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This section outlines the development of record linkage methodology, methods of automated record linkage and practical considerations in relation to linkage. These issues are discussed more extensively elsewhere (Jaro, 1995; Gill, 2001; Blakely and Salmond, 2002; Herzog et al., 2007). We consider here methods for linking two or more different records that are believed to relate to the same person. Usually the records come from separate sources and so are in separate data files, though record linkage may also be attempted within data files to identify duplicate records. Record linkage methodology involves comparing individual identifiers that appear on each record in order to determine which pairs of records relate to the same person. The aim of record linkage is to maximise the number of pairs of records that are correctly linked together, i.e. to maximise the number of linked records that have been correctly linked (true positives). However, it is possible for pairs of records relating to different people to be incorrectly linked (false positives). Similarly, true matches may not be linked (false negatives). Minimising false positive links will tend to lead to an increase

where r11 (s, t) := 2 r(s, t)/ s t. This shows differentiability in q.m. We assume, using the remark in the proof above, that X is centered and satis es the conditions of the proposition. It is easy to prove that E(X(s)X (t)) = r01 (s, t) := r (s, t), t

2 (tk+1 tk ) 2 R2 (t1 , . . . , tk , tk+1 ) .

These pancakes are an excellent breakfast choice they re a good source of protein, and they ll keep you energized until lunch. Remember to use light syrup. Preparation Time: 8 minutes Cooking Time: 3 minutes Yield: 11 pancakes 1 cup (250 ml) 1% cottage cheese 1 egg 2 egg whites 3 tbsp (45 ml) wheat germ

5 2 (t)

2.

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We now turn to obtaining a bound for E(MT ). A classical result in this direction is the next theorem. The proof is taken from Talagrand (1996). Let {X(t) : t T } be a stochastic process not necessarily Gaussian. As in Section 1.4.4, we de ne the canonical distance by d(s, t) := E (X(t) X(s))2 , identifying as usual points s, t when d(s, t) = 0. We de ne the covering number N := N (T , d, ) as in De nition 1.17 using this metric. Theorem 2.10 (Dudley). With the preceding notations, assume that E(X(t)) = 0 P(|X(t) X(s)| > u) 2 e (1/2)[u Then E(sup X(t)) K

moving at near light speed would inhabit a bizarre world where nothing behaved normally. The implications of Einstein s daydream are mindblowing. Reality stops working the usual way as you get close to the speed of light. Fixing light s speed has the effect of freeing up other factors that had until then seemed unchangeable. It s like trying to pin down an animal so a vet can give it an inoculation. Hold it by the legs and its head moves too much. Grab for the neck and the legs are suddenly kicking out. When Einstein pinned down light s speed, it was mass, size and time itself that sprang free. An object moving at near light speed shrinks and becomes hugely heavy. Time slows down for it, getting slower and slower until it stops entirely at the speed of light. If it were possible for the object to move even faster, to exceed light speed, it would begin to move backwards in time. Since Einstein s rst remarkable insight, all this has been proved by experiment. The distortions of special relativity are most dramatic near the speed of light, but today s instruments can expose relativity s mysterious in uence in the everyday world. Atomic clocks, slicing time into fragments of less than a billionth of a second, are small enough to t into a suitcase. Take two of these hyper-accurate timepieces and synchronize them exactly. Fly one around the world, while the other stays rmly on the ground. Place the clock from the plane back alongside its earthbound equivalent. Compare the times now and the clock that has made the journey will have fallen behind, perhaps by 30 billionths of

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self-administered surveys (Schuman and Presser, 1981; Dillman and Tarnai, 1991; De Leeuw, 1992). It has also been suggested that telephone respondents are more likely to acquiesce than are face-to-face respondents (Krosnick, 1999). A longstanding concern with longitudinal surveys is that respondents may be conditioned by earlier data collection to respond in certain ways to questions in later surveys. Evidence for conditioning is especially evident in web panel surveys where respondents are surveyed frequently (Cartwright and Nancarrow, 2006; Nancarrow and Cartwright, 2007). It also seems plausible that respondents who responded by a particular mode in an earlier data collection may be conditioned to respond similarly to a different mode, but experimental data on such an effect are lacking. 8.3.4 How Answers to Scalar Questions are Affected by Visual vs. Aural Communication

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