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Before hiring any investment rm or investment manager, you should make a point of conducting some base level of reference and background checks. Ask the investment professionals who manage the product to furnish at least two references. I generally like to speak to a current/former client and to a former business associate. When talking to references, bear in mind that they will likely paint a pretty rosy picture of the performer whose references you are checking (that s why you were given their names in the rst place). Don t be embarrassed to ask any of the questions that I outlined in 13. I have found out plenty from calling these handpicked references over the years. 13 also outlines the process for checking into a person s background. I personally do not conduct complete background checks on anyone. I hire professional investigative rms to do this for me. But I do conduct spot checks when I do not feel a complete check is needed. A spot check can be a simple veri cation of an individual s educational background, a check of employment history, or a veri cation of any professional certi cations or designations listed in a biography.

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.

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.

The evidence suggests that DI improves cross-wave continuity: both reactive and proactive DI reduce misclassification and under-reporting of events; in addition, reactive DI reduces keying error and proactive DI with routing reduces coding variability and consequent spurious change Returning to Figure 61, DI reduces spurious changes at the seam and constant wave responses caused by under-reporting of events or statuses, especially for respondents who experienced a change during the reference period.

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.

In the opening paragraph of their chapter about the BAI, Inbau et al. (2001, p. 173) state that research has demonstrated that innocent subjects tend to respond differently to these specialized questions than do