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Overreliance on Statistical Testing Logic in the Empirical Testing of Theories and Hypotheses
Hyun-Chul Cho,Shuzo Abe 한국마케팅과학회 2011 마케팅과학연구 Vol.21 No.1
本文?述了有?一些?念和方法?上有??度依???????,??反?的??。?管??假???只是????整??程中的一部分,但是有?多硏究者把???????解???理???整??程中普遍适用的工具。本文作者?明了?????反?的?分依???致的?多??:(1)?念性??,例如混淆硏究假?和??假?,忽?了?了接受含有硏究者??的“??性”原假?而必要的???明(2)方法?的??,如不??的??硏究方法(例如在??理???中,打破了“演?”和“??”的界限和适?的?序),?“??性”零假????果的不正?的解?以及??反?(??零假?)和??主?(??理?)之?的混淆。 ?了消除?些由?度依???反?造成的?念和方法?上的混淆,本文作者提出了一???理???的七步?模型:第一步(理?)→第二步(建立硏究假?:RHEF[存在形式的硏究假?]/RHNF[非存在形式的硏究假?]→第二步(建立???假?:把RHEF或RHNF???H?和H₁)→第四步(用??反?或???明??????假?)→第五步(??硏究假?:RHEF或RHNF成立或不成立)→第六步(????理?:?理???成立或不成立)→第七步(?硏究者的科?的角度?解?????的?果,例如??主?或????主?)(?了更好的理解,??考??部分的?一)。如果硏究假?是通??察得出而非理?的?,那?第一步,第六步和第七步?得无??要。 ?于??七步?模型的?用,作者提出了一下四点。第一,??假???不成立的?,硏究者不要使用??七步模型(例如,“H?:太???地球”和“H₁:地球??太?”[Johnson 1999]; “H?:被告是无辜的”和“Ha:被告是有罪的” [Anderson et al. 1999])。第二,理?(T),通?理?得出的硏究假?(RH)以及把硏究假??化成的???假?之?有?著的??。第二,?使在理?或模型是可以?行????的情?下,???比的理?或模型不??被看做是????的直接?象。例如“H?:模型 A vs. H₁:模型 B”(比如“H?:假?的或???的模型”和“H₁:更一般的模型” [例如,Arora 1982]; “H?:无?行走模型”和“H₁:Markov机制??模型”(反之亦然)[例如,Cheung和Erlandsson 2005])。要?????比理?或模型,通常需要分?使用七步或六步模型。最后,前三步??是理?的演?路?,而后面的第四到第六步代表着???本?据的??路?。因此,硏究者必?有序的?察演?路?和??路?之?的界限和适?的?序。如果硏究者可以忠?的遵守?四点,那?就可以避免不必要的混淆。 ?者相信??& This article explicates some conceptual and methodological problems involved in the tendency of overreliance on statistical testing logic, logic of disproof. Although statistical hypothesis testing is only a subset of the whole process of empirical testing, a number of researchers tend to misconceive that statistical testing logic is universally applicable to the whole process of empirical theory testing. The authors show that this overreliance on logic of disproof leads to many problems: (1) conceptual problems such as confusion between the research hypothesis and the statistical hypothesis, and oversight of the necessity of logic of proof for accepting the “substantive” null hypothesis containing the researcher’s assertion per se, and (2) methodological problems such as a non-rigorous way of conducting empirical research (i.e., the breakdown of the boundary and the proper sequence between the deductive route and the inductive route in empirical theory testing), an incorrect interpretation of test results associated with the testing of the “substantive” null hypothesis, and confusion between logic of disproof (for testing the null hypothesis) and falsificationism (for testing theory). To remove such conceptual and methodological points of confusion caused by overreliance on logic of disproof, the authors have proposed a seven-step model of empirical theory testing: Step 1 (Theory) → Step 2 (Setting up the research hypothesis: RHEF [i.e., the research hypothesis in existential form]/RHNF [i.e., the research hypothesis in non-existential form]) → Step 3 (Setting up the statistical hypothesis: Translating RHEF or RHNF into H? and H₁) → Step 4 (Testing the statistical hypothesis by logic of disproof/proof) → Step 5 (Testing the research hypothesis: RHEF or RHNF is supported/not supported) → Step 6 (Testing theory empirically: The theory is empirically supported/not supported) → Step 7 (Interpretation of this empirical test result from the researcher’s scientific standpoint, such as falsificationism or logical empiricism) (for better understanding, see Figure 1 in the conclusion section). If the research hypothesis is inductively derived from observations rather than theory, then Step 1, Step 6, and Step 7 become irrelevant. With regard to the application of the seven step model, the authors have noted the following four points. Firstly, researchers should not intend to apply this seven step model to cases where statistical hypothesis testing is not valid (e.g., “H?: The Sun revolves around the Earth” and “H₁: The Earth revolves around the Sun” [Johnson, 1999]; “H?: The defendant is innocent” and “Ha: The defendant is guilty” [Anderson et al., 1999]). Secondly, a theory (T), the research hypothesis (RH) derived from the theory, and the statistical hypothesis (H? and H₁) translated from the research hypothesis should be distinguished from each other clearly. Thirdly, even in cases of empirically testable theories or models, two competing theories or models should not be regarded as being the direct objects of statistical testing such as “H?: model A vs. H₁: model B” (e.g., “H?: the hypothesized or simpler model” and “H₁: the more general model” [e.g., Arora 1982]; “H?: the random-walk model” and “H₁: the Markov switching model” (and vice versa) [e.g., Cheung and Erlandsson, 2005]). To test two competing theories or models, they usually have to use two separate seven-step or six-step models. Finally, the former Steps 1-3 correspond to the deductive route ruled by theory and the later Steps 4-6 stand for the inductive route ruled by empirical sample data. Thus, the boundary and the proper sequence between the deductive route and the inductive route must be observed in that order. If researchers observe these four points faithfully, unnecessary confusion can be avoided.
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