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Under these circumstances the fundamental question to ask is Do we know something about these observations that suggest they come from a different process than the process we are seeking to understand In experimentally oriented psychology, rogue values could be caused by numerous unintended processes: failure to understand instructions (especially during opening trials), failure to follow the instructions, failure to pay attention to the task (especially during closing trials), or equipment or data transcription failures Under such circumstances, it is clear the data are not in the domain of the phenomenon to be studied, and the data should be set aside and the situation noted In other cases, extreme values present themselves with little auxiliary information to explain the reason for the extremeness.

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In such a situation we may rst assess how much damage the values create in the model by constructing the model with all the data involved as well as with the questionable data set aside For example, Behrens (1997b) conducted a metaanalysis of correlations between subscales of the White Racial Identity Attitude Scale (Helms, 1997) Initial review of the data suggested the distributions were not homogeneous and that some study results differed dramatically from the average To assess the effect of these extreme values, Behrens calculated the average correlations, rst, using all the data, and second, using a 20% trimmed mean Results were consistent across approaches, suggesting the data could remain or be set aside with little impact on the inferences he was to make.

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the services that she needs The goal of recommendation-based service discovery is to make it easier to nd and activate the services that are relevant in the current situation Ideally, the user interface should display only a brief list of relevant services from which she can pick up the ones that she needs However, the relevance of a given service depends on both the particular user and the current situation Thus, to be able to recommend relevant services, a user model that captures the interests of each user is needed To construct the user models, an assumption is made that the recent behaviour of a user in a similar context re ects the user s current needs Therefore, in recommendation-based service discovery, the generic framework of context-dependent user modelling, as described in Section 433, is used.

What would have happened if, on the other hand, the trimmed results deviated from the full-data results In such a case both sets of analysis should be conducted and reported and the difference between the two results considered as a measure of the effect of the rogue values The most important aspect in either case is that a careful and detailed description of the full data, the reduced data, and the impact of the rogue data be reported Unfortunately, the extremely terse and data-avoidant descriptions of much research reporting is inconsistent with this highly descriptive approach..

This framework makes it possible to (1) learn context-dependent user models based on the user s past behaviour, (2) store the user models, and (3) use the models to provide recommendations As the recommendations are based on the previous behaviour of the user, care should be taken as the number of different contexts and the number of different services may be huge, so one cannot assume that enough past data on any given service in any given situation is available Furthermore, recommendations are needed also in completely new situations (eg travelling in an unfamiliar country) and in the case of completely new services (eg a new competing service) To make the user models generalise to new contexts and new services, the higher-level structure of the data is exploited First, one should make use of higher-level context clusters instead of raw context data.

Click the Share This Printer option (see Figure 8-5).

SOCIAL AND CULTURAL INFLUENCES ON WOMEN S HEALTH Earlier in this chapter, we discussed the advances and shortcomings in the research and health care of women. It is important that strides continue to be made to better understand how women s expression of disease symptoms, potential warning signs, and risk factors for both psychological and

This way one can reason on types of context rather than single instances eliminating irrelevant details Second, one should make use of types of services instead of individual services, and then use the service categorisation mechanism described in Section 442 to map from the type of service to the concrete service that will be proposed to the user..

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