[R] matafor package - categorical moderator interpretation question

Calin-Jageman, Robert rcalinjageman at dom.edu
Mon Apr 3 23:32:06 CEST 2017

What does it mean if a categorical moderator is significant overall but has no significant pairwise contrasts between moderator levels?

I'm using metaphor to conduct a meta-analysis with a categorical moderator with 3 levels; this yields a significant result:

              Test of Moderators (coefficient(s) 1,2,3):

      F(df1 = 3, df2 = 37) = 4.6052, p-val = 0.0078

Model Results:
                                          estimate      se    tval    pval    ci.lb   ci.ub
factor(sample_data$Participants)Adults      0.3920  0.2847  1.3771  0.1768  -0.1848  0.9688
factor(sample_data$Participants)Online      0.1403  0.1283  1.0935  0.2812  -0.1197  0.4004
factor(sample_data$Participants)Students    0.2350  0.0717  3.2747  0.0023   0.0896  0.3803  **

But then I conduct contrasts between each moderator level, and none of these are significant (no correction for multiple comparisons applied):

                Linear Hypotheses:

                       Estimate Std. Error z value Pr(>|z|)

      Online - Adults == 0    -0.2517     0.3123  -0.806    0.420

      Students - Adults == 0  -0.1571     0.2936  -0.535    0.593

      Students - Online == 0   0.0946     0.1470   0.643    0.520

      (Adjusted p values reported -- none method)

Any thoughts or guides to interpretation are appreciated!  My code and sample data are at the end of the email.  My interpretation is that while one of the moderator levels may have be a significant factor in the overall analysis, the comparisons between moderator levels are noisier because they test to see if there is a difference in the weights between the two levels.  Given this pattern of results, I conclude the different moderator levels are probably not strong predictors of effect size.  I'm a bit uncertain if this is correct, and would appreciate any feedback.



Robert Calin-Jageman

Professor, Psychology

Neuroscience Program Director

Dominican University

Parmer 210

7900 West Division

River Forest, IL 60305

rcalinjageman at dom.edu



Sample data link:



#load required libraries



sample_data <- read.csv("red_effect_males.csv")

#Overall test of categorical moderator, reports significant result

mod_test = rma(yi, vi, mods = ~factor(sample_data$Participants) - 1, data=sample_data, knha = TRUE)


#Now do pairwise contrasts - but these show no significant contrasts....why?

cont_holder <- c(1:length(unique(sample_data$Participants)))

names(cont_holder) <- sort(unique(sample_data$Participants))

print(summary(glht(mod_test, linfct=contrMat(cont_holder, "Tukey")), test=adjusted("none")))

#Now print individual meta-analysis for each subgroub... Effect sizes estimates and CIs aren't the same as in overall analysis...why?

subgroup_list <- split(sample_data, sample_data$Participants, drop=FALSE)

for (subgroup in subgroup_list) {

  print(paste("Individual results for: ", subgroup$Participants[1]))

  print(rma(yi, vi, data=subgroup, knha=TRUE))


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