A recent review found that 11% of published factor models are hierarchical models with second-order factors. However, dedicated recommendations for evaluating hierarchical model fit have yet to emerge. Traditional benchmarks like RMSEA <0.06 or CFI >0.95 are often consulted, but they were never intended to generalize to hierarchical models.…

Developmental researchers commonly utilize longitudinal data to decompose reciprocal and dynamic associations between repeatedly measured constructs to better understand the temporal precedence between constructs. Although the cross-lagged panel model (CLPM) is commonly used in developmental research, it has been criticized for its potential to…

This study examined the impact of omitting covariates interaction effect on parameter estimates in multilevel multiple-indicator multiple-cause models as well as the sensitivity of fit indices to model misspecification when the between-level, within-level, or cross-level interaction effect was left out in the models. The parameter estimates…

For the most part, the effects of lexical-distributional properties of words on visual word recognition are well-established. More uncertainty remains, however, about the influence of these properties on lexical processing for nonwords. The work presented here investigates the mechanisms that guide nonword processing through an analysis of lexical…

It is common to include multiple dependent variables (DVs) in single-case experimental design (SCED) meta-analyses. However, statistical issues associated with multiple DVs in the multilevel modeling approach (i.e., possible dependency of error, heterogeneous treatment effects, and heterogeneous error structures) have not been fully investigated.…

When selecting a multilevel model to fit to a dataset, it is important to choose both a model that best matches characteristics of the data's structure, but also to include the appropriate fixed and random effects parameters. For example, when researchers analyze clustered data (e.g., students nested within schools), the multilevel model can be…

This dissertation informed researchers about the performance of different level-specific and target-specific model fit indices in Multilevel Latent Growth Model (MLGM) using unbalanced design and different trajectories. As the use of MLGMs is a relatively new field, this study helped further the field by informing researchers interested in using…

Several researchers have recommended that level-specific fit indices should be applied to detect the lack of model fit at any level in multilevel structural equation models. Although we concur with their view, we note that these studies did not sufficiently consider the impact of intraclass correlation (ICC) on the performance of level-specific…

The last 20 years has seen an uptick in research on missing data problems, and most software applications now implement one or more sophisticated missing data handling routines (e.g., multiple imputation or maximum likelihood estimation). Despite their superior statistical properties (e.g., less stringent assumptions, greater accuracy and power),…

Assessment instruments composed of two-tier multiple choice (TTMC) items are widely used in science education as an effective method to evaluate students' sophisticated understanding. In practice, however, there are often concerns regarding the common scoring methods of TTMC items, which include pair scoring and individual scoring schemes. The…

Hierarchical linear modeling (HLM) is a useful tool when analyzing data collected from groups. There are many decisions to be made when constructing and estimating a model in HLM including which estimation technique to use. Three of the estimation techniques available when analyzing data with HLM are maximum likelihood, restricted maximum…

Purpose: Research in communication sciences and disorders frequently involves the collection of clusters of observations, such as a series of scores for each individual receiving treatment over the course of an intervention study. However, little discipline-specific guidance is currently available on the subject of building and interpreting…

Bayesian Knowledge Tracing (BKT) models were in active use in the Intelligent Tutoring Systems (ITS) field for over 20 years. They have been intensively studied, and a number of useful extensions to them were proposed and experimentally tested. Among the most widely researched extensions to BKT models are various types of individualization.…

When a word is read more than once, reading time generally decreases in the successive occurrences. This Repetition Effect has been used to study word encoding and memory processes in a variety of experimental measures. We studied naturally occurring repetitions of words within normal texts (stories of around 3,000 words). Using linear mixed…

Growth modeling has been of interest in many assessment programs, including both highstakes and low-states tests. Growth could be modeled using different approaches. This study models growth with an Item Response Theory (IRT) based approach that utilizes item response data. It investigates the impact of complex student clustering structure where…