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enter or leave the panel. Subsequently, one has to deal with such events at the estimation stage. In this chapter, we will present different possible follow-up rules as they occur in some of the largest ongoing panel studies and will show that many follow-up procedures can be incorporated into the estimator of totals as some sort of weighting of the individuals in the households. In practice, dealing with follow-up is not the only challenge. As with other surveys, panel studies usually suffer from selective nonresponse and attrition (see Watson and Wooden, 10 of this volume). Since follow-up rules can be incorporated into weighting estimators, it seems natural to take one step further to adjust the weights in order to lessen the impact of nonresponse and attrition. This can be done using a calibration approach (Deville and Sarndal, 1992). We will discuss the implementation and effects of adjustments for nonresponse (Section 15.5) and calibration in panel surveys in general (Section 15.4).

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For high-risk applications, and specifically for online access to the network PVR, it is recommended that CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) be used [3]. This works as a challenge response test mechanism that presents images and expects characters to be provided as a response. Automatic software is not able to read the text on the images, as it is obfuscated and will tend to fail. Table 6.10 includes references to DRM disk countermeasures.

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R2 is the same value as X. Thus, the outcome of a sequence of two XORs using the same value produces the original value. To see this feature of the XOR in ...

This is a polynomial in age (a) of order Ql that can vary with l, the order determined from the data but with Ql usually less than max(Tijk) Using polynomials is a flexible and convenient way of representing nonlinear change with age Spline functions are another possibility, as described by Snijders and Bosker (1999, 12) To reduce the difficulties created by the correlations between higher order polynomials, it is usual to centre age around its overall mean This centring also helps with the interpretation of the estimates from the model Note that all the coefficients the intercept terms b0 and the growth parameters b1, b2, etc can vary from response to response (l), from cohort member to cohort member (i), from area to area (j) and from cohort to cohort (k).

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Total RM scores did not discriminate between truth telling and lying in 5 6 year olds but did distinguish between truths and lies told by participants who were 10 years of age or older This nding supports the RM assumption that RM cannot be used with young children Interestingly, CBCA scores did differentiate between 5 6 year old truth tellers and liars in the same study ( 8), indicating that it is the sensitivity of the RM instrument rather than anything else that caused failure in the RM tool to detect the young children s lies Computerised Versus Manual RM Coding Bond and Lee (2005) differed from other researchers in that they used an automatic, computerised, RM coding system The advantages of employing a computerised coding system are obvious.

In other words, we treat them as random effects and focus on the estimation of their variances and, within levels, their covariances We assume that random effects are uncorrelated across levels and also that: E eltijk 2 l l0 ; t t0 ; i i0 ; j j0 ; k k0 l e ll 0 e l 6 l0 ; t t0 ; i i0 ; j j0 ; k k0 0; otherwise: In other words, we assume that the variances of the within-individual residuals do not vary across occasions (ie with age) and that the residuals for the responses are correlated at each occasion but that there is no correlation across occasions, either within or between responses (ie no autocorrelation) We see later how to relax some of these assumptions As we shall see, we can expand the model (Equation 16.

1) to show how the random effects blqijk vary across levels, in principle systematically with explanatory variables zijk (characteristics of the cohort member), zjk (characteristics of the area) and zk (characteristics of the cohort), and hence how the covariance structure is defined at higher levels The model does not explicitly include either the siblings or the children of the cohort members, although implicitly they are included; the older siblings as members of cohorts born before the cohort member and their children as members of cohorts born some time after the cohort member What is missing, however, are any links that bind the cohort members themselves to their siblings on the one hand and to their children on the other Rather than elaborate an already rather complicated model, we introduce these.

In this chapter, the technical requirements and characteristics of Internet Protocol (IP)-based services are discussed in a generic context. An understanding of services from a technical viewpoint is central to making adequate service models. This chapter builds on the discussion of services in a business setting in s 1 and 2 and takes into account related standardization efforts discussed in 5. This chapter approaches requirements from the viewpoint of services, whereas 7, for example, will discuss requirements from the service management viewpoint. Internet Protocol (IP) is an important enabler for bringing greater exibility to accessing services. Summarized in an Internet Engineering Task Force (IETF) plenary speech a few years ago, the basic philosophy of IP is everything over IP, IP over everything .

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