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Over the past decade the number of panel surveys has increased dramatically This is partly due to the growing popularity of online panels and the potential of the Internet, which makes large-scale national and international panels affordable When assessing the field one sees two different worlds: the world of governmental and academic research and the world of commercial, market and opinion research In the first world, the (semi)governmental and academic world, mostly single purpose panels are used in which one major research topic is studied over a longer period of time with the emphasis on measurement of change The data are processed and analysed in detail by specialists, both substantively and methodologically In the second world, the world of market and opinion research, the majority of the panels are multipurpose panels or access panels run by commercial organizations working for a large variety of clients.

>java JMSPublish2 TopicConnectionFactory message from JMSPublish2" 5 JMSPublish beginning. creating an instance of JMSPublish... sending message: Test message from sending message: Test message from sending message: Test message from sending message: Test message from sending message: Test message from cleaning up JMSPublish... JMSPublish exiting. > topic/testTopic "Test

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For many commercial projects only cross-sectional data from the panels are used, but there is also a substantial class of research problems for which the longitudinal aspect is essential Obvious examples are tracking studies to evaluate the effect of advertising campaigns and investigating trends in markets such as the IT market or the leisure time market In this chapter, we attempt to bring these worlds closer together by reviewing how auxiliary data can be used for statistical adjustment in both kinds of panels The emphasis is on longitudinal surveys and we discussed calibration in general and over multiple waves We also discussed the use of imputation and multiple imputation for handling missing data Although most effort in longitudinal research is focused on the prevention and adjustment of panel attrition, one of the most crucial parts is the recruitment stage, when the majority of nonresponse is encountered.

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It is during recruitment and panel formation that selective nonresponse and the opportunity for nonresponse bias are greatest In the described case of the CentERpanel, only 10 % of the initial sample agreed to become a panel member; in the Gallup consumer panel 27 % of those contacted agreed to participate In our opinion both in the commercial world and the scientific world far more attention should be devoted to the recruitment process; this should be carefully described in terms of inclusion probability and amount and type of nonresponse But one should go one step further and also collect data in the recruitment process with the explicit goal of using these to adjust for initial nonresponse and differences between those approached, those willing to grant a recruitment interview and the final panel members.

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Infrared spectroscopy has proved to be a very effective tool for the investigation of solid pollutants. For example, contaminants, such as pesticides, organic and inorganic sprays, or dusts on plants, may be examined by using this technique. GC IR spectroscopy has been used to investigate the degradation products of Mirex (dodecachloropentacyclodecane), a pesticide used in the control of re ants [36]. One of the requirements for pesticides is that they rapidly decompose in the environment, and GC IR spectroscopy can be used to identify Mirex derivatives. This technique has an advantage over GC MS, for instance,

This approach goes much further than the standard adjustment procedures in which demographic variables are used, such as age and sex It is desirable to have datasets that are representative with respect to standard demographic variables, but this will only reduce nonresponse bias if these demographic variables are strongly related to the missingness pattern and the target variables of the study In other words, is that one old gentleman in an online panel like all the other old gentlemen not in the panel, or does he differ for instance in computer knowledge or.

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