 |
Questions? Call 888-624-8373 |
|
|
|
|
Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter.
Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 5
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
OCR for page 6
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,
OCR for page 7
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
OCR for page 8
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.
Representative terms from entire chapter:
expectancy students
Items in cart [0]
