tips:multiple_factors_interactions

The example below shows how to test/examine multiple factors and their interaction in (mixed-effects) meta-regression models.

For the example, we will use the data from the meta-analysis by Raudenbush (1984) (see also Raudenbush & Bryk, 1985) of studies examining teacher expectancy effects on pupil IQ (`help(dat.raudenbush1985)`

provides a bit more background). The data can be loaded with:

library(metafor) dat <- dat.raudenbush1985

I copy the dataset into 'dat', which is a bit shorter and therefore easier to type further below.

For illustration purposes, we will categorize the `weeks`

variable (i.e., the number of weeks of contact prior to the expectancy induction) into three levels (0 weeks = "none", 1-9 weeks = "some", and 10+ weeks = "high"):

dat$weeks <- cut(dat$weeks, breaks=c(0,1,10,100), labels=c("none","some","high"), right=FALSE) dat

study author year weeks setting tester yi vi 1 1 Rosenthal et al. 1974 some group aware 0.03 0.0156 2 2 Conn et al. 1968 high group aware 0.12 0.0216 3 3 Jose & Cody 1971 high group aware -0.14 0.0279 4 4 Pellegrini & Hicks 1972 none group aware 1.18 0.1391 5 5 Pellegrini & Hicks 1972 none group blind 0.26 0.1362 6 6 Evans & Rosenthal 1969 some group aware -0.06 0.0106 7 7 Fielder et al. 1971 high group blind -0.02 0.0106 8 8 Claiborn 1969 high group aware -0.32 0.0484 9 9 Kester 1969 none group aware 0.27 0.0269 10 10 Maxwell 1970 some indiv blind 0.80 0.0630 11 11 Carter 1970 none group blind 0.54 0.0912 12 12 Flowers 1966 none group blind 0.18 0.0497 13 13 Keshock 1970 some indiv blind -0.02 0.0835 14 14 Henrikson 1970 some indiv blind 0.23 0.0841 15 15 Fine 1972 high group aware -0.18 0.0253 16 16 Grieger 1970 some group blind -0.06 0.0279 17 17 Rosenthal & Jacobson 1968 some group aware 0.30 0.0193 18 18 Fleming & Anttonen 1971 some group blind 0.07 0.0088 19 19 Ginsburg 1970 some group aware -0.07 0.0303

Variable `yi`

provides the standardized mean differences and `vi`

the corresponding sampling variances for these 19 studies. For this illustration, we will focus on variables `weeks`

(as described above) and `tester`

(whether the test administrator was aware or blind to the researcher-provided designations of the children's intellectual potential) as possible moderator variables.

The `cut()`

command as used above automatically turns the `weeks`

variable directly into a factor. Moreover, the `none`

level is automatically set as the reference level (followed by levels `some`

and `high`

). To turn the `tester`

variable into a factor and to ensure that the `blind`

level becomes the reference level in the models to be fitted below, we can use the following command:

dat$tester <- relevel(factor(dat$tester), ref="blind")

A table with the number of factor level combinations (and margin counts) can be obtained with:

addmargins(table(dat$weeks, dat$tester))

blind aware Sum none 3 2 5 some 5 4 9 high 1 4 5 Sum 9 10 19

So, all factor level combinations are present in this dataset.

We start by fitting a model that only contains the main effects of the two factors:

res.a1 <- rma(yi, vi, mods = ~ weeks + tester, data=dat) res.a1

Mixed-Effects Model (k = 19; tau^2 estimator: REML) tau^2 (estimated amount of residual heterogeneity): 0.0094 (SE = 0.0134) tau (square root of estimated tau^2 value): 0.0972 I^2 (residual heterogeneity / unaccounted variability): 24.90% H^2 (unaccounted variability / sampling variability): 1.33 R^2 (amount of heterogeneity accounted for): 49.85% Test for Residual Heterogeneity: QE(df = 15) = 24.1362, p-val = 0.0628 Test of Moderators (coefficient(s) 2,3,4): QM(df = 3) = 9.9698, p-val = 0.0188 Model Results: estimate se zval pval ci.lb ci.ub intrcpt 0.4020 0.1300 3.0924 0.0020 0.1472 0.6567 ** weekssome -0.2893 0.1360 -2.1271 0.0334 -0.5558 -0.0227 * weekshigh -0.4422 0.1464 -3.0205 0.0025 -0.7291 -0.1552 ** testeraware -0.0511 0.0927 -0.5512 0.5815 -0.2327 0.1305 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Let's start with the table at the bottom containing the model results. The first coefficient in the table is the model intercept ($b_0 = .4020$), which is the estimated average standardized mean difference for levels `none`

and `blind`

of the `weeks`

and `tester`

factors, respectively. For this combination of levels, the effect is significantly different from zero ($p = .0020$), although the 95% confidence interval ($.1472$ to $.6567$) indicates quite some uncertainty how large the effect really is.

The second and third coefficients correspond to the dummy variables `weekssome`

and `weekshigh`

($b_1 = -.2893$ and $b_2 = -.4422$, respectively). These coefficients estimate how much higher/lower the average effect is for these two levels of the `weeks`

factor, compared to the reference level (i.e., `none`

). Therefore, these coefficients directly indicate the contrast between the `none`

and the other two levels. Both coefficients are significantly different from zero ($p = .0334$ and $p = .0025$, respectively), so the average effect is significantly different (and in fact lower) when teachers had some or a high amount of contact with the students prior to the expectancy induction (instead of no contact).

Note that we have not yet tested the `weeks`

factor as a whole. To do so, we have to test the null hypothesis that both coefficients are equal to zero simultaneously (see also the illustration of how to test factors and linear combinations of parameters in meta-regression models). We can do this with:

anova(res.a1, btt=2:3)

Test of Moderators (coefficient(s) 2,3): QM(df = 2) = 9.1550, p-val = 0.0103

This is a Wald-type chi-square test with 2 degrees of freedom, indicating that the factor is in fact significant ($p = .0103$).

We have also not yet tested whether there is a difference between levels `some`

and `high`

of this factor. One could change the reference level of the `weeks`

factor and refit the model to obtain this test. Alternatively, and more elegantly, we can just test the difference between these two coefficients directly. We can do this with:

anova(res.a1, L=c(0,1,-1,0))

Hypothesis: 1: weekssome - weekshigh = 0 Results: estimate se zval pval 1: 0.1529 0.1016 1.5050 0.1323 Test of Hypothesis: QM(df = 1) = 2.2650, p-val = 0.1323

The `linearHypothesis()`

function from the `car`

package also allows us to conduct such a test. After (installing and) loading the package, either one of the following commands leads to the same results:

library(car) linearHypothesis(res.a1, c(0,1,-1,0)) linearHypothesis(res.a1, c("weekssome - weekshigh = 0"))

Linear hypothesis test Hypothesis: weekssome - weekshigh = 0 Model 1: restricted model Model 2: res.a1 Res.Df Df Chisq Pr(>Chisq) 1 16 2 15 1 2.265 0.1323

So, we actually do not have enough evidence to conclude that there is a significant difference between these two levels ($p = .1323$).

The `glht()`

function from the `multcomp`

package also allows for such tests and actually makes it easy to conduct all pairwise comparisons between factor levels (with or without adjusted p-values due to multiple testing). To test all three linear combinations against each other, we would use:

library(multcomp) summary(glht(res.a1, linfct=rbind(c(0,1,0,0), c(0,0,1,0), c(0,1,-1,0))), test=adjusted("none"))

Simultaneous Tests for General Linear Hypotheses Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) 1 == 0 -0.2893 0.1360 -2.127 0.03341 * 2 == 0 -0.4422 0.1464 -3.021 0.00252 ** 3 == 0 0.1529 0.1016 1.505 0.13232 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- none method)

No adjustment to the p-values has been used for these tests (`test=adjusted("none")`

), but other options are possible for the `test`

argument (see `help(summary.glht)`

and the illustration of how to test factors and linear combinations of parameters). Note that the p-values for the three tests above are exactly the same as the corresponding two p-values from the model table (coefficients 2 and 3) and the one obtained with the `linearHypothesis()`

function above.

Finally, the last (4th) coefficient in the model table corresponds to the dummy variable `testeraware`

($b_3 = -.0511$) and estimates how much higher/lower the average effect is for studies where the test administrator was aware of the children's designation, compared to studies where the test administrator was blind. So, again, this is a contrast between these two levels. The coefficient is not significantly different from zero ($p = .5815$), so this factor does not appear to account for (some of the) heterogeneity in the effects.

The output includes some additional results. First of all, the results given under `Test of Moderators (coefficient(s) 2,3,4)`

is the omnibus test of coefficients 2, 3, and 4 (the first being the intercept, which is not included in the test by default). This Wald-type chi-square test is significant ($Q_M = 9.97, df = 3, p = .02$), so we can reject the null hypothesis that all of these coefficients are equal to zero (which would imply that there is no relationship between the size of the effect and these two factors). Moreover, the pseudo-$R^2$ value given suggests that roughly 50% of the total amount of heterogeneity is accounted for when including these two factors in the model. Finally, the `Test for Residual Heterogeneity`

just falls short of being significant ($Q_E = 24.14, df = 15, p = .06$), which would suggest that there is no significant amount of residual heterogeneity in the true effects when including these two factors in the model.

Based on the fitted model, we can also obtain the estimated average effect for each combination of the factor levels. For this, we can use the `predict()`

function, specifying how the dummy variables should be filled in. For levels `none`

, `some`

, and `high`

of the `weeks`

factor and level `blind`

of the `tester`

factor, we would use:

predict(res.a1, newmods=rbind(c(0,0,0),c(1,0,0),c(0,1,0)))

pred se ci.lb ci.ub pi.lb pi.ub 1 0.4020 0.1300 0.1472 0.6567 0.0839 0.7200 2 0.1127 0.0804 -0.0448 0.2702 -0.1345 0.3599 3 -0.0402 0.1020 -0.2401 0.1597 -0.3163 0.2359

For the same levels of the `weeks`

factor and level `aware`

of the `tester`

factor, we would use:

predict(res.a1, newmods=rbind(c(0,0,1),c(1,0,1),c(0,1,1)))

pred se ci.lb ci.ub pi.lb pi.ub 1 0.3509 0.1297 0.0966 0.6051 0.0332 0.6685 2 0.0616 0.0735 -0.0824 0.2056 -0.1772 0.3004 3 -0.0913 0.0857 -0.2592 0.0767 -0.3452 0.1627

Note that, by default, the intercept is automatically included in the calculation of these predicted values (so only a vector of length 3 or a matrix with 3 columns should be specified via the `newmods`

argument). The values under `ci.lb`

and `ci.ub`

are the bounds of the 95% confidence intervals, while the values under `pi.lb`

and `pi.ub`

are the bounds of the 95% prediction intervals (see `help(predict.rma)`

for more details).

We can use an alternative model specification, where we leave out the model intercept:

res.a2 <- rma(yi, vi, mods = ~ weeks + tester - 1, data=dat) res.a2

Mixed-Effects Model (k = 19; tau^2 estimator: REML) tau^2 (estimated amount of residual heterogeneity): 0.0094 (SE = 0.0134) tau (square root of estimated tau^2 value): 0.0972 I^2 (residual heterogeneity / unaccounted variability): 24.90% H^2 (unaccounted variability / sampling variability): 1.33 Test for Residual Heterogeneity: QE(df = 15) = 24.1362, p-val = 0.0628 Test of Moderators (coefficient(s) 1,2,3,4): QM(df = 4) = 12.6719, p-val = 0.0130 Model Results: estimate se zval pval ci.lb ci.ub weeksnone 0.4020 0.1300 3.0924 0.0020 0.1472 0.6567 ** weekssome 0.1127 0.0804 1.4021 0.1609 -0.0448 0.2702 weekshigh -0.0402 0.1020 -0.3941 0.6935 -0.2401 0.1597 testeraware -0.0511 0.0927 -0.5512 0.5815 -0.2327 0.1305 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Note that the first three coefficients now directly correspond to the estimated average effects for levels `none`

, `some`

, and `high`

of the `weeks`

factor and level `blind`

of the `tester`

factor. The last (4th) coefficient again indicates how much higher/lower the average effect is when the level of the `tester`

factor is changed to `aware`

. Also, note that the omnibus test now includes all four coefficients by default and has a different interpretation: It is essentially testing the null hypothesis that the true average effect is zero across all factor level combinations.

The model including only the main effects of the two factors assumes that the influence of the two factors is additive. We can illustrate the implications graphically by plotting the estimated average effects for all factor level combinations:

plot(coef(res.a2)[1:3], type="o", pch=19, xlim=c(0.8,3.2), ylim=c(-0.1,0.5), xlab="Amount of Contact Prior to Expectancy Induction", ylab="Standardized Mean Difference", xaxt="n", bty="l") axis(side=1, at=1:3, labels=c("none","some","high")) lines(coef(res.a2)[1:3] + coef(res.a2)[4], type="o", pch=15, lty="dotted") legend("topright", legend=c("Test Administrator Blind", "Test Administrator Aware"), lty=c("solid", "dotted"), pch=c(19,15), inset=0.01) title("Estimated Average Effects based on the Additive Model")

Note how the lines are parallel to each other, as expected under this model. Also, the `tester`

factor appears to have only a minor influence on the effect as compared to the `weeks`

factor. Finally, while we have treated `weeks`

as a factor, the decrease in the estimated average effect appears roughly linear as we move from the `none`

to the `some`

and `high`

levels. Therefore, one could also consider fitting a model where this variable is treated as a quantitative covariate. An example along those lines can be found in the discussion on how to test factors and linear combinations of parameters in mixed-effects meta-regression models.

Now let's move on to a model where we allow the two factors in the model to interact (note that it is questionable whether such a complex model should even be considered for these data; again, these analyses are for illustration purposes only). Such a model can be fitted with:

res.i1 <- rma(yi, vi, mods = ~ weeks * tester, data=dat) res.i1

Mixed-Effects Model (k = 19; tau^2 estimator: REML) tau^2 (estimated amount of residual heterogeneity): 0.0223 (SE = 0.0214) tau (square root of estimated tau^2 value): 0.1494 I^2 (residual heterogeneity / unaccounted variability): 42.22% H^2 (unaccounted variability / sampling variability): 1.73 R^2 (amount of heterogeneity accounted for): 0.00% Test for Residual Heterogeneity: QE(df = 13) = 23.4646, p-val = 0.0364 Test of Moderators (coefficient(s) 2,3,4,5,6): QM(df = 5) = 9.3154, p-val = 0.0971 Model Results: estimate se zval pval ci.lb ci.ub intrcpt 0.3067 0.1857 1.6518 0.0986 -0.0572 0.6707 . weekssome -0.1566 0.2159 -0.7255 0.4682 -0.5797 0.2665 weekshigh -0.3267 0.2597 -1.2584 0.2082 -0.8357 0.1822 testeraware 0.1759 0.2687 0.6547 0.5127 -0.3508 0.7026 weekssome:testeraware -0.2775 0.3072 -0.9034 0.3663 -0.8795 0.3245 weekshigh:testeraware -0.2634 0.3435 -0.7667 0.4433 -0.9366 0.4099 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

As before, the first coefficient in the table is the model intercept ($b_0 = .3067$), which is the estimated average effect for levels `none`

and `blind`

of the `weeks`

and `tester`

factors, respectively. The second and third coefficients ($b_1 = -.1566$ and $b_2 = -.3267$, respectively) indicate how much higher/lower the estimated average effect is for levels `some`

and `high`

of the `weeks`

factor when the level of the `tester`

factor is held at `blind`

. The fourth coefficient ($b_3 = .1759$) provides an estimate of how much higher/lower the average effect is for the `aware`

level of the `tester`

factor when the level of the `weeks`

factor is held at `none`

. Finally, the last two coefficients ($b_4 = -.2775$ and $b_5 = -.2634$, respectively) indicate how much higher/lower the contrast between the `aware`

and `blind`

levels of the `tester`

factor are for levels `some`

and `high`

of the `weeks`

factor (or equivalently, how much higher/lower the contrasts between the `some`

and the `none`

levels and the `high`

and the `none`

levels of the `weeks`

factor are for level `aware`

of the `tester`

factor).

These last two coefficients allow the influence of the `aware`

factor to differ across the various levels of the `weeks`

factor (and vice-versa). Therefore, if these two coefficients are in reality both equal to zero, then there is in fact no interaction. So, in order to test whether an interaction is actually present, we just need to test these two coefficients, which we can do with:

anova(res.i1, btt=5:6)

Test of Moderators (coefficient(s) 5,6): QM(df = 2) = 0.8576, p-val = 0.6513

Based on this Wald-type chi-square test, we find no significant evidence that an interaction is actually present ($Q_M = 0.86, df = 2, p = .65$).

Alternatively, we could use a likelihood ratio test (LRT) to test the interaction. For this, we must fit both models with maximum likelihood (ML) estimation and then conduct a model comparison:

res.a1.ml <- rma(yi, vi, mods = ~ weeks + tester, data=dat, method="ML") res.i1.ml <- rma(yi, vi, mods = ~ weeks * tester, data=dat, method="ML") anova(res.a1.ml, res.i1.ml)

df AIC BIC AICc logLik LRT pval QE tau^2 R^2 Full 7 8.5528 15.1639 18.7346 2.7236 23.4646 0.0000 Reduced 5 5.2247 9.9469 9.8401 2.3876 0.6719 0.7147 24.1362 0.0000 71.65%

The results from the LRT ($\chi^2 = 0.67, df = 2, p = .71$) lead to the same conclusion.

For illustration purposes, let us however proceed with a further examination of this model. As before, the `anova()`

, `linearHypothesis()`

, and `glht()`

functions could be used to test factor levels against each other and to obtain pairwise comparisons. Also, the `predict()`

function can be used to obtain the estimated average effect for particular factor level combinations. For example, to obtain the estimated average effect for level `high`

of the `weeks`

factor and level `aware`

of the `tester`

factor, we would use:

predict(res.i1, newmods=c(0,1,1,0,1))

pred se ci.lb ci.ub pi.lb pi.ub 1 -0.1074 0.1134 -0.3296 0.1148 -0.4751 0.2602

We can also make use of an alternative parameterization of the model, which is particularly useful in this context:

res.i2 <- rma(yi, vi, mods = ~ weeks:tester - 1, data=dat) res.i2

Mixed-Effects Model (k = 19; tau^2 estimator: REML) tau^2 (estimated amount of residual heterogeneity): 0.0223 (SE = 0.0214) tau (square root of estimated tau^2 value): 0.1494 I^2 (residual heterogeneity / unaccounted variability): 42.22% H^2 (unaccounted variability / sampling variability): 1.73 Test for Residual Heterogeneity: QE(df = 13) = 23.4646, p-val = 0.0364 Test of Moderators (coefficient(s) 1,2,3,4,5,6): QM(df = 6) = 11.9111, p-val = 0.0640 Model Results: estimate se zval pval ci.lb ci.ub weeksnone:testerblind 0.3067 0.1857 1.6518 0.0986 -0.0572 0.6707 . weekssome:testerblind 0.1502 0.1100 1.3646 0.1724 -0.0655 0.3658 weekshigh:testerblind -0.0200 0.1815 -0.1102 0.9122 -0.3757 0.3357 weeksnone:testeraware 0.4827 0.1942 2.4850 0.0130 0.1020 0.8634 * weekssome:testeraware 0.0486 0.1001 0.4851 0.6276 -0.1477 0.2448 weekshigh:testeraware -0.1074 0.1134 -0.9476 0.3433 -0.3296 0.1148 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Now, the table with the model results directly provides the estimated average effect for each factor level combination. It is now also quite easy to test particular factor level combinations against each other. For example, to test the difference between levels `some`

and `high`

of the `weeks`

factor within the `blind`

level of the `tester`

factor, we could use:

anova(res.i2, L=c(0,1,-1,0,0,0))

Hypothesis: 1: weekssome:testerblind - weekshigh:testerblind = 0 Results: estimate se zval pval 1: 0.1702 0.2122 0.8017 0.4227 Test of Hypothesis: QM(df = 1) = 0.6428, p-val = 0.4227

Or with the `linearHypothesis()`

function (abbreviated output shown):

linearHypothesis(res.i2, c("weekssome:testerblind - weekshigh:testerblind = 0"))

Res.Df Df Chisq Pr(>Chisq) 1 14 2 13 1 0.6428 0.4227

To test the same contrast within the `aware`

level of the `tester`

factor, we would use:

anova(res.i2, L=c(0,0,0,0,1,-1))

Hypothesis: 1: weekssome:testeraware - weekshigh:testeraware = 0 Results: estimate se zval pval 1: 0.1560 0.1513 1.0314 0.3024 Test of Hypothesis: QM(df = 1) = 1.0638, p-val = 0.3024

Or with the `linearHypothesis()`

function (abbreviated output shown):

linearHypothesis(res.i2, c("weekssome:testeraware - weekshigh:testeraware = 0"))

Res.Df Df Chisq Pr(>Chisq) 1 14 2 13 1 1.0638 0.3024

If one were inclined to do so, one could even test all 6 factor level combinations against each other (i.e., all possible pairwise comparisons). The `glht()`

function combined with the `contMat()`

function makes this relatively easy:

summary(glht(res.i2, linfct=contrMat(c("none:blind"=1,"some:blind"=1,"high:blind"=1, "none:aware"=1,"some:aware"=1,"high:aware"=1), type="Tukey")), test=adjusted("none"))

Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) some:blind - none:blind == 0 -0.15660 0.21585 -0.725 0.46816 high:blind - none:blind == 0 -0.32675 0.25965 -1.258 0.20824 none:aware - none:blind == 0 0.17592 0.26872 0.655 0.51269 some:aware - none:blind == 0 -0.25818 0.21098 -1.224 0.22105 high:aware - none:blind == 0 -0.41418 0.21757 -1.904 0.05696 . high:blind - some:blind == 0 -0.17015 0.21223 -0.802 0.42271 none:aware - some:blind == 0 0.33252 0.22324 1.490 0.13635 some:aware - some:blind == 0 -0.10158 0.14877 -0.683 0.49475 high:aware - some:blind == 0 -0.25759 0.15799 -1.630 0.10302 none:aware - high:blind == 0 0.50267 0.26582 1.891 0.05862 . some:aware - high:blind == 0 0.06857 0.20727 0.331 0.74076 high:aware - high:blind == 0 -0.08744 0.21398 -0.409 0.68282 some:aware - none:aware == 0 -0.43410 0.21852 -1.986 0.04698 * high:aware - none:aware == 0 -0.59010 0.22490 -2.624 0.00869 ** high:aware - some:aware == 0 -0.15601 0.15126 -1.031 0.30235 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- none method)

As before, no adjustment to the p-values has been used for these tests, but this would probably be advisable when conducting so many tests.

We can also illustrate the results from the interaction model by plotting the estimated average effects for all factor level combinations:

plot(coef(res.i2)[1:3], type="o", pch=19, xlim=c(0.8,3.2), ylim=c(-0.1,0.5), xlab="Amount of Contact Prior to Expectancy Induction", ylab="Standardized Mean Difference", xaxt="n", bty="l") axis(side=1, at=1:3, labels=c("none","some","high")) lines(coef(res.i2)[4:6], type="o", pch=15, lty="dotted") legend("topright", legend=c("Test Administrator Blind", "Test Administrator Aware"), lty=c("solid", "dotted"), pch=c(19,15), inset=0.01) title("Estimated Average Effects based on the Interaction Model")

Now, the lines are no longer constrained to be parallel and in fact cross each other (but as we saw before, there is hardly any evidence to suggest that there is a significant interaction).

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.

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

tips/multiple_factors_interactions.txt · Last modified: 2020/06/26 06:49 by Wolfgang Viechtbauer

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