In this issue, we have the following one invited paper, five original papers, one note, and two shot notes.The invited paper "WAIC and WBIC for Mixture Models" is presented by Watanabe (2021). This paper introduces mathematical foundation and computing methods of WAIC and WBIC in a normal mixture which is a typically singular statistical model, and discuss their properties in statistical inference. Also this paper demonstrates the case that samples are not independently and identically distributed, for example, they are conditional independent or exchangeable.The original paper "Monte Carlo detection of examinees with item preknowledge" by Dimitry Belov develops a new Monte Carlo approach for detecting examinees with Item Preknowledge (IP) by estimating mean of a performance gain on a random sample of items (drawn from the administered test) relative to another random sample (Belov 2021). Two samples are constructed such that for an examinee without IP, the gain should be low; meanwhile, for an examinee with IP, if the first sample has more compromised items than the second sample, the gain should not be low. Comparison study with the previous studies using data simulating the general case of IP demonstrated a dramatic improvement of detection rates (over five times on average) when using the Monte Carlo approach. Even higher improvement (over 25 times) was observed in experiments with two publicly available real datasets. Recommendations for practitioners and extensions of the Monte Carlo approach are provided.The original paper "Small and Negative Correlations Among Clustered Observations: Limitations of the Linear Mixed Effects Model" by Natalie M. Nielsen, Wouter A. C. Smink, and Jean-Paul Fox addresses the linear mixed effects model (Nielsen et al. 2021). Random effects in a mixed effects model can model a positive correlation among clustered observations but not a negative correlation. As negative clustering effects are largely unknown to the sheer majority of the research community, they conducted a simulation study to detail the bias that occurs when analyzing negative clustering effects with the linear mixed effects model. They also demonstrate that ignoring a small negative correlation leads to deflated Type-I
