Intraclass Correlations (ICC1, ICC2, ICC3 from Shrout and Fleiss)
The Intraclass correlation is used as a measure of association when studying the reliability of raters. Shrout and Fleiss (1979) outline 6 different estimates, that depend upon the particular experimental design. All are implemented and given confidence limits. Uses either aov or lmer depending upon options. lmer allows for missing values.
ICC(x,missing=TRUE,alpha=.05,lmer=TRUE,check.keys=FALSE)
x |
a matrix or dataframe of ratings |
missing |
if TRUE, remove missing data – work on complete cases only (aov only) |
alpha |
The alpha level for significance for finding the confidence intervals |
lmer |
Should we use the lmer function from lme4? This handles missing data and gives variance components as well. TRUE by default. |
check.keys |
If TRUE reverse those items that do not correlate with total score. This is not done by default. |
Shrout and Fleiss (1979) consider six cases of reliability of ratings done by k raters on n targets.
ICC1: Each target is rated by a different judge and the judges are selected at random. (This is a one-way ANOVA fixed effects model and is found by (MSB- MSW)/(MSB+ (nr-1)*MSW))
ICC2: A random sample of k judges rate each target. The measure is one of absolute agreement in the ratings. Found as (MSB- MSE)/(MSB + (nr-1)*MSE + nr*(MSJ-MSE)/nc)
ICC3: A fixed set of k judges rate each target. There is no generalization to a larger population of judges. (MSB - MSE)/(MSB+ (nr-1)*MSE)
Then, for each of these cases, is reliability to be estimated for a single rating or for the average of k ratings? (The 1 rating case is equivalent to the average intercorrelation, the k rating case to the Spearman Brown adjusted reliability.)
ICC1 is sensitive to differences in means between raters and is a measure of absolute agreement.
ICC2 and ICC3 remove mean differences between judges, but are sensitive to interactions of raters by judges. The difference between ICC2 and ICC3 is whether raters are seen as fixed or random effects.
ICC1k, ICC2k, ICC3K reflect the means of k raters.
The intraclass correlation is used if raters are all of the same “class". That is, there is no logical way of distinguishing them. Examples include correlations between pairs of twins, correlations between raters. If the variables are logically distinguishable (e.g., different items on a test), then the more typical coefficient is based upon the inter-class correlation (e.g., a Pearson r) and a statistic such as alpha or omega might be used. alpha and ICC3k are identical.
If using the lmer option, then missing data are allowed. In addition the lme object returns the variance decomposition. (This is simliar to testRetest which works on the items from two occasions.
The check.keys option by default reverses items that are negatively correlated with total score. A message is issued.
results |
A matrix of 6 rows and 8 columns, including the ICCs, F test, p values, and confidence limits |
summary |
The anova summary table or the lmer summary table |
stats |
The anova statistics (converted from lmer if using lmer) |
MSW |
Mean Square Within based upon the anova |
lme |
The variance decomposition if using the lmer option |
The results for the Lower and Upper Bounds for ICC(2,k) do not match those of SPSS 9 or 10, but do match the definitions of Shrout and Fleiss. SPSS seems to have been using the formula in McGraw and Wong, but not the errata on p 390. They seem to have fixed it in more recent releases (15).
Starting with psych 1.4.2, the confidence intervals are based upon (1-alpha)% at both tails of the confidence interval. This is in agreement with Shrout and Fleiss. Prior to 1.4.2 the confidence intervals were (1-alpha/2)%. However, at some point, this error slipped back again. It has been fixed in version 1.9.5 (5/21/19).
William Revelle
Shrout, Patrick E. and Fleiss, Joseph L. Intraclass correlations: uses in assessing rater reliability. Psychological Bulletin, 1979, 86, 420-3428.
McGraw, Kenneth O. and Wong, S. P. (1996), Forming inferences about some intraclass correlation coefficients. Psychological Methods, 1, 30-46. + errata on page 390.
Revelle, W. (in prep) An introduction to psychometric theory with applications in R. Springer. (working draft available at https://personality-project.org/r/book/
sf <- matrix(c(
9, 2, 5, 8,
6, 1, 3, 2,
8, 4, 6, 8,
7, 1, 2, 6,
10, 5, 6, 9,
6, 2, 4, 7),ncol=4,byrow=TRUE)
colnames(sf) <- paste("J",1:4,sep="")
rownames(sf) <- paste("S",1:6,sep="")
sf #example from Shrout and Fleiss (1979)
ICC(sf,lmer=FALSE) #just use the aov procedure
#data(sai)
sai <- psychTools::sai
sai.xray <- subset(sai,(sai$study=="XRAY") & (sai$time==1))
xray.icc <- ICC(sai.xray[-c(1:3)],lmer=TRUE,check.keys=TRUE)
xray.icc
xray.icc$lme #show the variance components as wellPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.