Phenomenology (science)

Phenomenology (science)

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The term phenomenology in science is used to describe a body of knowledge which relates several different empirical observations of phenomena to each other, in a way which is consistent with fundamental theory, but is not directly derived from theory. For example, we find the following definition in the Concise Dictionary of Physics:

Phenomenological Theory. A theory which expresses mathematically the results of observed phenomena without paying detailed attention to their fundamental significance.^[1]

The name is derived from phenomenon (from Greek φαινόμενoν, pl. φαινόμενα - phenomena) is any occurrence that is observable.

Contents [hide] 1 Phenomenology in physical sciences 1.1 Examples in physics 2 Phenomenology in social statistics 3 References 4 See also

[edit] Phenomenology in physical sciences

There are cases in physics when it is not possible to derive a theory for describing observed results using first principles (such as Newton's laws of motion or Maxwell's equations of electromagnetism). There may be several reasons for this: For example, the underlying theory is not yet understood or non existent, or the mathematics to describe the observations is too complex. Sometimes different length, mass and time scales are used to build a phenomenological theory.

In these cases sometimes simple algebraic ｅxpressions may be used to model observations or experimental results and used to make predictions about the results of other observations or experiments, despite the fact that the ｅxpressions themselves cannot be (or have not yet been) derived from the fundamental theory of that domain of knowledge.^{[citation needed]}

Another way of describing phenomenology is that it is intermediate between experiment and theory. It is more abstract and includes more logical steps than experiment, but is more directly tied to experiment than theory.^{[citation needed]}

The boundaries between theory and phenomenology, and between phenomenology and experiment, are somewhat fuzzy and to some extent depend on the preconceptions of the scientist describing these and the particular field in which the scientist works.^{[citation needed]}

The philosopher of science Nancy Cartwright does not believe in the fundamental laws but merely in the phenomenological laws of science^[2]

[edit] Examples in physics

The examples below are in chronological order.

• Rutherford model also known as planetary model (1911) describes the structure of an atom based on the experimental results. It has a number of essential modern features, including a relatively high central charge concentrated into a very small volume in comparison to the rest of the atom. It resembles the planetary system, a known physical object larger by several orders of magnitude. It was superseded in 1913 by the Bohr model, which used some of the early quantum mechanical results to give locational structure to the behavior of the orbiting electrons, confining them to certain circular (and later elliptical) orbits.
• Landau theory of second order phase transitions (1936).
• Bloch equations (1946).
• Ginzburg-Landau theory of superconductivity (1950).

[edit] Phenomenology in social statistics

In the science of Statistics, the collection of quantifiable data from people involves a phenomenological step. Namely, in order to obtain that data, survey questions must be designed to collect measurable responses which are categorized in a logically sound and practical way, such that the form in which the questions are asked does not bias the results. If this is not done, data distortions due to question-wording effects (response error) occur, and the data obtained may have no validity at all, because observations are counted up which do not have the same meaning (it would be like "adding up apples and pears"). A prerequisite of a good survey is that all respondents are really able to give a definite and unambiguous answer to the questions, and that they understand what is asked of them in the same way. one could for example ask farmers "How much risk do you run on your farm?" with a scale of response options ranging from e.g. "a lot of risk" to "no risk". But this yields quantitatively meaningless data which is not objective, since the interpretations of risk by farmers could focus on e.g. on the number, size, frequency, severity or consequence of risks, and each farmer will have his own idiosyncratic idea about that. All farmers may suffer e.g. from a lack of rainfall, but some will personally consider it a large risk, others a low risk and some not a risk at all. Furthermore, in actually asking the questions of respondents and subsequently coding the responses to numerical values, a technique must be found to ensure that no misinterpretation occurs of a type that would lead to errors. In other words, in designing the survey instrument, the researcher must somehow find a satisfactory "bridge" of meaning between the logical and practical requirements of the survey statistician, a statistical classification scheme, the awareness of respondents and the processors of the raw data. Finding this "bridge" involves an abstraction process which necessarily goes beyond logical inference, theory and experiment and involves an element of "art", because it must establish an appropriate connection between the language used, the intersubjective interactions between the surveyor and the respondent, and how respondents and those who process the data construct the meaning of what is being asked of them. For this cognitive process, it is impossible to provide a standard procedure which will always work, only "rules of thumb"; it requires a "practical" human insight.