Part V Advanced Programming Techniques in Java

Printer PDF 417 in Java Part V Advanced Programming Techniques

Illustrated TCP/IP by Matthew G. Naugle Wiley Computer Publishing, John Wiley & Sons, Inc. ISBN: 0471196568 Pub Date: 11/01/98 download
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P (x, y) log Q(x) H (X) = 0, P (x, y) log Q(y) H (Y ) = 0,
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When decisions are taken conditioned to another variable, like a received vector Y, the LLR is of the form of equation (54) of 7, and considering equation (55) of 7, it can be written as L(bi /Y) = ln P(bi = +1/Y) P(bi = 1/Y) = ln P(bi = +1) P(bi = 1) + ln P(yi /bi = +1) P(yi /bi = 1) (63)
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smallest number of classes) that provides adequate t could be selected as the one with the most appropriate number of latent classes. This approach is taken in the empirical example on adolescent depression presented later in this chapter. Assessing the Quality of Categorical Measures Latent class models provide a basis for assessing the reliability of categorical items (Clogg & Manning, 1996; Collins, 2001). Psychometric theory for instruments intended to measure continuous latent variables is inappropriate for instruments measuring categorical latent variables (Collins, 2001). However, reliability of such instruments can be examined by means of the parameters in the latent class model. Clogg and Manning (1996) present two types of reliability for categorical items, item-speci c reliability and item-set reliability. Item-speci c reliability refers to pairwise relations between single items and the latent class variable. For example, consider the parameters. These values quantify how well membership in a latent class predicts item response, which is one form of reliability. An item that is highly associated with a latent class is a reliable indicator of that class. Another item-speci c form of reliability presented by Clogg and Manning (1996) is how well an item response predicts membership in a latent class. Additional methods Clogg and Manning present for assessing item-speci c reliability include using odds ratios and transformed odds ratios that are similar to correlations. Item-set reliability, in contrast to item-speci c reliability, refers to the reliability of a set of items with respect to each latent class that is, how well a particular response pattern predicts latent class membership. Flaherty (in press) uses both item-speci c and item-set reliability to examine the measurement quality of four measures of tobacco use in a national survey. Collins (2001) has developed a reliability index that is similar conceptually to the familiar Cronbach s . Rather than examine reliability for item and class pairs, or for response patterns and each latent class, Collins (2001) takes the information contained in the item-set reliabilities and develops a single number that summarizes the reliability of the items. Collins (2001) also extends this notion of reliability to the assessment of the reliability of dynamic (changing) categorical latent variables. Reliability analysis is closely related to item development and selection. Biemer and Wiesen (2002) use the latent class model to assess the consistency of people s reports of marijuana use in a national survey of substance use. Using this method, they were able to identify poor items and adjust prevalence estimates of marijuana use. code 128 font
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Yi values yielding Vi = (Yi y )/ y . Let (x) = max[ 1, min(1, x)]. Set Ai = (Ui ) and Bi = correlation is estimated to be r pb = (Vi ). The percentage bend Ai Bi Ai2 Bi2
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