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medium, large, and extra large; respondents answer either yes or no ; manufactured parts either pass or do not pass quality control; or dessert choice is sorbet, tiramisu, chocolate mousse, or apple tart In many analyses, discrete variables are the grouping variables (treatment group vs control) for a main analysis such as analysis of variance (ANOVA) or logistic regression Assumptions for discrete variables relate to the frequency of cases in the various categories Problems arise when there are too few cases in some of the categories, as discussed later Grouped and Ungrouped Research Designs Assumptions are assessed differently depending on whether the data are to be grouped or ungrouped during analysis The most common goal in grouped analyses is to compare the central tendency in two or more groups; the most common goal in ungrouped analyses is to study relationships among variables.



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, & Anthony, J C (1998) A latent transition approach for modeling progression of health-risk behavior Multivariate Behavioral Research, 33, 457 478 Resnick, M D, Bearman, P S, Blum, R W, Bauman, K E, Harris, K M, Jones, J, Tabor, J, Beuhring, T, Sieving, R E, Shew, M, Ireland, M, Bearinger, L H, & Udry, J R (1997) Protecting adolescents from harm: Findings from the National Longitudinal Study of Adolescent Health Journal of the American Medical Association, 278, 823 832 Rindskopf, D (1983) A general framework for using latent class analysis to test hierarchical and nonhierarchical learning models Psychometrika, 48, 85 97 Rubin, D B (1984) Bayesianly justi able and relevant frequency calculations for the applied statistician Annals of Statistics, 12, 1151 1172 Rubin, D B (1987) Multiple imputation for nonresponse in surveys New York: Wiley Rubin, D.





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Likewise, the flow may have priorities in its packets, and may choose to discard an already queued packet in favor of an arriving packet when its queue is full Essentially, the approach is to limit the scope of the scheduler to determine only which flow is allocated the channel next, and let each flow make its own decision about which packet in the flow it wishes to transmit In our scheduling model, we support any queueing and packet dropping policy at the flow level because we decouple slot queues from packet queues*.

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Grouped data are appropriately analyzed using univariate or multivariate analysis of variance (ANOVA and MANOVA, including pro le analysis of repeated measures), logistic regression, or discriminant analysis Ungrouped data are analyzed through bivariate or multiple regression, canonical correlation, cluster analysis, or factor analysis Some techniques apply to either grouped or ungrouped data For example, time-series analysis and survival analysis can be used to track behavior over time for a single group of cases or to compare behavior over time for different groups Chisquare and multiway frequency analysis can be used to compare contingencies in responses among categorical variables for a single group or to look for differences in responses among different groups Similarly, structural equations can be used to model responses of a single group or compare models among groups Tests of assumptions are performed differently depending on whether data are to be grouped or ungrouped during analysis.

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B, & Stern, H S (1994) Testing in latent class models using a posterior predictive check distribution In A Von Eye &.

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Basically, ungrouped data are examined as a single set, while grouped data are examined separately within each group or have entirely different criteria for assessing t to some assumptions, as discussed later Single Versus Multiple Responses Participants provide a single response in the classical betweensubjects ANOVA or chi-square designs In other designs participants may provide several responses, and those responses may be measured either on the same or on different scales Multivariate statistical techniques deal with multiple responses on different scales and are analyzed using such methods as MANOVA, canonical correlation, discriminant analysis, factor analysis, and structural equation modeling..

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