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5 analyze the situations of interest, (b) to determine whether mediating mechanisms shown to be important in traditional research areas are likely to be present in the new areas, and (c) to estimate the extent to which expectancy effects court be influential in the new area. The present paper undertakes such an analysis. Mediation of Interpersonal Expectancy Effects Basic Issues A primary question of interest with respect to expectancy effects is the question of mediation: How are one person's expectations communicated to another person so as to create a self-fulfilling prophecy? This question in turn can be broken down into two components. The first component is the differential behaviors that are displayed by the expecter as a result of holding differential expectancies (the expecter-behavior link). For example, in what ways do teachers treat their high expectancy students differently? The second component is the differential behaviors that are associated with actual change in expectee behavior and self-concept (the behavior-outcome link). For example, what teacher behaviors result in better academic performance by the students? Both these aspects are critical in understanding expectancy mediation, for even if we could show an enormous effect of expectancy on expecter behavior (e.g., teachers smile more at high expectancy students), that behavior would not be important in expectancy mediation unless it actually impacted on the expectee to create better outcomes (e.g., being smiled at leads to better grades). The Four-Factor "Theory" Rosenthal (1973a, 1973b) proposed a four-factor "theory" of the mediation of teacher expectancy effects. In this view, four broad groupings of teacher
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6 behaviors are hypothesized to be involved in teacher expectancy effects. The first factor is climate, referring to the warmer socioemotional climate that teachers may create for their high expectancy students. This factor includes warmth communicated in both verbal and nonverbal channels. The second factor, feedback, refers to teachers' tendency to give more differentiated feedback to . . . high expectancy students. The third factor, input, refers to the tendency to teach more material and more difficult material to high expectancy students. The fourth factor is output, or the tendency for teachers to spend more time with high expectancy students and provide them with greater opportunities for responding. Although the four factor theory was originally proposed to account for the mediation of teacher expectancy effects, it seems reasonable to think that these factors may also operate in other domains where expectancy effects may be operating. Meta-analysis of Expectancy Mediation The question of how expectancy effects are mediated is ultimately an empirical one. Luckily, many studies address the mediation of expectancy effects, and we have conducted a meta-analysis of this literature (Harris & Rosenthal, 1985~. Essentially, we read all the studies we could find that examined expectancy mediation (resulting in an initial pool of 180 studies) and classified them according to the mediating variables that were investigated. This resulted in 31 mediating behaviors each of which was examined in at least four studies. We then computed an overall significance level and effect size for each of the 31 categories, separately for the expectancy-behavior effects and the behavior-outcome effects. The results of this meta-analysis pointed to the practical importance of 16 behaviors in mediation: negative climate, physical distance, input,
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7 positive climate, off-task behavior, duration of interactions, frequency of interactions, asking questions, encouragement, eye contact, smiles, praise, accepting students' ideas, corrective feedback, nods, and wait-time for responses. Table 1 summarizes the results of the meta-analysis for these 16 behaviors, presenting the effect sizes for the expectancy-behavior links and the behavior-outcome links separately. An intuitive way of understanding these effect sizes is given by the Binomial Effect Size Display (BESD; Rosenthal & Rubin, 1982-). The BESD expresses correlations in terms of percent increase in "success" rates due to a given "treatment," with the treatment group success rate computed as .50+(r/2) and the control group success rate computed as .50-(r/2). So, for example, the correlation of .21 for Positive Climate can be interpreted using the BE SD as meaning that the percentage of teachers exhibiting above average amounts of Positive Climate will increase from 39.5% [.50-(.21/2)] for low expectancy students to 60.5: [.50+(.21/2)] for high expectancy students. The other effect sizes can be similarly interpreted. Note that in Table 1 the effect sizes for behavior-outcome relations tend to be larger than the effect sizes for expectancy-behavior relations. One possible reason for this is that expectancies are manifested in myriad ways, meaning that the relationship between expectations and any particular behavior is not likely to be very strong. However, we can more accurately predict a person's response to a particular behavior once we know that a particular behavior has occurred. In other words, if we can condition on the behaviors emitted, we are in a better position to make more accurate predictions. We also presented a summary analysis evaluating the four factor theory. The ten behavior categories with the most studies (and therefore providing the most stable estimates) were reclassified into the four factors of climate, feedback, input, and output. We then computed an overall significance level
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8 w and effect size for each of the four factors, again separately for the expectancy-behavior and behavior-outcome links. For the expectancy-behavior link, the four factors were highly statistically significant ant associated with small to medium effect sizes: climate, r=.20; feedback, r=.13, input, r=.26, and output, r=.19. With respect to the behavior-outcome link, again all four factors were statistically significant, but in terms of effect size, feedback did not seem to be very important: climate, r=.36; feedback, r-.07; input, r=.33; and output ~ r=.20 . Human Performance Technologies and Expectancy Effects We now turn to a more focused discussion of the possible influence of expectancy effects on research on techniques for the enhancement of human performance. In this next section, we (a) describe paradigmatic examples of each of five research areas concerned with improving human performance, and (b) offer opinions about the extent to which expectancy effects may be influencing research results in these areas. The five areas that will be covered are those targeted for evaluation by the Committee on Techniques for the Enhancement of Human Performance; these areas are research on accelerated learning, neurolinguistic programming, mental practice, biofeedback, and parapsychology. One caveat should be emphasized in advance: It is not possible for us to conduct meta-analyses of each of these areas; instead, we will have to rely on a light review of each area and focus on some examples of typical experiments.-Consequently, we need to stress that our overall assessment is accurate only to the extent that our samples are representative. Meta-analyses of these domains would be of great value and should be undertaken for any domains for which they are not yet available.