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1988; Melton, Liang & Pulver, 1994; van der Heijden, Dessens, & Bockenholt, 1996), the transition probabilities in latent transition problems (Humphreys & Janson, 2000; Pfefferman, Skinner & Humphreys, 1998; Reboussin, Reboussin, Liang, & Anthony, 1998) and the conditional response probabilities (Pfefferman et al, 1998) Concomitant predictors are added to latent class and latent transition models via logistic or multinomial logistic regression Because these concomitant predictors are unmodeled, they can be discrete, continuous, or higher order terms, like powers or interactions For example, consider our latent class model of depression Perhaps it would be interesting to see whether alcohol use predicts membership in particular depression latent classes With this model, it is possible to investigate questions like the following: For a unit increase in alcohol consumption, how does the probability of membership in the depression latent class change The ability to include interactions makes the model quite attractive.

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For example, it might be reasonable to hypothesize that the effects of alcohol consumption on predicting level of depression could be different for males and females Perhaps female alcohol-consuming adolescents are most likely to be members of one latent class, but male alcohol-consuming adolescents are most likely to be members of another latent class The interaction becomes important if non alcohol-consuming male and female adolescents are not likely to be in the same depression latent classes as their alcohol-consuming counterparts Concomitant predictors can also be brought into the measurement side of the latent class model in other words, used to predict the conditional response probabilities in a latent class model In a study of employment, Pfefferman et al (1998) included variables thought to be related to the reporting of employment status, such as receiving unemployment compensation Suppose the variable measuring whether unemployment compensation is received is dichotomous.

It is Thursday morning at The Times Hotel. The reservations manager has printed the list of reservations for the day. The front of ce staff has prepared 252 packets for guests who have preregistered for the Pet Owners of the Americas Conference. The Times Hotel has been designated the headquarters for the cat owners, while The Sebastian Hotel, located two blocks away, has been designated the headquarters for the dog owners. The participants in the Pet Owners of the Americas Conference are supposed to start arriving at noon. The Times Hotel had a full house on Wednesday night. A planning group (179 rooms) for the Biology Researchers Conference was in the hotel. They held a meeting into the early hours of Thursday morning. Several of the guests posted do not disturb signs on their doors. Yoon-Whan Li, the executive engineer, has noticed the air-conditioning going on and off on the fth and sixth oors. Yoon-Whan investigated the

Then using it as a concomitant predictor amounts to estimating the conditional response probabilities separately for people who do not receive unemployment compensation and for those who do receive unemployment compensation It is important to note that this model would not be used to investigate whether those receiving unemployment compensation are more likely to be employed or not; rather, the model speci es that the probability of a particular response to the employment question, conditional on latent class membership, may differ between people receiving unemployment compensation and people not receiving unemployment compensation One can think of this as a differential error rate for those receiving and those not receiving unemployment compensation In LTA models, concomitant predictors have been used to predict transitions among latent statuses Reboussin et al..

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Error codes in Device Manager in Windows - Microsoft Support
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