psych (version 1.8.3.3)

ICC: Intraclass Correlations (ICC1, ICC2, ICC3 from Shrout and Fleiss)

Description

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.

Usage

ICC(x,missing=TRUE,alpha=.05,lmer=TRUE,check.keys=FALSE)

Arguments

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.

Value

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

Details

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.

References

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/

Examples

# NOT RUN {
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.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 well
# }