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Introduction

The progress of science is, among others, dependent on two factors: controlled observation or experimentation and structural information, that is, the availability of data as patterns or structures. There are several ways to eliminate the effects of unintended factors in experiments, such as Method of Removal and the Method of Constancy. If possible, all unintended factors should be removed from the experimental situation. However, as is often the case, the effects of certain known unintended factors cannot be completely or even partly eliminated. Control over the variable of interest in such instances can be secured by means of keeping the variable constant during the experiment. (Townsend, 1953, p. 64). Controlled observation and experimentation are needed to limit the number of unintended factors, which may be involved. The more such factors play a role in the emergence of the final results, the more difficult it will be to develop appropriate models for the observed data. Structural information is needed in order to make predictions possible. Predictions are about properties of data structures, not about single data points. For example, in the case of the development of atomic theory the data consisted of spectral lines, that is patterns of spectral lines. Experimentation was needed to study the spectral lines of elements, and not of compound of elements, such as molecules and mixtures of molecules. Similarly, the first attempt to built an explanatory theory of intelligence tests was based on the development of a model for the intercorrelations between intelligence test (Spearman, 1904). In this particular case the data structure consisted of a two-way pattern of product moment correlations coefficients. Using the well-known principle of parsimony, a principle which is almost always used in the development of theories, Spearman came to his famous Two-factor theory of intelligence. The name is still somewhat confusing as this theory actually is a single factor theory. Assuming only one common factor corresponds to the most simple representation of the data (principle of parsimony). The factor was called the general factor g. The theory, or more appropriate, the model could be tested using the well-known tetrad difference. Today, one may compute goodness-of-fit statistics for a maximum-likelihood extraction of a single factor. In some cases the single factor model held and in some cases not. But even in the cases where the model could not be rejected, and that therefore g certainly exists, Spearman still had to admit, that "... this would not of itself afford the smallest indication as to the nature of what this factor represents." Naturally, Spearman proposed several possible explanations of g, referring to such concepts as general intelligence, the power of attention and mental energy. However, these explanations scarcely exceeded the level of pure name giving and naming is not a substitute for explaining. Showing the existence of a phaenomenon is quite different from declaring what it is alike, as Spearman very well knew.

"But notice must be taken that this general factor g, like all measurements anywhere, is primarily not any concrete thing but only a value or magnitude. Further, that which this magnitude measures has not been defined by declaring what it is like, but only by pointing out where it can be found." Spearman, 1927, page 75).
By asking "... which this magnitude measures ..." the suggestion is made that it is al least a unitary faculty. However, Spearman had even doubts about the unitarity of g.
next up previous
Next: Spearman's Plurality of Sub-factors Up: The g-factor in Intelligence Previous: The g-factor in Intelligence

AHGS van der Ven