A didactic on latent growth curve modeling for ordinal outcomes is presented. The conceptual aspects of modeling growth with ordinal variables and the notion of threshold invariance are illustrated graphically using a hypothetical example. The ordinal growth model is described in terms of 3 nested models: (a) multivariate normality of the…

This article describes the development of a classification system to measure the expertise levels of practicing paediatric rehabilitation therapists. Seventy-five therapists from five disciplines (physical, occupational, speech-language, behaviour, and recreational therapy) were involved, along with 170 peers, and 188 parents of children with…

Canonical correlation (CC) analysis is discussed with a view toward providing an intuitive understanding of how the technique operates. CC analysis entails calculation of one or more sets of canonical variate coefficients (CVC), i.e., weights which can be applied to the variables in a study. A canonical function (CF) always consists of exactly two…

When selection occurs on the basis of two or more predictors, multivariate restriction of range can reduce various parameters of a validation study. A Statistical Analysis System (SAS) and a Fortran IV program are described that allow for correction of criterion standard deviation(s) and zero-order validities. (Author)

This paper presents three variable deletion strategies in canonical correlation analysis. All three strategies are illustrated by examples. The first strategy uses the canonical communality (h2) coefficients of the three functions to decide which variable to delete. The second function also uses the canonical communality coefficients, but only…

Data are often a continuous function of a variable such as time observed over some interval. One or more such functions might be observed for each subject. The extension of classical data analytic techniques to such functions is discussed. (Author/JKS)

The key ideas of multiple imputation for multivariate missing data problems are reviewed. Software programs available for this analysis are described, and their use is illustrated with data from the Adolescent Alcohol Prevention Trial (W. Hansen and J. Graham, 1991). (SLD)

Discusses the propriety and practical advantages of specifying multivariate time series models in the context of structural equation modeling for time series and longitudinal panel data. For time series data, the multiple indicator model specification improves on classical time series analysis. For panel data, the multiple indicator model…

Proposes a class of multivariate Tobit models with a factor structure on the covariance matrix. Such models are useful in the exploratory analysis of multivariate censored data and the identification of latent variables from behavioral data. The factor structure provides a parsimonious representation of the censored data. Models are estimated with…

Cluster analysis was used to identify groups defined by the patterning of fathers' and mothers' sources of knowledge about adolescents' experiences in a sample of 179 families with adolescents (M=16.5 years). Three clusters emerged for fathers (relational, relies on spouse, relies on others) and mothers (relational, questioners, relies on others).…

In this paper, we present a data-based user modeling framework that uses both unsupervised and supervised classification to build student models for exploratory learning environments. We apply the framework to build student models for two different learning environments and using two different data sources (logged interface and eye-tracking data).…

Institutions of higher education have a vested interest in the health of their student populations, even without the traditional strictures of "in loco parentis". Student health issues involve stress management, healthy diet and exercise, the building of healthy social lives, effective relationship management, and the development of life…

This paper illustrates how canonical correlation analysis can be employed to implement all the parametric tests that canonical methods subsume as special cases. The point is heuristic: all analyses are correlational, all apply weights to measured variables to create synthetic variables, and all yield effect sizes analogous to "r"…

Canonical correlation analysis is the most general linear model subsuming all other univariate and multivariate cases (N. Kerlinger & E. Pedhazur, 1973; B. Thompson, 1985, 1991). Because "reality" is a complex place, a multivariate analysis such as canonical correlation analysis is demanded to match the research design. The purpose…

Multivariate techniques have been implemented with greater and greater frequency. In order to use multivariate techniques researchers must understand the fundamental assumptions. The purpose of this paper is to evaluate one of the assumptions of multivariate analysis, normality. Overall, normal distributions are unimodal and symmetrical, and they…