External Validity of Conduct and Oppositional Defiant Disorders Determined by the NIMH Diagnostic Interview Schedule for Children

We administered the Diagnostic Interview Schedule for Children (DISC) two times to a group of youth (222 boys, 147 girls) entering residential care, once at their time of entry and once 1 year later. We then compared their DISC outcomes on Conduct Disorder (CD) and Oppositional Defiant Disorder (ODD) against changes in independent direct observations of diagnostically relevant behavior obtained over the course of that year. Results from hierarchical linear modeling analyses showed significant discriminative relationships between results from the DISC and the independent observations. Specifically, observations of symptomatic behaviors (CD or ODD) decreased for youth who met diagnostic criteria at the first administration of the DISC but not at the second, increased for youth who did not meet criteria at the first administration but did at the second, and did not change for youth who met criteria at both administrations. These results extend the data on the validity of the DISC and support continued research efforts to determine its clinical utility.     

Long-term efficacy of exercise across diagnostically heterogenous mental disorders and the mediating role of affect regulation skills

BackgroundExercise interventions are efficacious in reducing disorder-specific symptoms in various mental disorders. However, little is known about long-term transdiagnostic efficacy of exercise across heterogenous mental disorders and the potential mechanisms underlying treatment effects.MethodsPhysically inactive outpatients, with depressive disorders, anxiety disorders, insomnia or attention deficit hyperactivity disorder were randomized to a standardized 12-week exercise intervention, combining moderate exercise with behavior change techniques (BCTs) (n = 38), or a passive control group (n = 36). Primary outcome was global symptom severity (Symptom Checklist-90, SCL-90-R) and secondary outcomes were self-reported exercise (Physical Activity, Exercise, and Sport Questionnaire), exercise-specific affect regulation (Physical Activity-related Health Competence Questionnaire) and depression (SCL-90-R) assessed at baseline (T1), post-treatment (T2) and one year after post-treatment (T3). Intention-to-treat analyses were conducted using linear mixed models and structural equations modeling.ResultsFrom T1 to T3, the intervention group significantly improved on global symptom severity (d = −0.43, p = .031), depression among a depressed subsample (d = −0.62, p = .014), exercise (d = 0.45, p = .011) and exercise-specific affect regulation (d = 0.44, p = .028) relative to the control group. The intervention group was more likely to reveal clinically significant changes from T1 to T3 (p = .033). Increases in exercise-specific affect regulation mediated intervention effects on global symptom severity (ß = −0.28, p = .037) and clinically significant changes (ß = −0.24, p = .042).ConclusionsThe exercise intervention showed long-term efficacy among a diagnostically heterogeneous outpatient sample and led to long-lasting exercise behavior change. Long-term increases in exercise-specific affect regulation within exercise interventions seem to be essential for long-lasting symptom reduction.     

定长CD-CAT中两种新的题目曝光控制方法

提出了两种适用于定长CD-CAT的题目曝光控制方法（HIRP、HIRT）,这些方法在保证较高分类准确率的同时还有较合理的题目曝光率,新方法由二分化方法和RP及RT方法进行结合并适当调整而得到。模拟研究比较了其与RP、RT、SM、SMIE、RHA和SDBS的表现,结果表明: （1）HIRP的分类准确率和题目曝光率均好于SM、SMIE和SDBS;（2）HIRT的题目曝光率较RP、SM、SMIE、RHA和SDBS稍差,但分类准确率更高;（3）HIRP的分类准确率低于RT和RP,但题目曝光控制要更好。     

Flexible Computerized Adaptive Tests to Detect Misconceptions and Estimate Ability Simultaneously

The Scaling Individuals and Classifying Misconceptions (SICM) model is an advanced psychometric model that can provide feedback to examinees’ misconceptions and a general ability simultaneously. These two types of feedback are represented by a discrete and a continuous latent variable, respectively, in the SICM model. The complex structure of the SICM model brings difficulties in estimating both misconception profile and ability efficiently in a linear test. To overcome this challenge, this study proposes a flexible computerized adaptive test (FCAT) design as a new test delivery method to increase test efficiency by administering an individualized test to examinees. We propose three item selection methods and two transition criteria to determine adaptive steps based on the needs of estimating one or two latent variables. Through two simulation studies, we demonstrate how to select an appropriate item selection method for an adaptive step and what transition criterion should be used between two adaptive steps. Results reveal the combination of the item selection method and the transition criterion could improve the estimation accuracy of a specific latent variable to a different extent and thus provide further guidance in designing an FCAT.     

Optimal Hierarchical Learning Path Design With Reinforcement Learning

E-learning systems are capable of providing more adaptive and efficient learning experiences for learners than traditional classroom settings. A key component of such systems is the learning policy. The learning policy is an algorithm that designs the learning paths or rather it selects learning materials for learners based on information such as the learners’ current progresses and skills, learning material contents. In this article, the authors address the problem of finding the optimal learning policy. To this end, a model for learners’ hierarchical skills in the E-learning system is first developed. Based on the hierarchical skill model and the classical cognitive diagnosis model, a framework to model various mastery levels related to hierarchical skills is further developed. The optimal learning path in consideration of the hierarchical structure of skills is found by applying a model-free reinforcement learning method, which does not require any assumption about learners’ learning transition processes. The effectiveness of the proposed framework is demonstrated via simulation studies.     

认知诊断计算机化自适应测验中新的选题策略:结合项目区分度指标

当前国内外大部分认知诊断计算机化自适应测验(CD-CAT)主要采用PWKL作为选题策略进行研究。PWKL结合后验分布信息对KL指标进行加权,提高了判准率,但该方法仅利用个体层面信息加权,忽视了项目本身能够提供的信息,属于单源指标。本研究结合认知诊断中的项目区分度信息,对PWKL进行修正,提出了4种新的多源选题策略:GIDPWKL、AIDPWKL、CIDPWKL和KLEDPWKL方法,并在加入曝光控制下与PWKL和互信息法(MIM)进行比较。模拟研究结果表明:(1)在定长测验情景下的绝大多数实验结果表明,测验长度越短,新方法的判准率越高。平均属性/模式判准率最高的是GIDPWKL,之后是AIDPWKL,而CIDPWKL、KLEDPWKL和MIM方法的优势随实验条件不同而不同。(2)在定长测验情景下的绝大多数实验结果表明,题目质量越高,新方法的优势越明显。(3)Q矩阵结构的复杂性会影响不同选题策略的表现。(4)在变长测验情景下,4种新方法和MIM的平均测验长度均要低于PWKL方法,表现最好的是GIDPWKL方法。因此,若实际测验情景与本研究的模拟情景相似,推荐GIDPWKL方法。     

Psychometric properties of the mini-social phobia inventory (Mini-SPIN) in a large online treatment-seeking sample

The Mini-Social Phobia Inventory (Mini-SPIN) is a brief, three-item measure designed as a screening tool for social anxiety disorder (SAD). This study investigated the Mini-SPIN’s psychometric properties in a series of trials of Internet-delivered treatment. Participants were 993 people seeking Internet-delivered cognitive behavioural therapy for a range of anxiety and mood disorders. Participants completed the Mini-SPIN, and were diagnosed using the Mini International Neuropsychiatric Interview Version 5.0.0 (MINI). They also completed measures of depression, general anxiety, panic, neuroticism and general impairment. The Mini-SPIN’s ability to discriminate between people with and without SAD, within a large sample of people seeking treatment for a range of psychological disorders, was assessed at initial assessment and three-month follow-up. The Mini-SPIN’s criterion group validity, internal consistency, test–retest reliability, construct validity and responsiveness to treatment were also examined. Results demonstrated that the Mini-SPIN has an excellent ability to discriminate between those with and without SAD in a highly comorbid clinical sample, and also has good criterion group validity. The Mini-SPIN also exhibited excellent internal consistency, good test–retest reliability, and was responsive to treatment. These results highlight the Mini-SPIN’s potential as an efficient and reliable measure of SAD in heterogenous populations.     

Invariance Properties for General Diagnostic Classification Models

In item response theory (IRT), the invariance property states that item parameter estimates are independent of the examinee sample, and examinee ability estimates are independent of the test items. While this property has long been established and understood by the measurement community for IRT models, the same cannot be said for diagnostic classification models (DCMs). DCMs are a newer class of psychometric models that are designed to classify examinees according to levels of categorical latent traits. We examined the invariance property for general DCMs using the log-linear cognitive diagnosis model (LCDM) framework. We conducted a simulation study to examine the degree to which theoretical invariance of LCDM classifications and item parameter estimates can be observed under various sample and test characteristics. Results illustrated that LCDM classifications and item parameter estimates show clear invariance when adequate model data fit is present. To demonstrate the implications of this important property, we conducted additional analyses to show that using pre-calibrated tests to classify examinees provided consistent classifications across calibration samples with varying mastery profile distributions and across tests with varying difficulties.     

Fitting the Reduced RUM with Mplus: A Tutorial

The Reduced Reparameterized Unified Model (Reduced RUM) is a diagnostic classification model for educational assessment that has received considerable attention among psychometricians. However, the computational options for researchers and practitioners who wish to use the Reduced RUM in their work, but do not feel comfortable writing their own code, are still rather limited. One option is to use a commercial software package that offers an implementation of the expectation maximization (EM) algorithm for fitting (constrained) latent class models like Latent GOLD or Mplus. But using a latent class analysis routine as a vehicle for fitting the Reduced RUM requires that it be re-expressed as a logit model, with constraints imposed on the parameters of the logistic function. This tutorial demonstrates how to implement marginal maximum likelihood estimation using the EM algorithm in Mplus for fitting the Reduced RUM.