Skip to main content Accessibility help
×
Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-21T18:26:02.436Z Has data issue: false hasContentIssue false

General Methods

Published online by Cambridge University Press:  27 January 2017

John T. Cacioppo
Affiliation:
University of Chicago
Louis G. Tassinary
Affiliation:
Texas A & M University
Gary G. Berntson
Affiliation:
Ohio State University
Get access

Summary

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2016

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

References

Abelson, R. P. (1995). Statistics as Principled Argument. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Abelson, R. P. & Prentice, D. A. (1997). Contrast tests of interaction hypotheses. Psychological Methods, 2: 315328.Google Scholar
Aiken, L. S. & West, S. G. (1991). Multiple Regression: Testing and Interpreting Interactions. Newbury Park, CA: Sage.Google Scholar
Algina, J. & Keselman, H. J. (1997). Detecting repeated measures effects with univariate and multivariate statistics. Psychological Methods, 2: 208218.Google Scholar
Altmann, E. M. (2004). Advance preparation in task switching: what work is being done? Psychological Science, 15: 616622.Google Scholar
Amodio, D. M. & Bartholow, B. D. (2011). Event-related-potential methods in social cognition. In Klauer, C., Voss, A., & Stahl, C. (eds.), Cognitive Methods in Social Psychology (pp. 303339). New York: Guilford Press.Google Scholar
Arruda, J. E., McGee, H. A., Zhang, H., & Stanny, C. J. (2011). The effects of EEG data transformations on the solution accuracy of principal component analysis. Psychophysiology, 48: 370376.Google Scholar
Baron, R. M. & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51: 11731182.Google Scholar
Berntson, G. G., Bigger, J. Jr., Eckberg, D. L., Grossman, P., Kaufmann, P. G., Malik, M., … & van der Molen, M. W. (1997). Heart rate variability: origins, methods, and interpretive caveats. Psychophysiology, 34: 623648.Google Scholar
Berntson, G. G., Cacioppo, J. T., Quigley, K. S., & Fabro, V. T. (1994a). Autonomic space and physiological response. Psychophysiology, 31, 4461.CrossRefGoogle Scholar
Berntson, G. G., Quigley, K. S., Lang, J. F., & Boysen, S. T. (1990). An approach to artifact identification: application to heart period data. Psychophysiology, 27: 586598.Google Scholar
Berntson, G. G., Uchino, B. N., & Cacioppo, J. T. (1994b). Origins of baseline variance and the law of initial value. Psychophysiology, 31: 204210.CrossRefGoogle Scholar
Blumenthal, T. D., Cuthbert, B. N., Gilion, D. L., Hackley, S., Lipp, O. V., & van Boxtel, A. (2005). Committee report. Guidelines for human startle eyeblink electromyographic studies. Psychophysiology, 42: 115.Google Scholar
Borenstein, M., Cohen, J., & Rothstein, H. (1997). Power and Precision. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Boucsein, W., Fowles, D. C., Grimnes, S., Ben-Shakhar, G., Roth, W. T., Dawson, M. E., & Filion, D. L. (2012). Publication recommendations for electrodermal measurements. Psychophysiology, 49: 10171034.Google Scholar
Box, G. E. P. (1954). Some theorems on quadratic forms applied in the study of analysis of variance problems: I. Effects of inequality of variance in the one-way classification. Annals of Mathematical Statistics, 25: 290302.Google Scholar
Box, G. E. P. & Jenkins, G. M. (1970). Time Series Analysis. San Francisco, CA: Holden Day.Google Scholar
Bryk, A. S. & Raudenbush, S. W. (1987). Application of hierarchical linear models to assessing change. Psychological Bulletin, 101: 147158.Google Scholar
Bush, L. K., Hess, U., & Wolford, G. (1993). Transformations for within-subject designs: a Monte Carlo investigation. Psychological Bulletin, 113: 566579.Google Scholar
Cacioppo, J. T. & Tassinary, L. G. (1990). Inferring psychological significance from physiological signals. American Psychologist, 45: 1628.Google Scholar
Cacioppo, J. T., Tassinary, L. G., & Fridlund, A. J. (1990). The skeletomotor system. In Cacioppo, J. T. & Tassinary, L. G. (eds.), Principles of Psychophysiology: Physical, Social, and Inferential Elements (pp. 325384). Cambridge University Press.Google Scholar
Cary, N. C. (1989). SAS/IML Software: Usage and Reference, Version 6. SAS Institute.Google Scholar
Cary, N. C. (1996). SAS/STAT Software: Changes and Enhancements through Release 6.11. SAS Institute.Google Scholar
Casella, G. (1985). An introduction to empirical Bayesian data analysis. American Statistician, 39: 8387.Google Scholar
Charness, G., Gneezy, U., & Kuhn, M. A. (2012). Experimental methods: between-subject and within-subject design. Journal of Economic Behavior & Organization, 81: 18.Google Scholar
Cheung, M. N. (1981). Detection of and recovery from errors in cardiac interbeat intervals. Psychophysiology, 18: 341346.CrossRefGoogle ScholarPubMed
Chi, E. M. & Reinsel, G. C. (1989). Models of longitudinal data with random effects and AR-1 errors. Journal of the American Statistical Association, 84: 452459.Google Scholar
Chow, S. L. (1996). Statistical Significance: Rationale, Validity, and Utility. Thousand Oaks, CA: Sage.Google Scholar
Cleveland, W. S. (1985). The Elements of Graphing Data. Monterey, CA: Wadsworth.Google Scholar
Cohen, J. (1977). Statistical Power Analysis for the Behavioral Sciences, rev. edn. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Cohen, J. (1992). A power primer. Psychological Bulletin, 112: 155159.CrossRefGoogle ScholarPubMed
Cohen, J. (1994). The earth is round (p <.05). American Psychologist, 49: 9971003.Google Scholar
Cohen, J. & Cohen, P. (1975). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Cohen, J. & Cohen, P. (1983). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 2nd edn. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Cole, J. W. L. & Grizzle, J. E. (1966). Application of multivariate analysis of variance to repeated measures experiments. Biometrics, 22: 810828.CrossRefGoogle Scholar
Coles, M. G. H. (1989). Modern mind–brain reading: psychophysiology, physiology, and cognition. Psychophysiology, 26: 251269.Google Scholar
Coles, M. G. H., Gratton, G., & Donchin, E. (1988). Detecting early communication: using measures of movement-related potentials to illuminate human information processing. Biological Psychology, 26: 6989.Google Scholar
Cook, E. W. & Miller, G. A. (1992). Digital filtering: background and tutorial for psychophysiologists. Psychophysiology, 29: 350367.CrossRefGoogle ScholarPubMed
Cook, R. D. & Weisberg, S. (1994). An Introduction to Regression Graphics. New York: John Wiley.Google Scholar
Cooper, H., Camic, P. M., Long, D. L., Panter, A. T., Rindskopf, D., & Sher, K. J. (2012). APA Handbook of Research Methods in Psychology, vol 1: Foundations, Planning, Measures, and Psychometrics. Washington, DC: American Psychological Association.CrossRefGoogle Scholar
Cumming, G. (2014). The new statistics: why and how. Psychological Science, 25: 729.Google Scholar
Cumming, G. & Finch, S. (2001). A primer on the understanding, use, and calculation of confidence intervals that are based on central and noncentral distributions. Educational and Psychological Measurement, 61: 532574.Google Scholar
Curtin, J. J., Lozano, D. L., & Allen, J. J. B (2007). The psychophysiology laboratory. In Coan, J. A. & Allen, J. J. B. (eds.), The Handbook of Emotion Elicitation and Assessment (pp. 398425). Oxford University Press.Google Scholar
D’Amico, E. J., Neilands, T. B., & Zambarano, R. (2001). Power analysis for multivariate and repeated measures designs: a flexible approach using the SPSS MANOVA procedure. Behavior Research Methods, Instruments, & Computers, 33: 479484.Google Scholar
Darlington, R. B. (1990). Regression and Linear Models. New York: McGraw-Hill.Google Scholar
Davidson, R. J. (1995). Cerebral asymmetry, emotion, and affective style. In Davidson, R. J. & Hugdahl, K. (eds.), Brain Asymmetry (pp. 361388). Cambridge, MA: MIT Press.Google Scholar
Duncan, C. C., Barry, R. J., Connolly, J. F., Fischer, C., Michie, P. T., Naatanen, R., … & Van Petten, C. (2009). Event-related potentials in clinical research: guidelines for eliciting, recording, and quantifying mismatch negativity, P300, and N400. Clinical Neurophysiology, 120: 18831908.Google Scholar
Efron, B. & Tibshirani, R. (1991). Statistical data analysis in the computer age. Science, 253: 390395.Google Scholar
Elmes, D. G., Kantowitz, B. H., & Roedinger, H. L. III (2012). Research Methods, 9th edn. Belmont, CA: Wadsworth.Google Scholar
Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39: 175191.Google Scholar
Fluchêre, F., Deveaux, M., Burle, B., Vidal, F., van den Wildenberg, W. P., Witjas, T., … & Hasbroucq, T. (2015). Dopa therapy and action impulsivity: subthreshold error activation and suppression in Parkinson’s disease. Psychopharmacology, 232: 17351746.CrossRefGoogle ScholarPubMed
Frick, R. W. (1996). The appropriate use of null hypothesis testing. Psychological Methods, 1: 379390.Google Scholar
Fridlund, A. J. & Cacioppo, J. T. (1986). Guidelines for human electromyographic research. Psychophysiology, 23: 567589.CrossRefGoogle ScholarPubMed
Gianaros, P. J., Quigley, K. S., Muth, E. R., Levine, M. E., Vasko, R. C. J., & Stern, R. M. (2003). Relationship between temporal changes in cardiac parasympathetic activity and motion sickness severity. Psychophysiology, 40: 3944.Google Scholar
Gibbons, R. D., Hedeker, D., Elkin, I., Waternaux, C., Kraemer, H. C., Greenhouse, J. B., … & Watkins, J. T. (1993). Some conceptual and statistical issues in analysis of longitudinal psychiatric data: application to the NIMH Treatment of Depression Collaborative Research Program Dataset. Archives of General Psychiatry, 50: 739750.CrossRefGoogle Scholar
Gibbons, R. D., Hedeker, D., Waternaux, C., & Davis, J. M. (1988). Random regression models: a comprehensive approach to the analysis of longitudinal psychiatric date. Psychopharmacological Bulletin, 24: 438443.Google Scholar
Gratton, G., Coles, M. G. H., & Donchin, E. (1983). A new method for off-line removal of ocular artifact. Electroencephalography & Clinical Neurophysiology, 55: 468484.Google Scholar
Greenen, R. & van de Vijver, F. J. R. (1993). A simple test of the law of initial values. Psychophysiology, 30: 525530.Google Scholar
Greenhouse, J. B. & Junker, B.W. (1992). Exploratory statistical methods, with applications to psychiatric research. Psychoneuroendocrinology, 17: 423441.Google Scholar
Greenhouse, S. W. & Geisser, S. (1959). On methods in the analysis of profile data. Psychometrika, 24: 95112.CrossRefGoogle Scholar
Greenwald, A. G., Gonzalez, R., Harris, R. H., & Guthrie, D. (1996). Effect sizes and p-values: what should be reported and what should be replicated? Psychophysiology, 33: 175183.Google Scholar
Gueorguieva, R. & Krystal, J. H. (2004). Move over anova: progress in analyzing repeated-measures data andits reflection in papers published in the archives of general psychiatry. Archives of General Psychiatry, 61: 310317.CrossRefGoogle Scholar
Guilford, J. P. (1954). Psychometric Methods. New York: McGraw-Hill.Google Scholar
Harris, R. J. (1991). Significance tests are not enough: the role of effect size estimation in theory corroboration. Theory and Psychology, 1: 375382.Google Scholar
Hayes, A. F. (2013). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. New York: Guilford Press.Google Scholar
Hayes, A. F. & Matthes, J. (2009). Computational procedures for probing interactions in OLS and logistic regression: SPSS and SAS implementations. Behavior Research Methods, 41: 924936.Google Scholar
Hays, W. L. (1994). Statistics. Orlando, FL: Rinehart and Winston.Google Scholar
Hedeker, D. & Gibbons, R. D. (2006). Longitudinal Data Analysis. Hoboken, NJ: John Wiley.Google Scholar
Hintze, J. (2004). NCSS PASS. Retrieved from www.ncss.com/pass.htmlGoogle Scholar
Hoffman, L. (2015). Longitudinal Analysis: Modeling Within-Person Fluctuation and Change. New York: Routledge.Google Scholar
Howell, G. T. & Lacroix, G. L. (2012). Decomposing interactions using GLM in combination with the COMPARE, LMATRIX and MMATRIX subcommands in SPSS. Tutorials in Quantitative Methods for Psychology, 8: 122.Google Scholar
Huster, R. J., Debener, S., Eichele, T., & Herrmann, C. S. (2012). Methods for simultaneous EEG-fMRI: an introductory review. Journal of Neuroscience, 32: 60536060.Google Scholar
Huynh, H. & Feldt, L. S. (1970). Conditions under which mean square ratios in repeated measurement designs have exact F distributions. Journal of the American Statistical Association, 65: 15821589.CrossRefGoogle Scholar
Huynh, H. & Feldt, L. S. (1976). Estimation of the Box correction for degrees of freedom from sample data in randomized block and split-plot designs. Journal of Educational Statistics, 1: 6982.Google Scholar
Jaccard, J., Becker, M. A., & Wood, G. (1984). Pairwise multiple comparison procedures: a review. Psychological Bulletin, 96: 589596.Google Scholar
Jackson, A. F. & Bolger, D. J. (2014). The neurophysiological basis of EEG and EEG measurement: a review for the rest of us. Psychophysiology, 51: 10611071.CrossRefGoogle Scholar
Jacob, R. G., Thayer, J. F., Manuck, S. B., Muldoon, M. F., Tamres, L. K., Williams, D. M., … & Gatsonis, C. (1999). Ambulatory blood pressure responses and the circumplex model of mood: a 4-day study. Psychosomatic Medicine, 61: 319333.Google Scholar
Jacoby, W. G. (1997). Statistical Graphics for Univariate and Bivariate Data: Quantitative Applications in Social Sciences. Thousand Oaks, CA: Sage.Google Scholar
James, G. S. (1951). The comparison of several groups of observations when the ratios of the population variances are unknown. Biometrika, 38: 324329.Google Scholar
James, G. S. (1954). Tests of linear hypotheses in univariate and multivariate analysis when the ratios of the population variances are unknown. Biometrika, 41: 1943.Google Scholar
Janicki-Deverts, D. & Kamarck, T. W. (2008). Ambulatory blood pressure monitoring. In Luecken, L. J. & Gallo, L. C. (eds.), Handbook of Physiological Research Methods in Health Psychology (pp. 159182). Thousand Oaks, CA: Sage.Google Scholar
Jennings, J. R. (1986). Bodily changes during attending. In Coles, M. G. H., Donchin, E., & Porges, S. W. (eds.), Psychophysiology: Systems, Processes and Applications (pp. 268289). New York: Guilford Press.Google Scholar
Jennings, J. R., Berg, W. K., Hutcheson, J. S., Obrist, P., Porges, S. W., & Turpin, G. (1981). Publication guidelines for heart rate studies in men. Psychophysiology, 18: 226231.Google Scholar
Jennings, J. R., Kamarck, T., Stewart, C., Eddy, M., & Johnson, P. (1992). Alternate cardiovascular baseline assessment techniques: vanilla or resting baseline? Psychophysiology, 29: 742750.Google Scholar
Jennings, J. R. & McKnight, J. D. (1994). Inferring vagal tone from heart rate variability. Psychosomatic Medicine, 56: 194196.Google Scholar
Jennings, J. R. & Wood, C. C. (1976). The epsilon-adjusted procedure for repeated measures analyses of variance. Psychophysiology, 13: 277278.Google Scholar
Johnson, P. O. & Neyman, J. (1936). Tests of certain linear hypotheses and their application to some educational problems. Statistical Research Memoirs, 1: 5793.Google Scholar
Judd, C. M., McClelland, G. H., & Smith, E. R. (1996). Testing treatment by covariate interactions when treatment varies within participants. Psychological Methods, 1: 366378.Google Scholar
Kamarck, T. W., Jennings, J. R., Debski, T. W., Glickman-Weiss, E., Eddy, M. J., & Manuck, S. B. (1992). Reliable measures of behaviorally-evoked cardiovascular reactivity from a PC-based test battery: results from student and community samples. Psychophysiology, 29: 1728.Google Scholar
Kamarck, T. W., Schwartz, J. E., Janicki, D. L., Shiffman, S., & Raynor, D. A. (2003). Correspondence between laboratory and ambulatory measures of cardiovascular reactivity: a multilevel modeling approach. Psychophysiology, 40: 675683.Google Scholar
Kamarck, T. W., Schwartz, J. E., Shiffman, S., Muldoon, M. F., Sutton-Tyrrell, K., & Janicki, D. L. (2005). Psychosocial stress and cardiovascular risk: what is the role of daily experience? Journal of Personality, 73: 17491774.Google Scholar
Kamarck, T. W., Shiffman, S., Sutton-Tyrrell, K., Muldoon, M. F., & Tepper, P. (2012). Daily psychological demands are associated with 6-year progression of carotid artery atherosclerosis: the Pittsburgh Healthy Heart Project. Psychosomatic Medicine, 74: 432439.Google Scholar
Kamarck, T. W., Shiffman, S., & Wethington, E. (2011). Measuring psychosocial stress using ecological momentary assessment methods. In Contrada, R. J. & Baum, A. (eds.), The Handbook of Stress Science: Biology, Psychology, and Health (pp. 597617). New York: Springer.Google Scholar
Kamen, R. (1987). Introduction to Signals and Systems. New York: Macmillan.Google Scholar
Keil, A., Debener, S., Gratton, G., Junghofer, M., Kappenman, E. S., Luck, S. J., … & Yee, C. M. (2014). Committee report. Publication guidelines and recommendations for studies using electroencephalography and magnetoencephalography. Psychophysiology, 51: 121.Google Scholar
Kenny, D. A. (1979). Correlation and Causality. New York: John Wiley.Google Scholar
Keppel, G. (1991). Design and Analysis: A Researcher’s Handbook, 3rd edn. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Keppel, G. & Wickens, T. D. (2004). Design and Analysis: A Researcher’s Handbook, 4th edn. Upper Saddle River, NJ: Pearson/Prentice-Hall.Google Scholar
Kerlinger, F. N. L. & Lee, H. B. (2000). Foundations of Behavioral Research, 4th edn. New York: Harcourt College.Google Scholar
Keselman, H. J., Carriere, K. C., & Lix, L. M. (1993). Testing repeated measures hypotheses when covariance matrices are heterogeneous. Journal of Educational Statistics, 18: 305319.Google Scholar
Keselman, H. J., Keselman, J. C., & Lix, L. M. (1995). The analysis of repeated measurements: univariate tests, multivariate tests, or both? British Journal of Mathematical and Statistical Psychology, 48: 319338.Google Scholar
Keselman, H. J., Kowalchuk, R. K., Algina, J., Lix, L. M., & Wilcox, R. R. (2000). Testing treatment effects in repeated measures designs: Trimmed means and bootstrapping. British Journal of Mathematical and Statistical Psychology, 53: 175191.Google Scholar
Keselman, H. J., Rogan, J. C., Mendoza, J. L., & Breen, L. J. (1980). Testing the validity conditions of repeated measures F tests. Psychological Bulletin, 87: 479481.CrossRefGoogle Scholar
Keselman, J. C. & Keselman, H. J. (1990). Analyzing unbalanced repeated measures designs. British Journal of Mathematical and Statistical Psychology, 43: 265282.Google Scholar
Khatree, R. & Naik, D. N. (1995). Applied Multivariate Statistics with SAS Software. Cary, NC: SAS Institute.Google Scholar
Kirk, R. E. (1995). Experimental Design: Procedures for the Behavioral Sciences, 3rd edn. Monterey, CA: Brooks/Cole.Google Scholar
Kline, R. B. (2004). Beyond Significance Testing: Reforming Data Analysis Methods in Behavioral Research. Washington, DC: American Psychological Association.Google Scholar
Krantz, D. S. & Manuck, S. B. (1984). Acute psychophysiologic reactivity and risk of cardiovascular disease: a review and methodologic critique. Psychological Bulletin, 96: 435464.Google Scholar
Kristjansson, S. D., Kircher, J. C., & Webb, A. K. (2007). Multilevel models for repeated measures research designs in psychophysiology: an introduction to growth curve modeling. Psychophysiology, 44: 728736.Google Scholar
Laird, N. M. & Ware, J. H. (1982). Random effects models for longitudinal data. Biometrics, 38: 963974.Google Scholar
Lavori, P. (1990). ANOVA, MANOVA, my black hen: comments on repeated measures. Archives of General Psychiatry, 47: 775778.CrossRefGoogle ScholarPubMed
Law, L. N., Levey, A. B., & Martin, I. (1980). Response detection and measurement. In Martin, I. & Venables, P. H. (eds.), Techniques in Psychophysiology (pp. 629663). Chichester: John Wiley.Google Scholar
Levey, A. B. (1980). Measurement units in psychophysiology. In Martin, I. & Venables, P. H. (eds.), Techniques in Psychophysiology (pp. 597628). Chichester: John Wiley.Google Scholar
Levey, M. N. (1977). Parasympathetic control of the heart. In Randall, W. C. (ed.), Neural Regulation of the Heart (pp. 95129). Oxford University Press.Google Scholar
Levey, M. N. & Martin, P. (1984). Parasympathetic control of the heart. In Randall, W. C. (ed.), Nervous Control of Cardiovascular Function (pp. 6894). Oxford University Press.Google Scholar
Lippold, O. C. J. (1967). Electromyography. In Venables, P. H. & Martin, I. (eds.), A Manual of Psychophysiological Methods (pp. 246297). New York: John Wiley.Google Scholar
Little, T. D. (2013). Longitudinal Structural Equation Modeling. New York: Guilford Press.Google Scholar
Lix, L. M. &Keselman, H. H. (1995). Approximate degrees of freedom tests: a unified perspective on testing for mean equality. Psychological Bulletin, 117: 547560.Google Scholar
Llabre, M. M., Spitzer, S. B., Saab, P. G., Ironson, G. H., & Schneiderman, N. (1991). The replicability and specificity of delta versus residualized change as measures of cardiovascular reactivity to behavioral challenges. Psychophysiology, 28: 701711.Google Scholar
Loewenfeld, I. E. (1993). The Pupil: Anatomy, Physiology, and Clinical Applications. Ames, IA: Iowa State University Press.Google Scholar
Loftus, G. R. M. (1994). Why psychology will never be a real science until we change the way that we analyze data. Paper presented at the 102nd Annual Convention of the American Psychological Association, Los Angeles, California.Google Scholar
Loftus, G. R. M. & Masson, M. E. J. (1994). Using confidence intervals in within-subject designs. Psychonomic Bulletin and Review, 1: 476490.Google Scholar
Lykken, D. T. (1972). Range correction applied to heart rate and GSR data. Psychophysiology, 9: 373379.CrossRefGoogle ScholarPubMed
Maxwell, S. E. & Delaney, H. D. (1993). Bivariate median splits and spurious statistical significance. Psychological Bulletin, 113: 181200.Google Scholar
Maxwell, S. E. & Delaney, H. D. (2004). Designing Experiments and Analyzing Data: A Model Comparison Approach, 2nd edn. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Meyers, D. L. (1991). Misinterpretation of interaction effects: a reply to Rosnow and Rosenthal. Psychological Bulletin, 110: 571573.Google Scholar
Michell, J. (1986). Measurement scales and statistics: a clash of paradigms. Psychological Bulletin, 100: 398407.Google Scholar
Miller, G. A. & Chapman, J. P. (2001). Misunderstanding analysis of covariance. Journal of Abnormal Psychology, 110: 4048.Google Scholar
Moriarty, J., Hogan, M., & Stewart, I. (2011). Starting slow: the effects of response-switching frequency on patterns of cardiovascular reactivity. Psychology, Health & Medicine, 16: 1218.Google Scholar
Mortensen, J. A., Lehn, H., Evensmoen, H. R., & Haberg, A. K. (2015). Evidence for an antagonistic interaction between reward and punishment sensitivity on striatal activity: a verification of the joint subsystems hypothesis. Personality and Individual Differences, 74: 214219.Google Scholar
Muller, K. E. & Barton, C. N. (1989). Approximate power for repeated measures ANOVA lacking sphericity. Journal of the American Statistical Association, 84: 549555.Google Scholar
Muller, K. E. & Barton, C. N. (1991). Correction to “Approximate power for repeated measures ANOVA lacking sphericity.” Journal of the American Statistical Association, 86: 255256.Google Scholar
Muller, K. E., LaVange, L. M., Ramey, S. L., & Ramey, C. T. (1992). Power calculations for general linear multivariate models including repeated measures applications. Journal of the American Statistical Association, 87: 12091226.Google Scholar
Myers, N. D., Brincks, A. M., Ames, A. J., Prado, G. J., Penedo, F. J., & Benedict, C. (2012). Multilevel modeling in psychosomatic medicine research. Psychosomatic Medicine, 74: 925936.Google Scholar
Myrtek, M. & Foerster, F. (1986). The law of initial value: a rare exception. Biological Psychology, 22: 227237.Google Scholar
Nicol, A. A. M. & Pexman, P. M. (2010). Displaying Your Findings: A Practical Guide for Creating Figures, Posters, and Presentations, 6th edn. Washington, DC: American Psychological Association.Google Scholar
Nussbaum, E. M. (2015). Categorical and Nonparametric Data Analysis: Choosing the Best Statistical Technique. New York: Routledge.Google Scholar
O’Brien, R. G. & Muller, K. E. (1993). Unified power analysis for t-tests through multivariate hypotheses. In Edwards, L. K. (ed.), Applied Analysis of Variance in the Behavioral Sciences (pp. 297344). New York: Marcel Dekker.Google Scholar
Osterhout, L., Bersick, M., & McKinnon, R. (1997). Brain potentials elicited by words: word length and frequency predict the latency of an early negativity. Biological Psychology, 46: 143168.Google Scholar
Overall, J. E. & Tonidandel, S. (2010). The case for use of simple difference scores to test the significance of differences in mean rates of change in controlled repeated measurements designs. Multivariate Behavioral Research, 45: 806827.Google Scholar
Petrinovich, L. & Widaman, K. F. (1984). An evaluation of statistical strategies to analyze repeated-measures data. In Peeke, H. V. S. & Petrinovich, L. (eds.), Habituation, Sensitivation, and Behavior (pp. 105201). Orlando, FL: Academic Press.Google Scholar
Picton, T. W., Bentin, S., Berg, P., Donchin, E., Hillyard, S. A., Johnson, R. Jr., … & Taylor, M. J. (2000). Guidelines for using human event-related potentials to study cognition: recording standards and publication criteria. Psychophysiology, 37: 127152.Google Scholar
Pivik, R. T., Broughton, R. J., Coppola, R., Davidson, R. J., Fox, N., & Nuwer, M. R. (1993). Guidelines for the recording and quantitative analysis of electroencephalographic activity in research contexts. Psychophysiology, 30: 547548.Google Scholar
Porges, S. W. (1995). Orienting in a defensive world: mammalian modifications of our evolutionary heritage – a polyvagal theory. Psychophysiology, 32: 301318.Google Scholar
Porges, S. W. & Bohrer, R. E. (1990). The analysis of periodic processes in psychophysiological research. In Cacioppo, J. T. & Tassinary, L. G. (eds.), Principles of Psychophysiology (pp. 708753). Cambridge University Press.Google Scholar
Poulton, E. C. (1973). Unwanted range effects from using within-subject experimental designs. Psychological Bulletin, 80: 113121.Google Scholar
Poulton, E. C. (1982). Influential companions: effects of one strategy on another in the within-subjects designs of cognitive psychology. Psychological Bulletin, 91: 673690.CrossRefGoogle Scholar
Poulton, E. C. & Edwards, R. S. (1979). Asymmetric transfer in within-participants experiments on stress interaction. Ergonomics, 22: 945961.Google Scholar
Poulton, E. C. & Freeman, P. R. (1966). Unwanted asymmetrical transfer effects with balanced experimental designs. Psychological Bulletin, 66: 18.Google Scholar
Preacher, K. J. & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36: 717731.Google Scholar
Quigley, K. S. & Berntson, G. G. (1990). Autonomic interactions and chronotropic control of the heart: heart period versus heart rate. Psychophysiology, 33: 605611.Google Scholar
Ritz, T., Dahme, B., Dubois, A. B., Folgering, H., Fritz, G. K., Harver, A., … & Van de Woestijne, K. P. (2002). Guidelines for mechanical lung function measurements in psychophysiology. Psychophysiology, 39: 546567.Google Scholar
Rogosa, D., Brandt, D., & Zimowski, M. (1982). A growth curve approach to the measurement of change. Psychological Bulletin, 92: 726748.Google Scholar
Rosenthal, R. & Rosnow, R. L. (1985). Contrast Analysis: Focused Comparisons in the Analysis of Variance. New York: Holt, Rinehart, & Winston.Google Scholar
Rosenthal, R. & Rosnow, R. L. (1991). Essentials of Behavioral Research: Explanation and Prediction, 2nd edn. New York: McGraw-Hill.Google Scholar
Rosenthal, R., Rosnow, R. L., & Rubin, D. B. (2000). Contrasts and Effect Sizes in Behavioral Research: A Correlational Approach. Cambridge University Press.Google Scholar
Rosnow, R. L. & Rosenthal, R. (1989a). Definition and interpretation of interaction effects. Psychological Bulletin, 105: 143146.Google Scholar
Rosnow, R. L. & Rosenthal, R. (1989b). Statistical procedures and the justification of knowledge in psychological science. American Psychologist, 44: 12761284.Google Scholar
Rosnow, R. L. & Rosenthal, R. (1991). If you’re looking at the cell means, you’re not looking at only the interaction (unless all main effects are zero). Psychological Bulletin, 110: 574576.Google Scholar
Rosnow, R. L. & Rosenthal, R. (1995). Some things you learn aren’t so: Cohen’s paradox, Asch’s paradigm, and the interpretation of interaction. Psychological Science, 6: 39.Google Scholar
Rozeboom, W. W. (1960). The fallacy of the null hypothesis significance test. Psychological Bulletin, 57: 416428.Google Scholar
Russell, D. W. (1990). The analysis of psychophysiological data: multivariate approaches. In Cacioppo, J. T. & Tassinary, L. G. (eds.), Principles of Psychophysiology (pp. 775801). Cambridge University Press.Google Scholar
Schroeder, L. D., Sjoquist, D. L., & Stephan, P. E. (1986). Understanding Regression Analysis: An Introductory Guide. Newbury Park, CA: Sage.Google Scholar
Selig, J. P. & Preacher, K. J. (2009). Mediation models for longitudinal data in developmental research. Research in Human Development, 6: 144164.Google Scholar
Shapiro, D., Lane, J. D., Light, K. C., Myrtek, M., Suwada, Y., & Steptoe, A. (1996). Blood pressure publication guidelines. Psychophysiology, 33: 112.Google Scholar
Sherwood, A., Allen, M. T., Fahrenberg, J., Kelsey, R. M., Lovallo, W. R., & van Doornen, L. J. P. (1990). Methodological guidelines for impedance cardiography. Psychophysiology, 27: 123.Google Scholar
Sidani, S. & Lynn, M. R. (1993). Examining amount and pattern of change: comparing repeated measures ANOVA and individual regression analysis. Nursing Research, 42: 283286.Google Scholar
Siegal, S. (1956). Nonparametric Statistics. New York: McGraw-Hill.Google Scholar
Stearns, S. D. & David, R. A. (1993). Signal Processing Algorithms in Fortran and C. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Stemmler, G. & Fahrenberg, J. (1989). Psychophysiological assessment: conceptual, psychometric, and statistical issues. In Turpin, G. (ed.), Handbook of Clinical Psychophysiology (pp. 71104). Chichester: John Wiley.Google Scholar
Stern, R. M., Ray, W. J., & Quigley, K. S. (2001). Psychophysiological Recording, 2nd edn. Oxford University Press.Google Scholar
Stevens, S. S. (1951). Mathematics, measurement, and psychophysics. In Stevens, S. S. (ed.), Handbook of Experimental Psychology (pp. 149). New York: John Wiley.Google Scholar
Stiratelli, R., Laird, N. M., & Ware, J. H. (1984). Random-effects models for serial observations with binary response. Biometrics, 40: 961971.Google Scholar
Tabachnick, B. G. & Fidell, L. S. (2014). Cleaning up your act. In Tabachick, B. G. & Fidell, L. S., Using Multivariate Statistics, 6th edn. (pp. 93152). Harlow: Pearson.Google Scholar
Thede, L. (1996). Analog and Digital Filter Design Using C. Upper Saddle River, NJ: Prentice-Hall.Google Scholar
Tufte, E. R. (1983). The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press.Google Scholar
Tufte, E. R. (1990). Envisioning Information. Cheshire, CT: Graphics Press.Google Scholar
Tufte, E. R. (1997). Visual Explanations: Images and Quantities, Evidence and Narrative. Cheshire, CT: Graphics Press.Google Scholar
Tukey, J. W. (1977). Exploratory Data Analysis. Reading, MA: Addison-Wesley.Google Scholar
van Boxtel, G. J. M. (1998). Computational and statistical methods for analyzing event-related potential data. Behavior Research Methods, Instruments, & Computers, 30: 87102.Google Scholar
van Boxtel, G. J. M., van den Boogaart, B., & Brunia, C. H. M. (1993). The contingent negative variation in a choice reaction time task. Journal of Psychophysiology, 7: 1123.Google Scholar
van Ravenswaaij-Arts, C. M. A., Kolle’e, L. A. A., Hopman, J. C. W., Stoelinga, G. B. A., & van Geijn, H. P. (1993). Heart rate variability. Annals of Internal Medicine, 118: 463447.Google Scholar
Wainer, H. & Thissen, D. (1981). Graphical data analysis. Annual Review of Psychology, 32: 191241.Google Scholar
Wainer, H. & Thissen, D. (1993). Graphical data analysis. In Keren, G. & Lewis, C. (eds.), A Handbook for Data Analysis in the Behavioral Sciences: Statistical Issues (pp. 391457). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Ware, J. H. (1985). Linear models for the analysis of longitudinal studies. The American Statistician, 39: 95101.Google Scholar
Wasserman, S. B. & Bockenholt, U. (1989). Bootstrapping: applications to psychophysiology. Psychophysiology, 26: 208221.Google Scholar
Weiss, S. (2014). The fault in our stats. Observer, 27: 2930.Google Scholar
Welch, B. L. (1947). The generalization of “Student’s” problem when several different population variances are unequal. Biometrika, 29: 350362.Google Scholar
Welch, B. L. (1951). On the comparison of several mean values: an alternative approach. Biometrika, 38: 330336.Google Scholar
White, T. L. & McBurney, D. H. (2013). Research Methods, 9th edn. Belmont, CA: Wadsworth.Google Scholar
Wilder, J. (1958). Modern psychophysiology and the law of initial value. American Journal of Psychotherapy, 12: 199221.Google Scholar
Wilson, R. S. (1967). Analysis of autonomic reaction patterns. Psychophysiology, 4: 125142.Google Scholar
Woodman, G. F. (2010). A brief introduction to the use of event-related potentials in studies of perception and attention. Attention, Perception, & Psychophysics, 72: 20312046.Google Scholar
Xhyheri, B., Manfrini, O., Mazzolini, M., Pizzi, C., & Bugiardini, R. (2012). Heart rate variability today. Progress in Cardiovascular Diseases, 55: 321331.Google Scholar
Zahn, T. P. & Kreusi, M. J. P. (1993). Autonomic activity in boys with disruptive behavior disorders. Psychophysiology, 30: 605614.Google Scholar
Zuckerman, M., Hodgins, H. S., Zuckerman, A., & Rosenthal, R. (1993). Contemporary issues in the analysis of data. Psychological Sciences, 4: 4953.Google Scholar

References

Algina, J. & Penfield, R. D. (2009). Classical test theory. In Millsap, R. E. & Maydeu-Olivares, A. (eds.), The Sage Handbook of Quantitative Methods in Psychology (pp. 93122). Thousand Oaks, CA: Sage.Google Scholar
Boucsein, W., Fowles, D. C., Grimnes, S., Ben-Shakhar, G., Roth, W. T., Dawson, M. E., & Filion, D. L. (2012). Publication recommendations for electrodermal measurements. Psychophysiology, 49: 10171034.Google Scholar
Brennan, R. L. (1992). Elements of Generalizability Theory, rev. edn. Iowa City, IA: American College Testing.Google Scholar
Brennan, R. L. (1995). The conventional wisdom about group mean scores. Journal of Educational Measurement, 32: 385396.CrossRefGoogle Scholar
Brennan, R. L. (2001). Generalizability Theory. New York: Springer.Google Scholar
Brennan, R. L. (ed.) (2006). Educational Measurement, 4th edn. Lanham, MD: Rowman & Littlefield.Google Scholar
Brennan, R. L., Gao, X., & Colton, D. A. (1995). Generalizability analyses of work keys listening and writing tests. Educational and Psychological Measurement, 55: 157176.Google Scholar
Brennan, R. L. & Kane, M. T. (1977). An index of dependability for mastery tests. Journal of Educational Measurement, 14: 277289.Google Scholar
Burgess, A. P. & Gruzelier, J. H. (1996). The reliability of event-related desynchronisation: a generalisability study analysis. International Journal of Psychophysiology, 23: 163169.Google Scholar
Burt, K. B. & Obradović, J. (2013). The construct of psychophysiological reactivity: statistical and psychometric issues. Developmental Review, 33: 2957.Google Scholar
Bush, N. R., Alkon, A., Obradović, J., Stamperdahl, J., & Boyce, W. T. (2011). Differentiating challenge reactivity from psychomotor activity in studies of children’s psychophysiology: considerations for theory and measurement. Journal of Experimental Child Psychology, 110: 6279.Google Scholar
Cacioppo, J. T. & Tassinary, L. G. (1990a). Inferring psychological significance from physiological signals. American Psychologist, 45: 1628.Google Scholar
Cacioppo, J. T. & Tassinary, L. G. (eds.) (1990b). Principles of Psychophysiology. Cambridge University Press.Google Scholar
Campbell, D. T. & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56: 81105.Google Scholar
Campbell, N. R. (1957). Foundations of Science: The Philosophy of Theory. New York: Dover.Google Scholar
Cardinet, J., Johnson, S., & Pini, G. (2009). Applying Generalizability Theory Using EduG. New York: Routledge.Google Scholar
Cardinet, J., Tourneur, Y., & Allal, L. (1976). The symmetry of generalizability theory: application to educational measurement. Journal of Educational Measurement, 13: 119135.Google Scholar
Cardinet, J., Tourneur, Y., & Allal, L. (1981). Extension of generalizability theory and its application in educational measurement. Journal of Educational Measurement, 18: 183204.Google Scholar
Clayson, P. E. & Larson, M. J. (2013). Psychometric properties of conflict monitoring and conflict adaptation indices: response time and conflict N2 event-related potentials. Psychophysiology, 50: 12091219.Google Scholar
Coan, J. A., Allen, J. J. B., & McKnight, P. E. (2006). A capability model of individual differences in frontal EEG asymmetry. Biological Psychology, 72: 198207.Google Scholar
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2002). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 3rd edn. New York: Routledge.Google Scholar
Cole, D. A., Howard, G. S., & Maxwell, S. E. (1981). Effects of mono- versus multiple-operationalization in construct validation efforts. Journal of Consulting and Clinical Psychology, 49: 395405.Google Scholar
Crocker, L. & Algina, J. (2006). Introduction to Classical and Modern Test Theory. Independence, KY: Cengage.Google Scholar
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16: 292334.Google Scholar
Cronbach, L. J., Gleser, G. C., Nanda, H., & Rajaratnam, N. (1972). The Dependability of Behavioral Measurements: Theory of Generalizability of Scores and Profiles. New York: John Wiley.Google Scholar
Cronbach, L. J. & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52: 281302.Google Scholar
de Ayala, R. J. (2008). The Theory and Practice of Item Response Theory. New York: Guilford Press.Google Scholar
Di Nocera, F., Ferlazzo, F., & Borghi, V. (2001). G theory and the reliability of psychophysiological measures: a tutorial. Psychophysiology, 38: 796806.Google Scholar
Embretson, S. & Reise, S. P. (2000). Item Response Theory for Psychologists. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Fahrenberg, J., Foerster, F., Schneider, H. J., Müller, W., & Myrtek, M. (1986). Predictability of individual differences in activation processes in a field setting based on laboratory measures. Psychophysiology, 23: 323333.Google Scholar
Feldt, L. S. & Brennan, R. L. (1989). Reliability. In Lin, R. L. (ed.), Educational Measurement, 3rd edn. (pp. 105146). New York: Macmillan.Google Scholar
Fiske, D. W. (1987). Construct invalidity comes from method effects. Educational and Psychological Measurement, 47: 285307.Google Scholar
Gao, X. & Harris, D. J. (2012). Generalizability theory. In Cooper, H., Camic, P. M., Long, D. L., Panter, A. T., Rindskopf, D., & Sher, K. J. (eds.), APA Handbook of Research Methods in Psychology, vol. 1: Foundations, Planning, Measures, and Psychometrics (pp. 661681). Washington, DC: American Psychological Association.Google Scholar
Garćia-Vera, M. & Sanz, J. (1999). How many self-measured blood pressure readings are needed to estimate hypertensive patients’ “true” blood pressure? Journal of Behavioral Medicine, 22: 93113.Google Scholar
Ghiselli, E. E., Campbell, J. P., & Zedeck, S. (1981). Measurement Theory for the Behavioral Sciences. San Francisco, CA: Freeman.Google Scholar
Guion, R. M. (1978). Scoring of content domain samples. Journal of Applied Psychology, 63: 449506.Google Scholar
Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of Item Response Theory. Newbury Park, CA: Sage.Google Scholar
Hammond, K. R., Hamm, R. M., & Grassia, J. (1986). Generalizing over conditions by combining the multitrait–multimethod matrix and the representative design of experiments. Psychological Bulletin, 100: 257269.Google Scholar
Hecimovich, M. D., Peiffer, J. J., & Harbaugh, A. G. (2014). Development and psychometric evaluation of a post exercise exhaustion scale utilizing the Rasch measurement model. Psychology of Sports and Exercise, 15: 569579.Google Scholar
Hoyt, W. T. (2000). Rater bias in psychological research: when is it a problem and what can we do about it? Psychological Methods, 5: 6486.Google Scholar
Kamarck, T. W., Debski, T. T., & Manuck, S. B. (2000). Enhancing the laboratory-to-life generalizability of cardiovascular reactivity using multiple occasions of measurement. Psychophysiology, 37: 533542.Google Scholar
Kane, M. T. & Brennan, R. L. (1977). The generalizability of class means. Review of Educational Research, 47: 267292.Google Scholar
Kelley, T. L. (1927). Interpretation of Educational Measurements. New York: Macmillan.Google Scholar
Kenny, D. A. (1995). The multitrait–multimethod matrix: design, analysis, and conceptual issues. In Shrout, P. E. & Fiske, S. T. (eds.), Personality, Research, Methods, and Theory: A Festschrift Honoring Donald W. Fiske (pp. 111124). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Llabre, M. M., Ironson, G. H., Spitzer, S. B., Gellman, M. D., Weidler, D. J. & Schneiderman, N. (1988). How many blood pressure measurements are enough? An application of generalizability theory to the study of blood pressure reliability. Psychophysiology, 25: 97106.Google Scholar
Llabre, M. M., Spitzer, S. B., Saab, P. G, Ironson, G. H., & Schneiderman, N. (1991). The reliability and specificity of delta versus residualized change as measures of cardiovascular reactivity to behavioral challenges. Psychophysiology, 28: 701711.Google Scholar
Marcoulides, G. A. (1994). Selecting weighting schemes in multivariate generalizability studies. Educational and Psychological Measurement, 54: 37.Google Scholar
Marcoulides, G. A. & Goldstein, Z. (1990). The optimization of generalizability studies with resource constraints. Educational and Psychological Measurement, 50: 761768.Google Scholar
Marsh, H. W. & Grayson, D. (1995). Latent variable models of multitrait–multimethod data. In Hoyle, R. H. (ed.), Structural Equation Modeling: Concepts, Issues, and Applications (pp. 117198). Thousand Oaks, CA: Sage.Google Scholar
Maxwell, S. E. & Delaney, H. D. (2003). Designing Experiments and Analyzing Data: A Model Comparison Perspective, 2nd edn. New York: Routledge.Google Scholar
Messick, S. (1981). Constructs and their vicissitudes in educational and psychological measurement. Psychological Bulletin, 89: 575588.Google Scholar
Messick, S. (1989). Validity. In Linn, R. L. (ed.), Educational Measurement, 3rd edn. (pp. 13103). New York: Macmillan.Google Scholar
Myers, J. E., Well, A. D., & Lorch, R. F. Jr. (2010). Research Design and Statistical Analysis, 3rd edn. New York: Routledge.Google Scholar
Nunnally, J. C. & Bernstein, I. H. (1994). Psychometric Theory, 3rd edn. New York: McGraw-Hill.Google Scholar
Nussbaum, A. (1984). Multivariate generalizability theory in educational measurement: an empirical study. Applied Psychological Measurement, 8: 219230.Google Scholar
Pennebaker, J. W. (1982). The Psychology of Physical Symptoms. New York: Springer-Verlag.Google Scholar
Pickering, T. G., Harshfield, G. A., Kleinert, H. D., Blank, S., & Laragh, J. H. (1982). Blood pressure during normal daily activities, sleep, and exercise. Journal of the American Medical Association, 247: 992996.Google Scholar
Raykov, T. & Marcoulides, G. A. (2010). Introduction to Psychometric Theory. New York: Routledge.Google Scholar
Sarter, M., Berntson, G. G., & Cacioppo, J. T. (1996). Brain imaging and cognitive neuroscience: toward strong inference in attributing function to structure. American Psychologist, 51: 1321.Google Scholar
Schmidt, F. L. & Hunter, J. E. (1996). Measurement error in psychological research: lessons from 26 research scenarios. Psychological Methods, 1: 199223.Google Scholar
Schwerdtfeger, A. R., Schienle, A., Leutgeb, V., & Rathner, E. M. (2014). Does cardiac reactivity in the laboratory predict ambulatory heart rate? Baseline counts. Psychophysiology, 51: 565572.Google Scholar
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2001). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston, MA: Houghton Mifflin.Google Scholar
Shavelson, R. J. & Webb, N. M. (1991). Generalizability Theory: A Primer. Newbury Park, CA: Sage.Google Scholar
Shavelson, R. J., Webb, N. M., & Rowley, G. L. (1989). Generalizability theory. American Psychologist, 44: 922932.Google Scholar
Stevens, J. P. (2009). Applied Multivariate Statistics for the Social Sciences, 5th edn. New York: RoutledgeGoogle Scholar
Strube, M. J. (1989). Assessing subjects’ construal of the laboratory situation. In Schneiderman, N., Weiss, S. M., & Kaufman, P. (eds.), Handbook of Research Methods in Cardiovascular Behavioral Medicine (pp. 527542). New York: Plenum Press.Google Scholar
Thomas, M. L., Brown, G. G., Thompson, W. K., Voyvodic, J., Greve, D. N., Turner, J. A., … & Potkin, S. G. (2013). An application of item response theory to fMRI data: prospects and pitfalls. Psychiatry Research: Neuroimaging, 212: 167174.Google Scholar
Thurston, R. C., Hernandez, J., Del Rio, J. M., & De La Torre, F. (2010). Support vector machines to improve physiologic hot flash measures: applications to the ambulatory setting. Psychophysiology, 48: 10151021.Google Scholar
Torrents-Rodas, D., Fullana, M. A., Bonillo, A., Andion, O., Molinuevo, B., Caseras, X., & Torrubia, R. (2014). Testing the temporal stability of individual differences in the acquisition and generalization of fear. Psychophysiology, 51: 697705.Google Scholar
Vanleeuwen, D. M. & Mandabach, K. H. (2002). A note on the reliability of ranked items. Sociological Methods & Research, 31: 87105.Google Scholar
Webb, N. M. & Shavelson, R. J. (1981). Multivariate generalizability of general educational development ratings. Journal of Educational Measurement, 18: 1322.Google Scholar
Westen, D. & Rosenthal, R. (2003). Quantifying construct validity: two simple measures. Journal of Personality and Social Psychology, 84: 608618.Google Scholar
Whitley, B. E. Jr. & Kite, M. E. (2012). Principles of Research in Behavioral Science, 3rd edn. New York: Routledge.Google Scholar
Winer, B. J., Brown, D. R., & Michels, K. M. (1991). Statistical Principles in Experimental Design, 3rd edn. New York: McGraw-Hill.Google Scholar
Wohlgemuth, W. K., Edinger, J. D., Fins, A. I., & Sullivan, R. J. Jr. (1999). How many nights are enough? The short-term stability of sleep parameters in elderly insomniacs and normal sleepers. Psychophysiology, 36: 233244.Google Scholar
Wothke, W. (1996). Models for multitrait-multimethod matrix analysis. In Marcoulides, G. A. & Schumacker, R. E. (eds.), Advanced Structural Equation Modeling: Issues and Techniques (pp. 756). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Youngstrom, E. A. & De Los Reyes, A. (2015). Commentary. Moving toward cost-effectiveness in using psychophysiological measures in clinical assessment: validity, decision making, and adding value. Journal of Clinical Child & Adolescent Psychology, 44: 352361.Google Scholar
Zillmann, D. (1978). Attribution and misattribution of excitatory reactions. In Harvey, J. H., Ickes, W., & Kidd, R. F. (eds.), New Directions in Attribution Research, vol. 2 (pp. 335368). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar

References

Barrett, G., Shibasaki, H., & Neshige, R. (1986). Cortical potentials preceding voluntary movement: evidence for three periods of preparation in man. Electroencephalography & Clinical Neurophysiology, 63: 327339.Google Scholar
Basar, E., Basar-Eroglu, C., Karakas, S., & Schurmann, M. (1999). Are cognitive processes manifested in event-related gamma, alpha, theta and delta oscillations in the EEG? Neuroscience Letters, 259: 165168.Google Scholar
Ben-Shakhar, G. (1985). Standardization within individuals: a simple method to neutralize individual differences in skin conductance. Psychophysiology, 22: 292299.Google Scholar
Bradley, M. M., Cuthbert, B. N., & Lang, P. J. (1991). Startle and emotion: lateral acoustic probes and the bilateral blink. Psychophysiology, 28: 285295.Google Scholar
Bullmore, E. & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10: 186198.Google Scholar
Cacioppo, J. T. & Dorfman, D. D. (1987). Waveform moment analysis in psychophysiological research. Psychological Bulletin, 102: 421438.Google Scholar
Catani, M. & de Schotten, M. T. (2008). A diffusion tensor imaging tractography atlas for virtual in vivo dissections. Cortex, 44: 11051132.Google Scholar
Chauveau, N., Franceries, X., Doyon, B., Rigaud, B., Morucci, J. P., & Celsis, P. (2004). Effects of skull thickness, anisotropy, and inhomogeneity on forward EEG/ERP computations using a spherical three-dimensional resistor mesh model. Human Brain Mapping, 21: 8697.Google Scholar
Cherry, S. R. & Phelps, M. E. (1996). Imaging brain function with positron emission tomography. In Toga, A. W. & Mazziotta, J. C. (eds.), Brain Mapping: The Methods (pp. 191222). San Diego, CA: Academic Press.Google Scholar
Chiarelli, A. M., Maclin, E. L., Low, K. A., Fabiani, M., & Gratton, G. (2015). A comparison of procedures for coregistering scalp-recording locations to anatomical MRI images. Journal of Biomedical Optics, 20: 016009.Google Scholar
Cohen, M. S. (1996). Rapid MRI and functional applications. In Toga, A. W. & Mazziotta, J. C. (eds.), Brain Mapping: The Methods (pp. 223258). San Diego, CA: Academic Press.Google Scholar
Cook, E. W. & Miller, G. A. (1992). Digital filtering: background and tutorial for psychophysiologists. Psychophysiology, 29: 350367.Google Scholar
De Martino, F., Valente, G., Staeren, N., Ashburner, J., Goebel, R., & Formisano, E. (2008). Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns. NeuroImage, 43: 4458.Google Scholar
Dehaene, S., Posner, M. I., & Tucker, D. M. (1994). Localization of a neural system for error detection and compensation. Psychological Science, 5: 303305.Google Scholar
Demiralp, T., Yordanova, J., Kolev, V., Ademoglu, A., Devrim, M., & Samr, V. J. (1999). Time-frequency analysis of single-sweep event-related potentials by means of fast wavelet transform. Brain and Language, 66: 129145.Google Scholar
Donchin, E. (1969). Discriminant analysis in average evoked response studies: the study of single trial data. Electroencephalography & Clinical Neurophysiology, 27: 311314.Google Scholar
Donchin, E. & Heffley, E. (1978). Multivariate analysis of event-related potential data: a tutorial review. In Otto, D. (ed.), Multidisciplinary Perspectives in Event-Related Brain Potential Research (EPA-600/9-77-043) (pp. 555572). Washington, DC: US Government Printing Office.Google Scholar
Donchin, E. & Herning, R. I. (1975). A simulation study of the efficacy of stepwise discriminant analysis in the detection and comparison of event related potentials. Electroencephalography & Clinical Neurophysiology, 38: 5168.Google Scholar
Dorfman, D. D. & Cacioppo, J. T. (1990). Waveform moment analysis: topographical analysis of nonrhythmic waveforms. In Tassinary, L. G. & Cacioppo, J. T. (eds.), Principles of Psychophysiology (pp. 661707). Cambridge University Press.Google Scholar
Elui, R. (1969). Gaussian behavior of the EEG: changes during performance of mental tasks. Science, 164: 328331.Google Scholar
Estes, W. K. (1956). The problem of inference from curves based on group data. Psychological Bulletin, 53: 133140.Google Scholar
Evans, A. C., Collins, D. L., Mills, S. R., Brown, E. D., Kelly, R. L., & Peters, T. M. (1993). 3D statistical neuroanatomical models from 305 MRI volumes. In Proceedings of the IEEE Nuclear Science Symposium and Medical Imaging Conference (pp. 18131817). Piscataway, NJ: IEEE.Google Scholar
Evans, A. C., Marrett, S., Neelin, P., Collins, L., Worsley, K., Dai, W., … & Bub, D. (1992). Anatomical mapping of functional activation in stereotactic coordinate space. NeuroImage, 1: 4353.Google Scholar
Fabiani, M., Gordon, B. A., Maclin, E. L., Pearson, M., Brumback, C. R., Low, K. A., … & Gratton, G. (2014). Neurovascular coupling in normal aging: a combined optical, ERP and fMRI study. NeuroImage, 1: 592607.Google Scholar
Fabiani, M., Gratton, G., Corballis, P., Cheng, J., & Friedman, D. (1998). Bootstrap assessment of the reliability of maxima in surface maps of brain activity of individual subjects derived with electrophysiological and optical methods. Behavior Research Methods, Instruments, & Computers, 30: 7886.Google Scholar
Fabiani, M., Gratton, G., Karis, D., & Donchin, E. (1987). Definition, identification, and reliability of measurement of the P300 component of the event-related brain potential. In Ackles, P. K., Jennings, J. R., & Coles, M. G. (eds.), Advances in Psychophysiology, vol. 2 (pp. 178). Greenwich, CT: JAI Press.Google Scholar
Farwell, L. A., Martinerie, J. M., Bashore, T. R., Rapp, P. E., & Goddard, P. H. (1993). Optimal digital filters for long-latency components of the event-related brain potential. Psychophysiology, 30: 306315.Google Scholar
Fischl, B. (2012). FreeSurfer. NeuroImage, 62: 774781.Google Scholar
Fortgens, C. & de Bruin, M. P. (1983). Removal of eye movement and ECG artifacts from the non-cephalic reference EEG. Electroencephalography & Clinical Neurophysiology, 56: 9096.Google Scholar
Fox, P. T. & Raichle, M. E. (1984). Stimulus rate dependence of regional cerebral blood flow in human striate cortex, demonstrated by positron emission tomography. Journal of Neurophysiology, 51: 11091120.Google Scholar
Friston, K. J. (1996). Statistical parametric mapping and other analyses of functional imaging data. In Toga, A. W. & Mazziotta, J. C. (eds.), Brain Mapping: The Methods (pp. 363388). San Diego, CA: Academic Press.Google Scholar
Friston, K. J. (2011). Functional and effective connectivity: a review. Brain Connectivity, 1: 1336.Google Scholar
Gordon, B. A., Tse, C.-H., Gratton, G., & Fabiani, M. (2014). Spread of activation and spread of inhibition: does age matter? Frontiers in Aging Neuroscience, 6: 288.Google Scholar
Gratton, G. (1997). Attention and probability effects in the human occipital cortex: an optical imaging study. NeuroReport, 8: 17491753.Google Scholar
Gratton, G., Coles, M. G. H., & Donchin, E. (1983). A new method for offline removal of ocular artifact. Electroencephalography & Clinical Neurophysiology, 55: 468484.Google Scholar
Gratton, G., Coles, M. G., & Donchin, E. (1989a). A procedure for using multi-electrode information in the analysis of components of the event-related potential: vector filter. Psychophysiology, 26: 222232.Google Scholar
Gratton, G., Kramer, A. F., Coles, M. G., & Donchin, E. (1989b). Simulation studies of latency measures of components of the event-related brain potential. Psychophysiology, 26: 233248.Google Scholar
Gratton, C., Sreenivasan, K. K., Silver, M. A., & D’Esposito, M. (2013). Attention selectively modifies the representation of individual faces in the human brain. Journal of Neuroscience, 33: 69796989.Google Scholar
Hackley, S. A. & Johnson, L. N. (1996). Distinct early and late subcomponents of the photic blink reflex: response characteristics in patients with retrogeniculate lesions. Psychophysiology, 33: 239251.Google Scholar
Herrmann, C. S., Rach, S., Vosskuhl, J., & Strüber, D. (2014). Time-frequency analysis of event-related potentials: a brief tutorial. Brain Topography, 27: 438450.Google Scholar
Himberg, J., Hyvarinen, A., & Esposito, F. (2004). Validating the independent components of neuroimaging time series via clustering and visualization. NeuroImage, 22: 12141222.Google Scholar
Hubel, D. H. & Wiesel, T. N. (1968). Receptive fields and functional architecture of monkey striate cortex. Journal of Physiology, 195: 215243.Google Scholar
Huberty, C. J. & Morris, J. D. (1989). Multivariate analysis versus multiple univariate analyses. Psychological Bulletin, 105: 302308.Google Scholar
Jennings, J. R., Kamarck, T., Stewart, C., Eddy, M., & Johnson, P. (1992). Alternate cardiovascular baseline assessment techniques: vanilla or resting baseline. Psychophysiology, 29: 742750.Google Scholar
Jennings, J. R., van der Molen, M. W., Somsen, R. J., & Ridderinkhof, K. R. (1991). Graphical and statistical techniques for cardiac cycle time (phase) dependent changes in interbeat interval. Psychophysiology, 28: 596606.Google Scholar
Jennings, J. R. & Wood, C. C. (1976). Letter. The epsilon-adjustment procedure for repeated-measures analyses of variance. Psychophysiology, 13: 277278.Google Scholar
Jung, T.-P., Makeig, S., Westerfield, M., Townsend, J., Courchesne, E., & Sejnowski, T. J. (2000). Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects. Clinical Neurophysiology, 111: 17451758.Google Scholar
Karis, D., Fabiani, M., & Donchin, E. (1984). “P300” and memory: individual differences in the von Restorff effect. Cognitive Psychology, 16: 177216.Google Scholar
Karniski, W., Blair, R. C., & Snider, A. D. (1994). An exact statistical method for comparing topographic maps, with any number of subjects and electrodes. Brain Topography, 6: 203210.Google Scholar
Kennedy, J. J. (1983). Analyzing Qualitative Data: Introductory Log-Linear Analysis for Behavioral Research. New York: Praeger.Google Scholar
Lacey, J. I., Kagan, J., Lacey, B. C., & Moss, H. A. (1963). The visceral level: situational determinants and behavioral correlates of autonomic response patterns. In Knapp, P. H. (ed.), Expression of the Emotions in Man (pp. 161196). New York: International Universities Press.Google Scholar
Lachaux, J.-P., Lutz, A., Rudrauf, D., Cosmelli, D., Le Van Quyen, M., Martinerie, J., & Varela, F. J. (2002). Estimating the time-course of coherence between single-trial brain signal: an introduction to wavelet coherence. Clinical Neurophysiology, 32: 157174.Google Scholar
Lachaux, J.-P., Rodriguez, E., Martinerie, J., & Varela, F. J. (1999). Measuring phase synchrony in brain signals. Human Brain Mapping, 8: 194208.Google Scholar
Lamothe, R. & Stroink, G. (1991). Orthogonal expansions: their applicability to signal extraction in electrophysiological mapping data. Medical & Biological Engineering & Computing, 29: 522528.Google Scholar
Le Bihan, D., Mangin, J.-F., Poupon, C., Clark, C. A., Pappata, S., Molko, N., & Chabriat, H. (2001). Diffusion tensor imaging: concepts and applications. Journal of Magnetic Resonance Imaging, 13: 534546.Google Scholar
Maier, J., Dagnelie, G., Spekreijse, H., & van Dijk, B. W. (1987). Principal components analysis for source localization of VEPs in man. Vision Research, 27: 165177.Google Scholar
Makeig, S., Jung, T. P., Bell, A. J., Ghahremani, D., & Sejnowski, T. (1997). Blind separation of auditory event-related brain responses into independent components. Proceedings of the National Academy of Sciences of the USA, 94: 1097910984.Google Scholar
Makeig, S., Westerfield, M., Jung, T.-P., Enghoff, S., Townsend, J., Courchesne, E., & Sejnowski, T. J. (2002). Dynamic brain sources of visual evoked responses. Science, 295: 690694.Google Scholar
Mathewson, K., Beck, D., Ro, T., Maclin, E. L., Low, K. A., Fabiani, M., & Gratton, G. (2014). Dynamics of alpha control: fronto-parietal modulators of preparatory alpha oscillations revealed with combined EEG and event-related optical signals (EROS). Journal of Cognitive Neuroscience, 26: 24002415.Google Scholar
Mathewson, K., Gratton, G., Fabiani, M., Beck, D., & Ro, A. (2009). To see or not to see: pre-stimulus alpha phase predicts visual awareness. Journal of Neuroscience, 29: 27252732.Google Scholar
Mattout, J., Phillip, C., Penny, W. D., Rugg, M. D., & Friston, K. J. (2006). MEG source localization under multiple constraints: an extended Bayesian framework. NeuroImage, 30: 753767.Google Scholar
McCallum, W. C. & Curry, S. H. (1984). A comparison of early event-related potentials in two target detection tasks. Annals of the New York Academy of Sciences, 425: 242249.Google Scholar
McCarthy, G. & Wood, C. C. (1985). Scalp distributions of event-related potentials: an ambiguity associated with analysis of variance models. Electroencephalography & Clinical Neurophysiology, 62: 203208.Google Scholar
Miller, J., Patterson, T., & Ulrich, R. (1998). Jackknife-based method for measuring LRP onset latency differences. Psychophysiology, 35: 99115.Google Scholar
Möcks, J. (1986). The influence of latency jitter in principal component analysis of event-related potentials. Psychophysiology, 23: 480484.Google Scholar
Möcks, J. (1988). Decomposing event-related potentials: a new topographic components model. Biological Psychology, 26: 199215.Google Scholar
Möcks, J., Köhler, W., Gasser, T., & Pham, D. T. (1988). Novel approaches to the problem of latency jitter. Psychophysiology, 25: 217226.Google Scholar
Möcks, J. & Verleger, R. (1985). Nuisance sources of variance in principal components analysis of event-related potentials. Psychophysiology, 22: 674688.Google Scholar
Monk, T. H. (1987). Parameters of the circadian temperature rhythm using sparse and irregular sampling. Psychophysiology, 24: 236242.Google Scholar
Monk, T. H. & Fookson, J. E. (1986). Circadian temperature rhythm power spectra: is equal sampling necessary? Psychophysiology, 23: 472479.Google Scholar
Nitsche, M. A. & Paulus, W. (2000). Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation. Journal of Physiology, 527: 633639.Google Scholar
Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006). Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10: 424430.Google Scholar
Parks, N. A., Maclin, E. L., Low, K. A., Beck, D. M., Fabiani, M., & Gratton, G. (2012). Examining cortical dynamics and connectivity with concurrent simultaneous single-pulse transcranial magnetic stimulation and fast optical imaging. NeuroImage, 59: 25042510.Google Scholar
Pascual-Leone, A., Valls-Sole, J., Wassermann, E. M., & Hallett, M. (1994). Responses to rapid-rate transcranial magnetic stimulation of the human motor cortex. Brain, 117: 847858.Google Scholar
Pascual-Marqui, R. D., Esslen, M., Kochi, K., & Lehmann, D. (2002). Functional imaging with low resolution brain electromagnetic tomography (LORETA): review, new comparisons, and new validation. Japanese Journal of Clinical Neurophysiology, 30: 8194.Google Scholar
Pascual-Marqui, R. D., Michel, C. M., & Lehmann, D. (1994). Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. International Journal of Psychophysiology, 18: 4965.Google Scholar
Pereira, F., Mitchell, T., & Botvinick, M. (2009). Machine learning classifiers and fMRI: a tutorial review. NeuroImage, 45: S199S209.Google Scholar
Perrin, F., Pernier, J., Bertrand, O., Giard, M. H., & Echallier, J. F. (1987). Mapping of scalp potentials by surface spline interpolation. Electroencephalography & Clinical Neurophysiology, 66: 7581.Google Scholar
Pfurtscheller, G. & Neuper, C. (1992). Simultaneous EEG 10 Hz desynchronization and 40 Hz synchronization during finger movements. NeuroReport, 3: 10571060.Google Scholar
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., … & Petersen, S. E. (2011). Functional organization of the human brain. Neuron, 72: 665678.Google Scholar
Quigley, K. S. & Berntson, G. G. (1996). Autonomic interactions and chronotropic control of the heart: heart period versus heart rate. Psychophysiology, 33: 605611.Google Scholar
Roach, B. J. & Mathalon, D. H. (2008). Event-related EEG time-frequency analysis: an overview of measures and an analysis of early gamma band phase locking in schizophrenia. Schizophrenia Bulletin, 34: 907926.Google Scholar
Ruchkin, D. S., Sutton, S., & Stega, M. (1980). Emitted P300 and slow wave event-related potentials in guessing and detection tasks. Electroencephalography & Clinical Neurophysiology, 49: 114.Google Scholar
Rykhlevskaia, E., Fabiani, M., & Gratton, G. (2006). Lagged covariance structure models for studying functional connectivity in the brain. NeuroImage, 30: 12031218.Google Scholar
Rykhlevskaia, E., Fabiani, M., & Gratton, G. (2008). Combining structural and functional neuroimaging data for studying brain connectivity: a review. Psychophysiology, 45: 173187.Google Scholar
Samar, V. J., Bopardikar, A., Rao, R., & Swartz, K. (1999). Wavelet analysis of neuroelectric waveforms: a conceptual tutorial. Brain and Language, 66: 760.Google Scholar
Scherg, M. & von Cramon, D. (1986). Evoked dipole source potentials of the human auditory cortex. Electroencephalography & Clinical Neurophysiology, 65: 344360.Google Scholar
Serences, J. T., Saproo, S., Scolari, M., Ho, T., & Muftuler, T., (2009). Estimating the influence of attention on population codes in human visual cortex using voxel-based tuning functions. NeuroImage, 44: 223231.Google Scholar
Siegel, M., Engel, A. K., & Donner, T. H. (2011). Cortical network dynamics of perceptual decision-making in the human brain. Frontiers in Human Neuroscience, 5: 00021.Google Scholar
Siegler, R. S. (1987). The perils of averaging data over strategies: an example from children’s addition. Journal of Experimental Psychology: General, 116: 250264.Google Scholar
Skrandies, W. & Lehmann, D. (1982). Spatial principal components of multichannel maps evoked by lateral visual half-field stimuli. Electroencephalography & Clinical Neurophysiology, 54: 662667.Google Scholar
Smulders, F. T., Kenemans, J. L., & Kok, A. (1996). Effects of task variables on measures of the mean onset latency of LRP depend on the scoring method. Psychophysiology, 33: 194205.Google Scholar
Spencer, K. M., Dien, J., & Donchin, E. (1997). Temporal-spatial analysis of the late positive components of the ERP. Psychophysiology, 34: S6.Google Scholar
Spencer, K. M., Dien, J., & Donchin, E. (1999). Componential analysis of the ERP elicited by novel events using a dense electrode array. Psychophysiology, 36: 409414.Google Scholar
Squires, K. C. & Donchin, E. (1976). Beyond averaging: the use of discriminant functions to recognize event related potentials elicited by single auditory stimuli. Electroencephalography & Clinical Neurophysiology, 41: 449459.Google Scholar
Squires, N. K., Squires, K. C., & Hillyard, S. A. (1975). Two varieties of long-latency positive waves evoked by unpredictable auditory stimuli in man. Electroencephalography & Clinical Neurophysiology, 38: 387401.Google Scholar
Srinivasan, R., Nunez, P. L., Tucker, D. M., Silberstein, R. B., & Cadusch, P. J. (1996). Spatial sampling and filtering of EEG with spline laplacians to estimate cortical potentials. Brain Topography, 8: 355366.Google Scholar
Steinmetz, H., Furst, G., & Meyer, B.-U. (1989). Craniocerebral topography within the international 10–20 system. Electroencephalography & Clinical Neurophysiology, 72: 499506.Google Scholar
Stemmler, G. (1987). Standardization within subjects: a critique of Ben-Shakhar’s conclusions. Psychophysiology, 24: 243246.Google Scholar
Stiber, B. Z. & Sato, S. (1997). Visualization of EEG using time-frequency distributions. Methods of Information in Medicine, 36: 298301.Google Scholar
Talairach, J. & Tournoux, P. (1988). Co-Planar Stereotactic Atlas of the Human Brain: 3-Dimensional Proportional System: An Approach to Cerebral Imaging. Stuttgart: Thieme.Google Scholar
Tallon-Buadry, C., Bertrand, O., Delpuech, C., & Pernier, J. (1996). Stimulus specificity of phase-locked and non-phase-locked 40 Hz visual responses in human. Journal of Neuroscience, 16: 42404249.Google Scholar
Tomoda, H., Celesia, G. G., & Toleikis, S. C. (1991). Effect of spatial frequency on simultaneous recorded steady-state pattern electroretinograms and visual evoked potentials. Electroencephalography & Clinical Neurophysiology: Evoked Potentials, 80: 8188.Google Scholar
Van Essen, D. C., Drury, H. A., Joshi, S., & Miller, M. I. (1998). Functional and structural mapping of human cerebral cortex: solutions are in the surfaces. Proceedings of the National Academy of Sciences of the USA, 95: 788795.Google Scholar
Vasey, M. W. & Thayer, J. F. (1987). The continuing problem of false positives in repeated measures ANOVA in psychophysiology: a multivariate solution. Psychophysiology, 24: 479486.Google Scholar
Wainer, H. (1991) Adjusting for differential base rates: Lord’s Paradox again. Psychological Bulletin, 109: 147151.Google Scholar
Walter, W. G., Cooper, R., Aldridge, V. J., McCallum, W. C., & Winter, A. L. (1964). Contingent negative variation: an electrical sign of sensorimotor association and expectancy in the human brain. Nature, 203: 380384.Google Scholar
Wang, J. Z., Williamson, S. J., & Kaufman, L. (1992). Magnetic source images determined by a lead-field analysis: the unique minimum-norm least-squares estimation. IEEE Transactions on Biomedical Engineering, 39: 665675.Google Scholar
Wasserman, S. & Bockenholt, U. (1989). Bootstrapping: applications to psychophysiology. Psychophysiology, 26: 208221.Google Scholar
Wickens, C. D., Kramer, A. F., Vanasse, L., & Donchin, E. (1983). Performance of concurrent tasks: a psychophysiological analysis of the reciprocity of information-processing resources. Science, 221: 10801082.Google Scholar
Wiener, N. (1964). Extrapolation, Interpolation, and Smoothing of Stationary Time Series. Cambridge, MA: MIT Press.Google Scholar
Wilder, J. (1967). Stimulus and Response: The Law of Initial Value. Bristol: John Wright & Sons.Google Scholar
Wood, C. C. & McCarthy, G. (1984). Principal component analysis of event-related potentials: simulation studies demonstrate misallocation of variance across components. Electroencephalography & Clinical Neurophysiology, 59: 249260.Google Scholar
Woody, C. D. (1967). Characterization of an adaptive filter for the analysis of variable latency neuroelectrical signal. Medical and Biological Engineering, 5: 539553.Google Scholar
Yantis, S., Meyer, D. E., & Smith, J. K. (1991). Analyses of multinomial mixture distributions: new tests for stochastic models of cognition and action. Psychological Bulletin, 110: 350374.Google Scholar
Yule, G. U. (1927). On a method of investigating periodicities in disturbed series, with special reference to Wolfer’s sunspot numbers. Philosophical Transactions of the Royal Society of London, Series A, 226: 267298.Google Scholar

References

Aguinis, H., Gottfredson, R. K., & Culpepper, S. A. (2013). Best-practice recommendations for estimating cross-level interaction effects using multilevel modeling. Journal of Management, 39: 14901528.Google Scholar
Aiken, L. S. & West, S. G. (1991). Multiple Regression: Testing and Interpreting Interactions. Thousand Oaks, CA: Sage.Google Scholar
Carlson, J. M., Foti, D., Mujica-Parodi, L. R., Harmon-Jones, E., & Hajcak, G. (2011). Ventral striatal and medial prefrontal BOLD activation is correlated with reward-related electrocortical activity: a combined ERP and fMRI study. NeuroImage, 57: 16081616.Google Scholar
Cohen, J. (1992). A power primer. Psychological Bulletin, 112: 155159.Google Scholar
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 3rd edn. New York: Routledge.Google Scholar
DeYoung, D. G., Quilty, L. C., & Peterson, J. B. (2007). Between facets and domains: 10 aspects of the Big Five. Journal of Personality and Social Psychology, 93: 880896.Google Scholar
Edwards, L. J., Muller, K. E., Wolfinger, R. D., Qaqish, B. F., & Schabenberger, O. (2008). An R2 statistic for fixed effects in the linear mixed model. Statistics in Medicine, 27: 61376157.Google Scholar
Foti, D., Weinberg, A., Dien, J., & Hajcak, G. (2011). Event-related potential activity in the basal ganglia differentiates rewards from nonrewards: temporospatial principal components analysis and source localization of the feedback negativity. Human Brain Mapping, 32: 22072216.Google Scholar
Gehring, W. J. & Willoughby, A. R. (2002). The medial frontal cortex and the rapid processing of monetary gains and losses. Science, 295: 22792282.Google Scholar
Hayes, A. F. (2006). A primer on multilevel modeling. Human Communication Research, 32: 385410.Google Scholar
Mathieu, J. E., Aguinis, H., Culpepper, S. A., & Chen, G. (2012). Understanding and estimating the power to detect cross-level interaction effects in multilevel modeling. Journal of Applied Psychology, 97: 951966.Google Scholar
Searle, S. R., Speed, F. M., & Milliken, G. A. (1980). Population marginal means in the linear model: an alternative to least squares means. The American Statistician, 34: 216221.Google Scholar
Tritt, S. M., Page-Gould, E., Peterson, J. B., & Inzlicht, M. (2014). System justification and electrophysiological responses to feedback: support for a positivity bias. Journal of Experimental Psychology: General, 143: 10041010.Google Scholar
West, S. G., Aiken, L. S., & Krull, J. L. (1996). Experimental personality designs: analyzing categorical by continuous variable interactions. Journal of Personality, 64: 148.Google Scholar

References

Albers, J. (1975). Interaction of Color. New Haven, CT: Yale University Press.Google Scholar
Allen, E. A., Erhardt, E. B., & Calhoun, V. D. (2012). Data visualization in the neurosciences: overcoming the curse of dimensionality. Neuron, 74: 603608.Google Scholar
Baird, J. C. (1970a). A cognitive theory of psychophysics I. Scandinavian Journal of Psychology, 11: 3546.Google Scholar
Baird, J. C. (1970b). A cognitive theory of psychophysics II. Scandinavian Journal of Psychology, 11: 89102.Google Scholar
Belia, S., Fidler, F., Williams, J., & Cumming, G. (2005). Researchers misunderstand confidence intervals and standard error bars. Psychological Methods, 10: 389396.Google Scholar
Brewer, C. A. (1994). Guidelines for use of the perceptual dimensions of color for mapping and visualization. In Proceedings of the International Society for Optical Engineering (SPIE), vol. 2171 (pp. 5463). San Jose: International Society for Optics and Photonics.Google Scholar
Buckner, R. L., Andrews-Hanna, J. R., & Schacter, D. L. (2008). The brain’s default network. Annals of the New York Academy of Sciences of the USA, 1124: 138.Google Scholar
Bullmore, E. & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10: 186198.Google Scholar
Chambers, J., Cleveland, W., Kleiner, B., & Tukey, P. (1983). Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.Google Scholar
Chernoff, H. (1973). The use of faces to represent points in k-dimensional space graphically. Journal of the American Statistical Association, 68: 361368.Google Scholar
Cleveland, W. S. (1984). Graphs in scientific publications. The American Statistician, 38: 261269.Google Scholar
Cleveland, W. S. (1994). The Elements of Graphing Data, rev. edn. Summit, NJ: Hobart Press.Google Scholar
Cleveland, W. S., McGill, M. E., & McGill, R. (1988). The shape parameter of a two-variable graph. Journal of the American Statistical Association, 83: 289300.Google Scholar
Cleveland, W. S. & McGill, R. (1983). A color-caused optical illusion on a statistical graph. The American Statistician, 7: 101105.Google 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: 531554.Google Scholar
Cleveland, W. S. & McGill, R. (1985). Graphical perception and graphical methods for analyzing scientific data. Science, 229: 828833.Google Scholar
Cowan, N. (2001). Metatheory of storage capacity limits. Behavioral and Brain Sciences, 24: 154176.Google Scholar
Cumming, G., Fidler, F., and Vaux, D. L. (2007). Error bars in experimental biology. The Journal of Cell Biology, 177(1): 711.Google Scholar
Cumming, G. & Finch, S. (2005). Inference by eye: confidence intervals and how to read pictures of data. American Psychologist, 60: 170180.Google Scholar
Eichele, H., Juvodden, H. T., Ullsperger, M., & Eichele, T. (2010). Mal-adaptation of event-related EEG responses preceding performance errors. Frontiers in Human Neuroscience, 4: 65.Google Scholar
Ellis, W. D. (1999). A Source Book of Gestalt Psychology, vol. 2. New York: Psychology Press.Google Scholar
Emerson, J. W., Green, W. A., Schloerke, B., Crowley, J., Cook, D., Hofmann, H., & Wickham, H. (2011). The generalized pairs plot. Journal of Computational and Graphical Statistics, 22: 7991.Google Scholar
Fechner, G. (1860). Elemente Der Psychophysik. Leipzig: Breitkopf & Härtel.Google Scholar
Feinberg, B. M. & Franklin, C. A. (1975). Social Graphics Bibliography. Washington, DC: Bureau of Social Science Research.Google Scholar
Feinberg, R. A. & Wainer, H. (2011). Extracting sunbeams from cucumbers. Journal of Computational and Graphical Statistics, 20: 793810.Google Scholar
Few, S. (2007). Save the pies for dessert. Visual Business Intelligence Newsletter, August.Google Scholar
Friendly, M. (2002). Corrgrams: exploratory displays for correlation matrices. The American Statistician, 56: 316324.Google Scholar
Gehlenborg, N. & Wong, B. (2012). Points of view: into the third dimension. Nature Methods, 9: 851851.Google Scholar
Griethe, H. & Schumann, H. (2006). The visualization of uncertain data: methods and problems. In Proceedings of SimVis ’06 (pp. 143156). San Diego, CA: SCS Publishing House.Google Scholar
Habeck, C. & Moeller, J. R. (2011). Intrinsic functional-connectivity networks for diagnosis: just beautiful pictures? Brain Connectivity, 1: 99103.Google Scholar
Haemer, K. W. (1947a). Hold that line. The American Statistician, 1: 25.Google Scholar
Haemer, K. W. (1947b). The perils of perspective. The American Statistician, 1: 19.Google Scholar
Haemer, K. W. (1951). The pseudo third dimension. The American Statistician, 5: 28.Google Scholar
Harlow, L. L., Mulaik, S. A., & Steiger, J. H. (2013). What If There Were No Significance Tests? New York: Psychology Press.Google Scholar
Heer, J. & Bostock, M. (2010). Crowdsourcing graphical perception: using mechanical turk to assess visualization design. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 203212). New York: ACM.Google Scholar
Hengl, T. (2003). Visualisation of uncertainty using the HSI colour model: computations with colours. In Proceedings of the 7th International Conference on GeoComputation (pp. 817). University of Southampton.Google Scholar
Hintze, J. L. & Nelson, R. D. (1998). Violin plots: a box plot-density trace synergism. The American Statistician, 52: 181184.Google Scholar
Hoekstra, R., Morey, R. D., Rouder, J. N., & Wagenmakers, E.-J. (2014). Robust misinterpretation of confidence intervals. Psychonomic Bulletin & Review, 21: 11571164.Google Scholar
Jarvenpaa, S. L. & Dickson, G. W. (1988). Graphics and managerial decision making: research-based guidelines. Communications of the ACM, 31: 764774.Google Scholar
Kampstra, P. (2008). Beanplot: a boxplot alternative for visual comparison of distributions. Journal of Statistical Software, 28(1): 19.Google Scholar
Kiehl, K. A., Laurens, K. R., Duty, T. L., Forster, B. B., & Liddle, P. F. (2001). Neural sources involved in auditory target detection and novelty processing: an event-related fMRI study. Psychophysiology, 38: 133142.Google Scholar
Kosslyn, S. M. (1985). Graphics and human information processing: a review of five books. Journal of the American Statistical Association, 80: 499512.Google Scholar
Krzywinski, M. (2013a). Points of view: axes, ticks and grids. Nature Methods, 10: 183.Google Scholar
Krzywinski, M. (2013b). Points of view: elements of visual style. Nature Methods, 10: 371.Google Scholar
Krzywinski, M. & Altman, N. (2013). Points of significance: error bars. Nature Methods, 10: 921922.Google Scholar
Krzywinski, M. & Wong, B. (2013). Points of view: plotting symbols. Nature Methods, 10: 451.Google Scholar
Lane, D. M. & Sándor, A. (2009). Designing better graphs by including distributional information and integrating words, numbers, and images. Psychological Methods, 14: 239257.Google Scholar
Lewandowsky, S. & Spence, I. (1989). Discriminating strata in scatterplots. Journal of the American Statistical Association, 84: 682688.Google Scholar
Margulies, D. S., Böttger, J., Watanabe, A., & Gorgolewski, K. J. (2013). Visualizing the human connectome. NeuroImage, 80: 445461.Google Scholar
Moret-Tatay, C. & Perea, M. (2011). Do serifs provide an advantage in the recognition of written words? Journal of Cognitive Psychology, 23: 619624.Google Scholar
Munsell, A. H. (1947). A Color Notation, 12th edn. Baltimore, MD: Munsell Color Company.Google Scholar
Potter, K., Rosen, P., & Johnson, C. R. (2012). From quantification to visualization: a taxonomy of uncertainty visualization approaches. In Dienstfrey, A. M. & Boisvert, R. F. (eds.), Uncertainty Quantification in Scientific Computing (pp. 226249). New York: Springer.Google Scholar
Sammon, J. W. (1969). A nonlinear mapping for data structure analysis. IEEE Transactions on Computers, 18: 401409.Google Scholar
Schmid, C. F. (1983). Statistical Graphics: Design Principles and Practices. New York: John Wiley.Google Scholar
Spitzer, M., Wildenhain, J., Rappsilber, J., & Tyers, M. (2014). BoxPlotR: a web tool for generation of box plots. Nature Methods, 11: 121122.Google Scholar
Stevens, S. S. (1975). Psychophysics. Piscataway, NJ: Transaction Publishers.Google Scholar
Talbot, J., Gerth, J., & Hanrahan, P. (2012). An empirical model of slope ratio comparisons. IEEE Transactions on Visualization and Computer Graphics, 18: 26132620.Google Scholar
Tomasi, D. & Volkow, N. D. (2011). Association between functional connectivity hubs and brain networks. Cerebral Cortex, 21: 20032013.Google Scholar
Tufte, E. R. (2001). The Visual Display of Quantitative Information, 2nd edn. Cheshire, CT: Graphics Press.Google Scholar
Tukey, J. (1977). Exploratory Data Analysis. Boston, MA: Addison-Wesley.Google Scholar
Van der Maaten, L. & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9: 85.Google Scholar
Vaux, D. L. (2004). Error message. Nature, 428: 799.Google Scholar
Wainer, H. (1984). How to display data badly. The American Statistician, 38: 137147.Google Scholar
Wainer, H. (1996). Depicting error. The American Statistician, 50: 101111.Google Scholar
Wainer, H. (2008). Visual revelations: improving graphic displays by controlling creativity. Chance, 21: 4652.Google Scholar
Wallgren, A., Wallgren, B., Persson, R., Jorner, U., & Haaland, J.-A. (1996). Graphing Statistics & Data: Creating Better Charts. Thousand Oaks, CA: Sage.Google Scholar
Wand, H., Iversen, J., Law, M., & Maher, L. (2014). Quilt plots: a simple tool for the visualisation of large epidemiological data. PloS One, 9: e85047.Google Scholar
Ward, M. O. (2008). Multivariate data glyphs: principles and practice. In Chen, C.-H., Härdle, W., & Unwin, A. (eds.), Handbook of Data Visualization (pp. 179198). Berlin: Springer.Google Scholar
Wickham, H. (2009). ggplot2: Elegant Graphics for Data Analysis. New York: Springer.Google Scholar
Wickham, H. (2013). Graphical criticism: some historical notes. Journal of Computational and Graphical Statistics, 22: 3844.Google Scholar
Wilkinson, L. (2005). The Grammar of Graphics, 2nd edn. New York: Springer.Google Scholar
Wong, B. (2010a). Points of view: color coding. Nature Methods, 7: 573.Google Scholar
Wong, B. (2010b). Points of view: design of data figures. Nature Methods, 7: 665.Google Scholar
Wong, B. (2011a). Points of view: arrows. Nature Methods, 8: 701.Google Scholar
Wong, B. (2011b). Points of view: salience to relevance. Nature Methods, 8: 889.Google Scholar
Wong, B. (2011c). Points of view: simplify to clarify. Nature Methods, 8: 611.Google Scholar
Wong, B. & Kjærgaard, R. S. (2012). Points of view: pencil and paper. Nature Methods, 9: 1037.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org 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
×