A Comparative Study of Item Response Theory Models for Mixed Discrete-Continuous Responses.

Language proficiency assessments are pivotal in educational and professional decision-making. With the integration of AI-driven technologies, these assessments can more frequently use item types, such as dictation tasks, producing response features with a mixture of discrete and continuous distributions. This study evaluates novel measurement models tailored to these unique response features.

Mattering in the community: Domain and demographic differences in a US sample.

Mattering is defined as experiences of feeling valued and adding value in different domains of life: self, relationships, work, and community. Mattering is a construct with great relevance across psychological and social issues. Research has suggested there may be value in understanding group differences in mattering.

Assessing multidimensional mattering: Development and exploratory validation of the Mattering in Domains of Life Scale (MIDLS).

Mattering, defined as feeling valued and adding value, is a basic psychological need with significant explanatory power. Although several specific measures have been introduced to assess the construct, no integrated, multidimensional measure exists. This limits the ability of researchers to investigate mattering in ecological contexts.

Illustrating the Applicability of IRT to Implementation Science: Examining an Instrument of Therapist Attitudes.

Pragmatic instruments with psychometric support are important to advance dissemination and implementation (D&I) research, but few well-researched D&I instruments exist. Item response theory (IRT), an approach that is underutilized in D&I, can help with the development of actionable and brief instruments. This paper provides an overview of IRT for D&I researchers and examines an instrument of therapist attitudes using IRT measurement models.

Improving the measurement of food insecurity among people with HIV in South Africa: a psychometric examination.

Objective: Food insecurity is a structural barrier to HIV care in peri-urban areas in South Africa (SA), where approximately 80 % of households are moderately or severely food insecure. For people with HIV (PWH), food insecurity is associated with poor antiretroviral therapy adherence and survival rates. Yet, measurement of food insecurity among PWH remains a challenge.

Differential Item Functioning Effect Size From the Multigroup Confirmatory Factor Analysis for a Meta-Analysis: A Simulation Study.

This study presents a new approach to synthesizing differential item functioning (DIF) effect size: First, using correlation matrices from each study, we perform a multigroup confirmatory factor analysis (MGCFA) that examines measurement invariance of a test item between two subgroups (i.e., focal and reference groups).

Infant Movement Response to Auditory Rhythm.

Rhythmic entrainment occurs when an auditory rhythm drives an internal movement oscillator, thus providing a continuous time reference that improves temporal and spatial movement parameters. Entrainment processes and outcomes are well known for adults, but research is lacking for infants who might benefit from diagnosis and treatment of irregular rhythms within biological, sensorimotor, cognitive, and social domains. The present study used a combination of inertial measurement units and custom-made software to determine the amount, tempo, and regularity of movement in 28 infants aged 6-10 months while they were exposed to silence, an irregular rhythmic cue, or a regular rhythmic cue with tempo changes.

A Finite Mixture Item Response Theory Model for Continuous Measurement Outcomes.

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 behavior can be revealed. In addition to the real data application, the effectiveness of the heuristic estimation approach under real data analytic conditions was examined through a Monte Carlo simulation study.

Detecting Examinees With Item Preknowledge in Large-Scale Testing Using Extreme Gradient Boosting (XGBoost).

Researchers frequently use machine-learning methods in many fields. In the area of detecting fraud in testing, there have been relatively few studies that have used these methods to identify potential testing fraud. In this study, a technical review of a recently developed state-of-the-art algorithm, Extreme Gradient Boosting (XGBoost), is provided and the utility of XGBoost in detecting examinees with potential item preknowledge is investigated using a real data set that includes examinees who engaged in fraudulent testing behavior, such as illegally obtaining live test content before the exam.

Validation of Response Similarity Analysis for the Detection of Academic Cheating: An Experimental Study.

The purpose of the present study was to evaluate various analytical means to detect academic cheating in an experimental setting. The omega index was compared and contrasted given a gold criterion of academic cheating which entailed a discrepant score between two administrations using an experimental study with real test takers. Participants were 164 elementary school students who were administered a mathematics exam followed by an equivalent mock exam under conditions of strict and relaxed, invigilation, respectively.

Acoustic parameters of infant-directed singing in mothers of infants with down syndrome.

This study compared the acoustic parameters and degree of perceived warmth in two types of infant-directed (ID) songs - the lullaby and the playsong - between mothers of infants with Down syndrome (DS) and mothers of typically-developing (TD) infants. Participants included mothers of 15 DS infants and 15 TD infants between 3 and 9 months of age. Each mother's singing voice was digitally recorded while singing to her infant and subjected to feature extraction and data mining.

Classification Performance of Answer-Copying Indices Under Different Types of IRT Models.

Test fraud has recently received increased attention in the field of educational testing, and the use of comprehensive integrity analysis after test administration is recommended for investigating different types of potential test frauds. One type of test fraud involves answer copying between two examinees, and numerous statistical methods have been proposed in the literature to screen and identify unusual response similarity or irregular response patterns on multiple-choice tests. The current study examined the classification performance of answer-copying indices measured by the area under the receiver operating characteristic (ROC) curve under different item response theory (IRT) models (one- [1PL], two- [2PL], three-parameter [3PL] models, nominal response model [NRM]) using both simulated and real response vectors.

Evaluating the Sampling Performance of Exploratory and Cross-Validated DETECT Procedure with Imperfect Models.

Among the methods proposed for identifying the number of latent traits in multidimensional IRT models, DETECT has attracted the attention of both methodologists and applied researchers as a nonparametric counterpart to other procedures. The current study investigated the overall performance of the DETECT procedure and its outcomes using a real-data sampling design recommended by MacCallum (2003) and compared the results from a purely simulated data set that was generated with a well-specified "perfect" model. The comparison revealed that the sampling behavior of the maximized DETECT value and R-ratio statistics was quite robust to minor factors and other model misspecifications that potentially exist in the real data set, as there were negligible differences between the results of the real and simulated data sets.

A Finite Mixture of Nonlinear Random Coefficient Models for Continuous Repeated Measures Data.

Nonlinear random coefficient models (NRCMs) for continuous longitudinal data are often used for examining individual behaviors that display nonlinear patterns of development (or growth) over time in measured variables. As an extension of this model, this study considers the finite mixture of NRCMs that combine features of NRCMs with the idea of finite mixture (or latent class) models. The efficacy of this model is that it allows the integration of intrinsically nonlinear functions where the data come from a mixture of two or more unobserved subpopulations, thus allowing the simultaneous investigation of intra-individual (within-person) variability, inter-individual (between-person) variability, and subpopulation heterogeneity.

Fitting a linear-linear piecewise growth mixture model with unknown knots: A comparison of two common approaches to inference.

A linear-linear piecewise growth mixture model (PGMM) is appropriate for analyzing segmented (disjointed) change in individual behavior over time, where the data come from a mixture of 2 or more latent classes, and the underlying growth trajectories in the different segments of the developmental process within each latent class are linear. A PGMM allows the knot (change point), the time of transition from 1 phase (segment) to another, to be estimated (when it is not known a priori) along with the other model parameters. To assist researchers in deciding which estimation method is most advantageous for analyzing this kind of mixture data, the current research compares 2 popular approaches to inference for PGMMs: maximum likelihood (ML) via an expectation-maximization (EM) algorithm, and Markov chain Monte Carlo (MCMC) for Bayesian inference.

Longitudinal mathematics development of students with learning disabilities and students without disabilities: a comparison of linear, quadratic, and piecewise linear mixed effects models.

Effective instructional planning and intervening rely heavily on accurate understanding of students' growth, but relatively few researchers have examined mathematics achievement trajectories, particularly for students with special needs. We applied linear, quadratic, and piecewise linear mixed-effects models to identify the best-fitting model for mathematics development over elementary and middle school and to ascertain differences in growth trajectories of children with learning disabilities relative to their typically developing peers. The analytic sample of 2150 students was drawn from the Early Childhood Longitudinal Study - Kindergarten Cohort, a nationally representative sample of United States children who entered kindergarten in 1998.

Rotation to a partially specified target matrix in exploratory factor analysis in practice.

The purpose of the present study was to explore the influence of the number of targets specified on the quality of exploratory factor analysis solutions with a complex underlying structure and incomplete substantive measurement theory. We extended previous research in this area by (a) exploring this phenomenon in situations in which both the common factor model and the targeted pattern matrix contained specification errors and (b) comparing the performance of target rotation to an easier-to-use default rotation criterion (i.e.

The effects of baseline estimation on the reliability, validity, and precision of CBM-R growth estimates.

This study examined the effect of baseline estimation on the quality of trend estimates derived from Curriculum Based Measurement of Oral Reading (CBM-R) progress monitoring data. The authors used a linear mixed effects regression (LMER) model to simulate progress monitoring data for schedules ranging from 6-20 weeks for datasets with high and low levels of residual variance (poor and good quality datasets respectively). Three observations per day for the first three days of data collection were generated for baseline estimation.

Curriculum-based measurement of oral reading: multi-study evaluation of schedule, duration, and dataset quality on progress monitoring outcomes.

Curriculum-Based Measurement of Oral Reading (CBM-R) is used to collect time series data, estimate the rate of student achievement, and evaluate program effectiveness. A series of 5 studies were carried out to evaluate the validity, reliability, precision, and diagnostic accuracy of progress monitoring across a variety of progress monitoring durations, schedules, and dataset quality conditions. A sixth study evaluated the relation between the various conditions of progress monitoring (duration, schedule, and dataset quality) and the precision of weekly growth estimates.

A comparison of two estimation algorithms for Samejima's continuous IRT model.

This study compares two algorithms, as implemented in two different computer softwares, that have appeared in the literature for estimating item parameters of Samejima's continuous response model (CRM) in a simulation environment. In addition to the simulation study, a real-data illustration is provided, and CRM is used as a potential psychometric tool for analyzing measurement outcomes in the context of curriculum-based measurement (CBM) in the field of education. The results indicate that a simplified expectation-maximization (EM) algorithm is as effective and efficient as the traditional EM algorithm for estimating the CRM item parameters.