The original paper and the main claims
The data are from this paper:
Fedorenko, E., Gibson, E., & Rohde, D. (2006). The nature of working memory capacity in sentence comprehension: Evidence against domain-specific working memory resources. Journal of Memory and Language, 54(4), 541-553.
Ev was kind enough to give me the data; many thanks to her for this.
Design
The study is a self-paced reading experiment, with a 2x2x2 design, the examples are as follows:
- Factor: One noun in memory set or three nouns
easy: Joel
hard: Joel-Greg-Andy
- Factor: Noun type, either proper name or occupation
name: Joel-Greg-Andy
occ: poet-cartoonist-voter
- Factor: Relative clause type (two versions, between subjects but within items—we ignore this detail in this blog post):
- Subject-extracted, version 1: The physician | who consulted the cardiologist | checked the files | in the office.
- Subject-extracted, version 2: The cardiologist | who consulted the physician | checked the files | in the office.
- Object-extracted, version 1: The physician | who the cardiologist consulted | checked the files | in the office.
- Object-extracted, version 2: The cardiologist | who the physician consulted | checked the files | in the office.
As hinted above, this design is not exactly 2x2x2; there is also a within items manipulation of relative clause version. That detail was ignored in the published analysis, so we ignore it here as well.
The main claims in the paper
The main claim of the paper is laid out in the abstract above, but I repeat it below:
“A significant on-line interaction was found between syntactic complexity and similarity between the memory-nouns and the sentence-nouns in the three memory-nouns conditions, such that the similarity between the memory-nouns and the sentence-nouns affected the more complex object-extracted relative clauses to a greater extent than the less complex subject-extracted relative clauses.”
The General Discussion explains this further. I highlight the important parts.
“The most interesting result presented here is an interaction between syntactic complexity and the memory- noun/sentence-noun similarity during the critical region of the linguistic materials in the hard-load (three memory-nouns) conditions: people processed object-extracted relative clauses more slowly when they had to maintain a set of nouns that were similar to the nouns in the sentence than when they had to maintain a set of nouns that were dissimilar from the nouns in the sentence; in contrast, for the less complex subject-extracted relative clauses, there was no reading time difference between the similar and dissimilar memory load conditions. In the easy-load (one memory-noun) conditions, no interaction between syntactic complexity and memory-noun/sentence-noun similarity was observed. These results provide evidence against the hypothesis whereby there is a pool of domain-specific verbal working memory resources for sentence comprehension, contra Caplan and Waters (1999). Specifically, the results reported here extend the off-line results reported by Gordon et al. (2002) who—using a similar dual-task paradigm—provided evidence of an interaction between syntactic complexity and memory-noun/sentence-noun similarity in response-accuracies to questions about the content of the sentences. Although there was also a suggestion of an on-line reading time interaction in Gordon et al.’s experiment, this effect did not reach significance. The effect of memory-noun type in subject-extracted conditions was 12.7 ms per word (50.8ms over the four-word relative clause region), and the effect of memory-noun type in object-extracted conditions was 40.1 ms (160.4 ms over the four-word relative clause region). (Thanks to Bill Levine for providing these means). In the hard-load conditions of our experiment, the effect of memory-noun type in subject-extracted conditions over the four-word relative clause region is 12 ms in raw RTs (18 ms in residual RTs), and the effect of memory- noun type in object-extracted conditions is 324 ms in raw RTs (388 ms in residual RTs). This difference in effect sizes of memory-noun type between Gordon et al.’s and our experiment is plausibly responsible for the interaction observed in the current experiment, and the lack of such an interaction in Gordon et al.’s study. The current results therefore demonstrate that Gordon et al.’s results extend to on-line processing.”
The statistical evidence in the paper for the claim
Table 3 in the paper (last line) reports the critical interaction claimed for the hard-load conditions (I added the p-values, which were not provided in the table):
“Synt x noun type [hard load]: F1(1,43)=6.58, p=0.014;F2(1,31)=6.79, p=0.014”.
There is also a minF’ provided: \(minF'(1,72)=3.34, p=0.07\). I also checked the minF’ using the standard formula from a classic paper by Herb Clark:
Clark, H. H. (1973). The language-as-fixed-effect fallacy: A critique of language statistics in psychological research. Journal of Verbal Learning and Verbal Behavior, 12(4), 335-359.
[Aside: The journal “Journal of Verbal Learning and Verbal Behavior” is now called Journal of Memory and Language.]
Here is the minF’ function:
minf <- function(f1,f2,n1,n2){
fprime <- (f1*f2)/(f1+f2)
n <- round(((f1+f2)*(f1+f2))/(((f1*f1)/n2)+((f2*f2)/n1)))
pval<-pf(fprime,1,n,lower.tail=FALSE)
return(paste("minF(1,",n,")=",round(fprime,digits=2),"p-value=",round(pval,2),"crit=",round(qf(.95,1,n))))
}
And here is the p-value for the minF’ calculation:
minf(6.58,6.79,43,31)
## [1] "minF(1, 72 )= 3.34 p-value= 0.07 crit= 4"
The first thing to notice here is that even the published claim (about an RC type-noun type interaction in the hard load condition) is not statistically significant. I have heard through the grapevine that JML allows the authors to consider an effect as statistically significant even if the MinF’ is not significant. Apparently the reason is that the MinF’ calculation is considered to be conservative. But if they are going to ignore it, why the JML wants people to report a MinF’ is a mystery to me.
Declaring a non-significant result to be significant is a very common situation in the Journal of Memory and Language, and other journals. Some other JML papers I know of that report a non-significant minF’ as their main finding in the paper, but still act as if they have significance are:
Dillon et al 2013: “For total times this analysis revealed a three-way interaction that was significant by participants and marginal by items (\(F_1(1,39)=8.0,...,p=<0.01; F_1(1,47)=4.0,...,p=<0.055; minF'(1,81)=2,66,p=0.11\))”.
Van Dyke and McElree 2006: “The interaction of LoadxInterference was significant, both by subjects and by items. … minF’(1,90)=2.35, p = 0.13.”
Incidentally, neither of the main claims in these two studies replicated (which is not the same thing as saying that their claims are false—we don’t know whether they are false or true):
- Replication attempt of Dillon et al 2013: Lena A. Jäger, Daniela Mertzen, Julie A. Van Dyke, and Shravan Vasishth. Interference patterns in subject-verb agreement and reflexives revisited: A large-sample study. Journal of Memory and Language, 111, 2020.
- Replication attempt of Van Dyke et al 2006: Daniela Mertzen, Anna Laurinavichyute, Brian W. Dillon, Ralf Engbert, and Shravan Vasishth. Is there cross-linguistic evidence for proactive cue-based retrieval interference in sentence comprehension? Eye-tracking data from English, German and Russian. submitted, 2021
The second paper was rejected by JML, by the way.
Data analysis of the Fedorenko et al 2006 study
## Download and install library from here:
## https://github.com/vasishth/lingpsych
library(lingpsych)
data("df_fedorenko06")
head(df_fedorenko06)
## rctype nountype load item subj region RT
## 2 obj name hard 16 1 2 4454
## 6 subj name hard 4 1 2 5718
## 10 subj name easy 10 1 2 2137
## 14 subj name hard 20 1 2 903
## 18 subj name hard 12 1 2 1837
## 22 subj occ hard 19 1 2 1128
As explained above, we have a 2x2x2 design: rctype [obj,subj] x nountype [name, occupation] x load [hard,easy]. Region 2 is the critical region, the entire relative clause, and RT is the reading time for this region. Subject and item columns are self-explanatory.
We notice immediately that something is wrong in this data frame. Compare subject 1 vs the other subjects:
## something is wrong here:
xtabs(~subj+item,df_fedorenko06)
## item
## subj 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
## 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 8 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 12 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 13 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 14 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 15 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 17 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 18 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 19 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 20 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 21 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 22 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 23 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 24 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 25 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 26 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 28 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 29 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 30 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 31 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 32 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 33 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 34 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 35 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 36 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 37 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 38 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 39 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 40 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 41 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 42 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 43 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 44 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 46 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 47 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## item
## subj 29 30 31 32
## 1 2 2 2 2
## 2 1 1 1 1
## 3 1 1 1 1
## 4 1 1 1 1
## 5 1 1 1 1
## 6 1 1 1 1
## 7 1 1 1 1
## 8 1 1 1 1
## 9 1 1 1 1
## 11 1 1 1 1
## 12 1 1 1 1
## 13 1 1 1 1
## 14 1 1 1 1
## 15 1 1 1 1
## 16 1 1 1 1
## 17 1 1 1 1
## 18 1 1 1 1
## 19 1 1 1 1
## 20 1 1 1 1
## 21 1 1 1 1
## 22 1 1 1 1
## 23 1 1 1 1
## 24 1 1 1 1
## 25 1 1 1 1
## 26 1 1 1 1
## 28 1 1 1 1
## 29 1 1 1 1
## 30 1 1 1 1
## 31 1 1 1 1
## 32 1 1 1 1
## 33 1 1 1 1
## 34 1 1 1 1
## 35 1 1 1 1
## 36 1 1 1 1
## 37 1 1 1 1
## 38 1 1 1 1
## 39 1 1 1 1
## 40 1 1 1 1
## 41 1 1 1 1
## 42 1 1 1 1
## 43 1 1 1 1
## 44 1 1 1 1
## 46 1 1 1 1
## 47 1 1 1 1
Subject 1 saw all items twice! There was apparently a bug in the code, or maybe two different subjects were mis-labeled as subject 1. Hard to know. This is one more reason why one should not load and analyze data immediately; always check that the design was correctly implemented.
We could remove subject 1 from this data, leaving us with 43 subjects:
df_fedorenko06<-subset(df_fedorenko06,subj!=1)
sort(unique(df_fedorenko06$subj))
length(unique(df_fedorenko06$subj))
But we won’t do this yet.
A full 2x2x2 ANOVA analysis:
First, set up an ANOVA style coding for the data, in lmer:
library(lme4)
## Loading required package: Matrix
df_fedorenko06$noun<-ifelse(df_fedorenko06$nountype=="name",1/2,-1/2)
df_fedorenko06$ld<-ifelse(df_fedorenko06$load=="hard",1/2,-1/2)
df_fedorenko06$rc<-ifelse(df_fedorenko06$rctype=="obj",1/2,-1/2)
df_fedorenko06$nounxld<-df_fedorenko06$noun*df_fedorenko06$ld*2
df_fedorenko06$nounxrc<-df_fedorenko06$noun*df_fedorenko06$rc*2
df_fedorenko06$ldxrc<-df_fedorenko06$ld*df_fedorenko06$rc*2
df_fedorenko06$nounxldxrc<-df_fedorenko06$noun*df_fedorenko06$ld*df_fedorenko06$rc*4
I am using effect coding here (\(\pm 1/2\)), so that every estimate reflects the estimated difference in means for the particular comparison being made. When multiplying to get an interaction, I have to multiply with a factor of 2 or 4 to get the effect coding back; if I don’t do that I will get \(\pm 1/4\) or \(\pm 1/8\).
I’m using effect coding because I was interested in getting an estimate of the relative clause effect for our Bayesian textbook’s chapter 6, on prior self-elicitation.
The data frame now has new columns representing the contrast coding:
head(df_fedorenko06)
## rctype nountype load item subj region RT noun ld rc nounxld nounxrc
## 2 obj name hard 16 1 2 4454 0.5 0.5 0.5 0.5 0.5
## 6 subj name hard 4 1 2 5718 0.5 0.5 -0.5 0.5 -0.5
## 10 subj name easy 10 1 2 2137 0.5 -0.5 -0.5 -0.5 -0.5
## 14 subj name hard 20 1 2 903 0.5 0.5 -0.5 0.5 -0.5
## 18 subj name hard 12 1 2 1837 0.5 0.5 -0.5 0.5 -0.5
## 22 subj occ hard 19 1 2 1128 -0.5 0.5 -0.5 -0.5 0.5
## ldxrc nounxldxrc
## 2 0.5 0.5
## 6 -0.5 -0.5
## 10 0.5 0.5
## 14 -0.5 -0.5
## 18 -0.5 -0.5
## 22 -0.5 0.5
Display the estimated means in each condition, just to get some idea of what’s coming.
round(with(df_fedorenko06,tapply(RT,nountype,mean)))
## name occ
## 1636 1754
round(with(df_fedorenko06,tapply(RT,load,mean)))
## easy hard
## 1608 1782
round(with(df_fedorenko06,tapply(RT,rctype,mean)))
## obj subj
## 1909 1482
The authors report raw and residual RTs, but we analyze raw RTs, and then analyze log RTs. There’s actually no point in computing residual RTs (I will discuss this at some point in a different blog post), so we ignore the residuals analysis, although nothing much will change in my main points below even if we were to use residual RTs.
I fit the most complex model I could. If I were a Bayesian (which I am), I would have done the full Monty just because I can, but never mind.
## raw RTs
m1<-lmer(RT~noun+ld+rc+nounxld + nounxrc + ldxrc + nounxldxrc+(1+ld+rc+ nounxrc + nounxldxrc||subj) + (1+ld+rc+ nounxrc + nounxldxrc||item),df_fedorenko06)
#summary(m1)
## log RTs
m2<-lmer(log(RT)~noun+ld+rc+nounxld + nounxrc + ldxrc + nounxldxrc+(1+ld+ nounxrc||subj) + (1+ld+rc||item),df_fedorenko06)
#summary(m2)
Here is the summary output from the two models:
## raw RTs:
summary(m1)$coefficients
## Estimate Std. Error t value
## (Intercept) 1675.79757 125.88880 13.3117286
## noun -116.07053 58.60273 -1.9806337
## ld 155.14812 112.39971 1.3803249
## rc 422.73102 77.84215 5.4306182
## nounxld -103.39623 58.60364 -1.7643312
## nounxrc -110.87780 61.73703 -1.7959692
## ldxrc -23.47607 58.62151 -0.4004685
## nounxldxrc -95.76489 66.95281 -1.4303340
## log RTs:
summary(m2)$coefficients
## Estimate Std. Error t value
## (Intercept) 7.20041271 0.06585075 109.3444271
## noun -0.03127831 0.02457298 -1.2728741
## ld 0.01532113 0.04530999 0.3381403
## rc 0.21828914 0.02812620 7.7610591
## nounxld -0.04645901 0.02457339 -1.8906222
## nounxrc -0.02132791 0.02564043 -0.8318078
## ldxrc -0.03704690 0.02457244 -1.5076607
## nounxldxrc -0.01944255 0.02462971 -0.7893945
Both the models only show that object relatives are harder to process than subject relatives. No other main effect or interactions pan out. This (non-)result is actually consistent with what’s reported in the paper.
For the story about the hard load conditions (discussed below) to hold up, a prerequisite is that the nounxldxrc interaction be significant, but it is not.
Subset analyses using repeated measures ANOVA/paired t-test
The Syn x noun interaction in the hard load conditions reported in the paper as the main result was computed by first subsetting the data such that we have only the hard load conditions’ data. This leads to a 2x2 design. Let’s see what happens when we do this analysis by subsetting the data. We have dropped half the data:
dim(df_fedorenko06)
## [1] 1440 14
fed06hard<-subset(df_fedorenko06,load=="hard")
dim(fed06hard)
## [1] 720 14
(1440-720)/1440
## [1] 0.5
Let’s first do a repeated measures ANOVA (=paired t-tests) on raw RTs by subject and by item, because that is what Fedorenko et al did (well, they used residual RTs, but nothing will change if we use that dependent measure).
## aggregate by subject:
fedsubjRC<-aggregate(RT~subj+rctype,mean,data=fed06hard)
head(fedsubjRC)
## subj rctype RT
## 1 1 obj 3387.500
## 2 2 obj 892.125
## 3 3 obj 1373.000
## 4 4 obj 1738.625
## 5 5 obj 7074.000
## 6 6 obj 3119.625
t.test(RT~rctype,paired=TRUE,fedsubjRC)
##
## Paired t-test
##
## data: RT by rctype
## t = 3.5883, df = 43, p-value = 0.0008469
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 176.7331 630.3151
## sample estimates:
## mean of the differences
## 403.5241
fedsubjN<-aggregate(RT~subj+nountype,mean,data=fed06hard)
head(fedsubjN)
## subj nountype RT
## 1 1 name 2971.500
## 2 2 name 928.875
## 3 3 name 1161.500
## 4 4 name 1414.125
## 5 5 name 4472.250
## 6 6 name 1831.875
t.test(RT~nountype,paired=TRUE,fedsubjN)
##
## Paired t-test
##
## data: RT by nountype
## t = -2.6432, df = 43, p-value = 0.01141
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -383.91794 -51.61331
## sample estimates:
## mean of the differences
## -217.7656
## interaction: diff between obj and subj in name vs in occ conditions:
RCocc<-aggregate(RT~subj+rctype,mean,data=subset(fed06hard,nountype=="occ"))
diff_occ<-subset(RCocc,rctype=="obj")$RT-subset(RCocc,rctype=="subj")$RT
RCname<-aggregate(RT~subj+rctype,mean,data=subset(fed06hard,nountype=="name"))
diff_name<-subset(RCname,rctype=="obj")$RT-subset(RCname,rctype=="subj")$RT
## by-subject interaction:
t.test(diff_occ,diff_name,paired=TRUE)
##
## Paired t-test
##
## data: diff_occ and diff_name
## t = 2.131, df = 43, p-value = 0.03885
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 21.71398 787.90534
## sample estimates:
## mean of the differences
## 404.8097
One can of course do this with an ANOVA function:
fedsubjANOVA<-aggregate(RT~subj+rctype+nountype,mean,data=fed06hard)
head(fedsubjANOVA)
## subj rctype nountype RT
## 1 1 obj name 3030.25
## 2 2 obj name 962.50
## 3 3 obj name 1087.50
## 4 4 obj name 1687.00
## 5 5 obj name 5006.75
## 6 6 obj name 1064.50
library(rstatix)
##
## Attaching package: 'rstatix'
## The following object is masked from 'package:stats':
##
## filter
subj_anova_hard<-anova_test(data = fedsubjANOVA,
dv = RT,
wid = subj,
within = c(rctype,nountype)
)
get_anova_table(subj_anova_hard)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 rctype 1 43 12.876 0.000847 * 0.031
## 2 nountype 1 43 6.986 0.011000 * 0.009
## 3 rctype:nountype 1 43 4.541 0.039000 * 0.008
We see a significant interaction by subjects, as reported in the paper.
By items:
## aggregate by item:
feditemRC<-aggregate(RT~item+rctype,mean,data=fed06hard)
head(feditemRC)
## item rctype RT
## 1 1 obj 1978.545
## 2 2 obj 2076.667
## 3 3 obj 3483.273
## 4 4 obj 2489.000
## 5 5 obj 1322.091
## 6 6 obj 1268.800
t.test(RT~rctype,paired=TRUE,feditemRC)
##
## Paired t-test
##
## data: RT by rctype
## t = 3.9827, df = 31, p-value = 0.0003833
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 194.5260 602.8663
## sample estimates:
## mean of the differences
## 398.6962
feditemN<-aggregate(RT~item+nountype,mean,data=fed06hard)
head(feditemN)
## item nountype RT
## 1 1 name 1573.727
## 2 2 name 1566.455
## 3 3 name 1580.364
## 4 4 name 2808.667
## 5 5 name 1251.000
## 6 6 name 1261.818
t.test(RT~nountype,paired=TRUE,feditemN)
##
## Paired t-test
##
## data: RT by nountype
## t = -1.6978, df = 31, p-value = 0.09957
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -477.39928 43.65591
## sample estimates:
## mean of the differences
## -216.8717
## interaction: diff between obj and item in name vs in occ conditions:
RCocc<-aggregate(RT~item+rctype,mean,data=subset(fed06hard,nountype=="occ"))
diff_occ<-subset(RCocc,rctype=="obj")$RT-subset(RCocc,rctype=="subj")$RT
RCname<-aggregate(RT~item+rctype,mean,data=subset(fed06hard,nountype=="name"))
diff_name<-subset(RCname,rctype=="obj")$RT-subset(RCname,rctype=="subj")$RT
## by-item interaction:
t.test(diff_occ,diff_name,paired=TRUE)
##
## Paired t-test
##
## data: diff_occ and diff_name
## t = 2.0274, df = 31, p-value = 0.05129
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.45258 823.20437
## sample estimates:
## mean of the differences
## 410.3759
Using ANOVA:
feditemANOVA<-aggregate(RT~item+rctype+nountype,mean,data=fed06hard)
head(feditemANOVA)
## item rctype nountype RT
## 1 1 obj name 2206.000
## 2 2 obj name 1946.667
## 3 3 obj name 1966.167
## 4 4 obj name 2910.600
## 5 5 obj name 1011.500
## 6 6 obj name 1496.200
item_anova_hard<-anova_test(data = feditemANOVA,
dv = RT,
wid = item,
within = c(rctype,nountype)
)
get_anova_table(item_anova_hard)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 rctype 1 31 14.802 0.000557 * 0.085
## 2 nountype 1 31 2.867 0.100000 0.028
## 3 rctype:nountype 1 31 4.110 0.051000 0.023
Notice that even if the by-subjects and by-items analyses are significant, the MinF’ is not. As Clark points out in his paper, MinF’ is critical to compute in order to draw inferences about the hypothesis test.
Since \(F=t^2\), we can compute the MinF’.
F1<-2.13^2
F2<-2.03^2
minf(F1,F2,43,31)
## [1] "minF(1, 71 )= 2.16 p-value= 0.15 crit= 4"
We can do this MinF’ calculation with the ANOVA output as well:
minf(4.541,4.11,43,31)
## [1] "minF(1, 71 )= 2.16 p-value= 0.15 crit= 4"
We get exactly the same MinF’ in both the t-tests and ANOVA because they are doing the same test.
The upshot of the ANOVA/t-test analyses using subsetted data
The higher-order interaction is not significant. This diverges from the main claim in the paper.
Now, let’s do the same by-subjects and items analyses using logRTs:
fed06hard$logRT<-log(fed06hard$RT)
## aggregate by subject:
fedsubjRC<-aggregate(logRT~subj+rctype,mean,data=fed06hard)
head(fedsubjRC)
## subj rctype logRT
## 1 1 obj 7.980754
## 2 2 obj 6.710152
## 3 3 obj 7.125770
## 4 4 obj 7.179976
## 5 5 obj 8.625057
## 6 6 obj 7.569662
t.test(logRT~rctype,paired=TRUE,fedsubjRC)
##
## Paired t-test
##
## data: logRT by rctype
## t = 4.6431, df = 43, p-value = 3.227e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.1024094 0.2596785
## sample estimates:
## mean of the differences
## 0.1810439
fedsubjN<-aggregate(logRT~subj+nountype,mean,data=fed06hard)
head(fedsubjN)
## subj nountype logRT
## 1 1 name 7.835673
## 2 2 name 6.740148
## 3 3 name 6.988723
## 4 4 name 7.167295
## 5 5 name 8.249557
## 6 6 name 7.142238
t.test(logRT~nountype,paired=TRUE,fedsubjN)
##
## Paired t-test
##
## data: logRT by nountype
## t = -2.2556, df = 43, p-value = 0.02924
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.151468895 -0.008471231
## sample estimates:
## mean of the differences
## -0.07997006
## interaction: diff between obj and subj in name vs in occ conditions:
RCocc<-aggregate(logRT~subj+rctype,mean,data=subset(fed06hard,nountype=="occ"))
diff_occ<-subset(RCocc,rctype=="obj")$logRT-subset(RCocc,rctype=="subj")$logRT
RCname<-aggregate(logRT~subj+rctype,mean,data=subset(fed06hard,nountype=="name"))
diff_name<-subset(RCname,rctype=="obj")$logRT-subset(RCname,rctype=="subj")$logRT
## by-subject interaction:
t.test(diff_occ,diff_name,paired=TRUE)
##
## Paired t-test
##
## data: diff_occ and diff_name
## t = 1.0226, df = 43, p-value = 0.3122
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.07614228 0.23279035
## sample estimates:
## mean of the differences
## 0.07832404
Now the by-subjects t-test for the interaction is nowhere near significant (cf the raw RTs analysis above). This means that there were some extreme values in the by-subjects data driving the effect.
By items:
## aggregate by item:
feditemRC<-aggregate(logRT~item+rctype,mean,data=fed06hard)
head(feditemRC)
## item rctype logRT
## 1 1 obj 7.421371
## 2 2 obj 7.339790
## 3 3 obj 7.562565
## 4 4 obj 7.590270
## 5 5 obj 7.016257
## 6 6 obj 7.031505
t.test(logRT~rctype,paired=TRUE,feditemRC)
##
## Paired t-test
##
## data: logRT by rctype
## t = 3.6988, df = 31, p-value = 0.0008374
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.08105886 0.28033104
## sample estimates:
## mean of the differences
## 0.180695
feditemN<-aggregate(logRT~item+nountype,mean,data=fed06hard)
head(feditemN)
## item nountype logRT
## 1 1 name 7.216759
## 2 2 name 6.991090
## 3 3 name 7.192081
## 4 4 name 7.707697
## 5 5 name 7.018699
## 6 6 name 6.981104
t.test(logRT~nountype,paired=TRUE,feditemN)
##
## Paired t-test
##
## data: logRT by nountype
## t = -1.4239, df = 31, p-value = 0.1645
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.18854611 0.03351487
## sample estimates:
## mean of the differences
## -0.07751562
## interaction: diff between obj and item in name vs in occ conditions:
RCocc<-aggregate(logRT~item+rctype,mean,data=subset(fed06hard,nountype=="occ"))
diff_occ<-subset(RCocc,rctype=="obj")$logRT-subset(RCocc,rctype=="subj")$logRT
RCname<-aggregate(logRT~item+rctype,mean,data=subset(fed06hard,nountype=="name"))
diff_name<-subset(RCname,rctype=="obj")$logRT-subset(RCname,rctype=="subj")$logRT
## by-item interaction:
t.test(diff_occ,diff_name,paired=TRUE)
##
## Paired t-test
##
## data: diff_occ and diff_name
## t = 0.93122, df = 31, p-value = 0.3589
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.09504043 0.25475215
## sample estimates:
## mean of the differences
## 0.07985586
Here, too, the interaction by items is not significant.
Conclusion: the MinF’ values show that there is no evidence for an interaction in the hard conditions, regardless of whether we use raw or log RTs.
But there is actually more to this story, as I show below with linear mixed models analyses.
Incorrect and correct lmer analysis
Now, we do the same analyses as above, but in lmer instead of paired t-tests, and by subsetting the data as Fedorenko did.
In order to compare this model (m4) with the correct analysis (model m6 below), we change the coding to be \(\pm 1\) instead of \(\pm 1/2\).
## 2x2 anova by doing subsetting: this is the main result in the paper
fed06hard<-subset(df_fedorenko06,load=="hard")
fed06hard$noun<-fed06hard$noun*2
fed06hard$rc<-fed06hard$rc*2
fed06hard$nounxrc<-fed06hard$nounxrc*2
fed06hard<-fed06hard[,c("subj","item","RT","noun","rc","nounxrc")]
head(fed06hard)
## subj item RT noun rc nounxrc
## 2 1 16 4454 1 1 1
## 6 1 4 5718 1 -1 -1
## 14 1 20 903 1 -1 -1
## 18 1 12 1837 1 -1 -1
## 22 1 19 1128 -1 -1 1
## 26 1 31 2742 -1 1 -1
Here is the analysis using subsetting (this is incorrect):
m4<-lmer(RT~noun+rc+nounxrc+(1+rc+nounxrc||subj) + (1+noun+nounxrc||item),fed06hard)
summary(m4)
## Linear mixed model fit by REML ['lmerMod']
## Formula: RT ~ noun + rc + nounxrc + ((1 | subj) + (0 + rc | subj) + (0 +
## nounxrc | subj)) + ((1 | item) + (0 + noun | item) + (0 +
## nounxrc | item))
## Data: fed06hard
##
## REML criterion at convergence: 12401.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0317 -0.4256 -0.1341 0.2131 11.6983
##
## Random effects:
## Groups Name Variance Std.Dev.
## subj (Intercept) 915329 956.73
## subj.1 rc 42511 206.18
## subj.2 nounxrc 1570 39.62
## item (Intercept) 56296 237.27
## item.1 noun 4123 64.21
## item.2 nounxrc 11841 108.82
## Residual 1540203 1241.05
## Number of obs: 720, groups: subj, 44; item, 32
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1752.09 157.25 11.142
## noun -110.39 47.65 -2.317
## rc 200.80 55.86 3.595
## nounxrc -103.48 50.56 -2.047
##
## Correlation of Fixed Effects:
## (Intr) noun rc
## noun 0.000
## rc 0.000 0.000
## nounxrc 0.000 0.000 0.000
summary(m4)$coefficients
## Estimate Std. Error t value
## (Intercept) 1752.0945 157.24556 11.142410
## noun -110.3885 47.64559 -2.316866
## rc 200.8043 55.85609 3.595031
## nounxrc -103.4786 50.56305 -2.046527
The above analysis shows a significant interaction. But this analysis is incorrect because it’s dropping half the data, which contributes to misestimating the variance components.
Next, we will do the above analysis using all the data (no subsetting to load==hard conditions). We do this by defining nested contrasts
The design is:
## Nested analysis:
## load e e e e h h h h
## noun o o n n o o n n
## rctyp s o s o s o s o
# a b c d e f g h
#---------------------------------------
#load -1 -1 -1 -1 1 1 1 1
#nn_easy -1 -1 1 1 0 0 0 0
#rc_easy -1 1 -1 1 0 0 0 0
#nn_hard 0 0 0 0 -1 -1 1 1
#rc_hard 0 0 0 0 -1 1 -1 1
Define the nested coding. This time I use \(\pm 1\) coding; this is just in order to facilitate quick model specification by multiplying main effects in the lmer equation.
I could have used \(\pm 1/2\) and the only thing that would give me is that the estimated coefficients would reflect the difference in means, but I would need to define interactions as separate columsn (as done above). The statistical results will not change as a function of these two coding schemes.
df_fedorenko06$ld<-ifelse(df_fedorenko06$load=="hard",1,-1)
df_fedorenko06$nn_easy<-ifelse(df_fedorenko06$load=="easy" & df_fedorenko06$nountype=="occ",-1,
ifelse(df_fedorenko06$load=="easy" & df_fedorenko06$nountype=="name",1,0))
df_fedorenko06$nn_hard<-ifelse(df_fedorenko06$load=="hard" & df_fedorenko06$nountype=="occ",-1,
ifelse(df_fedorenko06$load=="hard" & df_fedorenko06$nountype=="name",1,0))
df_fedorenko06$rc_easy<-ifelse(df_fedorenko06$load=="easy" & df_fedorenko06$rctype=="subj",-1,
ifelse(df_fedorenko06$load=="easy" & df_fedorenko06$rctype=="obj",1,0))
df_fedorenko06$rc_hard<-ifelse(df_fedorenko06$load=="hard" & df_fedorenko06$rctype=="subj",-1,
ifelse(df_fedorenko06$load=="hard" & df_fedorenko06$rctype=="obj",1,0))
m6<-lmer(RT~ld + nn_hard*rc_hard + nn_easy*rc_easy +
(1+ld + nn_hard:rc_hard ||subj) +
(1+ld + nn_hard:rc_hard + nn_easy||item),df_fedorenko06)
summary(m6)
## Linear mixed model fit by REML ['lmerMod']
## Formula: RT ~ ld + nn_hard * rc_hard + nn_easy * rc_easy + ((1 | subj) +
## (0 + ld | subj) + (0 + nn_hard:rc_hard | subj)) + ((1 | item) +
## (0 + ld | item) + (0 + nn_easy | item) + (0 + nn_hard:rc_hard | item))
## Data: df_fedorenko06
##
## REML criterion at convergence: 24455.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7050 -0.4408 -0.1223 0.2148 13.3538
##
## Random effects:
## Groups Name Variance Std.Dev.
## subj (Intercept) 611484 781.97
## subj.1 ld 79352 281.70
## subj.2 nn_hard:rc_hard 20333 142.59
## item (Intercept) 36175 190.20
## item.1 ld 16443 128.23
## item.2 nn_easy 1915 43.76
## item.3 nn_hard:rc_hard 25223 158.82
## Residual 1252751 1119.26
## Number of obs: 1440, groups: subj, 44; item, 32
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1675.796 126.128 13.286
## ld 77.641 56.535 1.373
## nn_hard -110.046 41.745 -2.636
## rc_hard 200.372 41.749 4.799
## nn_easy -6.734 42.434 -0.159
## rc_easy 224.046 41.749 5.366
## nn_hard:rc_hard -103.208 54.818 -1.883
## nn_easy:rc_easy -8.443 41.821 -0.202
##
## Correlation of Fixed Effects:
## (Intr) ld nn_hrd rc_hrd nn_esy rc_esy nn_h:_
## ld 0.000
## nn_hard 0.000 0.000
## rc_hard 0.000 0.000 0.000
## nn_easy 0.000 0.000 0.000 0.000
## rc_easy 0.000 0.000 0.000 0.000 0.000
## nn_hrd:rc_h 0.000 0.000 0.000 0.000 0.000 0.000
## nn_sy:rc_sy 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#qqPlot(residuals(m6))
#summary(m6)$coefficients
## compare with a null model, without the
## nn_hard:rc_hard interaction:
m6null<-lmer(RT~ld + nn_hard+rc_hard + nn_easy*rc_easy +(1+ld + nn_hard:rc_hard ||subj) + (1+ld + nn_hard:rc_hard + nn_easy||item),df_fedorenko06)
anova(m6,m6null)
## refitting model(s) with ML (instead of REML)
## Data: df_fedorenko06
## Models:
## m6null: RT ~ ld + nn_hard + rc_hard + nn_easy * rc_easy + ((1 | subj) + (0 + ld | subj) + (0 + nn_hard:rc_hard | subj)) + ((1 | item) + (0 + ld | item) + (0 + nn_easy | item) + (0 + nn_hard:rc_hard | item))
## m6: RT ~ ld + nn_hard * rc_hard + nn_easy * rc_easy + ((1 | subj) + (0 + ld | subj) + (0 + nn_hard:rc_hard | subj)) + ((1 | item) + (0 + ld | item) + (0 + nn_easy | item) + (0 + nn_hard:rc_hard | item))
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m6null 15 24566 24645 -12268 24536
## m6 16 24565 24649 -12266 24533 3.4482 1 0.06332 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Removing subject 1 weakens the already non-significant interaction:
m6a<-lmer(RT~ld + nn_hard*rc_hard + nn_easy*rc_easy +(1+ld + nn_hard:rc_hard ||subj) + (1+ld + nn_hard:rc_hard + nn_easy||item),subset(df_fedorenko06,subj!=1))
#summary(m6a)
#qqPlot(residuals(m6))
#summary(m6)$coefficients
## compare with a null model, without the
## nn_hard:rc_hard interaction:
m6anull<-lmer(RT~ld + nn_hard+rc_hard + nn_easy*rc_easy +(1+ld + nn_hard:rc_hard ||subj) + (1+ld + nn_hard:rc_hard + nn_easy||item),subset(df_fedorenko06,subj!=1))
anova(m6a,m6anull)
## refitting model(s) with ML (instead of REML)
## Data: subset(df_fedorenko06, subj != 1)
## Models:
## m6anull: RT ~ ld + nn_hard + rc_hard + nn_easy * rc_easy + ((1 | subj) + (0 + ld | subj) + (0 + nn_hard:rc_hard | subj)) + ((1 | item) + (0 + ld | item) + (0 + nn_easy | item) + (0 + nn_hard:rc_hard | item))
## m6a: RT ~ ld + nn_hard * rc_hard + nn_easy * rc_easy + ((1 | subj) + (0 + ld | subj) + (0 + nn_hard:rc_hard | subj)) + ((1 | item) + (0 + ld | item) + (0 + nn_easy | item) + (0 + nn_hard:rc_hard | item))
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m6anull 15 23385 23463 -11677 23355
## m6a 16 23384 23467 -11676 23352 3.0927 1 0.07864 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Notice that the interaction (nn_hard:rc_hard) is gone once we take the full data into account.
## subsetted data:
summary(m4)$coefficients[4,]
## Estimate Std. Error t value
## -103.478646 50.563049 -2.046527
## full data:
summary(m6)$coefficients[7,]
## Estimate Std. Error t value
## -103.208049 54.817661 -1.882752
What has changed is the standard error of the interaction (in m4 it is 51 ms, in m6 it is 55 ms); the increase in the SE is due to the increased number of variance components in the full model with the nested contrast coding. The subsetted analysis (m4, and the paired t-tests) are hiding important sources of variance and giving a misleading result.
Here are the variance components in model m4 (the subsetted data):
Random effects:
Groups Name Variance Std.Dev.
subj (Intercept) 915329 956.73
subj.1 rc 42511 206.18
subj.2 nounxrc 1570 39.62
item (Intercept) 56296 237.27
item.1 noun 4123 64.21
item.2 nounxrc 11841 108.82
Residual 1540203 1241.05
Here are the variance components in the full model:
Random effects:
Groups Name Variance Std.Dev.
subj (Intercept) 611472 781.97
subj.1 ld 317412 563.39
subj.2 nn_hard:rc_hard 20321 142.55
item (Intercept) 36172 190.19
item.1 ld 65756 256.43
item.2 nn_easy 1912 43.73
item.3 nn_hard:rc_hard 25229 158.84
Residual 1252759 1119.27
Notice that there are still some problems in these models:
library(car)
## Loading required package: carData
qqPlot(residuals(m6))
![](data:image/png;base64,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)
## 2826 546
## 707 137
boxplot(RT~rctype,df_fedorenko06)
![](data:image/png;base64,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)
What if we limit reading time to 10 seconds? What percent of the data would we lose? It turns out, very little, 0.3%:
nfull<-dim(df_fedorenko06)[1]
nfull
## [1] 1440
n10000<-dim(subset(df_fedorenko06,RT<10000))[1]
100*(nfull-n10000)/nfull
## [1] 0.2777778
m7<-lmer(RT~ld + nn_hard*rc_hard + nn_easy*rc_easy +(1+ld + nn_hard:rc_hard ||subj) + (1+ld + nn_hard:rc_hard + nn_easy||item),subset(df_fedorenko06,RT<10000))
## boundary (singular) fit: see ?isSingular
summary(m7)$coefficients
## Estimate Std. Error t value
## (Intercept) 1641.543453 116.01909 14.1489087
## ld 75.945797 47.41424 1.6017508
## nn_hard -75.395584 33.93436 -2.2218064
## rc_hard 165.834591 33.92088 4.8888646
## nn_easy 6.713027 33.89550 0.1980507
## rc_easy 192.150808 33.91198 5.6661624
## nn_hard:rc_hard -67.536307 51.79075 -1.3040226
## nn_easy:rc_easy 5.684016 33.98754 0.1672382
Notice that the interaction has shrunk from -102 ms (SE 50) in model m6 to -68 (52) in m7. This means there were a very small number of data points (0.3%) that were influential and leading to a much larger estimate.
Visualize these extreme values:
boxplot(RT~rctype+nountype,df_fedorenko06)
![](data:image/png;base64,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)
Plot the data without 0.3% of the extreme data:
boxplot(RT~rctype+nountype,subset(df_fedorenko06,RT<10000))
![](data:image/png;base64,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)
On the log ms scale, the extreme values are down-weighted:
boxplot(log(RT)~rctype+nountype,df_fedorenko06)
![](data:image/png;base64,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)
This means we can analyze the log ms data without deleting any extreme values:
m8<-lmer(log(RT)~ld + nn_hard*rc_hard + nn_easy*rc_easy +(1+ld + nn_hard:rc_hard ||subj) + (1+ld + nn_hard:rc_hard||item),df_fedorenko06)
summary(m8)$coefficients
qqPlot(residuals(m8))
Basically, there is no hint of an interaction between noun type and RC in the hard conditions, contrary to the claim in the paper. This was the only finding in the paper, and it is not supported by the data. The key issue was subsetting the data, which inadvertently conceals important sources of variances.
Simulation demonstrating the subsetting problem
Here, we demonstrate what happens to SE if we subset data as described above. The steps I take are:
- Define code for simulating a 2x2x2 repeated measures design.
- Estimate parameters from the full model based on the Fedorenko data (nested analysis).
- Fit linear mixed models on simulated data: one model subsets the data, the other model uses the full data. 4. Compare the SEs of the critical interaction in the two models.
Define simulation code
library(MASS)
##
## Attaching package: 'MASS'
## The following object is masked from 'package:rstatix':
##
## select
## assumes that no. of subjects and no. of items is divisible by 8.
gen_fake_lnorm2x2x2<-function(ncond=8,
nitem=NULL,
nsubj=NULL,
beta=NULL,
Sigma_u=NULL, # subject vcov matrix
Sigma_w=NULL, # item vcov matrix
sigma_e=NULL){
grouping<-matrix(rep(NA,ncond*ncond),ncol=ncond)
grouping[1,]<-1:8
grouping[2,]<-c(2:8,1)
grouping[3,]<-c(3:8,1:2)
grouping[4,]<-c(4:8,1:3)
grouping[5,]<-c(5:8,1:4)
grouping[6,]<-c(6:8,1:5)
grouping[7,]<-c(7:8,1:6)
grouping[8,]<-c(8,1:7)
## prepare data frame for 8 condition latin square:
g1<-data.frame(item=1:nitem,
cond=rep(grouping[1,],nitem/ncond))
g2<-data.frame(item=1:nitem,
cond=rep(grouping[2,],nitem/ncond))
g3<-data.frame(item=1:nitem,
cond=rep(grouping[3,],nitem/ncond))
g4<-data.frame(item=1:nitem,
cond=rep(grouping[4,],nitem/ncond))
g5<-data.frame(item=1:nitem,
cond=rep(grouping[5,],nitem/ncond))
g6<-data.frame(item=1:nitem,
cond=rep(grouping[6,],nitem/ncond))
g7<-data.frame(item=1:nitem,
cond=rep(grouping[7,],nitem/ncond))
g8<-data.frame(item=1:nitem,
cond=rep(grouping[8,],nitem/ncond))
## assemble data frame:
gp1<-g1[rep(seq_len(nrow(g1)),
nsubj/ncond),]
gp2<-g2[rep(seq_len(nrow(g2)),
nsubj/ncond),]
gp3<-g3[rep(seq_len(nrow(g3)),
nsubj/ncond),]
gp4<-g4[rep(seq_len(nrow(g4)),
nsubj/ncond),]
gp5<-g5[rep(seq_len(nrow(g5)),
nsubj/ncond),]
gp6<-g6[rep(seq_len(nrow(g6)),
nsubj/ncond),]
gp7<-g7[rep(seq_len(nrow(g7)),
nsubj/ncond),]
gp8<-g8[rep(seq_len(nrow(g8)),
nsubj/ncond),]
fakedat<-rbind(gp1,gp2,gp3,gp4,gp5,gp6,gp7,gp8)
## add subjects:
fakedat$subj<-rep(1:nsubj,each=nitem)
## name conditions:
## Nested analysis:
# Cond: 1 2 3 4 5 6 7 8
## load e e e e h h h h
## noun o o n n o o n n
## rctyp s o s o s o s o
fakedat$load<-ifelse(fakedat$cond%in%c(1,2,3,4),"easy","hard")
fakedat$nountype<-ifelse(fakedat$cond%in%c(1,2,5,6),"occ","name")
fakedat$rctype<-ifelse(fakedat$cond%in%c(1,3,5,7),"subj","obj")
## nested contrast coding:
fakedat$ld<-ifelse(fakedat$load=="easy",1,-1)
fakedat$nn_easy<-ifelse(fakedat$load=="easy" & fakedat$nountype=="occ",-1,
ifelse(fakedat$load=="easy" & fakedat$nountype=="name",1,0))
fakedat$nn_hard<-ifelse(fakedat$load=="hard" & fakedat$nountype=="occ",-1,
ifelse(fakedat$load=="hard" & fakedat$nountype=="name",1,0))
fakedat$rc_easy<-ifelse(fakedat$load=="easy" & fakedat$rctype=="subj",-1,
ifelse(fakedat$load=="easy" & fakedat$rctype=="obj",1,0))
fakedat$rc_hard<-ifelse(fakedat$load=="hard" & fakedat$rctype=="subj",-1,
ifelse(fakedat$load=="hard" & fakedat$rctype=="obj",1,0))
## subject random effects:
u<-mvrnorm(n=length(unique(fakedat$subj)),
mu=c(rep(0,8)),Sigma=Sigma_u)
## item random effects
w<-mvrnorm(n=length(unique(fakedat$item)),
mu=c(rep(0,8)),Sigma=Sigma_w)
## generate data row by row:
N<-dim(fakedat)[1]
rt<-rep(NA,N)
for(i in 1:N){
rt[i] <- rlnorm(1,beta[1] +
u[fakedat[i,]$subj,1] +
w[fakedat[i,]$item,1] +
(beta[2]+u[fakedat[i,]$subj,2]+
w[fakedat[i,]$item,2])*fakedat$ld[i]+
(beta[3]+u[fakedat[i,]$subj,3]+
w[fakedat[i,]$item,3])*fakedat$nn_easy[i]+
(beta[4]+u[fakedat[i,]$subj,4]+
w[fakedat[i,]$item,4])*fakedat$nn_hard[i]+
(beta[5]+u[fakedat[i,]$subj,5]+
w[fakedat[i,]$item,5])*fakedat$rc_easy[i]+
(beta[6]+u[fakedat[i,]$subj,6]+
w[fakedat[i,]$item,6])*fakedat$rc_hard[i]+
(beta[7]+u[fakedat[i,]$subj,7]+
w[fakedat[i,]$item,7])*(fakedat$nn_easy[i]:fakedat$rc_easy[i])+
(beta[8]+u[fakedat[i,]$subj,8]+
w[fakedat[i,]$item,8])*(fakedat$nn_hard[i]:fakedat$rc_hard[i]),
sigma_e)
}
fakedat$rt<-rt
fakedat$subj<-factor(fakedat$subj)
fakedat$item<-factor(fakedat$item)
fakedat
}
Fit model to full data
In order to estimate all parameters, we will have to fit a Bayesian model.
library(brms)
priors <- c(
prior(normal(0,10), class = Intercept),
prior(normal(0,1), class = b),
prior(normal(0, 1), class = sd),
prior(normal(0, 1), class = sigma)
)
m_brms<-brm(RT~ 1+ld + nn_easy + rc_easy + nn_hard + rc_hard +
nn_easy:rc_easy + nn_hard:rc_hard+
(1+ld + nn_easy + rc_easy + nn_hard + rc_hard +
nn_easy:rc_easy + nn_hard:rc_hard|subj)+
(1+ld + nn_easy + rc_easy + nn_hard + rc_hard +
nn_easy:rc_easy + nn_hard:rc_hard|item),
family=lognormal(),
prior=priors,
chains=4,
iter = 2000,
warmup =1000,
control = list(adapt_delta = 0.99,
max_treedepth=15),
df_fedorenko06)
## save model to avoid recomputation:
#save(m_brms,file="m_brms.rda")
Simulate data
In this simulation, we will fit two models, one with the full data, and one with the subsetted data. Then, we will check how often the SE associated with the critical interaction is larger in the full vs the subsetted model.
I computed some other summary statistics too (like power for the interaction), but looking at SEs summarizes my main point.
nsim<-100
betaintsefull<-betaintsesubset<-sigmafull<-sigmasubset<-tvalsfull<-tvalssubset<-rep(NA,nsim)
for(i in 1:nsim){
print(paste("iteration",i,sep=" "))
fakedat<-gen_fake_lnorm2x2x2(ncond=8,
nitem=32,
nsubj=48,
beta=beta,
Sigma_u=Sigma_u, # subject vcov matrix
Sigma_w=Sigma_w, # item vcov matrix
sigma_e=sigma_e)
mfull<-lmer(log(rt)~ld + nn_easy+rc_easy + nn_hard+rc_hard + nn_easy:rc_easy + nn_hard:rc_hard +
(1+ld + nn_easy+rc_easy + nn_hard+rc_hard + nn_easy:rc_easy + nn_hard:rc_hard ||subj) +
(1+ld + nn_easy+rc_easy + nn_hard+rc_hard + nn_easy:rc_easy + nn_hard:rc_hard||item),fakedat,
control=lmerControl(optimizer="nloptwrap", calc.derivs = FALSE))
sigmafull[i]<-sigma(mfull)
tvalsfull[i]<-summary(mfull)$coefficients[8,3]
betaintsefull[i]<-summary(mfull)$coefficients[8,2]
## subset the data:
fakehard<-subset(fakedat,load=="hard")
fakehard$noun<-ifelse(fakehard$nountype=="occ",1,-1)
fakehard$rc<-ifelse(fakehard$rctype=="obj",1,-1)
msubset<-lmer(log(rt)~noun+rc+noun:rc+(1+noun+rc+noun:rc||subj) + (1+noun+rc+noun:rc||item),fakehard,
control=lmerControl(optimizer="nloptwrap", calc.derivs = FALSE))
sigmasubset[i]<-sigma(msubset)
tvalssubset[i]<-summary(msubset)$coefficients[4,3]
betaintsesubset[i]<-summary(msubset)$coefficients[4,2]
}
save(betaintsefull,file="betaintsefull.rda")
save(betaintsesubset,file="betaintsesubset.rda")
The proportion of times that the SE of the interaction in the full model is larger than in the subsetted model:
load("betaintsefull.rda")
load("betaintsesubset.rda")
mean(betaintsefull>betaintsesubset)
## [1] 0.44
This means that the interaction has an overly enthusiastic SE for the critical interaction, when we subset the data. That’s not a good thing.
PS If there are any mistakes in the code above, please let me know and I will correct them.