TY  - JOUR
PY  - 1999//
TI  - Correlating and predicting psychiatric symptom ratings: Spearman's r versus Kendall's tau correlation
JO  - Journal of psychiatric research
A1  - Arndt, S.
A1  - Turvey, C.
A1  - Andreasen, N. C.
SP  - 97
EP  - 104
VL  - 33
IS  - 2
N2  - Simple correlations play a large role in the analysis of psychiatric data. They are used to predict outcome, validate new instruments, establish treatment efficacy and find symptom patterns. Researchers and data analysts often face a question about which correlation coefficient to use in a study but are often unaware of the strengths and weaknesses of the alternative correlation measures. The presence of outliers, nonconstant variance, skewed distributions and unequal n are common in psychiatric data and this poses severe problems for many classic statistical methods. We compare Pearson, Spearman and Kendall's correlation coefficients using a large sample of subjects with schizophrenia spectrum disorders who were evaluated with 7 different psychiatric rating scales. Samples sizes ranging from 8 to 50 were evaluated using bootstrapping methods. The criteria for evaluation of the correlations were the type I error rates, power, bias and confidence interval width. Pearson's r did not always control for false positives at the nominal rate and was often unstable. Spearman's r performed better than Pearson's but provided a biased estimate of the true correlation. Spearman's r was also difficult to interpret. Our results suggest that Kendall's tau(b) has many advantages over Pearson's and Spearman's r; when applied to psychiatric data, tau(b) maintained adequate control of type I errors, was nearly as powerful as Pearson's r, provided much tighter confidence intervals and had a clear interpretation.<p /> <p>Language: en</p>
LA  - en
SN  - 0022-3956
UR  - http://dx.doi.org/
ID  - ref1
ER  -