Cognitive Diagnostic Modeling Using R

Cognitive diagnostic models (CDM) have been around for more than a decade but their application is far from widespread for mainly two reasons: (1) CDMs are novel, as compared to traditional IRT models. Consequently, many researchers lack familiarity with them and their properties, and (2) Software programs doing CDMs have been expensive and not readily available. The present paper presents a reader-friendly introduction to the CDMs and uses the CDM package (Robitzsch, Kiefer, Cathrice George, & Uenlue, 2014) in R to demonstrate the application of the generalized deterministic-input, noisy-and-gate model (G-DINA; de la Torre, 2011) and interpret the output. R is a free open source tool which can be utilized to conduct a range of CDMs which otherwise would need separate software programs to run each.Accessed 12,469 times on https://pareonline.net from April 13, 2015 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right

Some thoughts on analytical choices in the scaling model for test scores in international large-scale assessment studies

International large-scale assessments (LSAs), such as the Programme for International Student Assessment (PISA), provide essential information about the distribution of student proficiencies across a wide range of countries. The repeated assessments of the distributions of these cognitive domains offer policymakers important information for evaluating educational reforms and received considerable attention from the media. Furthermore, the analytical strategies employed in LSAs often define methodological standards for applied researchers in the field. Hence, it is vital to critically reflect on the conceptual foundations of analytical choices in LSA studies. This article discusses the methodological challenges in selecting and specifying the scaling model used to obtain proficiency estimates from the individual student responses in LSA studies. We distinguish design-based inference from model-based inference. It is argued that for the official reporting of LSA results, design-based inference should be preferred because it allows for a clear definition of the target of inference (e.g., country mean achievement) and is less sensitive to specific modeling assumptions. More specifically, we discuss five analytical choices in the specification of the scaling model: (1) specification of the functional form of item response functions, (2) the treatment of local dependencies and multidimensionality, (3) the consideration of test-taking behavior for estimating student ability, and the role of country differential items functioning (DIF) for (4) cross-country comparisons and (5) trend estimation. This article's primary goal is to stimulate discussion about recently implemented changes and suggested refinements of the scaling models in LSA studies

Complementary and competing factor analytic approaches for the investigation of measurement invariance

Sample-related invariance is an important topic in psychometric research. The generalizability of findings in a broad range of application samples requires equivalence of interpretations based on the measurement outcomes across respective samples. Contextual factors like gender, age, culture, ethnicity, socio-economical status etc. may affect the meaning and interpretation of psychological measures. Sample-related invariance is frequently investigated using Multiple-Group Mean and Covariance Structure (MGMCS) analyses. This method builds upon natural or artifical categories of contextual variables. Many contextual variables are continuous variables and their categorization is associated with an information loss and potentially overly simplistic data analyses. We present and discuss two complementary analytical approaches – Latent Moderated Structural (LMS) Equations and Local Structural Equation Models (LSEM). Both approaches allow treating contextual factors as continuous variables and are appropriate to detect non-linear relations. The use of these methods is exemplified based on real data. We investigated measurement equivalence of a battery of cognitive tests across age (N = 448; age range 18-82 years). Based on a higher-order factor model of cognitive abilities factorial equivalence could be established – contradicting the age-dedifferentiation hypothesis. Advantages and disadvantages of MGMCS, LMS, and LSEM and further implementations beyond aging-research are discussed

Examining moderators of vocabulary acquisition from kindergarten through elementary school using local structural equation modeling

Parental socio-economic status (SES) is often found to be associated with children's language competence in the first decade of life. To examine the effect of SES on children's vocabulary development, as well as potential compensatory effects of schooling and learning-related activities, we examined the joint and unique effects of parental education, occupational status, and learning environment at home on children's receptive vocabulary competence and growth in early childhood. We used latent growth curve models to assess pre-school receptive vocabulary and growth across primary school. Analyses were based on data from the German National Educational Panel Study (NEPS), a large-scale longitudinal study assessing vocabulary competence and family background from Kindergarten to the 3rd grade of elementary school. To examine the moderating effects of parental education, occupational status, and learning environment at home, we used local structural equation modeling. Results revealed a moderate to strong positive association between parental education and children's receptive vocabulary competence, which fully explained the effect of occupational status on this language skill. With the exception of the activity of reading aloud, we found no effect of learning environment at home. Initially lower performing children showed steeper growth trajectories across school, but rank-orders were relatively stable across time. In summary, the results suggest large initial differences in receptive vocabulary between children from different educational backgrounds, which are reduced, but not fully overcome across elementary school

Возобновляемая энергетика как приоритетное направление инновационного развития АР Крым

Целью данной статьи является комплексный анализ возобновляемых энергетических ресурсов (ВЭР) и возможных путей их использования в экономике АР Крым

An illustration of local structural equation modeling for longitudinal data:Examining differences in competence development in secondary schools

In this chapter, we discuss how a combination of longitudinal modeling and local structural equation modeling (LSEM) can be used to study how students’ context influence their growth in educational achievement. LSEM is a nonparametric approach that allows for the moderation of a structural equation model over a continuous variable (e.g., socio-economic status; cultural identity; age). Thus, it does not require the categorization of continuous moderators as applied in multi-group approaches. In contrast to regression-based approaches, it does not impose a particular functional form (e.g., linear) on the mean-level differences and can spot differences in the variance-covariance structure. LSEM can be used to detect nonlinear moderation effects, to examine sources of measurement invariance violations, and to study moderation effects on all parameters in the model. We showcase how LSEM can be implemented with longitudinal of the National Educational Panel Study (NEPS) using the R-package sirt. In more detail, we examine the effect of parental education on math and reading competence in secondary school across three measurement occasions, comparing LSEM to regression based approaches and multi-group confirmatory factor analysis. Results provide further evidence of the strong influence of the educational background of the family. This chapter offers a new approach to study inter-individual differences in educational development.</p

Cognitively diagnostic analysis using the G-DINA model in R

Cognitive diagnosis models (CDMs) have increasingly been applied in education and other fields. This article provides an overview of a widely used CDM, namely, the G-DINA model, and demonstrates a hands-on example of using multiple R packages for a series of CDM analyses. This overview involves a step-by-step illustration and explanation of performing Q-matrix evaluation, CDM calibration, model fit evaluation, item diagnosticity investigation, classification reliability examination, and the result presentation and visualization. Some limitations of conducting CDM analysis in R are also discusse

A semiparametric approach for item response function estimation to detect item misfit

When scaling data using item response theory, valid statements based on the measurement model are only permissible if the model fits the data. Most item fit statistics used to assess the fit between observed item responses and the item responses predicted by the measurement model show significant weaknesses, such as the dependence of fit statistics on sample size and number of items. In order to assess the size of misfit and to thus use the fit statistic as an effect size, dependencies on properties of the data set are undesirable. The present study describes a new approach and empirically tests it for consistency. We developed an estimator of the distance between the predicted item response functions (IRFs) and the true IRFs by semiparametric adaptation of IRFs. For the semiparametric adaptation, the approach of extended basis functions due to Ramsay and Silverman (2005) is used. The IRF is defined as the sum of a linear term and a more flexible term constructed via basis function expansions. The group lasso method is applied as a regularization of the flexible term, and determines whether all parameters of the basis functions are fixed at zero or freely estimated. Thus, the method serves as a selection criterion for items that should be adjusted semiparametrically. The distance between the predicted and semiparametrically adjusted IRF of misfitting items can then be determined by describing the fitting items by the parametric form of the IRF and the misfitting items by the semiparametric approach. In a simulation study, we demonstrated that the proposed method delivers satisfactory results in large samples (i.e., N ≥ 1,000). (DIPF/Orig.

Яскрава мить життя Ніни Сергіївни Голубкової (28.01.1932—24.08.2009)

24 серпня 2009 р. на 78-му році життя перестало битися серце видатного ліхенолога, доктора біологічних наук, професора, Заслуженого діяча науки Російської Федерації, лауреата медалі ім. Ахаріуса, без перебільшення визнаного неформального лідера всіх російських ліхенологів упродовж останніх 30-ти років — Ніни Сергіївни Голубкової