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The scientific study of the qualities of individual human lives, rather than of their average quantities in aggregations of lives

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  • Merton S. Krause

Abstract

The full variety of how individual human lives are lived and why so is what matters for scientific human Psychology (SHP) theory and practice research purposes. How representative of the human population are the fractions of each such variety sampled matters for social science and policy purposes. What varieties are obtained and how representative their sample fractions are of those in the human population depends upon how the sampling was done. The exact number of persons in these samples matters only for statistical significance testing purposes. Univariate means and variances and bi- or multi-variate regressions and correlations of variables are the Linear Model statistics SHP presently predominantly depends upon. These statistics are averages in aggregations of persons so not descriptive of individual persons, and why persons in such aggregations deviate from the average is generally not explored. A description of how a human life is lived and a causal explanation of why so necessarily involve quantities in the form of gradations on dimensions. Each description and explanation is a conjunction of gradations, one from each of several dimensions, so the essential difference between qualitative and quantitative SHP research is between dealing with each individual case and dealing only with the statistics of aggregations of cases.

Suggested Citation

  • Merton S. Krause, 2018. "The scientific study of the qualities of individual human lives, rather than of their average quantities in aggregations of lives," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(3), pages 1315-1329, May.
    • Handle: RePEc:spr:qualqt:v:52:y:2018:i:3:d:10.1007_s11135-017-0523-6
    DOI: 10.1007/s11135-017-0523-6
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    References listed on IDEAS

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    1. Michael Wood & Richard Christy, 1999. "Sampling for Possibilities," Quality & Quantity: International Journal of Methodology, Springer, vol. 33(2), pages 185-202, May.
    2. Carling, Kenneth, 2000. "Resistant outlier rules and the non-Gaussian case," Computational Statistics & Data Analysis, Elsevier, vol. 33(3), pages 249-258, May.
    3. Marlys K. Christianson & Maria T. Farkas & Kathleen M. Sutcliffe & Karl E. Weick, 2009. "Learning Through Rare Events: Significant Interruptions at the Baltimore & Ohio Railroad Museum," Organization Science, INFORMS, vol. 20(5), pages 846-860, October.
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