A graph for every analysis: Mapping visuals onto common analyses using flexplot
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Adams, J. A. (1989). Human factors engineering. New York, NY: Macmillan Publishing Co, Inc.
Anscombe, F. J. (1973). Graphs in statistical analysis. American Statistician, 27(1), 17–21. https://doi.org/10.1080/00031305.1973.10478966
Appelbaum, M. I., & Cramer, E. M. (1974). Some problems in the nonorthogonal analysis of variance. Psychological Bulletin, 81(6), 335.
Asparouhov, T., & Muthén, B. O. (2014). Using Mplus individual residual plots for diagnostics and model evaluation in SEM, (20), 1–14.
Baddeley, A. (1994). The Magical Number Seven: Still Magic After All These Years? Psychological Review, 101(2), 353–356. https://doi.org/10.1037/0033-295x.101.2.353
Bertin, J. (2010). Semiology of graphics: Diagrams, networks, maps. Redlands, CA: Esri Press.
Butler, D. L. (1993). Graphics in psychology: Pictures, data, and especially concepts. Behavior Research Methods, Instruments, & Computers (Vol. 25).
Card, S. (2009). Information visualization. In A. Sears & J. A. Jacko (Eds.), Human-computer interaction: Design issues, solutions, and applications (pp. 181–216). Boca Raton, Florida: CRC Press.
Chernoff, H. (1973). The use of faces to represent points in k-dimensional space graphically. Journal of the American Statistical Association, 68(342), 361–368.
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(387), 531–554.
Cohen, J. (1968). Multiple regression as a general data-analytic system. Psychological Bulletin, 426–443. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.476.6180
Correll, M. A. (2015). Visual Statistics. University of Wisconsin-Madison.
Correll, M. A., & Gleicher, M. (2014). Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error. IEEE Transactions on Visualization and Computer Graphics, 20(12), 2142–2151. https://doi.org/10.1109/TVCG.2014.2346298
Correll, M. A., Li, M., Kindlmann, G., & Scheidegger, C. (2019). Looks Good to Me: Visualizations As Sanity Checks. IEEE Transactions on Visualization and Computer Graphics, 25(1), 830–839. https://doi.org/10.1109/TVCG.2018.2864907
Counsell, A., & Harlow, L. L. (2017). Reporting practices and use of quantitative methods in Canadian journal articles in psychology. Canadian Psychology/Psychologie Canadienne, 58(2), 140–147. https://doi.org/10.1037/cap0000074
Cumming, G., Fidler, F., Leonard, M., Kalinowski, P., Christiansen, A., Kleinig, A., … Wilson, S. (2007). Statistical Reform in Psychology Is Anything Changing ? Psychological Science, 18(3), 1–4. https://doi.org/10.1111/j.1467-9280.2007.01881.x
Dimara, E., Franconeri, S., Plaisant, C., Bezerianos, A., & Dragicevic, P. (2018). A task-based taxonomy of cognitive biases for information visualization. IEEE Transactions on Visualization and Computer Graphics
Eklund, A. (2012). Beeswarm: the bee swarm plot, an alternative to stripchart. R Package Version 0.1, 5.
Fife, D. A. (2020b). The Eight Steps of Data Analysis: A Graphical Framework to Promote Sound Statistical Analysis. Perspectives on Psychological Science, 15(4), 1054–1075. https://doi.org/10.1177/1745691620917333
Fife, D., & Rodgers, J. L. (2019). Exonerating EDA, Expanding CDA: A Pragmatic Solution to the Replication Crisis. https://doi.org/10.31234/osf.io/5vfq6
Gigerenzer, G. (2004). Mindless statistics. The Journal of Socio-Economics, 33(5), 587–606. https://doi.org/10.1016/J.SOCEC.2004.09.033
Hallgren, K. A., McCabe, C. J., King, K. M., & Atkins, D. C. (2019). Beyond path diagrams: Enhancing applied structural equation modeling research through data visualization. Addictive Behaviors, 94, 74–82. https://doi.org/10.1016/j.addbeh.2018.08.030
Hansen, C. D., Chen, M., Johnson, C. R., Kaufman, A. E., & Hagen, H. (2014). Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization. London: Springer. Retrieved from http://www.springer.com/series/4562
Healey, C. G., Booth, K. S., & Enns, J. T. (1996). High-speed visual estimation using preattentive processing. ACM Transactions on Computer-Human Interaction (TOCHI), 3(2), 107–135.
Healy, K., & Moody, J. (2014). Data Visualization in Sociology. Annual Review of Sociology, 40(1), 105–128. https://doi.org/10.1146/ANNUREV-SOC-071312-145551
Hofman, J. M., Goldstein, D. G., & Hullman, J. (2020). How Visualizing Inferential Uncertainty Can Mislead Readers About Treatment Effects in Scientific Results. https://doi.org/10.1145/3313831.3376454
Hollands, J. G., & Spence, I. (1998). Judging Proportion with Graphs: The Summation Model. Applied Cognitive Psychology, 12(2), 173–190. https://doi.org/10.1002/(SICI)1099-0720(199804)12:2<173::AID-ACP499>3.0.CO;2-K
Hu, K., Orghian, D., & Hidalgo, C. (2018). DIVE: A mixed-initiative system supporting integrated data exploration workflows. Proceedings of the Workshop on Human-In-the-Loop Data Analytics, HILDA 2018. https://doi.org/10.1145/3209900.3209910
Inbar, O., Tractinsky, N., & Meyer, J. (2007). Minimalism in information visualization: attitudes towards maximizing the data-ink ratio. In ECCE (Vol. 7, pp. 185–188).
JASP Team. (2019). JASP (Version 0.10.2)[Computer software]. Retrieved from https://jasp-stats.org/
Jun, E., Daum, M., Roesch, J., Chasins, S., Berger, E., Just, R., & Reinecke, K. (2019). Tea: A High-level Language and Runtime System for Automating Statistical Analysis. In Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology (pp. 591–603).
Kale, A., Kay, M., & Hullman, J. (2020). Visual Reasoning Strategies for Effect Size Judgments and Decisions. Retrieved from http://arxiv.org/abs/2007.14516
Kandel, S., Parikh, R., Paepcke, A., Hellerstein, J. M., & Heer, J. (2012). Profiler: Integrated statistical analysis and visualization for data quality assessment. In Proceedings of the Workshop on Advanced Visual Interfaces AVI (pp. 547–554). New York, New York, USA: ACM Press. https://doi.org/10.1145/2254556.2254659
Kantowitz, B. H. (1987). 3. Mental workload. In Advances in psychology (Vol. 47, pp. 81–121). Elsevier.
Kohn, L. T., Corrigan, J., & Donaldson, M. S. (2000). To err is human: building a safer health system (Vol. 6). National academy press Washington, DC.
Kosslyn, S. M. (2006). Graph Design for Eye and Mind. New York, New York, USA: Oxford University Press. https://doi.org/10.1016/B978-1-4557-7896-6.00058-3
Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W. (2004). Applied linear statistical models. Applied linear statistical models. New York, NY: McGraw-Hill/Irwin.
Kyonka, E. G. E., Mitchell, S. H., & Bizo, L. A. (2019). Beyond inference by eye: Statistical and graphing practices in JEAB, 1992-2017. Journal of the Experimental Analysis of Behavior, 111(2), 155–165. https://doi.org/10.1002/jeab.509
Lee, M. D., Blanco, G., & Bo, N. (2017). Testing take-the-best in new and changing environments. Behavior Research Methods, 49(4), 1420–1431. https://doi.org/10.3758/s13428-016-0798-x
Lee, M. D., Reilly, R. E., & Butavicius, M. E. (2003). An empirical evaluation of Chernoff faces, star glyphs, and spatial visualizations for binary data. In Proceedings of the Asia-Pacific symposium on Information visualisation-Volume 24 (pp. 1–10).
Levine, S. S. (2018). Show us your data: Connect the dots, improve science. Management and Organization Review, 14(2), 433–437. https://doi.org/10.1017/mor.2018.19
Mackinlay, J. (1986). Automating the Design of Graphical Presentations of Relational Information. ACM Transactions on Graphics (TOG), 5(2), 110–141. https://doi.org/10.1145/22949.22950
Mackinlay, J., Hanrahan, P., & Stolte, C. (2007). Show me: Automatic presentation for visual analysis. IEEE Transactions on Visualization and Computer Graphics, 13(6), 1137–1144.
Matejka, J., & Fitzmaurice, G. (2017). Same stats, different graphs: generating datasets with varied appearance and identical statistics through simulated annealing. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 1290–1294).
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81.
Moritz, D., Wang, C., Nelson, G. L., Lin, H., Smith, A. M., Howe, B., & Heer, J. (2018). Formalizing visualization design knowledge as constraints: Actionable and extensible models in draco. IEEE Transactions on Visualization and Computer Graphics, 25(1), 438–448.
Muenchen, B. (2019). Is Scholarly Use of R Beating SPSS Already? | r4stats.com. Retrieved October 6, 2020, from http://r4stats.com/2019/07/15/is-scholarly-use-of-r-use-beating-spss-already/
Newman, G. E., & Scholl, B. J. (2012). Bar graphs depicting averages are perceptually misinterpreted: The within-the-bar bias. Psychonomic Bulletin & Review, 19(4), 601–607.
Nissen, S. B., Magidson, T., Gross, K., & Bergstrom, C. T. (2016). Publication bias and the canonization of false facts. ELife, 5(e21451). https://doi.org/10.7554/eLife.21451
Norman, D. (2014). Things that make us smart: Defending human attributes in the age of the machine. Diversion Books.
Oakes, M. (1986). Statistical inference: A commentary for the social and behavioral sciences. New York, NY, USA: John Wiley & Sons.
Pandey, A. V., Rall, K., Satterthwaite, M. L., Nov, O., & Bertini, E. (2015). How deceptive are deceptive visualizations?: An empirical analysis of common distortion techniques. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 1469–1478). ACM.
Pastore, M., Lionetti, F., & Altoè, G. (2017). When one shape does not fit all: A commentary essay on the use of graphs in psychological research. Frontiers in Psychology. Frontiers Media S.A. https://doi.org/10.3389/fpsyg.2017.01666
Peden, B. F., & Hausmann, S. E. (2000). Data Graphs in Introductory and Upper Level Psychology Textbooks: A Content Analysis. Teaching of Psychology, 27(2), 93–97. https://doi.org/10.1207/S15328023TOP2702_03
Potter, K., Kniss, J., Riesenfeld, R., & Johnson, C. R. (2010). Visualizing summary statistics and uncertainty. In Computer Graphics Forum (Vol. 29, pp. 823–832). Oxford England: Wiley Online Library.
Rodgers, J. L. (2010). The epistemology of mathematical and statistical modeling: a quiet methodological revolution. The American Psychologist, 65(1), 1–12. https://doi.org/10.1037/a0018326
Schild, A. H. E., & Voracek, M. (2013). Less is less: A systematic review of graph use in meta-analyses. - PsycNET. Research Synthesis Methods, 4(3), 209–219. Retrieved from https://psycnet.apa.org/record/2013-34734-001
Schmidt, F. L. (1996). Statistical significance testing and cumulative knowledge in psychology: Implications for training of researchers. Psychological Methods, 1(2), 115–129. https://doi.org/10.1037/1082-989X.1.2.115
Shrout, P. E., & Rodgers, J. L. (2018). Psychology, Science, and Knowledge Construction: Broadening Perspectives from the Replication Crisis. Annual Review of Psychology, 69(1), 487–510. https://doi.org/10.1146/annurev-psych-122216-011845
Szafir, D. A., Haroz, S., Gleicher, M., & Franconeri, S. (2016). Four types of ensemble coding in data visualizations. Journal of Vision, 16(5), 11.
Tay, L., Parrigon, S., Huang, Q., & LeBreton, J. M. (2016). Graphical Descriptives: A Way to Improve Data Transparency and Methodological Rigor in Psychology. Perspectives on Psychological Science, 11(5), 692–701. https://doi.org/10.1177/1745691616663875
The Jamovi Project. (2019). Jamovi (Version 0.9) [Computer Software]. Retrieved from https://www.jamovi.org
Tufte, E. R., & Graves-Morris, P. R. (1983). The visual display of quantitative information (Vol. 2). Graphics press Cheshire, CT.
Tyron, W. W. (1998). The inscrutable null hypothesis. American Psychologist, 53(7), 796–796. https://doi.org/10.1037/0003-066X.53.7.796.b
Vicente, K. (2010). The human factor: revolutionizing the way we live with technology. Vintage Canada.
Wainer, H. (2010). Prelude. In J. Berkson (Ed.), Semiology of Graphics: Diagrams, Networks, Maps (2nd ed., pp. ix–x). Redlands, CA: ESRI Press.
Ware, C. (2019). Information visualization: perception for design. Morgan Kaufmann.
Weissgerber, T. L., Milic, N. M., Winham, S. J., & Garovic, V. D. (2015). Beyond bar and line graphs: time for a new data presentation paradigm. PLoS Biology, 13(4), e1002128–e1002128. https://doi.org/10.1371/journal.pbio.1002128
Wickens, C. D. (2014). Effort in human factors performance and decision making. Human Factors, 56(8), 1329–1336.
Wickham, H. (2010). A Layered Grammar of Graphics. Journal of Computational and Graphical Statistics, 19(1), 3–28. https://doi.org/10.1198/jcgs.2009.07098
Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. Retrieved from https://ggplot2.tidyverse.org
Wilkinson, L., & Task Force on Statistical Inference. (1999). Statistical Methods in Psychology Journals: Guidelines and Explanations. American Psychologist, 54(8), 594–601.