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2 - Methodology in Psychology

Published online by Cambridge University Press:  18 May 2019

By
Daniel J. Denis  and
Briana Young
Edited by
Robert J. Sternberg  and
Wade E. Pickren
Robert J. Sternberg
Affiliation:
Cornell University, New York
Wade E. Pickren
Affiliation:
Ithaca College, New York
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Summary

Psychology, perhaps more than any other social science, has historically defined itself by the methodologies it employs in generating new knowledge. This chapter surveys the history of methodology in psychology. We begin by first defining the concept of a methodology, then survey early scientific methods of psychology with the work of Gustav Fechner in the 1860s. Fechner, along with other psychophycisists of the time, were of the first to promote a rigorous scientific method in the study and understanding of psychological phenomena. The quest for quantification in psychology would continue with the work of Francis Galton and his monumental discovery of empirical correlation almost thirty years later, which set the stage for a wealth of statistical methodologies that would arise in the early twentieth century. These included the birth of psychometrics and testing, as well as the development of the factor analysis method used by Charles Spearman and others. By the early 1920s, geneticist R. A. Fisher revolutioned the application of quantitative methods by promoting null hypothesis significance testing, while simultaneously packaging experimental design into the method of the analysis of variance. We close the chapter with a survey of Sewall Wright’s method of path analysis, which, along with factor analysis (and the advancement of the computer), set the stage for the birth of structural equation models of the mid-to-late twentieth century. While psychological science has been dominated by quantitative methods, the reemergence of qualitative methods is also noteworthy. We conclude with a comment that psychological methods of the future, to be successful, will likely require a merger of both quantitative and qualitative approaches.

Keywords

methodology in psychologystatisticsearly scientific methods in psychologyFechnerGaltonFisherhistory of statisticsquantitative and qualitative approachespath analysisfactor analysispsychometrics
Type
Chapter
Information
The Cambridge Handbook of the Intellectual History of Psychology , pp. 29 - 62
Publisher: Cambridge University Press
Print publication year: 2019

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References

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, 1173–1182.Google Scholar
Betz, F. (2011) Managing Science: Methodology and Organization of Research. New York: Springer.Google Scholar
Blalock, H. M. (1964). Causal Inferences in Nonexperimental Research. Chapel Hill: University of North Carolina Press.Google Scholar
Bollen, K. A. (1989). Structural Equations with Latent Variables. New York: Wiley.Google Scholar
Boring, E. G. (1919). Mathematical vs. scientific importance. Psychological Bulletin, 16, 335–338.Google Scholar
Burks, B. S. (1928). The relative influence of nature and nurture upon mental development: A comparative study of foster parent-foster child resemblance and true parent-true child resemblance. Twenty-Seventh Yearbook of the National Society for the Study of Education, 27, 219–316.Google Scholar
Cowan, R. S. (1972). Francis Galton’s statistical ideas: The influence of eugenics. Isis, 63, 509–528.Google Scholar
Cowles, M. (2001). Statistics in Psychology: An Historical Perspective. Mahwah, NJ: Erlbaum.Google Scholar
Danziger, K. (1980). Wundt’s psychological experiment in the light of his philosophy of science. Psychological Research, 42, 109–122.CrossRefGoogle Scholar
Danziger, K. (1985). The methodological imperative in psychology. Philosophy of the Social Sciences, 15, 1–13.Google Scholar
Danziger, K. (1988). On theory and method in psychology. In Baker, W. J.., Mos, L., & van Rappard, H. (Eds.), Recent Trends in Theoretical Psychology (pp. 87–94). New York: Springer.Google Scholar
Danziger, K. (1994). Constructing the Subject. Cambridge: Cambridge University Press.Google Scholar
Denis, D. (2001). The origins of correlation and regression: Francis Galton or Auguste Bravais and the error theorists? History and Philosophy of Psychology Bulletin, 13, 36–44.Google Scholar
Denis, D. (2004). The modern hypothesis testing hybrid: R. A. Fisher’s fading influence. Journale de la Société Française de Statistique, 145, 5–26.Google Scholar
Denis, D., & Docherty, K. (2007). Late nineteenth century Britain: A social, political, and methodological context for the rise of multivariate statistics. Journal Électronique d’Histoire des Probabilités et de la Statistique, 3, 1–41.Google Scholar
Denis, D., & Legerski, J. (2006). Causal modeling and the origins of path analysis. Theory & Science, 17. Retrieved from: http://theoryandscience.icaap.org/content/vol7.1/denis.html.Google Scholar
Duncan, O. D. (1966). Path analysis: Sociological examples. American Journal of Sociology, 72, 1–16.Google Scholar
Everitt, B. S. (2002). The Cambridge Dictionary of Statistics. New York: Cambridge University Press.Google Scholar
Fancher, R. E., & Rutherford, A. (2016). Pioneers of Psychology. New York: W. W. Norton.Google Scholar
Fechner, G. T. (1860/1966). Elements of Psychophysics. Translated by Adler, Helmut E.. Edited by Howes, Davis H. and Boring, Edwin G.. New York: Holt, Rinehart and Winston.Google Scholar
Ferguson, H. C. (2006). Hubble deep fields. In Murdin, P. (Ed.), Encyclopedia of Astronomy & Astrophysics. Philadelphia: IOP Publishing.Google Scholar
Fisher, R. A. (1925/1990). Statistical Methods for Research Workers. London: Oliver and Boyd (14th ed., 1973; reprinted, Oxford University Press, 1990).CrossRefGoogle Scholar
Fisher, R. A. (1935). The Design of Experiments. New York: Hafner.Google Scholar
Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, 466–475.Google Scholar
Friedman, M. (1999). Reconsidering Logical Positivism. Cambridge: Cambridge University Press.Google Scholar
Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute, 15, 246–263.Google Scholar
Galton, F. (1889). Natural Inheritance. London: Macmillan.Google Scholar
Gigerenzer, G. (2004). Mindless statistics. Journal of Socio-Economics, 33, 587–606.CrossRefGoogle Scholar
Goldberger, A. S. (1972). Structural equation models in the social sciences. Econometrica, 40, 979–1001.Google Scholar
Gosset, W. S. (1908). The probable error of a mean. Biometrika, 6, 1–25.Google Scholar
Green, C. D. (1992). Of immortal mythological beasts: Operationism in Psychology. Theory & Psychology, 2, 291–320.CrossRefGoogle Scholar
Hacking, I. (1990). The Taming of Chance. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Hacking, I. (2006). The Emergence of Probability: A Philosophical Study of Early Ideas about Probability, Induction and Statistical Inference. Cambridge: Cambridge University Press.Google Scholar
Hald, A. (1998). A History of Mathematical Statistics from 1750 to 1930. New York: Wiley.Google Scholar
Hathaway, S. R., & McKinley, J. C. (1940). A multiphasic personality schedule (Minnesota): Construction of the schedule. Journal of Psychology, 10, 249–254.Google Scholar
Heidelberger, M. (2004). Nature from Within: Gustav Theodor Fechner and His Psychological Worldview. Pittsburgh: University of Pittsburgh Press.Google Scholar
Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34, 183–202.CrossRefGoogle Scholar
Jöreskog, K. G. (1978). Structural analysis of covariance and correlation matrices. Psychometrika, 43, 443–477.Google Scholar
Kramer, G. P., Bernstein, D. A., & Phares, V. (2014). Introduction to Clinical Psychology. New York: Pearson.Google Scholar
Kuhn, T. S. (1962). The Structure of Scientific Revolutions. Chicago: University of Chicago Press.Google Scholar
Kunen, K. (2007). The foundations of mathematics. www.math.wisc.edu/~miller/old/m771-10/kunen770.pdf.Google Scholar
Lehmann, E. L. (1992). The Fisher, Neyman-Pearson theories of testing hypotheses: One theory or two? University of California, Department of Statistics, Technical Report No. 333, 1–21.Google Scholar
Lloyd, G. E. R. (1999). Magic, Reason, and Experience. Studies in the Origins and Development of Greek Science. New York: Hackett.Google Scholar
Mackenzie, D. A. (1981). Statistics in Britain, 1865–1930: The Social Construction of Scientific Knowledge. Edinburgh: Edinburgh University Press.Google Scholar
Michell, J. (1999). Measurement in Psychology: A Critical History of a Methodological Concept. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Mulaik, S. A. (1987). A brief history of the philosophical foundations of exploratory factor analysis. Multivariate Behavioral Research, 22, 267–305.Google Scholar
Nelson, D. (1998). Penguin Dictionary of Mathematics. New York: Penguin Books.Google Scholar
Neyman, J., & Pearson, E. S. (1928). On the use and interpretation of certain test criteria for purposes of statistical inference: Part I. Biometrika, 20A, 263–294.Google Scholar
Niles, H. E. (1922). Correlation, causation and Wright’s theory of “path coefficients.” Genetics, 7, 258–273.Google Scholar
Pearson, K. (1892). The Grammar of Science. London: Walter Scott.Google Scholar
Pearson, K. (1920). Notes on the history of correlation. Biometrika, 13, 25–45.Google Scholar
Poitevineau, J., & Lecoutre, B. (2001). Interpretation of significance levels by psychological researchers: The .05 cliff effect may be overstated. Psychonomic Bulletin & Review, 8, 847–850.Google Scholar
Porter, T. M. (1986). The Rise of Statistical Thinking: 1820–1900. Princeton, NJ: Princeton University Press.Google Scholar
Rutherford, A. (2009). Beyond the Box: B.F. Skinner’s Technology of Behaviour from Laboratory to Life, 1950s–1970s. Toronto: University of Toronto Press.CrossRefGoogle Scholar
Savage, L. J. (1954). The Foundations of Statistics. New York: Wiley.Google Scholar
Simon, H. A. (1957). Models of Man. New York: Wiley.Google Scholar
Spearman, C. (1904). “General intelligence,” objectively determined and measured. American Journal of Psychology, 15, 201–293.Google Scholar
Spearman, C. (1927). The Abilities of Man: Their Nature and Measurement. New York: MacMillan.Google Scholar
Steiger, J. H., & Schönemann, P. H. (1978). A history of factor indeterminacy. In Shye, S. (Ed.), Theory Construction and Data Analysis in the Behavioral Sciences (pp. 136–178). San Francisco: Jossey Bass.Google Scholar
Stigler, S. M. (1986). The History of Statistics: The Measurement of Uncertainty before 1900. Cambridge, MA: Belknap Press.Google Scholar
Timm, N. H. (2002). Applied Multivariate Analysis. New York: Springer.Google Scholar
Tucker, W. H. (2009). The Cattell Controversy: Race, Science, and Ideology. Chicago: University of Illinois Press.Google Scholar
Walker, H. M. (1929). Studies in the History of Statistical Method. Baltimore, MD: Williams & Wilkins.Google Scholar
Walsh, R. T. G., Teo, T., & Baydala, A. (2014). A Critical History and Philosophy of Psychology: Diversity of Context, Thought, and Practice. Cambridge: Cambridge University Press.Google Scholar
Wechsler, D. (1955). Manual for the Wechsler Adult Intelligence Scale. New York: Psychological Corporation.Google Scholar
Wertz, F. (2014). Qualitative inquiry in the history of psychology. Qualitative Psychology, 1, 4–16.Google Scholar
Wilson, E. B. (1928). Review of The Abilities of Man, their Nature and Measurement by C. Spearman, Science, 67, 244–248.Google Scholar
Wright, S. (1920). The relative importance of heredity and environment in determining the piebald pattern of guinea-pigs. Proceedings of the National Academy of Sciences of the United States of America, 6, 320–332.Google Scholar
Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20, 557–585.Google Scholar

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