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This chapter presents a latent class approach to correct gross flows from correlated classification errors. The emphasis is on the capacity to account for correlated classification errors across panel data without heavily depending on multiple indicators (admittedly, at the price of carefully specifying a parsimonious model with sufficient identifiability conditions). A case study serves to illustrate our modelling strategy. A model is formulated and estimated within a latent class analysis set-up, to adjust observed quarterly labour force gross flows of French young people, March 1990 March 1992. The data were taken from the FLFS, which collects information on labour force conditions mainly by means of retrospective interrogation. Based on evidence from the same French dataset and on arguments from survey methodology literature, the model accounts for correlated classification errors over time and allows for a seasonal component in the latent Markov process.

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Panel conditioning is more likely to occur for certain topics and certain types of questions than for others, meaning that the extent to which the researcher needs to be concerned about panel conditioning and the measures that might be introduced to minimise any negative consequences of it should depend on the survey content Panel conditioning in the context of attitudinal measures is discussed in 7 Sample Attrition Sample attrition (also referred to as panel attrition) refers to the continued loss of respondents from the sample due to nonresponse at each wave of a longitudinal survey The term attrition is usually used to refer to a monotone process whereby sample members can change from being respondents to nonrespondents but not vice versa.

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The solution to this problem is to construct the matrix of observed auxiliary variables. Let ci =1 if respondent i participates in wave , and ci = 0 if this is not the case. Then let X be the matrix X c1 ; c2 ; . . . cq ; X1 ; X2 ; . . . Xq 9:6

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In fact, many longitudinal surveys continue to attempt to collect data from sample members after they have been nonrespondents at one or more waves, and are often successful in doing so Generally, then, the issue is that many sample members may have been nonrespondents at one or more wave, whether or not their response pattern is one of monotone attrition The response rate at any one wave of a longitudinal survey may be just as good as that for any other survey but after, say, five waves the proportion of sample units that have responded at every wave may be quite low Thus, the effective response rate for longitudinal analysis for which data from every wave is required may be lower than the response rates that we are used to having on cross-sectional surveys After several waves there is also a risk that responding sample sizes can become unacceptably small.

Schmidt, Raymond Klefstad, and Angelo Corsaro show how well-designed Real-time CORBA middleware architecture can minimize footprint and facilitate customization for Object Request Brokers (ORBs) to support various classes of applications The authors also describe how highly optimized ORB designs can improve the performance and predictability of DRE systems and show that Java and Real-time Java features can be applied to an ORB to increase ease of use and predictability for Java-based DRE applications Finally, the authors describe the ZEN ORB which is a next generation of Real-time CORBA middleware that combines Real-time Java with Real-time CORBA resulting in an easy-to-use, extensible, and exible middleware with an appropriate footprint and QoS to meet the needs of many DRE systems 18: Middleware Support for Fault Tolerance Telecommunication management systems are large and complex pieces of software that are costly to maintain.

specific applications. In object-oriented programming, you create small software systems that can be plugged into any application. For example, the components of an old model of the Beetle car cannot be plugged into a Mercedes-Benz; however, a company may decide to manufacture a brake that can be plugged into any type of car. Using the principles of object-oriented programming, JSP provides special tags that can be plugged into Java components such as JavaBeans. As a result, the encapsulated code can be reused and the developer is saved from the task of rewriting common Java code. The most commonly used approaches for encapsulations of this type involve using the useBean tag and the XML-based custom tag. The use of the useBean tag facilitates the placement of the Java code in a bean, which is then shared with other pages of the application. The custom tag also facilitates this type of encapsulation, except that the component used is in the form of a class file instead of bean component. In this chapter, we will use JavaBeans as the pluggable components of a JSP page. It is obvious that for a task such as this, you need a basic understanding of a JavaBean. We will use JavaBean components primarily as pluggables, and therefore only a few concepts about JavaBeans will need to be summarized. For more details on JavaBeans, look up the documentation available at http://java.sun.com/beans/docs/. The later sections of this chapter discuss specific tags that facilitate the use of the JavaBean component in a JSP page. Toward the end of the chapter, we create a JSP page using a scenario-based example that will include a JavaBean component. Let s begin with a short recap of JavaBeans.

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