Non-statistical, substantive generalization: lessons from Q methodology

被引:9
|
作者
Ramlo, Susan [1 ]
机构
[1] Univ Akron, Engn & Sci Technol, 302 Buchtel Commons, Akron, OH 44325 USA
关键词
Q methodology; generalization; mixed research; qualitative; quantitative; external validity; MIXED METHODS; QUALITATIVE GENERALIZATION; COMPLEMENTARITY; JAMES; WILLIAM; BOHR; NIELS;
D O I
10.1080/1743727X.2023.2173735
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Considerations related to generalization of a study's findings are often interconnected to researchers' judgements regarding the 'quality' of the methodology and methodological pluralism. Too often, researchers consider generalization as only possible with respect to quantitative studies with large numbers of randomly selected participants (statistical generalization). Recently, Levitt suggests qualitative research can be generalized to phenomenon. Like others, she differentiates the type of generalization typically identified with quantitative research, which requires large samples and randomization so that findings can be generalized to the population, with the type of generalization that can be achieved with qualitative research. In turn, Levitt proposes the concept of qualitative generalization, an idea suggested by other researchers. However, her assertions about generalization in qualitative research resonate with the assertions of Thomas and Baas about generalizability in Q methodology. Q methodology offers a unique blend of qualitative and quantitative principles to study subjectivity and includes factor analysis. Yet Q findings are about developing theories, much like qualitative research. Q researchers may discuss substantive inference, connecting generalizations about phenomenon to a population, rather than statistical inference about a population. We discuss acceptance of two types of generalization within and their importance in relation to research equity.
引用
收藏
页码:65 / 78
页数:14
相关论文
共 50 条
  • [1] Non-statistical rational maps
    Amin Talebi
    Mathematische Zeitschrift, 2022, 302 : 589 - 608
  • [2] NON-STATISTICAL APPROACH TO SOLUTIONS
    GUTMANN, V
    RESCH, G
    PURE AND APPLIED CHEMISTRY, 1981, 53 (07) : 1447 - 1459
  • [3] Non-statistical weak measurements
    Tollaksen, Jeff
    QUANTUM INFORMATION AND COMPUTATION V, 2007, 6573
  • [4] Non-statistical rational maps
    Talebi, Amin
    MATHEMATISCHE ZEITSCHRIFT, 2022, 302 (01) : 589 - 608
  • [5] NON-STATISTICAL EFFECTS AND THE DOORWAY STATE
    ROHR, G
    INSTITUTE OF PHYSICS CONFERENCE SERIES, 1982, (62): : 322 - 335
  • [6] Towards non-statistical foundation of thermodynamics
    Zakharov, A. Yu
    INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE ON INNOVATIONS IN ENGINEERING AND TECHNOLOGY, 2018, 441
  • [7] Non-Statistical Effects in Neutron Capture
    Koehler, P. E.
    Bredeweg, T. A.
    Guber, K. H.
    Harvey, J. A.
    O'Donnell, J. M.
    Reifarth, R.
    Rundberg, R. S.
    Ullmann, J. L.
    Vieira, D. J.
    Wiarda, D.
    Wouters, J. M.
    CAPTURE GAMMA-RAY SPECTROSCOPY AND RELATED TOPICS, 2009, 1090 : 424 - +
  • [8] NON-STATISTICAL EMISSION FROM METASTABLE AUTOIONIZING STATES OF OI
    DEHMER, PM
    CHUPKA, WA
    BULLETIN OF THE AMERICAN PHYSICAL SOCIETY, 1974, 19 (10): : 1203 - 1204
  • [9] DUALITY THEOREM OF NON-STATISTICAL AND STATISTICAL BEAM PHASE SPACES
    YINBAO, C
    XI, X
    BULLETIN OF THE AMERICAN PHYSICAL SOCIETY, 1981, 26 (02): : 119 - 119
  • [10] Comparison of different non-statistical classification methods
    Popelka, Ondrej
    Hrebicek, Jiri
    Stencl, Michael
    Hodinka, Michal
    Trenz, Oldrich
    PROCEEDINGS OF 30TH INTERNATIONAL CONFERENCE MATHEMATICAL METHODS IN ECONOMICS, PTS I AND II, 2012, : 727 - +