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inattentiveness, or to put it down to chance. And Freud characterized the pertinent explanatory unconscious causes of slips as motives of unpleasure. Thus, when a young man forgot the Latin word aliquis in a quotation from Virgil, Freud diagnosed its interfering cause as the man s distressing unconscious fear that his girlfriend had become pregnant by him (1901, p. 9). If that latent fear was actually the motive of the slip, it was surely not apparent to anyone. Once it is clear what is meant by a bona fide Freudian slip, we need to ask whether there actually exist any such slips at all, that is, slips that appear to be psychologically unmotivated but are actually caused by repressed, unpleasant ideas. It is very important to appreciate how difficult it is to provide cogent evidence for such causation. K. Sch ttauf, J. Bredenkamp, and E. K. Specht (1997) claim to have produced just such evidence. They note that, according to psychoanalytic etiologic theory, obsessive-compulsive neurosis is attributable to an unconscious conflict whose repressed component features anal-erotic and sadistic wishes, which are presumably activated by regression. Then they reason that when such conflict-laden material is to be verbalized by obsessive-compulsive neurotics, Freudian theory expects a higher incidence of misspeakings (slips of the tongue) among them than among normal subjects. And these researchers report that all of their findings bore out that expectation. This investigation by Sch ttauf, Bredenkamp, and Specht differs from Br der s (1995) strategy, which was designed to inquire into the possible influence of unconscious information-processing on the frequency of specific speech-errors in an experimental setting. Thus, Br der and Bredenkamp (1996) claim to have produced experimental support for the weaker Freudian thesis of verbal slip generation by unconscious, rather than repressed, thoughts: Priming words that remain unconscious induce misspeaking errors with higher probability than consciously registered ones. As for the soundness of the design of Sch ttauf, Bredenkamp, and Specht, Hans Eysenck (Rosemarie Sand, personal communication, March 1, 1996) raised several objections: (1) As the author [Sch ttauf ] himself acknowledges, this is not an experiment, as ordinarily understood; it is a simple correlational study . . . correlation cannot be interpreted as causation, which he unfortunately attempts to do. (2) The members of the experimental group were severely neurotic, while the control group were normals. But the proper control group would have been severely [disturbed] neurotics suffering from a different form of neurosis than that of obsessive compulsive behaviour. (3) Freudian theory posits a causal relationship between the anal stage of development and obsessive compulsive neurosis; the author does not even try to document this hypothetical relationship. (4) Obsessive-compulsive neurotics suffer from fear of dirt and contamination, so that on those grounds alone they would be likely to react differentially to stimuli suggesting such contamination. . . . It is truly commonsensical to say that people whose neurosis consists of feelings of dirt will react differentially to verbal presentations of words related to dirt. Naturally, I sympathize with Sch ttauf and his coworkers in their avowed effort (sec. 4) to escape my criticism (Gr nbaum, 1984, pp. 202 205) of an earlier purported experimental confirmation of Freud s theory of slips by Motley (1980). I had complained that the independent variable Motley manipulated in his speech-error experiments did not involve unconscious antecedents but only conscious ones. As Sch ttauf, Bredenkamp, and Specht tell us, precisely to escape my criticism of Motley, they relied on Freud s etiology of obsessivecompulsive neurosis to infer that subjects who exhibit the symptoms of that neurosis fulfill the requirement of harboring repressions of anal-sadistic wishes. Thus, only on that etiologic assumption does their use of compulsive subjects and their manipulation of words
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Two Continuous Predictors: High Interpredictor Correlation Hypothesis 4 We now turn to the two continuous psychological predictors, bene ts and worry. We can easily test the linear relationship between each predictor and intention separately. Two ver sions of Equation 19.4, Y = b0 + b1 X 1 , are estimated, the rst using scores on bene ts as X1 and the second using scores on worry as X1. The results of these one-predictor regression analyses are given in Table 19.3, Panels A and B, respectively. We note that the slope b1 = 0.39, t (238) = 6.34, p < .001 for bene ts and that the slope b1 = 0.21, t (238) = 1.89, p = .060 for worry.
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Marsh placed this claim on SEMNET (an electronic mail network on the discussion of various structural equation modeling issues) and invited the 1,500 members to provide counterexamples. Although many interesting points were raised in response to this strawperson claim, no one
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Typical Downlink Power Budget. A link budget is a tabular method of calculating space communication system parameters. The approach is very similar to that used for LOS microwave links (see Section We start with the EIRP of the satellite for the downlink or the EIRP of the earth station for the uplink. The bottom line is C/ N 0 and the link margin, all calculated with dB notation. C/ N 0 is the carrier-to-noise ratio in 1 Hz of bandwidth at the input of the LNA. (Note: RSL, or receive signal level, and C are synonymous.) Expressed as an equation:
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The data preparation phase offers the data miner the opportunity to clarify relationships between variables and to derive new variables that may be useful for the analysis. For example, consider the following three variables: (1) amount spent (by customer) in the last month, (2) amount spent in the last three months, and (3) amount spent in the last six months. Clearly, the amount spent by the customer in the last month is also contained in the other two variables, the amount spent in the last three months and the last six months. Therefore, the amount spent in the last month is getting triplecounted. Now, the analyst may not wish for this most recent amount to be so heavily weighted. For example, in time-series models, the more recent measurements are the most heavily weighted. In this case, however, we prefer not to triple-count the most recent month and must therefore derive two new variables, as shown in Table 7.3.
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