DMAR (version 1.0.0)

teacher_expectancy: Teacher Expectancy Meta-Analysis Data (Raudenbush, 1984)

Description

The 19 effect sizes from Raudenbush's (1984) synthesis of 18 experiments testing the effect of teacher expectancy on pupil IQ, the meta-analysis that resolved the controversy started by Pygmalion in the Classroom (Rosenthal & Jacobson, 1968; the single famous study is shipped separately as pygmalion). In each experiment, teachers were told that randomly selected children were likely to bloom intellectually; the synthesis asks how large the resulting IQ advantage was and, centrally, why it varied across studies. Raudenbush's hypothesis, strongly supported, was that the longer teachers had known their pupils before the expectancy induction, the smaller the effect: credible deception is the Achilles' heel of the design.

Usage

teacher_expectancy

Format

A data frame with 19 rows (18 experiments; Pellegrini and Hicks, 1972, contributes a tester-aware and a tester-blind condition) and 10 variables.

study

Integer identifier, in the order of the paper's Table 1.

author

Study authors (with the Pellegrini and Hicks condition noted).

year

Year of publication.

weeks

Estimated weeks of teacher-student contact prior to the expectancy induction, 0 to 24. The moderator at the heart of the paper.

testing

Factor: group or individual IQ testing.

tester

Factor: test administrator aware of or blind to the expectancy designations.

n_experimental, n_control

Per-condition sample sizes (from the studies as tabulated in Raudenbush & Bryk, 1985; the 1984 table does not print them).

d

Standardized mean difference: the treatment effect in IQ points divided by the control group's posttest standard deviation (positive when the expectancy children gained more). The 1984 paper's Table 1 values.

p_one_tailed

One-tailed p-value reported for the study's expectancy effect.

Author

Ken Kelley [email protected]

Details

The study-level Pellegrini and Hicks values. For analyses with the 18 studies as units (the combined significance tests, the contrast on weeks of prior contact, and the heterogeneity statistic), Raudenbush merged the two Pellegrini and Hicks conditions into a single study-level entry with \(d = 0.52\) and one-tailed \(p = .010\) (Table 1 prints these on the study's header row above the two condition rows). Replace rows 4 and 5 with that pair to reconstruct his 18-study analyses, as the teacher expectancy vignette does. For the tester aware-versus-blind comparisons the two conditions enter separately, which is why the data ship at the condition level.

Relation to the 1985 version. Raudenbush and Bryk (1985) re-standardized the same literature for their empirical Bayes analysis (that version circulates as dat.raudenbush1985 in metafor), so its effect sizes differ from the d column here, which preserves the 1984 paper's metric. The sample sizes are common to both.

The teacher expectancy vignette (vignette("teacher_expectancy", package = "DMAR")) reproduces the paper's analyses with combine_p, meta_contrast, and meta_smd, and then reanalyzes the data with modern random effects machinery.

References

Raudenbush, S. W. (1984). Magnitude of teacher expectancy effects on pupil IQ as a function of the credibility of expectancy induction: A synthesis of findings from 18 experiments. Journal of Educational Psychology, 76(1), 85--97. tools:::Rd_expr_doi("10.1037/0022-0663.76.1.85")

Raudenbush, S. W., & Bryk, A. S. (1985). Empirical Bayes meta-analysis. Journal of Educational Statistics, 10(2), 75--98.

Rosenthal, R., & Jacobson, L. (1968). Pygmalion in the classroom: Teacher expectation and pupils' intellectual development. Holt, Rinehart and Winston.

See Also

pygmalion for the single Rosenthal and Jacobson study this literature grew from; meta_smd, meta_contrast, and combine_p for the analyses the vignette reproduces.

Examples

data(teacher_expectancy)
head(teacher_expectancy)

# The paper's central picture: effect size against weeks of prior contact.
plot(d ~ weeks, data = teacher_expectancy,
     xlab = "Weeks of teacher-student contact before induction",
     ylab = "Effect size d")

# The study-level (18-study) data Raudenbush used for the combined tests:
# merge the two Pellegrini & Hicks conditions into their study row.
study_level <- teacher_expectancy[-c(4, 5), ]
ph <- data.frame(study = 4, author = "Pellegrini & Hicks", year = 1972,
                 weeks = 0, testing = "group", tester = "aware",
                 n_experimental = 22, n_control = 22,
                 d = 0.52, p_one_tailed = .010)
study_level <- rbind(study_level[1:3, ], ph, study_level[4:17, ])
round(c(mean = mean(study_level$d), sd = sd(study_level$d)), 2)  # .11, .20