Extending exploratory diagnostic classification models: Inferring the effect of covariates

Diagnostic models provide a statistical framework for designing formative assessments by classifying student knowledge profiles according to a collection of fine-grained attributes. The context and ecosystem in which students learn may play an important role in skill mastery, and it is therefore important to develop methods for incorporating student covariates into diagnostic models. Including covariates may provide researchers and practitioners with the ability to evaluate novel interventions or understand the role of background knowledge in attribute mastery. Existing research is designed to include covariates in confirmatory diagnostic models, which are also known as restricted latent class models. We propose new methods for including covariates in exploratory RLCMs that jointly infer the latent structure and evaluate the role of covariates on performance and skill mastery. We present a novel Bayesian formulation and report a Markov chain Monte Carlo algorithm using a Metropolis-within-Gibbs algorithm for approximating the model parameter posterior distribution. We report Monte Carlo simulation evidence regarding the accuracy of our new methods and present results from an application that examines the role of student background knowledge on the mastery of a probability data set.     

Premature conclusions about the signal-to-noise ratio in structural equation modeling research: A commentary on Yuan and Fang (2023)

In a recent article published in this journal, Yuan and Fang (British Journal of Mathematical and Statistical Psychology, 2023) suggest comparing structural equation modeling (SEM), also known as covariance-based SEM (CB-SEM), estimated by normal-distribution-based maximum likelihood (NML), to regression analysis with (weighted) composites estimated by least squares (LS) in terms of their signal-to-noise ratio (SNR). They summarize their findings in the statement that “[c]ontrary to the common belief that CB-SEM is the preferred method for the analysis of observational data, this article shows that regression analysis via weighted composites yields parameter estimates with much smaller standard errors, and thus corresponds to greater values of the [SNR].” In our commentary, we show that Yuan and Fang have made several incorrect assumptions and claims. Consequently, we recommend that empirical researchers not base their methodological choice regarding CB-SEM and regression analysis with composites on the findings of Yuan and Fang as these findings are premature and require further research.     

跨期选择的变易程度正向预测中华文化圈国民的自评扛疫成效：亚非欧美大洋洲18国跨国研究

跨期选择是对不同时间点的得失的权衡与选择。伊索寓言《蚂蚁和蚱蜢》假借群居型昆虫的跨期选择偏爱暗喻投资未来的慢策略比只顾眼前的快策略更利于生存。用跨期选择领域通用的语言解读这一寓言便是：选择大而迟选项的蚂蚁比选择小而早选项的蚱蜢更可能扛过严冬而生存下来。为了探索何种跨期选择策略更有助于我们扛过疫情,本研究调查了亚非欧美大洋洲这5大洲18个国家共计26355名受测者对混合得失双结果的跨期选择偏爱,测量了人们平时和疫时跨期选择偏爱的变易程度(2类变易的程度指标),以及人们自评的扛疫成效。跨文化比较结果的主要发现是：不同通货的选择变易程度(指标1)和不同时期的选择变易程度(指标2)能联合预测中国/新加坡文化圈国民的自评扛疫成效;不同时期的选择变易程度(指标2)也可以单独预测印度/马来西亚/菲律宾/尼日利亚文化圈国民的自评扛疫成效;这2类选择偏爱变易的程度指标不能预测其他文化圈国民的自评扛疫成效(或者预测方向和假设相反)。基于易经“穷则变,变则通”的要旨和跨国比较的发现,我们认为：面临历史危机时善于变通的特长抑或成就了中华民族特有的竞争优势;在应对危机时,与中国文化距离越相近的国家或民族抑或也能...     

The factor structure of the Multiple Affect Adjective Check List-Revised (MAACL-R): Some statistical considerations

In response to Zuckerman's comments on my factor analyses of the MAACL-R, I focus on the statistical issues of the cutoff criterion for factor loadings and the evidence for the discriminant validity of the five-factor solution. Based upon the conclusions I draw in these two areas, I recommend that researchers either (a) use the two factor solution to the MAACL-R represented in the summary scores of Dysphoria and Positive Affect + Sensation Seeking or (b) include all MAACL-R scales in their studies and analyze their data to determine whether it is statistically appropriate to report results from a single scale (A, D, H, PA, or SS) rather than from the summary scores.     

On the sampling theory roundations of item response theory models

Item response theory (IT) models are now in common use for the analysis of dichotomous item responses. This paper examines the sampling theory foundations for statistical inference in these models. The discussion includes: some history on the stochastic subject versus the random sampling interpretations of the probability in IRT models; the relationship between three versions of maximum likelihood estimation for IRT models; estimating versus estimating -predictors; IRT models and loglinear models; the identifiability of IRT models; and the role of robustness and Bayesian statistics from the sampling theory perspective.A presidential address can serve many different functions. This one is a report of investigations I started at least ten years ago to understand what IRT was all about. It is a decidedly one-sided view, but I hope it stimulates controversy and further research. I have profited from discussions of this material with many people including: Brian Junker, Charles Lewis, Nicholas Longford, Robert Mislevy, Ivo Molenaar, Donald Rock, Donald Rubin, Lynne Steinberg, Martha Stocking, William Stout, Dorothy Thayer, David Thissen, Wim van der Linden, Howard Wainer, and Marilyn Wingersky. Of course, none of them is responsible for any errors or misstatements in this paper. The research was supported in part by the Cognitive Science Program, Office of Naval Research under Contract No. Nooo14-87-K-0730 and by the Program Statistics Research Project of Educational Testing Service.     

Continuous and discrete latent structure models for item response data

Relations are examined between latent trait and latent class models for item response data. Conditions are given for the two-latent class and two-parameter normal ogive models to agree, and relations between their item parameters are presented. Generalizationss are then made to continuous models with more than one latent trait and discrete models with more than two latent classes, and methods are presented for relating latent class models to factor models for dichotomized variables. Results are illustrated using data from the Law School Admission Test, previously analyzed by several authors.     

The use of the occupational stress indicator (OSI) in factory workers in China

The purpose of the study was to investigate occupational stress among Chinese factory workers (N=342), from three cities of South East China, using the shortened version of the Occupational Stress Indicator (OSI)-2. The results showed that the reliabilities and predictive validity of the OSI-2 subscales and other subscales used for the study were reasonably high. Quite a high percentage of workers perceived high work pressure. The main sources of stress were intrinsic to the job; and the coping strategies that were most frequently used to tackle stress were “control” methods. In addition, “satisfaction on environmental condition” seemed to be a common predictor for job satisfaction, and mental and physical well-being. The logical relationships between job satisfaction, mental well-being and physical well-being in Chinese workers have provided support to the findings obtained in Western countries.