MIMIC Models for Uniform and Nonuniform DIF as Moderated Mediation Models.

In this article, the authors describe how multiple indicators multiple cause (MIMIC) models for studying uniform and nonuniform differential item functioning (DIF) can be conceptualized as mediation and moderated mediation models. Conceptualizing DIF within the context of a moderated mediation model helps to understand DIF as the effect of some variable on measurements that is not accounted for by the latent variable of interest. In addition, useful concepts and ideas from the mediation and moderation literature can be applied to DIF analysis: (a) improving the understanding of uniform and nonuniform DIF as direct effects and interactions, (b) understanding the implication of indirect effects in DIF analysis, (c) clarifying the interpretation of the "uniform DIF parameter" in the presence of nonuniform DIF, and (d) probing interactions and using the concept of "conditional effects" to better understand the patterns of DIF across the range of the latent variable

An Overview of Models for Response Times and Processes in Cognitive Tests.

Response times (RTs) are a natural kind of data to investigate cognitive processes underlying cognitive test performance. We give an overview of modeling approaches and of findings obtained with these approaches. Four types of models are discussed: response time models (RT as the sole dependent variable), joint models (RT together with other variables as dependent variable), local dependency models (with remaining dependencies between RT and accuracy), and response time as covariate models (RT as independent variable). The evidence from these approaches is often not very informative about the specific kind of processes (other than problem solving, information accumulation, and rapid guessing), but the findings do suggest dual processing: automated processing (e.g., knowledge retrieval) vs. controlled processing (e.g., sequential reasoning steps), and alternative explanations for the same results exist. While it seems well-possible to differentiate rapid guessing from normal problem solving (which can be based on automated or controlled processing), further decompositions of response times are rarely made, although possible based on some of model approaches

Impacts of Innovation School System in Korea: A Latent Space Item Response Model with Neyman-Scott Point Process

South Korea's educational system has faced criticism for its lack of focus on critical thinking and creativity, resulting in high levels of stress and anxiety among students. As part of the government's effort to improve the educational system, the innovation school system was introduced in 2009, which aims to develop students' creativity as well as their non-cognitive skills. To better understand the differences between innovation and regular school systems in South Korea, we propose a novel method that combines the latent space item response model (LSIRM) with the Neyman-Scott (NS) point process model. Our method accounts for the heterogeneity of items and students, captures relationships between respondents and items, and identifies item and student clusters that can provide a comprehensive understanding of students' behaviors/perceptions on non-cognitive outcomes. Our analysis reveals that students in the innovation school system show a higher sense of citizenship, while those in the regular school system tend to associate confidence in appearance with social ability. We compare our model with exploratory item factor analysis in terms of item clustering and find that our approach provides a more detailed and automated analysis

CAGE Binds to Beclin1, Regulates Autophagic Flux and CAGE-Derived Peptide Confers Sensitivity to Anti-cancer Drugs in Non-small Cell Lung Cancer Cells

The objective of this study was to determine the role of CAGE, a cancer/testis antigen, in resistance of non-small cell lung cancers to anti-cancer drugs. Erlotinib-resistant PC-9 cells (PC-9/ER) with EGFR mutations (ex 19 del + T790M of EGFR), showed higher level of autophagic flux than parental sensitive PC-9 cells. Erlotinib and osimertinib increased autophagic flux and induced the binding of CAGE to Beclin1 in PC-9 cells. The inhibition or induction of autophagy regulated the binding of CAGE to Beclin1 and the responses to anti-cancer drugs. CAGE showed binding to HER2 while HER2 was necessary for binding of CAGE to Beclin1. CAGE was responsible for high level of autophagic flux and resistance to anti-cancer drugs in PC-9/ER cells. A peptide corresponding to the DEAD box domain of CAGE, 266AQTGTGKT273, enhanced the sensitivity of PC-9/ER cells to erlotinib and osimertinib, inhibited the binding of CAGE to Beclin1 and regulated autophagic flux in PC-9/ER cells. Mutant CAGE-derived peptide 266AQTGTGAT273 or 266AQTGTGKA273 did not affect autophagic flux or the binding of CAGE to Beclin1. AQTGTGKT peptide showed binding to CAGE, but not to Beclin1. FITC-AQTGTGKT peptide showed co-localization with CAGE. AQTGTGKT peptide decreased tumorigenic potentials of PC-9/ER and H1975 cells, non-small cell lung cancer (NSCLC) cells with EGFR mutation (L885R/T790M), by inhibiting autophagic fluxand inhibiting the binding of CAGE to Beclin1. AQTGTGKT peptide also enhanced the sensitivity of H1975 cells to anti-cancer drugs. AQTGTGKT peptide showed tumor homing potential based on ex vivo homing assays of xenograft of H1975 cells. AQTGTGKT peptide restored expression levels of miR-143-3p and miR-373-5p, decreased autophagic flux and conferred sensitivity to anti-cancer drugs. These results present evidence that combination of anti-cancer drug with CAGE-derived peptide could overcome resistance of non-small cell lung cancers to anti-cancer drugs

[Comment] Redefine statistical significance

The lack of reproducibility of scientific studies has caused growing concern over the credibility of claims of new discoveries based on “statistically significant” findings. There has been much progress toward documenting and addressing several causes of this lack of reproducibility (e.g., multiple testing, P-hacking, publication bias, and under-powered studies). However, we believe that a leading cause of non-reproducibility has not yet been adequately addressed: Statistical standards of evidence for claiming discoveries in many fields of science are simply too low. Associating “statistically significant” findings with P < 0.05 results in a high rate of false positives even in the absence of other experimental, procedural and reporting problems. For fields where the threshold for defining statistical significance is P<0.05, we propose a change to P<0.005. This simple step would immediately improve the reproducibility of scientific research in many fields. Results that would currently be called “significant” but do not meet the new threshold should instead be called “suggestive.” While statisticians have known the relative weakness of using P≈0.05 as a threshold for discovery and the proposal to lower it to 0.005 is not new (1, 2), a critical mass of researchers now endorse this change. We restrict our recommendation to claims of discovery of new effects. We do not address the appropriate threshold for confirmatory or contradictory replications of existing claims. We also do not advocate changes to discovery thresholds in fields that have already adopted more stringent standards (e.g., genomics and high-energy physics research; see Potential Objections below). We also restrict our recommendation to studies that conduct null hypothesis significance tests. We have diverse views about how best to improve reproducibility, and many of us believe that other ways of summarizing the data, such as Bayes factors or other posterior summaries based on clearly articulated model assumptions, are preferable to P-values. However, changing the P-value threshold is simple and might quickly achieve broad acceptance

A Constrained Confirmatory Mixture IRT Model: Extensions and Estimation of the Saltus model using Mplus

In this paper, I will discuss applications, extensions, and estimation of the Saltus model, a specialized confirmatory mixture IRT model. The Saltus model is confirmatory because the number/nature of latent classes is pre-specified and prior item information is utilized for class differentiation. Such a confirmatory model has not been fully utilized in applied research due to two main misconceptions: (1) the model is designed for specific purposes only and (2) a specialized software package is required to estimate the model. In this study, I will discuss that (1) such a confirmatory approach is applicable to various applications of mixture IRT modeling, (2) the model can actually be parameterized as a constrained mixture IRT model and (3) the model can be readily extended and estimated with regular, off-the-shelf mixture IRT software packages that allow for linear constraints on model parameters. An application and estimation of the constrained confirmatory IRT model is illustrated with an empirical example