Culture-free testing is far more hypothetical than real. It assumes, if not requires, there are no cultural influences in any measurement and assessment of an individual or group on some trait. This further suggests that measurement and assessment can be designed to only tap into true individual or group traits and not draw on any culture-related error variances that may and do occur. Historically, culture-free measurement was seen as merely error-reduced measurement. Error here means unintended and undefined variance thought of as being unavoidable yet reducible through solid methodology.
Culture-Free Testing in Two Test Paradigms or Models
However, the artifact of culture-free testing is easily recognizable in current research paradigms that presume with big enough samples that researchers can control and even eliminate cultural influences or patterns in the data through either matrix algebra or calculus-based statistical methods: classic test theory (Observed Score = True Score + Error) and modern test theory with its Raschian, function-based calculus, respectively.
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As such, culture-free testing may be the psychometric equivalent of a myth, or perhaps better stated as a cipher, at present. Namely, in the absence of any quantity or example of a bona fide culture-free test, scientific opinion cannot confirm culture-free testing as a reality, at present. Thus, it may be tempting to summarily dismiss culture-free testing without fully knowing why.
However, its guiding principles have been useful in discouraging, let alone discontinuing, the use and creation of “separate yet equal” versions of tests for men, women, and various ethno-cultural groups.
Historical Backdrop
Seminal works in the 1950s on the role of values in constructing a valid theory of human action and the need to improve the accuracy of what psychological tests measured have influenced the field to consider that culture serves as a context for understanding an individual. The subsequent waning of an inclusive theory of action and the booming growth of trait-factor testing and measurement meant that “culture as context” became supplanted by the notion of “culture as barrier.” Therefore, a standardized test was presumed to be largely culture free through the 1950s and into the 1960s. By the 1970s, culture-free testing was made an explicit assessment goal from both positivist and civil rights perspectives. As culture-free testing could not be validated by its own data, it did not last long, giving way to the notion of culture-fair testing in the late 1970s and into the early 1980s. Not surprisingly, culture-fair testing, in turn, was not supported by its data, despite the rigors of classic test theory test validation.
It should be noted, however, that the culture-free testing movement still affected testing significantly. For example, a single Strong Interest Inventory with overall, male, female, and other group norms exists instead of separately developed Strong Campbell instruments for each, as was the case in the not too distant past. Thus, the now nonremarkable modern testing practice of using the same instrument yet with different norms was a direct result of this notable psychometric evolution before it dead-ended in classic test theory.
Implications in Modern Test Theory
Culture-free testing may seem a noble, if not odd, idea that cannot be realized in classic test theory and its matrix algebra assumptions, but what about modern test theory with its calculus-based functions? These equations allow for group differences to be “smoothed out” by an algorithm made up of hundreds, if not thousands, of responses. Thus, modern test theory statistics are not presumably influenced by culture because they are “non-norm norms” or “not group yet group-based comparisons.” Hence, though methodologically debatable, key assumptions of culture-free testing can and do “live on” because of their modern test theory mathematics.
In practice, as might also be expected, cultural and other group effects can and do affect some item characteristic curves or other modern test theory-based data indices. Indeed, cultural data nonuniformly impact an item or a test’s accuracy and consistency. Thus, item/test function shape “morphs” or changes when “true,” nonuniform group differences occur, of which culture is one. In short, modern test theory’s inferred assumptions about being culture free or culture fair do not prevail, despite some mathematical support that it should.
True to postmodern and constructivist assumptions, tests generally work best with those resembling the norming, or even non-norm, group. This too may extend to test developers and test users. Similar to the ciphered culture-free test, confirming data supporting culture-free testing appears to be wanting, and though theoretically plausible in places, it does not now appear to be forthcoming in practice any time soon.
References:
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