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question, because if they do not, it means that the outcome of CBCA coding does not solely depend on the quality of the statement (as it should) but also on the interpretation of the individual CBCA coder To answer this inter-rater reliability question, I reviewed the available CBCA research I give a summary of the results here, and the full details are published elsewhere (Vrij, 2005b) For most individual criteria, good, albeit not perfect, inter-rater agreements were obtained in the majority of studies (there were exceptions for Criterion 2, unstructured production, and Criterion 14, spontaneous corrections) Moreover, the inter-rater agreement scores for the total CBCA scores were typically somewhat higher than those for the individual criteria These ndings suggest that different CBCA coders often agree that a particular sentence in a statement includes a CBCA criterion, but sometimes disagree about which criterion it is.

The high but not perfect inter-rater agreement ndings suggest that total CBCA scores are reliable but that individual coders sometimes obtain different results This makes it important that in real life at least two CBCA experts rate a statement rather than just one Only by asking two CBCA experts to evaluate the same statement independently from each other, can someone determine whether the CBCA score merely re ects the quality of the statement, or, at least in part, the private opinion of the individual CBCA coder In real-life cases, however, it is common practice for only one expert to examine the transcript7 Stage 4: Evaluation of the CBCA Outcome: The Validity Checklist A CBCA evaluation is in itself not suf cient to make a decision about the truthfulness of a statement, because CBCA scores may be affected by issues other than the veracity of the statement.

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For example, the interviewer could have guided the interviewee and could have lled in many gaps in the interviewee s story, or prior to the interview others could have instructed the interviewee about what to say In those situations even fabricated stories could be rich and detailed The reverse could also occur A truthful statement may be of poor quality and lack detail, for example because the interviewee is very young, or verbally unskilled, or too upset to say much, or because the interviewer did not give the interviewee enough opportunity to tell the whole story The fact that issues other than veracity in uence CBCA scores implies that CBCA is not a standardised test A standardised test has.

the presence of an abscessed tooth or extracting an infected tooth.5 A practitioner might have to attempt to differentiate whether a patient has a medial pterygoid myospasm or myositis, if the patient had an inferior alveolar injection to extract an infected tooth and returned complaining of signi cant medial pterygoid muscle pain limiting opening. If the patient has a fever or lymphadenopathy, this would suggest an infection is present, and antibiotics are indicated.

j3 1

where xi is the matrix of size T by p of explanatory variables for subject i, and V has the (jk)th element vjk = covp (yij, yik), where j,k = 1, . . . T (see Diggle et al., 1994).

Walnut, Pear, and Chicken Salad ............................................................................... 135

A formal test for a close precursor relationship between T1 and T2 as indicated in Section 19.2.1 (T1 a close precursor of T2) is given by the following: for suitable k(t1), let ! Z ^ ^ F2 t1 k t1 jT1 t1 F2 t1 k t1 ^ 19:2 Q dF1 t1 : ^ ^ F2 t1 jT1 t1 F2 t1 Under the null hypothesis of independence of T1 and T2, the mean of Q will be close to zero. Thus in order to test the null hypothesis, the value of Q may be compared with twice its estimated standard error, se(Q). Note that the difference within Equation (19.2) approximates to the difference between the hazard function conditional on T1 = t1 and the unconditional hazard. To compute Q, we first would obtain an estimate of the joint density of (T1, T2) within the observation window. Then, we would obtain numerically the corresponding marginal probability density functions and consequently the respective survivor functions (conditional and unconditional versions). As a check on the finite sample distribution of Q, we have performed simulations (available on request from the authors) using our density estimation method and models and parameter settings appropriate to the NPHS application in Section 19.5. These show a mean for Q under independence that is very close to zero (and validates the significance test of Section 19.5).

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