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# Causal Inference-Deductive Implications--论文代写范文精选

2016-01-22 来源: 51due教员组 类别: Essay范文

**51Due论文代写网精选essay代写范文：“**

**Causal Inference-Deductive Implications**

**” 任何因果关系是基于假设的解释性理论之间的关系,研究假设,实验假设等。模糊性演绎逻辑的事实归因于不可见的实体理论假设,它总是可以提供多个解释相同的现象。这篇社会essay代写范文讲述了推理演绎的相关问题，如果因果推论研究设计是独立的。这些归纳规则的作用是排除其他解释,描绘了随机单因素，明确的独立变量,四个控制变量和一些无关的变量。**

心理学家依赖的归纳方法更复杂，控制变量和解释变量所代表是两个层次的独立变量。这个特性是实验控制条件下的恒常性。假设有一个充分的理由排除可能的机会影响。下面的paper代写范文进行讲述。

Abstract

any causal relationship is based on the implicative relationships among the explanatory theory, the research hypothesis, the experimental hypothesis, the statistical hypothesis, and the data (see, e.g., the three embedding conditional syllogisms discussed in Chow, 1996, 1998). The causal conclusion owes its ambiguity to deductive logic as a result of the facts that (a) hypothetical properties are attributed to the unobservable theoretical entities postulated (Feigl, 1970; MacCorquodale & Meehl, 1948), (b) it is always possible to offer multiple explanations for the same phenomenon (Popper, 1968a, 1968b), and (c) affirming the consequent of a conditional proposition does not affirm its antecedent (Cohen & Nagel, 1934; Meehl, 1967, 1978). In other words, the report's treatment of random subject-assignment is not helpful when it incorrectly assigns to the research design the task of making causal inference possible. Nor is the ambiguity of drawing causal conclusions a difficulty in inductive logic, as said in the report that "the causal inference problem ... one of missing data" (Wilkinson & Task Force, 1999, p.600).

If causal inference is independent of research design in general (and the completely randomized design in particular), what precisely is the role of the design in empirical research? The answer to this question sets in high relief the unacceptability of the report's suggestion of replacing the control group with the contrast group if the researcher is concerned with conceptual rigor or methodological validity. Experimental Design and Induction Contrary to the induction by enumeration assumed in the report (recall the invocation of `missing data' on p. 600), underlying a valid research design is one of Mill's (1970) canons of induction (viz., Method of Difference, Joint Method of Agreement and Difference, Method of Concomitant Variation, and Method of Residues; see Cohen & Nagel, 1934, for the exclusion of Method of Agreement).

The function of these inductive rules is to exclude alternative explanations, as may be seen in Table 1, which depicts the formal structure of the completely randomized one-factor, two-level experiment described in the `Independent Observations from Non-randomly Selected Samples' sub-section above. Made explicit in Table 1 are the independent variable (viz., the similarity in sound among the ten words in the list), four control variables (viz., list length, number of lists, rate of presentation, and the length of the items used), the dependent variables (viz., the number of items recalled in the correct order), and some of an infinite number of extraneous variables. This formal arrangement of the independent, control and dependent variables satisfies the stipulation of Mill's (1973) Method of Difference.

That is, psychologists rely on an inductive method that is more sophisticated than the induction by enumeration envisaged in the report. Control Variables and Exclusion of Explanations Variables CI through C4 are control variables in the sense that they are represented by the same level at both levels of the independent variable. This feature is one type of the `constancy of condition' of experimental control (Boring, 1954, 1969). Suppose that there is a good reason to exclude chance influences as the explanation of the difference between X E and X c (i.e., the difference is statistically significant). This difference is found when there is no difference in any of the four control variables between the experimental and control conditions.

Consequently, it can be concluded that none of the control variables is responsible for the difference between X E and X c. This shows that experimental control in the form of using control variables serves to exclude explanations, not to affirm a causal relationship. Random Subject-assignment As A Control Procedure Extraneous variables of the experiment are defined by exclusion, namely, any variable that is neither the independent, the control or the dependent variable is an extraneous variable. As the symbol, C∝, in Table 1 indicates, there is an infinite number of extraneous variables. It follows that, in order to exclude any of them as an explanation of the data, these extraneous variables have to be controlled (in the sense of being held constant at both levels of the independent variable). Depending on the nature of the independent variable, the extraneous variables may be excluded from being confounding variables by (a) assigning subjects randomly to the experimental and control conditions (the only procedure recognized in the report), (b) using the repeated-measures design, and (c) using the matched-groups (or randomized block) designs. That is, instead of rendering possible causal inference, random subject-assignment is only one of several control procedures that serve to prevent extraneous variables from being confounding variables.

Control versus Contrast Group

That no contrast group can replace the control group may also be seen from Table 1. The control group and the experimental group are identical in terms of all the control variables. It is reasonable to assume that the two groups are comparable in terms of the extraneous variables to the extent that the completely randomized design is appropriate and that the random-assignment procedure is carried out successfully. Being different from the control group, the contrast group envisaged in the report has to be a group that differs from the experimental group in something else in addition to being different in terms of the independent variable. The additional variable involved cannot be excluded as an alternative explanation. That is, there is bound to be a confounding variable in the contrast group; otherwise it would be a control group. The subject's gender is treated as an extraneous variable in Table 1.

However, if there is a theoretical reason to expect that male and female students would perform differently on the task, gender would be controlled in one of several ways. First, gender may be used as an additional control variable (e.g., only male or female students would be used). Second, gender may be used as another independent variable, in which case the relevancy of gender may be tested by examining the interaction between acoustic similarity and gender. The third alternative is to use gender as a blocking variable, such that equal number of male and females are used in the two groups. Which male (or female) is used in the experimental or control condition is determined randomly. In other words, the choice of any variable (be it the independent, control or dependent variable) is informed by the theoretical foundation of the experiment. This gives the lie to the report's treating matching or blocking variables as 'nuisance' variables.

Conclusions

Psychologists can justify using non-randomly selected student-subjects because the representativeness of such samples is warranted on theoretical grounds. Moreover, using student-subjects does not violate the independence of observations requirement. Causal inference is made by virtue of the implicative relationships among the hypotheses at different levels of abstraction and data. Being one of several control procedures, random subject-assignment serves to exclude extraneous variables as alternative explanations of data. Psychologists can exclude many extraneous variables by using the repeated-measures or randomized-block design. Many of the observations made about psychologists' research practice would assume a more benign complexion if theoretical relevancy and some subtle distinctions are taken into account. For example, the evidential support for the experimenter's expectancy effects has to be re-considered if the distinction between meta-experiment and experiment is made. It is necessary for power-analysts to resolve the 'disparate levels of abstraction' difficulty and to explain how a conditional probability may be used as an exact probability. Despite what is said in the report, it is hoped that non-psychologist readers have a better opinion of psychologists' methodological sophistication, conceptual rigor or intellectual integrity.

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