Reliability and validity of the Basic Depression Questionnaire

Background/ObjectiveDepressive disorders have a high prevalence around the world. They present a great comorbidity with other disorders like anxiety, thereby making a differential diagnosis very difficult. The Basic Depression Questionnaire was designed to palliate this issue by isolating specific depression symptoms. Our aim is to study the reliability, factorial structure, and differential item functioning of this questionnaire. Method: The sample consisted of 1,397 adults without psychological problems (M_age = 29.76, SD = 11.25, 64.78% women) who completed the CBD. Results: We observed that none of the items presented differential functioning. A monofactorial structure was established. In this model a good fit was obtained by confirmatory factor analysis and a strict invariance by sex. The ordinal alpha was used to check the reliability and it fetched an index of .95. Conclusions: The Basic Depression Questionnaire has adequate psychometric properties. The absence of differential item functioning and the invariance by sex are guarantees of an adequate use to diagnose depression for men and women. So, its clinical use can help to differentiate between the specific diagnoses for depressive disorders and anxiety disorders.     

心理与教育测验中反应时模型应用的优势与举例

基于计算机形式的测验使得收集作答反应时信息成为可能,这些信息的有效利用对心理与教育测验的理论研究和实际应用产生了重大影响。首先,归纳并总结了测验中使用反应时信息的五大优势。其次,分别介绍了4种不同取向下较典型的反应时模型与模型特征,并分别进行评价。再次,较系统地梳理了反应时模型在实践中的应用,使读者了解反应时信息在测验中所发挥的作用。最后,探讨了未来将反应时应用于心理与教育测量领域的几个研究方向。     

Dark and bright sides of thriving – school burnout and engagement in the Finnish context

School burnout is defined as exhaustion, cynicism and inadequacy as a student, and engagement can be conceptualized as study-related vigor, absorption and dedication. School burnout is increasing, particularly among students on an academic track, while at the end of elementary school almost half of the students no longer find school meaningful. School burnout and engagement were investigated by applying the demand-resource and stage-environment models. The results show that high school demands lead to burnout, while personal and school resources lead to school engagement. Burnout from school-context can also spillover to later depression, drop out and internet addiction, and engagement to overall satisfaction with life and success in educational pathways. In line with the stage-environment fit theory, educational transitions play a role in changes in school burnout trajectories. Adoption of a person-oriented approach revealed several different burnout-engagement profiles, including a profile in which students are at the same time both exhausted and engaged. The social context of peers, immigrant status, parents and teachers also play an important role in engagement and burnout.     

Developing new online calibration methods for multidimensional computerized adaptive testing

Multidimensional computerized adaptive testing (MCAT) has received increasing attention over the past few years in educational measurement. Like all other formats of CAT, item replenishment is an essential part of MCAT for its item bank maintenance and management, which governs retiring overexposed or obsolete items over time and replacing them with new ones. Moreover, calibration precision of the new items will directly affect the estimation accuracy of examinees’ ability vectors. In unidimensional CAT (UCAT) and cognitive diagnostic CAT, online calibration techniques have been developed to effectively calibrate new items. However, there has been very little discussion of online calibration in MCAT in the literature. Thus, this paper proposes new online calibration methods for MCAT based upon some popular methods used in UCAT. Three representative methods, Method A, the ‘one EM cycle’ method and the ‘multiple EM cycles’ method, are generalized to MCAT. Three simulation studies were conducted to compare the three new methods by manipulating three factors (test length, item bank design, and level of correlation between coordinate dimensions). The results showed that all the new methods were able to recover the item parameters accurately, and the adaptive online calibration designs showed some improvements compared to the random design under most conditions.     

Non‐ignorable missingness item response theory models for choice effects in examinee‐selected items

Examinee‐selected item (ESI) design, in which examinees are required to respond to a fixed number of items in a given set, always yields incomplete data (i.e., when only the selected items are answered, data are missing for the others) that are likely non‐ignorable in likelihood inference. Standard item response theory (IRT) models become infeasible when ESI data are missing not at random (MNAR). To solve this problem, the authors propose a two‐dimensional IRT model that posits one unidimensional IRT model for observed data and another for nominal selection patterns. The two latent variables are assumed to follow a bivariate normal distribution. In this study, the mirt freeware package was adopted to estimate parameters. The authors conduct an experiment to demonstrate that ESI data are often non‐ignorable and to determine how to apply the new model to the data collected. Two follow‐up simulation studies are conducted to assess the parameter recovery of the new model and the consequences for parameter estimation of ignoring MNAR data. The results of the two simulation studies indicate good parameter recovery of the new model and poor parameter recovery when non‐ignorable missing data were mistakenly treated as ignorable.     

A Hierarchical Rater Model for Longitudinal Data

Research studies in psychology and education often seek to detect changes or growth in an outcome over a duration of time. This research provides a solution to those interested in estimating latent traits from psychological measures that rely on human raters. Rater effects potentially degrade the quality of scores in constructed response and performance assessments. We develop an extension of the hierarchical rater model (HRM), which yields estimates of latent traits that have been corrected for individual rater bias and variability, for ratings that come from longitudinal designs. The parameterization, called the longitudinal HRM (L-HRM), includes an autoregressive time series process to permit serial dependence between latent traits at adjacent timepoints, as well as a parameter for overall growth. We evaluate and demonstrate the feasibility and performance of the L-HRM using simulation studies. Parameter recovery results reveal predictable amounts and patterns of bias and error for most parameters across conditions. An application to ratings from a study of character strength demonstrates the model. We discuss limitations and future research directions to improve the L-HRM.     

善因营销的捐赠水平与消费者态度:一个有中介的调节效应模型检验

通过两个实验,探讨了善因营销的捐赠水平对消费者态度的影响,尤其是考察了道德提升感的中介作用以及产品-公益事业拟合度的调节作用。研究发现,善因营销的捐赠水平对消费者态度有显著的正向影响,且道德提升感在捐赠水平与消费者态度之间起中介作用。与此同时,产品-公益事业拟合度显著地调节了捐赠水平与道德提升感的关系,且该调节关系通过道德提升感的中介作用来进一步影响消费者态度。     

《中国大学生心理健康量表》的编制

本研究在文献研究、咨询案例分析、专家访谈和讨论、开放式问卷调查的基础上,采用实证法,编制了大学生心理健康量表,制定了全国大学生常模,并对其信度和效度进行了检验。结果表明:该量表具有良好的信度与效度,可以用于我国大学生心理健康水平的的检测。     

Higher-order latent trait models for cognitive diagnosis

Higher-order latent traits are proposed for specifying the joint distribution of binary attributes in models for cognitive diagnosis. This approach results in a parsimonious model for the joint distribution of a high-dimensional attribute vector that is natural in many situations when specific cognitive information is sought but a less informative item response model would be a reasonable alternative. This approach stems from viewing the attributes as the specific knowledge required for examination performance, and modeling these attributes as arising from a broadly-defined latent trait resembling theϑ of item response models. In this way a relatively simple model for the joint distribution of the attributes results, which is based on a plausible model for the relationship between general aptitude and specific knowledge. Markov chain Monte Carlo algorithms for parameter estimation are given for selected response distributions, and simulation results are presented to examine the performance of the algorithm as well as the sensitivity of classification to model misspecification. An analysis of fraction subtraction data is provided as an example. This research was funded by National Institute of Health grant R01 CA81068. We would like to thank William Stout and Sarah Hartz for many useful discussions, three anonymous reviewers for helpful comments and suggestions, and Kikumi Tatsuoka and Curtis Tatsuoka for generously sharing data.