DMAR (version 1.0.0)

meta_contrast: Contrast Among Study Effect Sizes (Rosenthal-Rubin)

Description

Tests a focused hypothesis about differences among independent study effect sizes by the method of Rosenthal and Rubin (1982): given effects \(y_i\) with sampling variances \(v_i\) and contrast weights \(\lambda_i\) summing to zero, $$z \;=\; \frac{\sum \lambda_i y_i}{\sqrt{\sum \lambda_i^2 v_i}}$$ is referred to the standard normal. This is how a meta-analyst asks a pointed moderator question (“do the effects decline with weeks of prior teacher-student contact?”) rather than the diffuse heterogeneity question (“do the effects differ at all?”). Raudenbush (1984) used exactly this test for the teacher expectancy literature, with weights inversely proportional to weeks of prior contact.

Usage

meta_contrast(yi, vi, weights, center = TRUE)

Value

A data.frame (class dmar_tbl) with the contrast estimate (\(\sum \lambda_i y_i\)), its se, the z statistic, the two-sided p_value, and k.

Arguments

yi

Numeric vector of study effect sizes (any metric whose sampling distribution is approximately normal; standardized mean differences and Fisher's Z correlations qualify).

vi

Sampling variances of yi, one per study.

weights

Contrast weights, one per study. If they do not already sum to zero they are mean-centered (with a message) when center = TRUE, the convenient route for weights built from a moderator such as 1 / (weeks + 2).

center

Logical: mean-center weights that do not sum to zero? Default TRUE.

Author

Ken Kelley [email protected]

Details

The two-sided p-value is reported; halve it for a directional hypothesis stated in advance (Raudenbush's \(z = 2.75\) carried the one-tailed \(p = .003\)). Dividing the squared contrast \(z^2\) by the total heterogeneity statistic \(Q\) from meta_es gives the proportion of between-study heterogeneity the contrast accounts for, the meta-analytic analog of a contrast's share of the between-group sum of squares.

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.

Rosenthal, R., & Rubin, D. B. (1982). Comparing effect sizes of independent studies. Psychological Bulletin, 92(2), 500--504.

See Also

meta_es for the pooled effect and the total heterogeneity the contrast partitions; combine_p for combined significance tests; contrast_test for the single-study ANOVA analog.

Other meta-analysis: combine_p(), meta_es(), meta_r(), meta_smd(), plot_forest()

Examples

# Raudenbush (1984): do expectancy effects decline with weeks of prior
# teacher-student contact? Weights inversely proportional to weeks + 2,
# study-level data (Pellegrini & Hicks merged), d variances from the
# standard large-sample formula.
data(teacher_expectancy)
study <- teacher_expectancy[-c(4, 5), ]
d  <- append(study$d, 0.52, after = 3)
wk <- append(study$weeks, 0, after = 3)
ne <- append(study$n_experimental, 22, after = 3)
nc <- append(study$n_control, 22, after = 3)
v  <- (ne + nc) / (ne * nc) + d^2 / (2 * (ne + nc))
meta_contrast(d, v, weights = 1 / (wk + 2))
# z near 2.75: the better teachers knew their pupils, the smaller the
# expectancy effect (one-tailed p = .003 in the paper).