The purpose of this study is to introduce a functional approach for modeling unfolding response data. Functional data analysis (FDA) has been used for examining cumulative item response data, but a functional approach has not been systematically used with unfolding response processes. A brief overview of FDA is presented and illustrated within the…

In modeling missing data, the missing data latent variable of the confirmatory factor model accounts for systematic variation associated with missing data so that replacement of what is missing is not required. This study aimed at extending the modeling missing data approach to tetrachoric correlations as input and at exploring the consequences of…

A mixture extension of Samejima's continuous response model for continuous measurement outcomes and its estimation through a heuristic approach based on limited-information factor analysis is introduced. Using an empirical data set, it is shown that two groups of respondents that differ both qualitatively and quantitatively in their response…

The unstructured multiple-attempt (MA) item response data in virtual learning environments (VLEs) are often from student-selected assessment data sets, which include missing data, single-attempt responses, multiple-attempt responses, and unknown growth ability across attempts, leading to a complex and complicated scenario for using this kind of…

An alternative to null hypothesis significance testing is presented and discussed. This approach, referred to as observation-oriented modeling, is centered on model building in an effort to explicate the structures and processes believed to generate a set of observations. In terms of analysis, this novel approach complements traditional methods…

This research note contributes to the discussion of methods that can be used to identify useful auxiliary variables for analyses of incomplete data sets. A latent variable approach is discussed, which is helpful in finding auxiliary variables with the property that if included in subsequent maximum likelihood analyses they may enhance considerably…

A challenge associated with traditional mixture regression models (MRMs), which rest on the assumption of normally distributed errors, is determining the number of unobserved groups. Specifically, even slight deviations from normality can lead to the detection of spurious classes. The current work aims to (a) examine how sensitive the commonly…

This article discusses the concept of an invariant item ordering (IIO) for polytomously scored items and proposes methods for investigating an IIO in real test data. Method manifest IIO is proposed for assessing whether item response functions intersect. Coefficient H[superscript T] is defined for polytomously scored items. Given that an IIO…

The authors investigated the factor structure of the Schutte Self-Report Emotional Intelligence (SSREI) scale on international students. Via confirmatory factor analysis, the authors tested the fit of the models reported by Schutte et al. and five other studies to data from 640 international students in the United States. Results show that…

Classification procedures are common and useful in behavioral, educational, social, and managerial research. Supervised classification techniques such as discriminant function analysis assume training data are perfectly classified when estimating parameters or classifying. In contrast, unsupervised classification techniques such as finite mixture…

Suggests two complementary frameworks for providing validity evidence for rating data. One conceptualizes raters as data collection instruments that should be subject to traditional procedures for establishing validity evidence; the other evaluates studies using raters from an experimental design perspective allowing the assessment of the study's…

This study explores the latent structure of scores on the Learning and Study Strategies Inventory (LASSI) and analyzes the relationship between this structure and students' academic performance. Two independent samples of college freshmen (n = 527) and seniors (n = 429) completed the LASSI. Data analysis of the first sample revealed acceptable…

Missing data are a common practical problem for longitudinal designs. Time-series analysis is a longitudinal method that involves a large number of observations on a single unit. Four different missing-data methods (deletion, mean substitution, mean of adjacent observations, and maximum likelihood estimation) were evaluated. Computer-generated…

There is disagreement among researchers as to whether creativity is a unidimensional or multidimensional trait. Much of the debate centers around the most widely used measure of creativity, the Torrance Tests of Creative Thinking (TTCT). This study used data from 1,000 kindergartners (ages 5-7), 1,000 third graders (ages 7-11) and 1,000 sixth…