Quality & Quantity

, Volume 52, Issue 3, pp 1315–1329 | Cite as

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

Article

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.

Keywords

Individual cases Aggregations of cases Narrative description Hyperspace location Statistical description 

References

  1. Baker, S.E., Edwards, R., Doidge, M. How many qualitative interviews is enough?: Expert voices and early career reflections on sampling and cases in qualitative research. National Centre for Research Methods Review Paper. (2012). brighton.ac.uk visited 11/9/16Google Scholar
  2. Bollen, K.A., Pearl, J.: Eight myths about causality and structural equation models. In: Morgan, S. (ed.) Handbook of causal analysis for social research, pp. 1–42. Springer, New York (2012)Google Scholar
  3. Breckenridge, J., Jones, D. Demystifying theoretical sampling in grounded theory research. Grounded Theor. Rev. Int. J. 8(2), 113–126. (2009). {or visit via https://www.researchgate.net/profile/Jenna_Breckenridge/publications [then scroll down]}. Visited 11/9/16
  4. Bruscaglioni, L.: Theorizing in grounded theory and creative abduction. Qual. Quant. 50, 2009–2024 (2016)CrossRefGoogle Scholar
  5. Carling, K.: Resistant outlier rules and the non-Gaussian case. Comput. Stat. Data Anal. 33(3), 249–258 (2000)CrossRefGoogle Scholar
  6. Chatterjee, S., Hadi, A.S.: Influential observations, high leverage points, and outliers in linear regression. Stat. Sci. 1, 379–393 (1986)CrossRefGoogle Scholar
  7. Chin, W.W.: How to write up and report PLS analyses. In: Vinzi, V.E., et al. (eds.) Handbook of partial least squares, pp. 655–690. Springer, Berlin (2010)CrossRefGoogle Scholar
  8. Chow, S.L.: Statistical significance: Rationale, validity, and utility. Sage, Beverly Hills (1996)Google Scholar
  9. Christianson, M.K., Farkas, M.T., Sutcliffe, K.M., Weick, K.E.: Learning through rare events: significant interruptions at the Baltimore and Ohio Railroad Museum. Organ. Sci. 20(5), 846–860 (2009)CrossRefGoogle Scholar
  10. Cleary, M., Horsfall, J., Hayter, M.: Data collection and sampling in qualitative research: Does size matter? J. Adv. Nurs. 70, 473–475 (2014)CrossRefGoogle Scholar
  11. Cohen, J.: Statistical power analysis. Curr. Dir. Psychol. Sci. 1(3), 98–101 (1992)CrossRefGoogle Scholar
  12. Corbin, J., Strauss, A.: Grounded theory research: procedures, canons and evaluative criteria. Zeitschrift für Sociologie 19(6), 418–427 (1990)Google Scholar
  13. Coyne, I.T.: Sampling in qualitative research. Purposeful and theoretical sampling; merging or clear boundaries? J. Adv. Nurs. 26(3), 623–630 (1997)CrossRefGoogle Scholar
  14. DeGroot, M.H.: Optimal statistical decisions. Wiley, New York (2005)Google Scholar
  15. Denzin, N.K.: The reflexive interview and a performative social science. Qual. Res. 1(1), 23–46 (2001)CrossRefGoogle Scholar
  16. Dourdouma, A., Mőrtl, K.: The creative journey of grounded theory analysis: a guide to its principles and applications. Ricerca in Psicoterapia/Res. Psychother. Psychopathol. Process Outcome 15, 96–106 (2012)Google Scholar
  17. Eby, L.T., Hurst, C.S., Butts, M.M.: Qualitative research: The redheaded stepchild in organizational and social science research. In: Lance, C.E., Vandenberg, R.J. (eds.) More statistical and methodological myths and urban legends: Doctrine, verity and fable in the organizational and social sciences, pp. 219–246. Routledge, New York (2014)Google Scholar
  18. Fann, K.T.: Peirce’s theory of abduction. Springer, New York (2012). (originally 1970) Google Scholar
  19. Feller, W.: An introduction to probability theory and its applications, vol. I, 3rd edn. Wiley, New York (1968)Google Scholar
  20. Flach, P.A., Hadjiantonis, A.M. (eds.): Abduction and induction: Essays on their relation and integration, vol. 18. Springer, New York (2013)Google Scholar
  21. Flick, U.: An introduction to qualitative research, 4th edn. Sage, Thousand Oaks (2009)Google Scholar
  22. Francis, J.J., Johnston, M., Robertson, C., Glidewell, L., Entwistle, V., Eccles, M.P., Grimshaw, J.M.: What is an adequate sample size? Operationalizing data saturation for theory-based interview studies. Psychol. Health 25(10), 1229–1245 (2010)CrossRefGoogle Scholar
  23. Goodman, S.N.: Toward evidence-based medical statistics. 1: the P value fallacy. Ann. Intern. Med. 130(12), 995–1004 (1999)CrossRefGoogle Scholar
  24. Goodman, S.N.: Aligning statistical and scientific reasoning: misunderstanding and misuse of statistical significance impede science. Science 352, 1180–1181 (2016)CrossRefGoogle Scholar
  25. Iglewicz, B., Hoaglin, D. How to detect and handle outliers. In Mytkytka, E.F. (ed.), The ASQC basic references in quality control: Statistical techniques, vol. 16. American Society for Quality, Milwaukee (1993). Retrievable (for a fee) from http://asq.org
  26. Kanji, G.: 100 statistical tests. Sage, Newbury Park (1993)Google Scholar
  27. Kendall, M.G., Stuart, A.: The advanced theory of statistics: Inference and relationship, vol. 2. Charles Griffin, London (1966a)Google Scholar
  28. Kendall, M.G., Stuart, A.: The advanced theory of statistics: Design and analysis, and time series, vol. 3. Charles Griffin, London (1966b)Google Scholar
  29. Kline, R.B.: Principles and practice of structural equation modelling, 3rd edn. Guilford, New York (2011)Google Scholar
  30. Koh, J.L.Y., Lee, M.L., Hsu, W., Lam, K.T.: Correlation-based detection of attribute outliers. In: Kotagiri, R., Krishna, P.R., Mohania, M., Nantajeewarawat, E. (eds.) Advances in Databases: Concepts, Systems and Applications, DASFAA 2007. Lecture Notes in Computer Science, vol. 4443, pp. 164–175. Springer, Berlin (2007)CrossRefGoogle Scholar
  31. Kraemer, H.C., Blasey, C.: How many subjects?: Statistical power analysis in research. Sage, Thousand Oaks (2015)Google Scholar
  32. Krause, M.S.: What are you doing? What am I doing? J. Theor. Philos. Psychol. 25, 257–279 (2005)CrossRefGoogle Scholar
  33. Krause, M.S.: Trying to discover sufficient condition causes. Methodology 6, 59–70 (2010)CrossRefGoogle Scholar
  34. Krause, M.S.: Significance testing and clinical trials. Psychotherapy 48, 217–222 & 234–236 (2011)Google Scholar
  35. Krause, M.S.: Measurement validity is fundamentally a matter of definition, not correlation. Rev. Gen. Psychol. 16, 391–400 (2012)CrossRefGoogle Scholar
  36. Krause, M.S.: The incompatibility of achieving a fully specified linear model and assuming that residual dependent-variable variance is random. Qual. Quant. 47, 3201–3204 (2013a)CrossRefGoogle Scholar
  37. Krause, M.S.: The data analytic implications of human psychology’s dimensions being ordinally scaled. Rev. Gen. Psychol. 17, 318–325 (2013b)CrossRefGoogle Scholar
  38. Krause, M.S.: Valid description of experiencings and thereby of behaviors and situations. J. Conscious. Explor. Res. 7, 1–20 (2016a)Google Scholar
  39. Krause, M.S.: Case sampling for psychotherapy practice, theory, and policy guidance: qualities and quantities. Psychother. Res. 26, 530–544 (2016b)CrossRefGoogle Scholar
  40. Krause, M.S.: Mathematical expression and sampling issues of treatment-contrasts: beyond significance testing and meta-analysis to clinically useful research synthesis. Psychother. Res. (2016c). doi: 10.1080/10503307.2016.1222459.
  41. Krause, M.S., Howard, K.I.: The linear model is a very special case: How to explore data for their full clinical implications. Psychother. Res. 12, 475–490 (2002)CrossRefGoogle Scholar
  42. Krause, M.S., Lutz, W.: Process transforms inputs to determine outcomes: therapists are responsible for managing process. Clin. Psychol. Sci. Pract. 13, 73–81 (2009)CrossRefGoogle Scholar
  43. Krause, M.S., Lutz, W., Bőhnke, J.R.: The role of sampling in clinical trial design. Psychother. Res. 21, 143–151 (2011)CrossRefGoogle Scholar
  44. Krause, M.S., Lutz, W., Saunders, S.M.: Empirically certified treatments or therapists: the issue of separability. Psychotherapy 44, 347–353 (2007)CrossRefGoogle Scholar
  45. Kraut, R.E., McConahay, J.B.: How being interviewed affects voting: an experiment. Public Opin. Q. 37(3), 398–406 (1973)CrossRefGoogle Scholar
  46. Lamiell, J.T.: Toward a critically personalistic general psychology in consideration of its unifying potential. Rev. Gen. Psychol. 18, 1–6 (2014)CrossRefGoogle Scholar
  47. Magnani, L.: Abduction, reason and science. Springer, New York (2001)CrossRefGoogle Scholar
  48. Mason, M. Sample size and saturation in PhD studies using qualitative interviews. Forum Qualitative Sozialforschung/Forum Qual. Soc. Res. 11(3), Art. 8, (2010). http://nbn-resolving.de/urn:nbn:de:0114-fqs100387. (visited 11/8/16)
  49. McDonald, R.P.: Structural models and the art of approximation. Perspect. Psychol. Sci. 5, 675–686 (2010)CrossRefGoogle Scholar
  50. Michell, J.: Measurement in psychology: Critical history of a methodological concept. Cambridge University Press, New York (1999)CrossRefGoogle Scholar
  51. Michell, J.: Is Psychometrics pathological science? Measurement 6, 7–24 (2008)Google Scholar
  52. Morse, J.M.: The significance of saturation. Qual. Health Res. 5, 147–149 (1995)CrossRefGoogle Scholar
  53. Morse, J.M.: Developing grounded theory. The second generation. Left Coast Press, Walnut Creek (2009)Google Scholar
  54. Nesselroade, J.R.: On an emerging third discipline of scientific Psychology. In: Molenaar, P.C.M., Newell, K.M. (eds.) Individual pathways of change: Statistical models for analyzing learning and development, pp. 209–218. APA, Washington (2010)CrossRefGoogle Scholar
  55. Nickerson, R.S.: Confirmation bias: a ubiquitous phenomenon in many guises. Rev. Gen. Psychol. 2(2), 175–220 (1998)CrossRefGoogle Scholar
  56. Nickerson, R.S.: Null hypothesis significance testing: A review of an old and continuing controversy. Psychol. Methods 5, 241–301 (2000)CrossRefGoogle Scholar
  57. Onwuegbuzie, A.J., Leech, N.L.: Sampling designs in qualitative research: making the sampling process more public. Qual. Rep. 12(2), 238–254 (2007)Google Scholar
  58. Orlinsky, D.E., Rønnestad, M.H., Willutzki, U.: Fifty years of psychotherapy process-outcome research: Continuity and change. In: Lambert, M.J. (ed.) Bergin and Garfields’ Handbook of psychotherapy and behavior change, 5th edn, pp. 307–389. Wiley, New York (2004)Google Scholar
  59. Osborne, J.W., Overbay, A.: The power of outliers (and why researchers should always check for them). Pract. Assess. Res. Eval. 9(6), 1–12 (2004)Google Scholar
  60. Porter, T.M.: Trust in numbers: The pursuit of objectivity in science and public life. Princeton University Press, Princeton (1996)CrossRefGoogle Scholar
  61. Reichertz, J.: Abduction: The logic of discovery of grounded theory. In: Bryant, A., Charmaz, K. (eds.) The Sage handbook of grounded theory, pp. 214–228. Sage, Thousand Oaks (2007)CrossRefGoogle Scholar
  62. Ritchie, J., Lewis, J., Gillian, E.: Designing and selecting samples. In: Ritchie, J., Lewis, J. (eds.) Qualitative research practice, pp. 77–108. Sage, Thousand Oaks (2003)Google Scholar
  63. Roberts, S., Pashler, H.: How persuasive is a good fit? A comment on theory testing. Psychol. Rev. 107, 358–367 (2000)CrossRefGoogle Scholar
  64. Robinson, O.C.: The idiographic/nomothetic dichotomy: tracing historical origins of contemporary confusions. Hist. Philos. Psychol. 13(2), 32–39 (2011)Google Scholar
  65. Rozeboom, W.W.: Some esoteric aspects of SEM that its practitioners should want to know. Multivar. Behav. Res. 44, 553–587 (2009)CrossRefGoogle Scholar
  66. Salvatore, S., Valsiner, J.: Between the general and the unique: overcoming the nomothetic versus idiographic opposition. Theor. Psychol. 20(6), 817–833 (2010)CrossRefGoogle Scholar
  67. Sandelowski, M.: One is the liveliest number: the case orientation of qualitative research. Res. Nurs. Health 19, 525–529 (1996)CrossRefGoogle Scholar
  68. Stigler, S.M.: The seven pillars of statistical wisdom. Harvard University Press, Cambridge (2016)CrossRefGoogle Scholar
  69. Strauss, A.L., Corbin, J.M. Basics of qualitative research: Techniques and procedures for developing grounded theory. 2nd edn. Sage, Thousand Oaks. (1998) (There is a Corbin and Strauss, 2015, 4th edn)Google Scholar
  70. Taylor, S.J., Bogdan, R., DeVault, M.: Introduction to qualitative research methods: A guidebook and resource. Wiley, New York (2015)Google Scholar
  71. Tietjen, G.L.: A topical dictionary of statistics. Chapman & Hall, New York (1986)Google Scholar
  72. Turner, C.J., Campbell, M.I., Crawford, R.H. Generic sequential sampling for meta-model approximations. In: ASME 2003 international design engineering technical conferences and computers and information in engineering conference (pp. 555–564). American Society of Mechanical Engineers. (2003, January). (go to www.asme.org and Search for this title). (visited 11/9/16)
  73. Ullman, J.B., Bentler, P.M.: Structural equation modeling. In: Weiner, I.B. (ed.) Handbook of psychology, vol. 2, 2nd edn, pp. 661–690. Wiley, New York (2012)Google Scholar
  74. Van Manen, M.: Researching lived experience: Human science for an action sensitive pedagogy. Left Coast Press, Walnut Creek (2015)Google Scholar
  75. Walter, S.: Epiphenomenalism. In: Binder, M.D., Hirokawa, N., Windhorst, U. (eds.) Encyclopedia of neuroscience, pp. 1137–1139. Springer, Berlin (2009)CrossRefGoogle Scholar
  76. Waterman, A.S.: The humanistic psychology—positive psychology divide: contrasts in philosophical foundations. Am. Psychol. 68, 124–133 (2013)CrossRefGoogle Scholar
  77. Wax, M., Shapiro, L.J.: Repeated interviewing. Am. J. Sociol. 62(2), 215–217 (1956)CrossRefGoogle Scholar
  78. Wall, P.D.: On the relation of injury to pain. Pain 6, 253–264 (1979)CrossRefGoogle Scholar
  79. Wallerstein, N.B., Duran, B.: Using community-based participatory research to address health disparities. Health Promot. Pract. 7(3), 312–323 (2006)CrossRefGoogle Scholar
  80. Welles, B.F.: On minorities and outliers: the case for making Big Data small. Big Data Soc. 1(1), 1–2 (2014). doi: 10.1177/2053951714540613 CrossRefGoogle Scholar
  81. Wertz, F.J., Charmaz, K., McMullen, L., Josselson, R., Anderson, R., McSpadden, E.: Five ways of doing qualitative analysis: Phenomenological psychology, grounded theory, discourse analysis, narrative research, and intuitive inquiry. Guilford, New York (2011)Google Scholar
  82. Wood, M., Christy, R.: Sampling for possibilities. Qual. Quant. 33, 185–202 (1999)CrossRefGoogle Scholar
  83. Yeo, A., Legard, R., Keegan, J., Ward, K., McNaughton-Nicholls, C., Lewis, J.: In-depth interviews. In: Ritchie, J., Lewis, J., Nicholls, C.M., Ormston, R. (eds.) Qualitative research practice: A guide for social science students and researchers, pp. 177–210. Sage, Thousand Oaks (2014)Google Scholar
  84. Yule, G.U., Kendall, M.G.: An introduction to the theory of statistics, 14th edn. Hafner, New York (1950)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.EvanstonUSA

Personalised recommendations