The nature of psychological measurement is still the subject of fierce controversy. A rather philosophical debate has been going on in this journal; therefore a closer look at physicists’ ideas on measurement may be helpful. In particular, we will try to clarify matters with the help of the crucial concepts of access (validity), precision (reliability), and invariance.
AndersonP.W. (1972). More is different: Broken symmetry and the nature of the hierarchical structure of science. Science, 177(4047), 393–396.
2.
BarrettP. (2008). The consequence of sustaining a pathology: Scientific stagnation [Peer commentary on the paper “Is psychometrics a pathological science?” by J. Michell]. Measurement, 6, 78–123.
3.
BennettJ.H. (1990). Statistical inference and analysis: Selected correspondence of R.A. Fisher. Oxford, UK: Clarendon Press.
4.
BoringE.G. (1920). The logic of the normal law of error in mental measurement. American Journal of Psychology, 31, 1–33.
5.
BorsboomD. (2005). Measuring the mind: Conceptual issues in contemporary psychometrics. Cambridge, UK: Cambridge University Press.
6.
BorsboomD.MellenberghG.J. (2004). Why psychometrics is not pathological: A comment on Michell. Theory & Psychology, 14, 105–120.
7.
BridgmanP.W. (1927). The logic of modern physics. New York, NY: Macmillan.
8.
CampbellN.R. (1920). Physics: The elements. Cambridge, UK: Cambridge University Press.
9.
CampbellN.R. (1928). An account of the principles of measurement and calculation. London, UK: Longmans, Green.
10.
CampbellN.R. (1953). What is science?New York, NY: Dover Reprint. (Original work published 1921)
11.
CohenP.CohenJ.AikenL.S.WestS.G. (1999). The problem of units and the circumstance for POMP. Multivariate Behavioral Research, 34, 315–346.
12.
CronbachL.J.RajaratnamN.GleserG.C. (1963). Theory of generalizability: A liberalization of reliability theory. British Journal of Statistical Psychology, 16, 137–163.
13.
DingleH. (1950). A theory of measurement. British Journal for the Philosophy of Science, 1, 5–26.
14.
DuncanO.D. (1984). Notes on social measurement: Historical and critical. New York, NY: Russell Sage Foundation.
15.
EstesW.K. (1975). Some targets for mathematical psychology. Journal of Mathematical Psychology, 12, 263–282.
16.
Feldman BarrettL. (2009). Understanding the mind by measuring the brain. Lessons from measuring behavior [Peer commentary on the paper “Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition” by E.H. Vul, P. Winkielman, & H. Pashler]. Perspectives on Psychological Science, 4, 314–318.
17.
FeynmanR.P. (1997). Surely you’re joking, Mr. Feynman: Adventures of a curious character. London, UK: W.W. Norton.
18.
FisherR.A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London, Ser. A, 222, 309–368.
19.
FisherR.A. (1973). Statistical methods and scientific inference (3rd ed.). New York, NY: Hafner Publishing Company.
20.
FisherW. P. (2003). Mathematics, measurement, metaphor and metaphysics II: Accounting for Galileo’s “fateful omission”. Theory & Psychology, 13, 791-828.
GelmanA.CarlinJ.B.SternH.S.RubinD. B. (2004). Bayesian data analysis. Boca Raton, FL: CRC Press.
24.
GigerenzerG. (2004). Mindless statistics. The Journal of Socio-Economics, 33, 587–606.
25.
GuttmanL. (1981). What is not what in theory construction. In BorgI. (Ed.), Multidimensional data representations: When and why (pp. 47–64). Ann Arbor, MI: Mathesis.
26.
HandD.J. (1996). Statistics and the theory of measurement. Journal of the Royal Statistical Society, Ser. A, 159, 445–492.
27.
HoodS.B. (2009). Validity in psychological testing. Theory & Psychology, 19, 451–473.
28.
HoshmandL.T. (2003). Can lessons of history and logical analysis ensure progress in psychological science?Theory & Psychology, 13, 39–44.
29.
HuffmanC.A. (1999). The Pythagorean tradition. In LongA.A. (Ed.) The Cambridge companion to early Greek philosophy (pp. 66–97). Cambridge, UK: Cambridge Univ. Press.
30.
JaynesE.T. (1976). Confidence intervals vs. Bayesian intervals. In HarperW.L.HookerC.A. (Eds.), Foundations of probability theory, statistical inference, and statistical theories of science (pp. 175–257). Dordrecht, The Netherlands: Reidel.
31.
JaynesE.T. (2003). Probability theory: The logic of science. Cambridge, UK: Cambridge University Press.
JonesL.V. (Ed.). (1986a). The collected works of J.W. Tukey: Vol. 3. Philosophy and principles of data analysis: 1949–1964. London, UK: Chapman & Hall.
34.
JonesL.V. (Ed.). (1986b). The collected works of J. W. Tukey: Vol. 4. Philosophy and principles of data analysis: 1965–1986. London, UK: Chapman & Hall.
35.
KelleyT.L. (1927). Interpretation of educational measurements. New York, NY: Macmillan.
36.
KelvinW.T. (1891). Popular lectures and addresses (Vol. 1). London, UK: Macmillan.
37.
KleinertA. (1988). “Messen, was messbar ist”: Über ein angebliches Galilei-Zitat [“Measuring what can be measured”: A quotation attributed to Galileo]. Berichte zur Wissenschaftsgeschichte, 11, 253–255.
38.
KochS. (1992). Psychology’s Bridgman vs. Bridgman’s Bridgman: An essay in reconstruction. Theory & Psychology, 2, 261–290.
39.
KrantzD.H.LuceR.D.SuppesP.TverskyA. (1971). Foundations of measurement (Vol. 1). New York, NY: Academic Press.
40.
KyngdonA. (2008). Conjoint measurement, error and the Rasch model. Theory & Psychology, 18, 125–131.
41.
LaughlinR.B. (2005). A different universe: Reinventing physics from the bottom down. New York, NY: Basic Books.
42.
LuceR. (1959). On the possible psychophysical laws. Psychological Review, 66, 81–95.
43.
MartinJ. (2003). Positivism, quantification and the phenomena of psychology. Theory & Psychology, 13, 33–38.
44.
MeehlP.E. (1967). Theory-testing in psychology and physics: A methodological paradox. Philosophy of Science, 34, 103–115.
45.
MeehlP.E. (1978). Theoretical risks and tabular asterisks: Sir Karl, Sir Ronald, and the slow progress of soft psychology. Journal of Consulting and Clinical Psychology, 46, 806–834.
46.
MeehlP.E. (1990). Apraising and amending theories: The strategy of Lakatosian defence and two principles that warrant it. Psychological Inquiry, 1, 108–141.
47.
MengerK. (2007). Calculus: A modern approach. Ginn, IL: Dover. (Original work published 1955)
48.
MichellJ. (1999). Measurement in psychology: A critical history of a methodological concept. Cambridge, UK: Cambridge University Press.
49.
MichellJ. (2000). Normal science, pathological science and psychometrics. Theory & Psychology, 10, 639–667.
50.
MichellJ. (2003a). The quantitative imperative: Positivism, naïve realism and the place of qualitative methods in psychology. Theory & Psychology, 13, 5–31.
51.
MichellJ. (2003b). Pragmatism, positivism, and the quantitative imperative. Theory & Psychology, 13, 45–52.
52.
MichellJ. (2004). Item response models, pathological science and the shape of error: Reply to Borsboom and Mellenbergh. Theory & Psychology, 14, 121–129.
53.
MichellJ. (2005). The meaning of the quantitative imperative: A response to Niaz. Theory & Psychology, 15, 257–263.
54.
MichellJ. (2008a). Conjoint measurement and the Rasch paradox: A response to Kyngdon. Theory & Psychology, 18, 119–124.
55.
MichellJ. (2008b). Is psychometrics pathological science?Measurement, 6, 7–24.
56.
NarensL. (2002). Theories of meaningfulness. London, UK: Erlbaum.
57.
NiazM. (2005). The quantitative imperative vs. the imperative of presuppositions. Theory & Psychology, 15, 247–256.
58.
OakesM. (1986). Statistical inference: A commentary for the social and behavioral sciences. New York, NY: Wiley.
59.
PeierlsR.E. (1960). Wolfgang Ernst Pauli. 1900–1958. Biographical Memoirs of Fellows of the Royal Society, 5, 174–192.
60.
PfanzaglJ. (1968). Theory of measurement. Würzburg, Germany: Physica Verlag.
61.
PopperK.R. (1959). The logic of scientific discovery. New York, NY: Basic Books.
62.
RubinD.B. (2006). Matched sampling for causal effects. Cambridge, UK: Cambridge University Press.
63.
Saint-MontU. (2011). Statistik im Forschungsprozess: Eine Philosophie der Statistik als Baustein einer integrativen Wissenschaftstheorie [Statistics in the process of research: A philosophy of statistics meant as a building block for an integrative philosophy of science]. Heidelberg, Germany: Springer.
64.
SalsburgD.S. (1985). The religion of statistics as practiced in medical journals. The American Statistician, 39, 220–223.
65.
SchönemannP.H. (1994). Measurement: The reasonable ineffectiveness of mathematics in the social sciences. In BorgI.MohlerP. (Eds.), Trends and perspectives in empirical social research (pp. 149–160). Berlin, Germany: Walter de Gruyter.
66.
SedlmeierP. (1996). Jenseits des Signifikanztest-Rituals: Ergänzungen und Alternativen [Beyond the ritual of significance testing: Alternative and supplementary methods]. Methods of Psychological Research Online, 1(4), 41–63.
67.
StevensS.S. (1951). Mathematics, measurement and psychophysics. In StevensS.S. (Ed.), Handbook of experimental psychology (pp. 1–49). New York, NY: Wiley.
68.
SuppesP.ZinnesJ.L. (1968). Basic measurement theory. In LuceR.D.BushR.R.GalanterE. (Eds.), Handbook of mathematical psychology: Vol. 1 (pp. 3–76). New York, NY: Wiley.
69.
ThorndikeE.L. (1918). The nature, purposes, and general methods of measurements of educational products. In WippleG.M. (Ed.), Seventeenth yearbook of the national society for the study of education: Vol. 2 (pp. 16–24). Bloomington, IL: Public School Publishing.
70.
TrendlerG. (2009). Measurement theory, psychology and the revolution that cannot happen. Theory & Psychology, 19, 579–599.
71.
TukeyJ.W. (1986). Data analysis and behavioral science or learning to bear the quantitative man’s burden by shunning badmandments. In JonesL.V. (Ed.), The collected works of J.W. Tukey: Vol. III. Philosophy and principles of data analysis: 1949–1964 (pp. 187–390). London, UK: Chapman & Hall.
72.
TukeyJ.W. (1991). The philosophy of multiple comparisons. Statistical Science, 6, 100-116.
73.
VellemanP.F.WilkinsonL. (1993). Nominal, ordinal, interval, and ratio typologies are misleading. The American Statistician, 47, 65–72.
74.
WignerE. (1949). Invariance in physical theory. Proceedings of the American Philosophical Society, 93, 521–526.
75.
ZuccatoE.ChiabrandoC.CastiglioniS.CalamariD.BagnatiR.SchiareaS.FanelliR. (2005). Cocaine in surface waters: A new evidence-based tool to monitor community drug abuse. Environmental Health, 4(14). doi: 10.1186/1476-069X-4-1410.1186/1476-069X-4-14. Retrieved from http://www.ehjournal.net/content/4/1/14