Impact of Not Fully Addressing Cross-Classified Multilevel Structure in Testing Measurement Invariance and Conducting Multilevel Mixture Modeling within Structural Equation Modeling Framework

In educational settings, researchers are likely to encounter multilevel data without strictly nested or hierarchical but cross-classified multilevel structure. However, due to the lack of familiarity and limitations of statistical software with cross-classified model, most substantive researchers adopt then the less optimal approaches to analyze cross-classified multilevel data. Two separate Monte Carlo studies were conducted to evaluate the impacts of misspecifying cross-classified structure data as hierarchical structure data in two different analytical settings under the structural equation modeling (SEM) framework. Study 1 evaluated the performance of conventional multilevel confirmatory factor analysis (MCFA) which assumes hierarchical multilevel data in testing measurement invariance, especially when the noninvariance exists at the between-level groups. We considered two design factors, intra-class correlation (ICC) and magnitude of factor loading differences. This simulation study showed low empirical power in detecting noninvariance under low ICC conditions. Furthermore, the low power was plausibly related to the underestimated ICC and the underestimated factor loading differences due to the redistribution of the variance component from the crossed factor ignored in the analysis. Study 2 examined the performance of conventional multilevel mixture models (MMMs), which assume hierarchical multilevel data, on the classification accuracy of class enumeration and individuals’ class assignment when the latent class variable is at the between (cluster)-level. We considered a set of study conditions, including cluster size, degree of partial cross-classification, and mixing proportion of subgroups. From the results of the study, ignoring a crossed factor caused overestimation of the variance component of the remaining crossed factor at the between-level which was redistributed from the ignored crossed factor in the analysis. Moreover, no SEM statistical program can conduct MMM and take into account of the cross-classified data structure simultaneously. Hence, a researcher should acknowledge this limitation and be cautioned when conventional MMM is utilized with cross-classified multilevel data given the inflated variance component associated with the remaining crossed factor. Implications of the findings and limitations for each study are discussed

The Meaning and Measurements of the UTAUT Model: An Invariance Analysis

The Unified Theory on Acceptance and Use of Technology (UTAUT), a recent model in the study of technology adoption, integrates eight theories of technology adoption and provides a comprehensive view of factors affecting users’ adoption behavior. In this study, the invariance of the UTAUT model’s measures was tested along three dimensions: country, technology, and gender. Data were collected from two countries (Korea and the U.S.) for two technologies (Internet banking and MP3 players). The results show that overall the UTAUT model is robust across different conditions. However, when applying the UTAUT model to different conditions and groups, possible differences due to measurement non-invariance should be taken into account, especially in cases of transnational or cross-technology comparison. The paper discusses implications of the study results and makes recommendations for future research

Commutative Pseudo Valuations on BCK-Algebras

The notion of a commutative pseudo valuation on a BCK-algebra is introduced, and its characterizations are investigated. The relationship between a pseudo valuation and a commutative pseudo-valuation is examined

Optical Images and Source Catalog of AKARI North Ecliptic Pole Wide Survey Field

We present the source catalog and the properties of the $B-, R-$, and $I-$band images obtained to support the {\it AKARI} North Ecliptic Pole Wide (NEP-Wide) survey. The NEP-Wide is an {\it AKARI} infrared imaging survey of the north ecliptic pole covering a 5.8 deg$^2$ area over 2.5 -- 6 \micron wavelengths. The optical imaging data were obtained at the Maidanak Observatory in Uzbekistan using the Seoul National University 4k $\times$ 4k Camera on the 1.5m telescope. These images cover 4.9 deg$^2$ where no deep optical imaging data are available. Our $B-, R-$, and $I-$band data reach the depths of $\sim$23.4, $\sim$23.1, and $\sim$22.3 mag (AB) at 5$\sigma$, respectively. The source catalog contains 96,460 objects in the $R-$band, and the astrometric accuracy is about 0.15\arcsec at 1$\sigma$ in each RA and Dec direction. These photometric data will be useful for many studies including identification of optical counterparts of the infrared sources detected by {\it AKARI}, analysis of their spectral energy distributions from optical through infrared, and the selection of interesting objects to understand the obscured galaxy evolution.Comment: 39 pages, 12 figure

North Ecliptic Pole Wide Field Survey of AKARI: Survey Strategy and Data Characteristics

We present the survey strategy and the data characteristics of the North Ecliptic Pole (NEP) Wide Survey of AKARI. The survey was carried out for about one year starting from May 2006 with 9 passbands from 2.5 to 24 micron and the areal coverage of about 5.8 sq. degrees centered on NEP. The survey depth reaches to 21.8 AB magnitude near infrared (NIR) bands, and ~ 18.6 AB maggnitude at the mid infrared (MIR) bands such as 15 and 18 micron. The total number of sources detected in this survey is about 104,000, with more sources in NIR than in the MIR. We have cross matched infrared sources with optically identified sources in CFHT imaging survey which covered about 2 sq. degrees within NEP-Wide survey region in order to characterize the nature of infrared sources. The majority of the mid infrared sources at 15 and 18 micron band are found to be star forming disk galaxies, with smaller fraction of early type galaxies and AGNs. We found that a large fraction (60~80 %) of bright sources in 9 and 11 micron stars while stellar fraction decreases toward fainter sources. We present the histograms of the sources at mid infrared bands at 9, 11, 15 and 18 micron. The number of sources per magnitude thus varies as m^0.6 for longer wavelength sources while shorter wavelength sources show steeper variation with m, where m is the AB magnitude.Comment: 18 pages, 11 figures, to appear in PASJ, Vol. 61, No. 2. April 25, 2009 issu

Machine learning approaches for detecting tropical cyclone formation using satellite data

This study compared detection skill for tropical cyclone (TC) formation using models based on three different machine learning (ML) algorithms-decision trees (DT), random forest (RF), and support vector machines (SVM)-and a model based on Linear Discriminant Analysis (LDA). Eight predictors were derived from WindSat satellite measurements of ocean surface wind and precipitation over the western North Pacific for 2005-2009. All of the ML approaches performed better with significantly higher hit rates ranging from 94 to 96% compared with LDA performance (~77%), although false alarm rate by MLs is slightly higher (21-28%) than that by LDA (~13%). Besides, MLs could detect TC formation at the time as early as 26-30 h before the first time diagnosed as tropical depression by the JTWC best track, which was also 5 to 9 h earlier than that by LDA. The skill differences across MLs were relatively smaller than difference between MLs and LDA. Large yearly variation in forecast lead time was common in all models due to the limitation in sampling from orbiting satellite. This study highlights that ML approaches provide an improved skill for detecting TC formation compared with conventional linear approaches