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In the 1950s Stephen Kuf er s laboratory at Johns Hopkins University was studying the visual response of retinal neurons using microelectrodes It was in 1958 that two young researchers who had come to work with Kuf er met: David H Hubel (b 1926) and Torsten N Wiesel (b 1924) They decided to look at the response of single neurons in the visual cortex to see if they had any differential responses to stimuli.



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estimated, the most commonly used procedure involves a one-parameter, or Rasch, model (Rasch, 1966) that focuses only upon the threshold parameter In this model, only respondent ability is assumed to affect responses items are assumed to be equally discriminating, and guessing is assumed to have no in uence upon responses When a twoparameter item response model is used, typically the guessing parameter is excluded and item threshold and item discrimination are each estimated In personality and psychopathology, one-parameter and two-parameter models are most useful because the concept of guessing typically has little meaning outside the area of ability testing.

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With respect to the threshold parameter, it is advisable to select items whose estimated thresholds fall along the full range of the construct, so that the resulting scale can provide discriminations in a wide range of applications; in a one-parameter model, this can be achieved by selecting items with varying levels of item dif culty, as described previously For the discrimination parameter, the desired values may vary according to the function of the scale For example, adaptive testing, which uses item response theory to tailor test questions to a respondent by selecting the most informative item as estimated from previous responses, works most ef ciently when items are sharply discriminating (ie, have steeper slopes at the point of in ection).

Any database management system needs to be able to deal with failures. There are many types of failures that a DBMS must handle. Before discussing failure types and commit protocols, we need to lay the foundation by de ning the terms we will use in the rest of this chapter.

Besides making it easier to refer to objects, using object variables can also help your code execute more quickly.

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On the other hand, a scale with relatively few items may be able to achieve a more even assessment across the continuum by selecting items with a less steep (although still positive) discrimination slope Although item response theory is most typically applied to scaling items with binary scoring, models have also been developed for items with graded response options, such as Likert-type scaling (Samijema, 1969) The Samejima graded response model treats the graded item as a series of dichotomies, and models the resulting item information as a function incorporating the component ICCs for these dichotomies For example, an item rated on a scale of 1 to 4 has three such dichotomies response 1 versus responses 2, 3, and 4; responses 1 and 2 versus 3 and 4; and responses 1, 2, and 3 versus response 4.

Optical ber cable is commonly available in 1-km sections; it is also available in longer sections, in some types up to 10 km or more. In any case there must be some way of connecting the ber to the source and to the detector as well as connecting the reels of cable together, whether in 1 km or more lengths, as required. There are two methods

The use of the graded approach introduces additional complexities to the interpretation of the results, and a simpler (although less informative) alternative is to dichotomize the graded items in some fashion In order for the results to be meaningful, the same dichotomizing strategy should be employed for all items on the scale (eg, responses above vs below the midpoint of the scale) Also, the dichotomy must be drawn based upon the raw responses rather than on any distributional properties of the items, such as a median split The latter approach would arti cially force all items to appear as if they had similar item dif culties and would seriously distort the estimation of the item parameters..

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Here are some of the methods available in the database class (the MTDatabaseBase inherits from the ezSQL class):

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