive events and positive reflection on work-stress and health. Academy of Management Journal, 56: 1601–1627.CrossRefGoogle Scholar
Bosco, F. A., Aguinis, H., Field, J. G., Pierce, C. A., & Dalton, D. R. (2016). HARKing’s threat to organizational research: Evidence from primary and meta-analytic sources. Personnel Psychology, 69, 709–750.CrossRefGoogle Scholar
Bosco, F. A., Aguinis, H., Singh, K., Field, J. G., & Pierce, C. A. (2015). Correlational effect size benchmarks. Journal of Applied Psychology, 100, 431–449.CrossRefGoogle ScholarPubMed
Bosco, F. A., Steel, P., Oswald, F. L., Uggerslev, K. L., & Field, J. G. (2015). Cloud-based meta-analysis to bridge science and practice: Welcome to metaBUS. Personnel Assessment and Decisions, 1, 3–17.CrossRefGoogle Scholar
Bosco, F. A., Uggerslev, K. L., & Steel, P. (2017). metaBUS as a vehicle for facilitating meta-analysis. Human Resource Management Review, 27, 237–254.CrossRefGoogle Scholar
Bosco, F. A., & Uggerslev, K. L. (2018). metaBUS, http://metaBUS.org, September 25.
Boyd, D. & Crawford, K. 2012. Critical questions for big data: provocations for a cultural, technological and scholarly phenomenon, Information, Community, & Society, 15(5), 662–679.CrossRefGoogle Scholar
Cheung, M. W.-L. (2015). metaSEM: an R package for meta-analysis using structural equation modeling. Frontiers in Psychology, 5, 1521. doi.org/10.3389/fpsyg.2014.01521.CrossRefGoogle Scholar
Cheung, M. W.-L. (2017). metaSEM: An R package for meta-analysis using structural equation modeling. Modified from the Frontiers in Psychology 2014 manuscript. https://cran.r-project.org/web/packages/metaSEM/vignettes/metaSEM.pdf.
Cortina, J. M., Green, J. P., Keeler, K. R., & Vandenberg, R. J. (2017). Degrees of freedom in SEM: Are we testing the models that we claim to test? Organizational Research Methods, 20(3), 350-378. doi.org/10.1177/1094428116676345.CrossRefGoogle Scholar
Cowls, J. & Schroeder, R. (2015). Causation, correlation, and Big Data in social science research. Policy & Internet, 7, (4), 447–472.CrossRefGoogle Scholar
Creswell, J., & Plano Clark, V. (2011). Choosing a mixed method design. In: Creswell, J. & Plano Clark, V. (Eds.), Designing and Conducting Mixed Methods Research (2nd edn., pp. 53–105). Thousand Oaks, CA: Sage.Google Scholar
Foster, I., Ghani, R., Jarmin, R. S., Kreuter, F., & Lane, J. (Eds.). (2016). Big data and social science: A practical guide to methods and tools. Boca Raton, FL: CRC Press/ Taylor & Francis Group.CrossRefGoogle Scholar
George, G., Osinga, E. C., Lavie, D., & Scott, B. A. (2016). Big data and data science methods for management research, Academy of Management Journal, 59(5), 1493–1507. doi: 10.5465/amj.2016.4005.CrossRefGoogle Scholar
Holland, S. J., Shore, D. B., & Cortina, J. M. (2016). Review and recommendations for integrated mediation and moderation. Organizational Research Methods, 20(4), 1–35. http://dx.doi.org/10 .1177/1094428116658958.Google Scholar
Ilies, R., Dimotakis, N., & De Pater, I. (2010). Psychological and physiological reactions to high workloads: Implications for well-being. Personnel Psychology, 63, 407–436. doi:10.1111/j.1744–6570.2010.01175.x.CrossRefGoogle Scholar
Kosoff, M. (2015). LinkedIn just bought online learning company Lynda for $1.5 billion, April 9, 2015. www.businessinsider.com/linkedin-buys-lyndacom-for-15-billion-2015–4.
Kozlowski, S. W., Chen, G., & Salas, E. (2017). One hundred years of the Journal of Applied Psychology: Background, evolution, and scientific trends. Journal of Applied Psychology, 102(3), 237.CrossRefGoogle ScholarPubMed
Larsen, K. R., Lee, J., Li, J., & Bong, C. H. (2010). A transdisciplinary approach to construct search and integration, 16th Americas Conference on Information Systems, Lima, Peru, August 12–15.
Larsen, K. R. (2017). Inter-Nomological Network. http://inn.colorado.edu, June 14.
Li, J. & Larsen, K. R.. (2011). “Establishing Nomological Networks for Behavioral Science: a Natural Language Processing Based Approach,” International Conference on Information Systems (ICIS), Shanghai, China, December 4th–7th, 2011.
Manning, C. D., Raghavan, P., & Schutze, H. (2009). Introduction to information retrieval. Cambridge, UK: Cambridge University Press.Google Scholar
Mell, P. & Grance, T. (2011). The NIST Definition of Cloud Computing (Technical report). National Institute of Standards and Technology: U.S. Department of Commerce. doi:10.6028/NIST.SP.800–145. Special publication 800–145.CrossRef
Mohan, C. (2013). “History Repeats Itself: Sensible and NonsenSQL Aspects of the NoSQL Hoopla”. Proceedings of the 16th International Conference on Extending Database Technology. Retrieved 2017–06-26 from http://openproceedings.org/2013/conf/edbt/Mohan13.pdf.
Najor, M. (2009). Web crawler architecture. In: LIU L., ÖZSU M.T. (Eds) Encyclopedia of Database Systems. Boston, MA: Springer,Google Scholar
Olmedilla, M., Martínez-Torres, M.R., & Toral, S.L. (2016). Harvesting Big Data in social science: A methodological approach for collecting online user-generated content, Computer Standards & Interfaces, 46, 79–87. doi.org/10.1016/j.csi.2016.02.003.CrossRefGoogle Scholar
Prajapati, V. (2013). Big data analytics with R and Hadoop. Birmingham, UK: Packt Publishing.Google Scholar
Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D., & Tufano, P. (2012). Analytics: The real-world use of Big Data – How innovative enterprises extract value from uncertain data, Executive Report, IBM Institute for Business Value.
Singh, S. (2013). Optical character recognition techniques: a survey. Journal of emerging Trends in Computing and information Sciences, 4(6), 545–550.Google Scholar
SINTEF. Big Data, for better or worse: 90 percent of world’s data generated over last two years. ScienceDaily. Retrieved June 20, 2017, from www.sciencedaily.com/releases/2013/05/130522085217.htm.
Sirosh, J. (2015). Microsoft Closes Acquisition of Revolution Analytics. Microsoft. Retrieved November 22, 2015 from blogs.technet.com.
Varian, H. R. (2014). Big data: New tricks for econometrics. The Journal of Economic Perspectives, 28: 3–27.CrossRefGoogle Scholar
Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3):1–48.CrossRefGoogle Scholar
VoltDB. SQL vs. NoSQL vs. NewSQL. White paper retrieved on 2017–6-26 from www.voltdb.com/wp-content/uploads/2017/05/VoltDB_SQL-vs-NoSQL-vs-NewSQL.pdf.
WIRED. (Jan 19, 2012). Amazon Goes Back to the Future With “NoSQL” Database. Retrieved June 26, 2017, from www.wired.com/2012/01/amazon-dynamodb/.
Xu, Z.W. (2014). Cloud-sea computing systems: Towards thousand-fold improvement in performance per watt for the coming zettabyte era. Journal of Computer science and technology, 29(2),177–181. doi: 10.1007/s11390-014–1420-2.CrossRefGoogle Scholar
Baker, M. (2016). Is there a reproducibility crisis? Nature, 533(7604), 3–5.Google Scholar
Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python: analyzing text with the natural language toolkit. Sebastopol, CA: O’Reilly Media.Google Scholar
Bogomolov, A., Lepri, B., Ferron, M., Pianesi, F., & Pentland, A. S. (November, 2014). Daily stress recognition from mobile phone data, weather conditions and individual traits. In Proceedings of the 22nd ACM international conference on Multimedia (pp. 477–486). ACM.CrossRef
Bollen, K., Cacioppo, J. T., Kaplan, R. M., Krosnick, J. A., & Olds, J. L. (2015). Social, behavioral, and economic sciences perspectives on robust and reliable science. Retrieved from www.nsf.gov/sbe/AC_Materials/SBE_Robust_and_Reliable_Research_Report.pdf.
Bosco, F. A., Steel, P., Oswald, F. L., Uggerslev, K., & Field, J. G. (2015). Cloud-based meta-analysis to bridge science and practice: Welcome to metaBUS. Personnel Assessment and Decisions, 1, 3–17.CrossRefGoogle Scholar
Brieman, L. (2001). Statistical modeling: The two cultures. Statistical Science, 16, 199–231.CrossRefGoogle Scholar
Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O.,… Varoquaux, G. (2013). API design for machine learning software: experiences from the scikit-learn project. Paper presented at the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases, Prague, Czech Republic.
Carter, N. T., Carter, D. R., & DeChurch, L. A. (2015). Implications of observability for the theory and measurement of emergent team phenomena. Journal of Management, 0149206315609402.
Chacon, S. & Straub, B. (2014). Pro git [pdf version]. Retrieved from https://github.com/progit/progit2/releases/download/2.1.48/progit.pdf.CrossRef
Chen, D. & Zhao, H. (March, 2012). Data security and privacy protection issues in cloud computing. In International Conference Computer Science and Electronics Engineering Proceedings (ICCSEE), 2012 (vol. 1, pp. 647–651). IEEE.CrossRef
Chen, X., Cho, Y., & Jang, S. Y. (April, 2015). Crime prediction using Twitter sentiment and weather. In Systems and Information Engineering Design Symposium Conference Proceedings (SIEDS), 2015 (pp. 63–68). IEEE.CrossRef
Crocker, J. (2011). The road to fraud starts with a single step. Nature, 479, 151.CrossRefGoogle ScholarPubMed
Denzin, N. K. (1970). The research act: A theoretical introduction to sociological methods. Chicago, IL: Aldine.Google Scholar
Dickersin, K. (1990). The existence of publication bias and risk factors for its occurrence. Jama, 263(10), 1385–1389.CrossRefGoogle ScholarPubMed
Dumbill, E. (2013). Making sense of big data. Big Data, 1(1), 1–2.CrossRefGoogle ScholarPubMed
Earp, B. D. & Trafimow, D. (2015). Replication, falsification, and the crisis of confidence in social psychology. Frontiers in Psychology, 6, 621.CrossRefGoogle ScholarPubMed
Finkel, E. J., Eastwick, P. W., & Reis, H. T. (2017). Replicability and other features of a high-quality science: Toward a balanced and empirical approach. Journal of Personality and Social Psychology, 113(2), 244.CrossRefGoogle Scholar
Furht, B. & Escalante, A. (2010). Handbook of cloud computing (vol. 3). New York, NY: Springer.CrossRefGoogle Scholar
Gatica-Perez, D. (2009). Automatic nonverbal analysis of social interaction in small groups: A review. Image and Vision Computing, 27(12), 1775–1787.CrossRefGoogle Scholar
Giles, J. (2012). Making the links. From e-mails to social networks, the digital traces left by the life in the modern world are transforming social science. Nature, 488(7412), 448–450. doi:10.1038/488448a.CrossRefGoogle Scholar
Guzzo, R. A., Fink, A. A., King, E., Tonidandel, S., & Landis, R. S. (2015). Big data recommendations for industrial–organizational psychology. Industrial and Organizational Psychology, 8, 491–508.CrossRefGoogle Scholar
Hambrick, D. C. (2007). Upper echelons theory: An update. Academy of Management Review, 32, 334–343.CrossRefGoogle Scholar
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98–115.CrossRefGoogle Scholar
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd edn.). New York, NY: Springer.CrossRefGoogle Scholar
Hernandez, I., Newman, D., & Jeon, G. (2016). Twitter analysis: Methods for data management and validation of a word count dictionary to measure city-level job satisfaction. In Tonidandel, S., King, E., & Cortina, J. (Eds.), Big data at work: The data science revolution and organizational psychology (pp. 64–114). New York, NY: Routledge.Google Scholar
Highhouse, S. & Schmitt, N.W. (2013). A snapshot in time: Industrial-organizational psychology today. In Weiner, I. B. (Ed.), Handbook of psychology (2nd edn., pp. 3–13). Hoboken, NJ: John Wiley & Sons.Google Scholar
Hitzler, P. & Janowicz, K. (2013). Linked data, big data, and the 4th paradigm. Semantic Web, 4, 233–235.Google Scholar
Howell, L. (2013). Digital wildfires in a hyperconnected world. WEF Report 2013. Retrieved from reports.weforum.org/global-risks-2013/risk-case-1/digital-wildfires-in-a-hyperconnected-world.
Huang, L. & Knight, A. P. (2017). Resources and relationships in entrepreneurship: an exchange theory of the development and effects of the entrepreneur-investor relationship. Academy of Management Review, 42, 80–102.CrossRefGoogle Scholar
Hung, H. & Gatica-Perez, D. (2010). Estimating cohesion in small groups using audio-visual nonverbal behavior. IEEE Transactions on Multimedia, 12(6), 563–575.CrossRefGoogle Scholar
Jalali, A., Olabode, O. A., & Bell, C. M. (2012). Leveraging cloud computing to address public health disparities: An analysis of the SPHPS. Online Journal of Public Health Informatics, 4.CrossRefGoogle ScholarPubMed
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning with applications in R. New York, NY: Springer.CrossRefGoogle Scholar
Jick, T. D. (1979). Mixing qualitative and quantitative methods: Triangulation in action. Administrative Science Quarterly, 24(4), 602–611.CrossRefGoogle Scholar
John, L. K., Loewenstein, G., & Prelec, D. (2012). Measuring the prevalence of questionable research practices with incentives for truth telling. Psychological Science, 23(5), 524–532. doi:10.1177/0956797611430953.CrossRefGoogle ScholarPubMed
Johnson, V. E., Payne, R. D., Wang, T., Asher, A., & Mandal, S. (2017). On the reproducibility of psychological science. Journal of the American Statistical Association, 112, 1–10. doi:10.1080/01621459.2016.1240079.CrossRefGoogle ScholarPubMed
Kerr, N. L. (1998). HARKing: Hypothesizing after the results are known. Personality and Social Psychology Review, 2(3), 196–217.CrossRefGoogle ScholarPubMed
Kidwell, M. C., Lazarević, L. B., Baranski, E., Hardwicke, T. E., Piechowski, S., Falkenberg, L. S., … & Errington, T. M. (2016). Badges to acknowledge open practices: a simple, low-cost, effective method for increasing transparency. PLoS Biology, 14(5), e1002456.CrossRefGoogle ScholarPubMed
Kirk, A. (2012). Data visualization: A successful design process. Birmingham, UK: PacktGoogle 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, 357–380.CrossRefGoogle Scholar
Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). Supervised machine learning: A review of classification techniques. Emerging Artificial Intelligence Applications in Computer Engineering, 160, 3–24.Google Scholar
Kozlowski, S. W., Chao, G. T., Chang, C. H., & Fernandez, R. (2015). Team dynamics: Using “big data” to advance the science of team effectiveness. In Tonidandel, S., King, E. B., & Cortina, J. M. (Eds.), Big data at work: The data science revolution and organizational psychology (pp. 273–309). New York, NY: Routledge.Google Scholar
Kozlowski, S. W., Chao, G. T., Grand, J. A., Braun, M. T., & Kuljanin, G. (2016). Capturing the multilevel dynamics of emergence: Computational modeling, simulation, and virtual experimentation. Organizational Psychology Review, 6, 3–33.CrossRefGoogle Scholar
Kuhn, M. & Johnson, K. (2013). Applied predictive modeling. New York, NY: Springer.CrossRefGoogle Scholar
Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., … & Hunt, T. (2017). Caret: Classification and Regression Training. R package version 6.0–78. https://CRAN.R-project.org/package=caret.
Landers, R. N. (October, 2016). A Crash Course in Data Visualization Platform Tableau. The Industrial Organizational Psychologist, 55(2).Google Scholar
Landers, R. N., Brusso, R. C., Cavanaugh, K. J., & Collmus, A. B. (2016). A primer on theory-driven web scraping: Automatic extraction of big data from the Internet for use in psychological research. Psychological Methods, 21, 475–492.CrossRefGoogle ScholarPubMed
Landers, R. N., Fink, A., & Collmus, A. B. (2017). Using big data to enhance staffing: Vast untapped resources or tempting honeypot? In Farr, J. L. & Tippins, N. T. (Eds.), Handbook of employee selection (2nd edn., pp. 949–966). New York, NY: Routledge.CrossRefGoogle Scholar
Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META Group Research Note, 6.Google Scholar
Lazer, D., Pentland, A. S., Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., … & Jebara, T. (2009). Life in the network: The coming age of computational social science. Science, 323(5915), 721–723.CrossRefGoogle Scholar
LeBel, E. P., Vanpaemel, W., McCarthy, R., & Earp, B., & Elson, M. (2017). A Unified Framework to Quantify the Trustworthiness of Empirical Research. Manuscript under review @ Advances in Methods and Practices in Psychological Science. Retrieved from https://osf.io/preprints/psyarxiv/uwmr8.
Lewis, P., Grierson, J., Weaver, M. (March 24, 2018). Cambridge Analytica academic’s work upset university colleagues. The Guardian. Retrieved from www.theguardian.com/education/2018/mar/24/cambridge-analytica-academics-work-upset-university-colleagues.
Locke, E. A. (2007). The case for inductive theory building. Journal of Management, 33(6), 867–890.CrossRefGoogle Scholar
Luciano, M. M., Mathieu, J. E., Park, S., & Tannenbaum, S. I. (2017). A Fitting Approach to Construct and Measurement Alignment: The Role of Big Data in Advancing Dynamic Theories. Organizational Research Methods, 1094428117728372.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.Google Scholar
Marshall, E. (2000). Scientific misconduct. How prevalent is fraud? That’s a million-dollar question. Science, 290(5497), 1662.CrossRefGoogle ScholarPubMed
Maxwell, S. E., Lau, M. Y., & Howard, G. S. (2015). Is psychology suffering from a replication crisis? What does “failure to replicate” really mean? American Psychologist, 70(6), 487–498.CrossRefGoogle ScholarPubMed
McAbee, S., Grubbs, J., & Zickar, M. (2018). Open science is robust science. Industrial and Organizational Psychology, 11(1), 54–61. doi:10.1017/iop.2017.85.CrossRefGoogle Scholar
McAbee, S. T., Landis, R. S., & Burke, M. I. (2017). Inductive reasoning: The promise of big data. Human Resource Management Review, 27, 277–290.CrossRefGoogle Scholar
McKelvey, K. R. & Menczer, F. (February, 2013). Truthy: Enabling the study of online social networks. In Proceedings of the 2013 conference on Computer supported cooperative work companion (pp. 23–26). ACM.CrossRef
McKinney, W. (2010). Data Structures for Statistical Computing in Python. In van der Walt, S. & Millman, J. (Eds.), Proceedings of the 9th Python in Science Conference (pp. 51–56).
McKnight, K. M., Sechrest, L., & McKnight, P. E. (2005). Psychology, psychologists, and public policy. Annual Review of Clinical Psychology, 1, 557–576.CrossRefGoogle ScholarPubMed
Mell, P. & Grance, T. (2011). The NIST definition of cloud computing. National Institute of Standards and Technology, U.S. Department of Commerce.CrossRef
Miguel, E., Camerer, C., Casey, K., Cohen, J., Esterling, K. M., Gerber, A., … & Laitin, D. (2014). Promoting transparency in social science research. Science, 343(6166), 30–31.CrossRefGoogle ScholarPubMed
Mourtada, R. & Salem, F. (2011). Civil movements: The impact of Facebook and Twitter. Arab Social Media Report, 1(2), 1–30.Google Scholar
Ohm, P. (2010). Broken promises of privacy: Responding to the surprising failure of anonymization. UCLA Law Review, 57, 1701–1776.Google Scholar
Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716. doi:10.1126/science.aac4716.CrossRef
Pandey, A. V., Manivannan, A., Nov, O., Satterthwaite, M., & Bertini, E. (2014). The persuasive power of data visualization. IEEE Transactions on Visualization and Computer Graphics, 20, 2211–2220.CrossRefGoogle ScholarPubMed
Park, C. L. (2010). Making sense of the meaning literature: an integrative review of meaning making and its effects on adjustment to stressful life events. Psychological Bulletin, 136, 257.CrossRefGoogle ScholarPubMed
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … & Vanderplas, J. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.Google Scholar
Phillips, G. W. & Jiang, T. (2016). Measurement error and equating error in power analysis. Practical Assessment, Research & Evaluation, 21 (9), 1–12.Google Scholar
Putka, D. J., Beatty, A. S., & Reeder, M. C. (2017). Modern prediction methods: New perspectives on a common problem. Organizational Research Methods, 1094428117697041.
R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. www.R-project.org/.
Rissman, J., Greely, H. T., & Wagner, A. D. (2010). Detecting individual memories through the neural decoding of memory states and past experience. Proceedings of the National Academy of Sciences, USA, 107, 9849–9854. doi:10.1073/ pnas.1001028107.CrossRefGoogle ScholarPubMed
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston, MA: Houghton Mifflin.Google Scholar
Schmidt, F. L. & Hunter, J. E. (2003). History, development, evolution, and impact of validity generalization and meta-analysis methods, 1975–2001. In Validity generalization: A critical review (pp. 31–65).
Shute, V. J. (2011). Stealth assessment in computer-based games to support learning. Computer Games and Instruction, 55(2), 503–524.Google Scholar
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22(11), 1359–1366.CrossRefGoogle ScholarPubMed
Sinar, E. F. (2015). Data visualization. In Tonidandel, S., King, E. B., & Cortina, J. M. (Eds.), Big data at work: The data science revolution and organizational psychology (pp. 115–157). New York, NY: Routledge.Google Scholar
Sinar, E. F. (2018). Data Visualization: Get Visual to Drive HR’s Impact and Influence. Society for Human Resource Management (SHRM)-Society for Industrial Organizational Psychology (SIOP) Science of HR White Paper Series.
Spector, P. E., Rogelberg, S. G., Ryan, A. M., Schmitt, N., & Zedeck, S. (2014). Moving the pendulum back to the middle: Reflections on and introduction to the inductive research special issue of Journal of Business and Psychology. Journal of Business and Psychology, 29(4), 499–502.CrossRefGoogle Scholar
Stanton, J. M. (2013). Introduction to data science. Retrieved from https://archive.org/details/DataScienceBookV3.
Stapel, D. (2014). Faking science: A true story of academic fraud. Trans. NJL Brown.). Retrieved from https://errorstatistics.files.wordpress.com/2014/12/fakingscience-20141214.pdf.
Stroebe, W., Postmes, T., & Spears, R. (2012). Scientific misconduct and the myth of self-correction in science. Perspectives on Psychological Science, 7(6), 670–688.CrossRefGoogle Scholar
Thurmond, V. A. (2001). The point of triangulation. Journal of Nursing Scholarship, 33(3), 253–258.CrossRefGoogle ScholarPubMed
Tonidandel, S., King, E. B., & Cortina, J. M. (2016). Big Data methods: Leveraging modern data analytic techniques to build organizational science. Organizational Research Methods, 1094428116677299.
Tumasjan, A., Sprenger, T. O., Sandner, P. G., & Welpe, I. M. (2010). Predicting elections with twitter: What 140 characters reveal about political sentiment. ICWSM, 10, 178–185.Google Scholar
Univers. (2012). Levelt: Fraud detected in 55 publications [Blog post]. Retrieved from https://universonline.nl/2012/11/28/levelt-report-fraud-detected-in-55-publications.
van ’t Veer, A. E. & Giner-Sorolla, R. (2016). Pre-registration in social psychology: A discussion and suggested template. Journal of Experimental Social Psychology, 67, 2–12.CrossRefGoogle Scholar
Wang, L., Wang, G., & Alexander, C. A. (2015). Big data and visualization: Methods, challenges and technology progress. Digital Technologies, 1, 33–38.Google Scholar
We Are Social. (January, 2018). Most famous social network sites worldwide as of January 2018, ranked by number of active users (in millions). Retrieved from www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/.
Wenzel, R. & Van Quaquebeke, N. (2017). The Double-Edged Sword of Big Data in Organizational and Management Research: A Review of Opportunities and Risks. Organizational Research Methods, 1094428117718627.
Westera, W., Nadolski, R., & Hummel, H. (2014). Serious gaming analytics: What students’ log files tell us about gaming and learning. International Journal of Serious Games, 1(2), 35–50.CrossRefGoogle Scholar
Wicherts, J. (2011). Psychology must learn a lesson from fraud case. Nature, 480.CrossRefGoogle Scholar
Wicherts, J. M., Borsboom, D., Kats, J., & Molenaar, D. (2006). The poor availability of psychological research data for reanalysis. American Psychologist, 61(7), 726–728. http://dx.doi.org.proxy.lib.odu.edu/10.1037/0003-066X.61.7.726.CrossRefGoogle ScholarPubMed
Wickham, H (2017). Tidyverse: Easily Install and Load the “Tidyverse.” R package version 1.2.1. https://CRAN.R-project.org/package=tidyverse.
Yarkoni, T. & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12, 1100–1122.CrossRefGoogle ScholarPubMed
Zhu, Y. (2007). Measuring effective data visualization. Advances in Visual Computing, 4842, 652–661.CrossRefGoogle Scholar
Alonso, O. & Lease, M. (February, 2011). Crowdsourcing 101: Putting the WSDM of crowds to work for you. In WSDM (pp. 1–2). New York, NY: ACM.Google Scholar
Alkhatib, A., Bernstein, M. S., & Levi, M. (2017). Examining Crowd Work and Gig Work Through The Historical Lens of Piecework. Paper presented at the Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, Denver, Colorado, USA.CrossRef
Amazon.com. (2017). Amazon Mechanical Turk Getting Started Guide, 2017 Retrieved from http://docs.aws.amazon.com/es_es/AWSMechTurk/latest/AWSMechanicalTurkGettingStartedGuide/amt-gsg.pdf.
Ashford, S. J., George, E., & Blatt, R. (2007). 2 old assumptions, new work: The opportunities and challenges of research on nonstandard employment. The Academy of Management Annals, 1(1), 65–117.CrossRefGoogle Scholar
Behrend, T. S., Sharek, D. J., Meade, A. W., & Wiebe, E. N. (2011). The viability of crowdsourcing for survey research. Behavior Research Methods, 43(3), 800.CrossRefGoogle ScholarPubMed
Baobao Zhang, . (August 19, 2014). How to launch a survey on Amazon Mechanical Turk (MTurk) [Video file]. Retrieved from www.youtube.com/watch?v=qPcVe4SOmz8.
Berinsky, A., Huber, G., & Lenz, G. (2012). Using Mechanical Turk as a subject recruitment tool for experimental research. Political Analysis, 20, 351–368.CrossRefGoogle Scholar
Buhrmester, M. D., Kwang, T., & Gosling, S. D. (2011). Amazon’s Mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6, 3–5.CrossRefGoogle ScholarPubMed
Casey, L. S., Chandler, J., Levine, A. S., Proctor, A., & Strolovitch, D. Z. (2017). Intertemporal differences among MTurk Workers: Time-based sample variations and implications for online data collection. SAGE Open, 7(2), 2158244017712774.CrossRefGoogle Scholar
Casler, K., Bickel, L., & Hackett, E. (2013). Separate but equal? A comparison of participants and data gathered via Amazon’s MTurk, social media, and face-to-face behavioral testing. Computers in Human Behavior, 29(6), 2156–2160.CrossRefGoogle Scholar
Chambers, S., Nimon, K., & Anthony-McMann, P. (2016). A primer for conducting survey research using MTurk: Tips for the field. International Journal of Adult Vocational Education and Technology (IJAVET), 7(2), 54–73.CrossRefGoogle Scholar
Chandler, J., Mueller, P., &Paolacci, G. (2014).Nonnaiveté among Amazon Mechanical Turk Workers: Consequences and solutions for behavioral researchers. Behavioral Research, 46, 112–130.CrossRefGoogle ScholarPubMed
Chandler, J. J. & Paolacci, G. (2017). Lie for a dime: When most prescreening responses are honest but most study participants are impostors. Social Psychological and Personality Science, 8, 500–508. doi: 10.1177/1948550617698203.CrossRefGoogle Scholar
Chandler, J., Paolacci, G., Peer, E., Mueller, P., & Ratliff, K. A. (2015). Using nonnaive participants can reduce effect sizes. Psychological Science, 26, 1131–1139. doi:10.1177/0956797615585115CrossRefGoogle ScholarPubMed
Chandler, J. & Shapiro, D. (2016). Conducting clinical research using crowdsourced convenience samples. Annual Review of Clinical Psychology, 12. 53–81. doi: 10.1146/annurev-clinpsy-021815-093623.CrossRefGoogle ScholarPubMed
Cheung, J. H., Burns, D. K., Sinclair, R. R., & Sliter, M. (2017). Amazon Mechanical Turk in organizational psychology: An evaluation and practical recommendations. Journal of Business and Psychology, 32(4), 347–361.CrossRefGoogle Scholar
Cotter v. Lyft, Inc., 60 F. Supp. 3d 1067, 1081–82 (ND Cal. 2015).
Devine, E. G., Waters, M. E., Putnam, M., Surprise, C., O’Malley, K., Richambault, C., … & Streeter, C. (2013). Concealment and fabrication by experienced research subjects. Clinical Trials, 10(6), 935–948.CrossRefGoogle ScholarPubMed
Deneme. (December 21, 2009). Deneme: A blog of experiments on Amazon Mechanical Turk. http://groups.csail.mit.edu/uid/deneme/?p=523.
Feitosa, J., Joseph, D., & Newman, D. (2015). Crowdsourcing and personality measurement equivalence: A warning about countries whose primary language is not English. Personality and Individual Differences, 75, 47–52.CrossRefGoogle Scholar
George, E., & Ng, C. K. (2010). APA Handbook of Industrial and Organizational Psychology. APA.Google Scholar
Harms, P. D., & DeSimone, J. A. (2015). Caution! MTurk workers ahead—Fines doubled. Industrial and Organizational Psychology, 8(2), 183–190.CrossRefGoogle Scholar
Hitlin, P. (2016). Research in the crowdsourcing age, a case study. Retrieved from http://assets. pewresearch.org/wpcontent/uploads/sites/14/2016/07/PI_2016.07.11_Mechanical-Turk_ FINAL.pdf.
Howe, , J. (2006). Crowdsourcing: a definition. Wired Blog Network: Crowdsourcing. Retrieved September 2018, from http://crowdsourcing.typepad.com/cs/2006/06/crowdsourcing_a.html
Huff, , C., & Tingley, , D. (2015). “Who are these people?” Evaluating the demographic characteristics and political preferences of MTurk survey respondents. Research & Politics, 2(3), 2053168015604648.CrossRefGoogle Scholar
Ipeirotis, P. (2010). Demographics of Mechanical Turk [Working Article]. New York University. Retrieved from www.archive.nyu.edu/bitstream/2451/29585/2/CeDER-10-01.pdf.
Keith, M. G. & Harms, P. D. (2016). Is Mechanical Turk the answer to our sampling woes?. Industrial and Organizational Psychology, 9(1), 162–167.CrossRefGoogle Scholar
Keith, M. G., Tay, L., & Harms, P. D. (2017). Systems perspective of Amazon Mechanical Turk for organizational research: Review and recommendations. Frontiers in Psychology, 8, 1359.CrossRefGoogle ScholarPubMed
Kittur, A., Nickerson, J. V., Bernstein, M., Gerber, E., Shaw, A., Zimmerman, J.,… Horton, J. (2013). The future of crowd work. Paper presented at the Proceedings of the 2013 conference on Computer supported cooperative work.CrossRef
Kleemann, F., Voß, G. G., & Rieder, K. (2008). Un(der)paid innovators: The commercial utilization of consumer work through crowdsourcing. Science, Technology & Innovation Studies, 4(1), 5–26.Google Scholar
Kuek, S. C., Paradi-Guilford, C., Fayomi, T., Imaizumi, S., Ipeirotis, P., Pina, P., & Singh, M. (2015). The global opportunity in online outsourcing. Washington, DC: The World Bank. Retrieved from http://documents.worldbank.org/curated/en/138371468000900555/The-global-opportunity-in-online-outsourcing.Google Scholar
Landers, R. & Behrend, T. (2015). An inconvenient truth: Arbitrary distinctions between organizational, Mechanical Turk, and other convenience samples. Industrial and Organizational Psychology: Perspectives on Science and Practice, 8(2), 142–164.CrossRefGoogle Scholar
Levay, K. E., Freese, J., & Druckman, J. N. (2016). The demographic and political composition of Mechanical Turk samples. Sage Open, 6(1),1–17.CrossRefGoogle Scholar
Litman, L., Robinson, J., & Abberbock, T. (2017). Behavior Research Methods, 49(2), 433–442.CrossRef
Mason, W. & Suri, S. (2011). Conducting behavioral research on Amazon’s Mechanical Turk. Behavioral Research, 44, 1–23.CrossRefGoogle Scholar
How, I., Meade, A. W., Behrend, T. S., & Lance, C. E. (2009). Dr. StrangeLOVE, or: How I learned to stop worrying and love omitted variables. In Lance, C. E. & Vandenberg, R. J. (Eds.), Statistical and methodological myths and urban legends: Doctrine, verity and fable in the organizational and social sciences (pp. 89–106). New York, NY: Routledge/Taylor & Francis Group.Google Scholar
Paolacci, G., Chandler, J., & Ipeirotis, P. G. (2010). Running experiments on Amazon Mechanical Turk. Judgment and Decision Making, 5, 411–419.Google Scholar
Peer, E., Samat, S., Brandimarte, L.,& Acquisti, A. (2016). Beyond the Turk: An Empirical Comparison of Alternative Platforms for Crowdsourcing Online Behavioral Research (May 1, 2016). Retrieved from SSRN. https://ssrn.com/abstract=2594183 or http://dx.doi.org/10.2139/ssrn.2594183.CrossRef
Peer, E., Brandimarte, L., Samat, S., & Acquisti, A. (2017). Beyond the Turk: Alternative platforms for crowdsourcing behavioral research. Journal of Experimental Social Psychology, 70, 153–163.CrossRefGoogle Scholar
Prassl, J. & Risak, M. (2015). Uber, Taskrabbit, and Co.: Platforms as ezmployers –Rethinking the legal analysis of crowdwork. Comparative Labor Law & Policy, 37, 619.Google Scholar
Rand, D. (2012). The promise of Mechanical Turk: How online labor markets can help theorists run behavioral experiments. Journal of Theoretical Biology, 299, 172–179.CrossRefGoogle ScholarPubMed
Retelny, D., Robaszkiewicz, S., To, A., Lasecki, W. S., Patel, J., Rahmati, N.,… Bernstein, M. S. (2014). Expert crowdsourcing with flash teams. Paper presented at the Proceedings of the 27th annual ACM symposium on User interface software and technology.CrossRef
Roulin, N. (2015). Don’t throw the baby out with the bathwater: Comparing data quality of crowdsourcing, online panels, and student samples. Industrial and Organizational Psychology, 8(2), 190–196.CrossRefGoogle Scholar
Shapiro, D., Chandler, J., &Mueller, P. (2013). Using Mechanical Turk to study clinical populations. Clinical Psychological Science, 1, 213–220.CrossRefGoogle Scholar
Sheehan, K. & Pittman, M. (2016). Amazon’s Mechanical Turk for academics: The HIT handbook for social science research. Irvine, CA: Melvin & Leigh.Google Scholar
Stewart, N., Ungemach, C., Harris, A. J., Bartels, D. M., Newell, B. R., Paolacci, G., & Chandler, J. (2015). The average laboratory samples a population of 7,300 Amazon Mechanical Turk Workers. Judgment and Decision making, 10(5), 479.Google Scholar
Williamson, V. (2016). On the ethics of crowdsourced research. PS: Political Science & Politics, 49(1), 77–81Google Scholar
Affective Signals (June 14, 2017). Affective Signals. Retrieved from www.affective-signals.com/.
Apple (2017). ResearchKit and CareKit. Retrieved from www.apple.com/de/researchkit/.
Apple (2018). IPhone X Specs. Retrieved from www.apple.com/iphone-Xs/specs/.
Audacity (2017). Audacity. Retrieved from www.audacity.de/.
Baltrušaitis, T., McDuff, D., Banda, N., Mahmoud, M., El Kaliouby, R., Robinson, P., & Picard, R. (2011). Real-time inference of mental states from facial expressions and upper body gestures. Presented at the IEEE International Conference on Automatic Face & Gesture Recognition, Santa Barbara, CA. doi:10.1109/FG.2011.5771372.CrossRef
Baltrušaitis, T., Robinson, P., & Morency, L.-P. (2016). Openface: An open source facial behavior analysis toolkit. In 2016 IEEE winter conference on applications of computer vision (WACV) (pp. 1–10). Lake Placid, NY: IEEE. doi:10.1109/wacv.2016.7477553.Google Scholar
Batrinca, L. M., Mana, N., Lepri, B., Pianesi, F., & Sebe, N. (2011). Please, tell me about yourself: Automatic personality assessment using short self-presentations. In Proceedings of the 13th international conference on multimodal interfaces (pp. 255–262). New York, NY: ACM Press. doi:10.1145/2070481.2070528.Google Scholar
Baur, T., Damian, I., Lingenfelser, F., Wagner, J., & André, E. (2013). NovA: Automated analysis of nonverbal signals in social interactions. Lecture Notes in Computer Science, 8212, 160–171. doi:10.1007/978–3-319–02714-2_14.CrossRef
Boersma, P. & Van Heuven, V. (2001). Speak and unSpeak with PRAAT. Glot International, 5, 341–347.Google Scholar
Bunderson, J. S., van der Vegt, G. S., Cantimur, Y., & Rink, F. (2016). Different views on hierarchy and why they matter: Hierarchy as inequality or as cascading influence. Academy of Management Journal, 42, 1265–1289. doi:10.5465/amj.2014.0601.CrossRefGoogle Scholar
Chatman, J. A., Boisnier, A. D., Spataro, S. E., Anderson, C., & Berdahl, J. L. (2008). Being distinctive versus being conspicuous: The effects of numeric status and sex-stereotyped tasks on individual performance in groups. Organizational Behavior and Human Decision Processes, 107, 141–160. doi:10.1016/j.obhdp.2008.02.006.CrossRefGoogle Scholar
Chartrand, T. L. & Bargh, J. A. (1999). The chameleon effect: The perception–behavior link and social interaction. Journal of Personality and Social Psychology, 76, 893–910. doi:10.1037//0022–3514.76.6.893.CrossRefGoogle ScholarPubMed
Ciocchetti, M., Massaroni, C., Saccomandi, P., Caponero, M., Polimadei, A., Formica, D., & Schena, E. (2015). Smart textile based on fiber Bragg grating sensors for respiratory monitoring: Design and preliminary trials. Biosensors, 5, 602–615. doi:10.3390/bios5030602.CrossRefGoogle ScholarPubMed
Cook, A. & Mayer, B. (2017). Assessing leadership behavior with observational and sensor-based methods: A brief overview. In Schyns, B., Hall, R. J., & Neves, P. (Eds.), Handbook of methods in leadership research (pp. 73–102). Cheltenham, UK: Edward Elgar.CrossRefGoogle Scholar
Cook, A. (S.) & Mayer, B., Gockel, C., & Zill, A. (in press). Adapting leadership perceptions across tasks: The micro origins of informal leadership transitions. Small Group Research.
Damian, I., Baur, T., & André, E. (2016). Measuring the impact of multimodal behavioural feedback loops on social interactions. In Proceedings of the 18th ACM International Conference on Multimodal Interaction (pp. 201–208). New York, NY: ACM Press. doi:10.1145/2993148.2993174.Google Scholar
Damian, I., Dietz, M., Gaibler, F., & André, E. (2016). Social signal processing for dummies. In Proceedings of the 18th ACM international conference on multimodal interaction (pp. 394–395). New York, NY: ACM Press. doi:10.1145/2993148.2998527.Google Scholar
De Looze, C., Scherer, S., Vaughan, B., & Campbell, N. (2014). Investigating automatic measurements of prosodic accommodation and its dynamics in social interaction. Speech Communication, 58, 11–34. doi:10.1016/j.specom.2013.10.002.CrossRefGoogle Scholar
Dinev, T. & Hart, P. (2004). Internet privacy concerns and their antecedents: Measurement validity and a regression model. Behaviour & Information Technology, 23, 413–422. doi:10.1080/01449290410001715723.CrossRefGoogle Scholar
Duchovski, A. (2007). Eye tracking methodology: Theory and practice. London, UK: Springer.Google Scholar
Ellgring, H. & Scherer, K. R. (1996). Vocal indicators of mood change in depression. Journal of Nonverbal Behavior, 20, 83–110. doi:10.1007/bf02253071.CrossRefGoogle Scholar
Eyben, F., Scherer, K. R., Schuller, B. W., Sundberg, J., Andre, E., Busso, C., … Truong, K. P. (2016). The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for voice research and affective computing. IEEE Transactions on Affective Computing, 7, 190–202. doi:10.1109/TAFFC.2015.2457417.CrossRefGoogle Scholar
Giddens, C. L., Barron, K. W., Byrd-Craven, J., Clark, K. F., & Winter, A. S. (2013). Vocal indices of stress: A review. Journal of Voice, 27, 21–29. doi:10.1016/j.jvoice.2012.12.010.CrossRefGoogle ScholarPubMed
Hawkins, S. A. & Hastie, R. (1990). Hindsight: Biased judgement of past events after the outcomes are known. Psychological Bulletin, 107, 311–327. doi:10.1037//0033–2909.107.3.311.CrossRefGoogle Scholar
Huang, J. L., Curran, P. G., Keeney, J., Poposki, E. M., & DeShon, R. P. (2012). Detecting and deterring insufficient effort responding to surveys. Journal of Business and Psychology, 27, 99–114. doi:10.1007/s10869-011–9231-8.CrossRefGoogle Scholar
Intel (2018). Intel RealSense technology. Retrieved from https://software.intel.com/en-us/realsense/d400.
Jehn, K. A. (1995). A multimethod examination of the benefits and detriments of intragroup conflict. Administrative Science Quarterly, 40, 256–282. doi:10.2307/2393638.CrossRefGoogle Scholar
Kim, T., Chang, A., Holland, L., & Pentland, A. S. (2008). Meeting mediator: Enhancing group collaboration using sociometric feedback. In Proceedings of the 2008 ACM conference on computer supported cooperative work (pp. 457–466). New York, NY: ACM Press. Retrieved from http://dl.acm.org/citation.cfm?id=1460636.Google Scholar
Krajewski, J. & Kröger, B. J. (2007). Using prosodic and spectral characteristics for sleepiness detection. Paper presented at the INTERSPEECH 2007, Antwerp, Belgium. Retrieved from www.ao.i2.psychologie.uni-wuerzburg.de/fileadmin/06020230/user_upload/1-_Krajewski_Kroeger_2007_Using_prosodic_and_spectral_07_erschienen_in_interspeech.pdf.
Langer, M., König, C. J., Gebhard, P., & André, E. (2016). Dear computer, teach me manners: Testing virtual employment interview training. International Journal of Selection and Assessment, 24, 312–323. doi:10.1111/ijsa.12150.CrossRefGoogle Scholar
Louwerse, M. M., Dale, R., Bard, E. G., & Jeuniaux, P. (2012). Behavior matching in multimodal communication is synchronized. Cognitive Science, 36, 1404–1426. doi:10.1111/j.1551–6709.2012.01269.x.CrossRefGoogle ScholarPubMed
Lu, H., Frauendorfer, D., Rabbi, M., Mast, M. S., Chittaranjan, G. T., Campbell, A. T., … Choudhury, T. (2012). StressSense: Detecting stress in unconstrained acoustic environments using smartphones. In Proceedings of the 2012 ACM conference on ubiquitous computing (pp. 351–360). New York, NY: ACM Press. Retrieved from http://dl.acm.org/citation.cfm?id=2370270.CrossRefGoogle Scholar
Luxton, D. D., McCann, R. A., Bush, N. E., Mishkind, M. C., & Reger, G. M. (2011). mHealth for mental health: Integrating smartphone technology in behavioral healthcare. Professional Psychology: Research and Practice, 42, 505–512. doi:10.1037/a0024485.CrossRefGoogle Scholar
Manson, J. H. & Robbins, M. L. (2017). New evaluation of the Electronically Activated Recorder (EAR): Obtrusiveness, compliance, and participant self-selection effects. Frontiers in Psychology, 8, 658. doi:10.3389/fpsyg.2017.00658.CrossRefGoogle ScholarPubMed
Matsumoto, D., Yoo, S. H., & Fontaine, J. (2008). Mapping expressive differences around the world: The relationship between emotional display rules and individualism versus collectivism. Journal of Cross-Cultural Psychology, 39, 55–74. doi:10.1177/0022022107311854.CrossRefGoogle Scholar
McDuff, D., Gontarek, S., & Picard, R. (2014). Remote measurement of cognitive stress via heart rate variability. Presented at the 36th annual international conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL. doi:10.1109/EMBC.2014.6944243.CrossRef
Mehl, M. R. (2017). The Electronically Activated Recorder (EAR): A method for the naturalistic observation of daily social behavior. Current Directions in Psychological Science, 26, 184–190. doi:10.1177/0963721416680611.CrossRefGoogle ScholarPubMed
Mehl, M. R. & Holleran, S. E. (2007). An empirical analysis of the obtrusiveness of and participants’ compliance with the Electronically Activated Recorder (EAR). European Journal of Psychological Assessment, 23, 248–257. doi:10.1027/1015–5759.23.4.248.CrossRefGoogle Scholar
Mehl, M. R., Pennebaker, J. W., Crow, D. M., Dabbs, J., & Price, J. H. (2001). The Electronically Activated Recorder (EAR): A device for sampling naturalistic daily activities and conversations. Behavior Research Methods, 33, 517–523.doi:10.3758/bf03195410.Google ScholarPubMed
Microsoft. (February 25, 2015). Kinect for Windows. Retrieved from www.microsoft.com/en-us/kinectforwindows/.
Microsoft. (2017). Developing with Kinect for Windows. Retrieved from https://developer.microsoft.com/en-us/windows/kinect/develop.
Miller, G. (2012). The smartphone psychology manifesto. Perspectives on Psychological Science, 7, 221–237. doi:10.1177/1745691612441215.CrossRefGoogle ScholarPubMed
Mori, M. (1970). Bukimi no tani [The uncanny valley]. Energy, 7, 33–5.Google Scholar
Mori, M., MacDorman, K., & Kageki, N. (2012). The uncanny valley. IEEE Robotics & Automation Magazine, 19, 98–100. doi:10.1109/MRA.2012.2192811.CrossRefGoogle Scholar
Muralidhar, S., Schmid Mast, M., & Gatica-Perez, D. (2017). How May I Help You? Behavior and Impressions in Hospitality Service Encounters. In Proceedings of 19th ACM international conference on multimodal interaction (pp. 312–320). New York, NY: ACM Press. doi:10.1145/3136755.3136771.Google Scholar
Naim, I., Tanveer, M. I., Gildea, D., & Hoque, M. E. (2015). Automated analysis and prediction of job interview performance: The role of what you say and how you say it. Presented at the 11th IEEE international conference and workshops on automatic face and gesture recognition, Ljubljana, Slovenia. doi:10.1109/fg.2015.7163127.CrossRef
Noah, J. A., Spierer, D. K., Gu, J., & Bronner, S. (2013). Comparison of steps and energy expenditure assessment in adults of Fitbit Tracker and Ultra to the Actical and indirect calorimetry. Journal of Medical Engineering & Technology, 37, 456–462. doi:10.3109/03091902.2013.831135.CrossRefGoogle Scholar
Ohly, S., Sonnentag, S., Niessen, C., & Zapf, D. (2010). Diary studies in organizational research: An introduction and some practical recommendations. Journal of Personnel Psychology, 9, 79–93. doi:10.1027/1866–5888/a000009.CrossRefGoogle Scholar
Oksüz, N., Biswas, R. S. I., Shcherbatyi, I., & Maass, W. (2018). Measuring biosignals of overweight and obese children for real-time feedback and predicting performance. In vom, J. Brocke, P.-Léger, M., & Randolph, A. (Eds.), Information systems and neuroscience– Gmunden retreat on NeuroIS 2017 (pp. 185–193). Cham, Switzerland: Springer.Google Scholar
Olguín, D. O. (2007). Sociometric badges: Wearable technology for measuring human behavior (Master thesis). Massachusetts Institute of Technology, Boston. Retrieved from http://hdl.handle.net/1721.1/42169.
Olguín, D. O. & Pentland, A. S. (2007). Sociometric badges: State of the art and future applications. In Doctoral colloquium presented at IEEE 11th International Symposium on Wearable Computers, Boston, MA. Retrieved from https://pdfs.semanticscholar.org/48f1/f30259586e2229682510a3ba90fa053004c8.pdf.
Pantelopoulos, A. & Bourbakis, N. G. (2010). A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Transactions on Systems, Man, and Cybernetics, 40, 1–12. Available at: doi:10.1109/tsmcc.2009.2032660.CrossRefGoogle Scholar
Podsakoff, P. M. & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of Management, 12, 531–44. doi:10.1177/014920638601200408.CrossRefGoogle Scholar
Ranganath, R., Jurafsky, D., & McFarland, D. A. (2013). Detecting friendly, flirtatious, awkward, and assertive speech in speed-dates. Computer Speech & Language, 27, 89–115. doi:10.1016/j.csl.2012.01.005.CrossRefGoogle Scholar
Samsung. (2017). Samsung Galaxy S7 Specs. Retrieved from www.samsung.com/de/smartphones/galaxy-s7/more/.
Schmid Mast, M., Frauendorfer, D., Nguyen, L. S., Gatica-Perez, D., Choudhury, T., & Odobez, J.-M. (2017). A step towards automatic applicant selection: Predicting job performance based on applicant nonverbal interview behavior. [Under Review.]
Schmid Mast, M., Gatica-Perez, D., Frauendorfer, D., Nguyen, L., & Choudhury, T. (2015). Social sensing for psychology automated interpersonal behavior assessment. Current Directions in Psychological Science, 24, 154–160. doi:10.1177/0963721414560811.CrossRefGoogle Scholar
Stahl, S. E., An, H.-S., Dinkel, D. M., Noble, J. M., & Lee, J.-M. (2016). How accurate are the wrist-based heart rate monitors during walking and running activities? Are they accurate enough? BMJ Open Sport & Exercise Medicine, 2, 1–7. doi:10.1136/bmjsem-2015–000106.CrossRefGoogle ScholarPubMed
Sundholm, M., Cheng, J., Zhou, B., Sethi, A., & Lukowicz, P. (2014). Smart-mat: Recognizing and counting gym exercises with low-cost resistive pressure sensing matrix. In Proceedings of the 16th ACM international joint conference on pervasive and ubiquitous computing (pp. 373–382). New York, NY: ACM Press. doi:10.1145/2632048.2636088.Google Scholar
Van Vaerenbergh, Y. & Thomas, T. D. (2013). Response styles in survey research: A literature review of antecedents, consequences, and remedies. International Journal of Public Opinion Research, 25, 195–217. doi:10.1093/ijpor/eds021.CrossRefGoogle Scholar
Wagner, J., Lingenfelser, F., Baur, T., Damian, I., Kistler, F., & André, E. (2013). The social signal interpretation (SSI) framework: Multimodal signal processing and recognition in real-time. In Proceedings of the 21st ACM international conference on Multimedia (pp. 831–834). New York, NY: ACM Press. Retrieved from http://dl.acm.org/citation.cfm?id=2502223.CrossRefGoogle Scholar
Wallen, M. P., Gomersall, S. R., Keating, S. E., Wisløff, U., & Coombes, J. S. (2016). Accuracy of heart rate watches: Implications for weight management. PLoS One, 11, e0154420. doi:10.1371/journal.pone.0154420.CrossRefGoogle ScholarPubMed
Wang, R., Blackburn, G., Desai, M., Phelan, D., Gillinov, L., Houghtaling, P., & Gillinov, M. (2017). Accuracy of wrist-worn heart rate monitors. JAMA Cardiology, 2, 104–106. doi:10.1001/jamacardio.2016.3340.CrossRefGoogle ScholarPubMed
Ziegler, M., MacCann, C., & Roberts, R. (Eds.) (2011). New perspectives on faking in personality assessment. Oxford, UK: Oxford University Press.CrossRefGoogle Scholar
Cleveland, W. S. & McGill, R. (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79, 531–554.CrossRefGoogle Scholar
Deng, W. S. & Sloutsky, V. M. (2016). Selective attention, diffused attention, and the development of categorization. Cognitive Psychology, 91, 24–62. doi:10.1016/j.cogpsych.2016.09.002CrossRefGoogle ScholarPubMed
De Mauro, A., Greco, M., & Grimaldi, M. (2016). A formal definition of big data based on its essential features. Library Review, 65, 122–135. doi:10.1108/LR-06–2015-0061.CrossRefGoogle Scholar
Elliot, A. (2015). Color and psychological functioning: A review of theoretical and empirical work. Frontiers in Psychology, 6, 368. doi:10.3389/fpsyg.2015.00368.CrossRefGoogle ScholarPubMed
Gazzaniga, M. S., & Mangun, G. R. (Eds.). (2014). Cognitive neurosciences (5th ed.). Cambridge, MA: MIT Press.Google Scholar
Hoffman, A. B. & Rehder, B. (2010). The costs of supervised classification: The effect of learning task on conceptual flexibility. Journal of Experimental Psychology: General, 139, 319–340. doi:1037/a0019042.CrossRefGoogle ScholarPubMed
Keim, D., Andrienko, G., Fekete, J. D., Görg, C., Kohlhammer, J., & Melançon, G. (2008). Visual analytics: Definition, process, and challenges. In Kerren, A., Stasko, J. T., Fekete, J. D., & North, C. (Eds.), Information visualization: Human-centered issues and perspectives (pp. 154–175). New York, NY: Springer.CrossRefGoogle Scholar
Norton, M. I., Mochon, D., & Ariely, D. (2012). The IKEA effect: When labor leads to love. Journal of Consumer Psychology, 22, 453–60. doi:10.1016/j.jcps.2011.08.002.CrossRefGoogle Scholar
Shneiderman, B. (September, 1996). The eyes have it: A task by data type taxonomy for information visualizations. Paper presented at the Proceedings of the IEEE Symposium on Visual Languages, Washington, DC.
Skitka, L. J., Mosier, K. L., & Burdick, M. (2000). Accountability and automation bias. International Journal of Human-Computer Studies, 52, 701–17. doi:10.1006/ijhc.1999.0349.CrossRefGoogle Scholar
Ware, C. (2004). Information visualization: Perception for design (2nd edn.). San Francisco, CA: Elsevier.Google Scholar