Most existing studies on the relationship between factor analysis (FA) and principal component analysis (PCA) focus on approximating the common factors by the first few components via the closeness between their loadings. Based on a setup in Bentler and de Leeuw (Psychometrika 76:461-470, 2011), this study examines the relationship between FA…

Most methods for structural equation modeling (SEM) focused on the analysis of covariance matrices. However, "Historically, interesting psychological theories have been phrased in terms of correlation coefficients." This might be because data in social and behavioral sciences typically do not have predefined metrics. While proper methods…

Within each of 170 physicians, patients were randomized to access e-assist, an online program that aimed to increase colorectal cancer screening (CRCS), or control. Compliance was partial: 78.34% of the experimental patients accessed e-assist while no controls were provided the access. Of interest are the average causal effect of assignment to…

Factor analysis is widely utilized to identify latent factors underlying the observed variables. This paper presents a comprehensive comparative study of two widely used methods for determining the optimal number of factors in factor analysis, the K1 rule, and parallel analysis, along with a more recently developed method, the bass-ackward method.…

The p-factor is a construct that is thought to explain and maybe even cause variation in all forms of psychopathology. Since its 'discovery' in 2012, hundreds of studies have been dedicated to the extraction and validation of statistical instantiations of the p-factor, called general factors of psychopathology. In this Perspective, we outline five…

Factor analysis is often used to model scales created to measure latent constructs, and internal structure validity evidence is commonly assessed with indices like SRMR, RMSEA, and CFI. These indices are essentially effect size measures and definitive benchmarks regarding which values connote reasonable fit have been elusive. Simulations from the…

Scale validation is vital to psychological research because it ensures that scores from measurement scales represent the intended construct. Factor analysis fit indices are commonly used to provide quantitative evidence that a proposed factor structure is plausible. However, there is mismatch between guidelines for evaluating fit of factor models…

Item factor analysis (IFA), also known as Multidimensional Item Response Theory (MIRT), is a general framework for specifying the functional relationship between a respondent's multiple latent traits and their response to assessment items. The key element in MIRT is the relationship between the items and the latent traits, so-called item factor…

Background: The Evidence-Based Practice Attitudes Scale (EBPAS) is widely used in implementation research, but it has not been adapted and validated for use among general education teachers, who are most likely to deliver evidence-based prevention programs in schools, the most common setting where youth access social, emotional, and behavioral…

To evaluate the fit of a confirmatory factor analysis model, researchers often rely on fit indices such as SRMR, RMSEA, and CFI. These indices are frequently compared to benchmark values of 0.08, 0.06, and 0.96, respectively, established by Hu and Bentler (1999). However, these indices are affected by model characteristics and their sensitivity to…

We describe a data mining pipeline to convert data from educational systems into knowledge component (KC) models. In contrast to other approaches, our approach employs and compares multiple model search methodologies (e.g., sparse factor analysis, covariance clustering) within a single pipeline. In this preliminary work, we describe our approach's…

Culturally responsive assessment practices include validated measures appropriate for use with diverse populations. Considering the increasing population of Latinx students in U.S. schools, measures need co-validated English and Spanish (SEHS) language forms. This study examined the Social Emotional Health Survey--Secondary with Latinx students…

Deciding which random effects to retain is a central decision in mixed effect models. Recent recommendations advise a maximal structure whereby all theoretically relevant random effects are retained. Nonetheless, including many random effects often leads to nonpositive definiteness. A typical remedy is to simplify the random effect structure by…

Assessing youth psychopathology involves collecting multiple informants' reports. Yet, multi-informant reports often disagree, necessitating integrative strategies that optimize predictive power. The "Trait" score approach leverages principal components analysis (PCA) to account for the context and perspective from which informants…

With many pretreatment covariates and treatment factors, the classical factorial experiment often fails to balance covariates across multiple factorial effects simultaneously. Therefore, it is intuitive to restrict the randomization of the treatment factors to satisfy certain covariate balance criteria, possibly conforming to the tiers of…