The reliability and validity of the Japanese version of the Daily Record of Severity of Problems (J-DRSP) and Development of a Short-Form version (J-DRSP (SF)) to assess symptoms of premenstrual syndrome among Japanese women

PURPOSE: To assess the validity and reliability of the Japanese version of the Daily Record of Severity of Problems (J-DRSP, 24 items) for evaluating symptoms of premenstrual syndrome (PMS), and to develop a short form version of the J-DRSP. METHODS: Using the "DRSP-JAPAN" smartphone app, we collected daily J-DRSP records from cycle day - 6 (CD - 6) to CD 10, with CD 1 representing the menstruation onset date. Factorial validity (exploratory factor analysis: EFA, confirmatory factor analysis: CFA) and criterion validity were examined, and test-retest reliability (intraclass correlation: ICC) evaluated. The short-form version of the J-DRSP was developed using classical test theory. RESULTS: In total, 304 women participated and 243 recorded symptoms on at least 4 days spanning the week of the luteal phase (CD - 6 to CD 0) and 4 days spanning the week of the follicular phase (CD 4 to CD 10), with CD 0 set as the day before menstruation started. The EFA revealed a two-factor structure. Kaiser-Meyer-Olkin was 0.992, and Bartlett's test of sphericity chi-square was 3653.89 (P < 0.001). However, the model fitness of CFA was found to be suboptimal (comparative fit index (CFI): 0.83, root mean square error of approximation (RMSEA): 0.12). Total scores for J-DRSP and the sum scores for each subscale were higher on CD 0 than on CD 10 (p < 0.001), suggesting validity for some criteria. ICC values for the total J-DRSP score from CD 0 to CD - 1, and between CD 9 to CD 10, were 0.60 (95% CI: 0.48-0.72) and 0.76 (95% CI: 0.69-0.82), respectively. Having eliminated some original items after considering factor loading for each item, we developed an 8-item Short-Form J-DRSP (J-DRSP (SF)) comprising 2 factors (S-Psychological and S-Physical, 4 items for each). CFA showed a better model fit (CFI: 0.99, RMSEA: 0.048), and ICC values in the luteal and follicular phases were 0.61 (95%CI: 0.51-0.68) and 0.70 (95%CI: 0.62-0.77), respectively. CONCLUSION: The J-DRSP has moderate to good reliability and a certain level of validity. The 8-item J-DRSP (SF) has a two-factor structure and can be used effectively among Japanese women to assess their PMS symptoms

測定精度の偏り軽減のための等質適応型テストの提案

適応型テストとは，受検者の能力を逐次的に推定し，その能力に応じて測定精度が最も高い項目を出題するコンピュータ・テスティングの出題形式である．この手法では，易しすぎる項目や難しすぎる項目の出題が減少するため，受検者の測定精度を減少させずに受検時間や項目数を軽減できる．しかし，従来の適応型テストでは，能力が同等な受検者には全く同じ項目群が出題される可能性が高く，実際に適応型テストを導入しているSPIやGTECの重要な問題になっている．本研究では，能力が同等な受検者であっても異なる項目を同一の測定精度を保ちつつ適応的に出題できる等質適応型テストを提案する．具体的には，提案手法では次のように項目出題を行う．1）2017年時点で最先端の複数等質テスト構成手法を用いて，異なる項目で構成されるが測定精度が等質になるような等質テストを多数構成する．2）受検者ごとに異なる等質テストを一つ割り当て，そのテスト内の項目集合をアイテムバンクとみなして適応型テストを実施する．本論では，シミュレーション実験と実データを用いた実験により提案手法の有効性を示す．Adaptive testing is a question format of computer testing that estimates an examinee\u27s ability sequentially and which produces question items with the highest estimation accuracy according to the examinee\u27s ability. The technique mitigates the creation of overly easy or overly difficult questions, which can reduce the time spent on a test, and reduces the number of items without reducing the estimation accuracy for the examinee\u27s ability. However, in conventional adaptive tests, it is highly likely that the exact same group of items will be prepared for examinees who have equivalent ability. The tests cannot be used practically under circumstances by which the same learner can take a test multiple times, such as SPI and GTEC. In this paper, we propose a multiple equivalent adaptive test that adaptively creates different items for examinees even if those with equivalent capabilities, maintaining the same evaluation accuracy. Specifically, we follow the procedure outlined below. 1) We compose an item cluster for a multiple equivalent test based on the amount of test information so that the measurement accuracy for examinees\u27ability can be equivalent despite consisting of different items. To compose a multiple equivalent test, we use a technique that employs the maximum clique problem to maximize the number of compositions from items within an item bank. 2) Regarding an item cluster for a multiple equivalent test as an item bank, we propose a multiple equivalent adaptive test that estimates the value of an examinee\u27s ability sequentially and which selects items with the greatest amount of information for the value of ability from an item cluster for a multiple equivalent test. This paper presents the effectiveness of the technique through a simulation experiment and with item banks used by actual test providers

Association between a Polymorphism of Aminolevulinate Dehydrogenase (ALAD) Gene and Blood Lead Levels in Japanese Subjects

This cross-sectional study investigated the relationship between the aminolevulinate dehydrogenase (ALAD) genotype and blood lead levels among 101 Japanese workers. Blood lead concentration measurement, biomarkers, and genotyping were performed. The minor allele frequency (MAF) for ALAD (ALAD2) was 0.08. Although the blood lead level in the subjects with heterozygous GC genotype was significantly higher than those with homozygous GG genotype, there were no significant differences for hemoglobin, hematocrit, serum and urinary ALA levels among genotypes. ALAD2 genotype was significantly associated with the blood lead concentration, even in the environmental lead exposed subjects. Further confirmation with a large sample size is needed

Time- and Learner-Dependent Hidden Markov Model for Writing Process Analysis Using Keystroke Log Data

Teaching writing strategies based on writing processes has attracted wide attention as a method for developing writing skills. The writing process can be generally defined as a sequence of subtasks, such as planning, formulation, and revision. Therefore, instructor feedback is often given based on sequence patterns of those subtasks. For such feedback, instructors need to analyze sequence patterns for all learners, which becomes problematic as the number of learners increases. To resolve this problem, this study proposes a new machine-learning method that estimates sequence patterns from keystroke log data. Specifically, we propose an extension of the Gaussian hidden Markov model that incorporates parameters representing temporal change in a subtask appearance distribution for each learner. Furthermore, we propose a collapsed Gibbs sampling algorithm as the parameter estimation method for the proposed model. We demonstrate effectiveness of the proposed model by applying it to actual keystroke log datasets