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Current recommended cutoffs for determining measurement invariance have typically derived from simulation studies that have focused on multigroup confirmatory factor analysis, often using continuous data. These cutoffs may be inappropriate for ordered categorical data in a longitudinal setting. This study conducts two Monte Carlo studies that evaluate the performance of four popular model fit indices used to determine measurement invariance. The comparative fit index (CFI), Tucker-Lewis Index (TLI), and root mean square error of approximation (RMSEA) were all found to be inconsistent across various simulation conditions as well as invariance tests, and thus were not recommended for use in longitudinal measurement invariance testing. The standardized root mean square residual (SRMR) was the most consistent and robust fit index across simulation conditions, and thus we recommended using ≥ 0.01 as a cutoff for determining longitudinal measurement invariance with ordered categorical indicators.

Title: Physics demonstrations for upper secondary school students - video study Author: Bc. Alexandr Nikitin Department: Department of Physics Education Supervisor: RNDr. Marie Snětinová, Ph.D., Department of Physics Education Abstract: Experiment is a key element not only for physics as science, but also for physics education. Even though attention has been lately focused more on students' hands-on experiments, demonstration experiments still play an important role in today's education. Department of Physics Education (Charles University, Faculty of Mathematics and Physics) has been performing physics demonstrations for upper secondary school students for more than 30 years. Seven different topics are currently offered by the Department. A survey conducted on a population of est. 5,100 students showed that the perception of these topics by students varies quite significantly, especially considering their intrinsic motivation and subjectively perceived value and usefulness of a given topic. The questions at hand are whether parameters that influence students' perception in negative or positive way do exist and how they are related to the choice of the experiments themselves or the lecturer's work with the audience. Aim of this thesis is to present a video study conducted on video recordings of all seven...

Clustering is an unsupervised learning technique widely used to group data into homogeneous clusters. For many real-world data containing categorical values, existing algorithms are often computationally costly in high dimensions, do not work well on noisy data with missing values, and rarely provide theoretical guarantees on clustering accuracy. In this thesis, we propose a general categorical data encoding method and a computationally efficient spectral based algorithm to cluster high-dimensional noisy categorical (nominal or ordinal) data. Under a statistical model for data on m attributes from n subjects in r clusters with missing probability epsilon, we show that our algorithm exactly recovers the true clusters with high probability when mn(1-epsilon) >= CMr² log³M, with M=max(n,m) and a fixed constant C. Moreover, we show that mn(1- epsilon)² >= r *delta/2 with 0< delta <1 is necessary for any algorithm to succeed with probability at least (1+delta)/2. In case, where m=n and r is fixed, for example, the sufficient condition matches with the necessary condition up to a polylog(n) factor, showing that our proposed algorithm is nearly optimal. We also show our algorithm outperforms several existing algorithms in both clustering accuracy and computational efficiency, both theoretically and numerically. In addition, we propose a spectral algorithm with standardization to cluster mixed data. This algorithm is computationally efficient and its clustering accuracy has been evaluated numerically on both real world data and synthetic data.

Although there is a wealth of knowledge on categorization early in life, there are still many unanswered questions about the nature of category representation in infancy. For example, it is unclear whether infants are sensitive to boundaries between complex categories, such as types of animals, or whether young infants exhibit such sensitivity without explicit experience in the lab. Using a morphing technique, we linearly altered the category composition of images and measured 6.5-month-olds’ attention to pairs of animal faces that either did or did not cross the categorical boundary, with the stimuli in each pair being equally dissimilar from one another across the two types of image pairs. Results indicated that infants dichotomize the continua between cats and dogs and between cows and otters, but only when the images are presented in their canonical, upright orientations. These findings demonstrate a propensity to dichotomize early in life that could have implications for social categorizations, such as race and gender.

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中国における都市部と農村部異なる制度的・社会経済的背景により、独特な環境意識を生まれていると考えられる。本研究は、現地調査によりデータを収集し、統計分析を駆使したことにより、都市部と農村部における環境意識の特有の構造と形成メカニズムを解明することを目的とする。先行研究の成果を踏まえ、都市部と農村部の社会構造を考慮し、環境意識に関する総合的な理論モデルを提案し、環境意識の三つのディメンションに分けて展開する。理論的に検討することと実証的なデータの分析結果を基に、環境意識形成の内在因子と外部影響要因を明らかにした。 / Long-time institutional and socioeconomic segmentations make rural China become a distinctive society from the urban China. The remarkable rural and urban division in China supplies us a good context to explore the formation and diverse social facets of environmental consciousness. This study aims to clarify the specific structure and formation mechanism of environmental consciousness under the different social backgrounds of rural and urban China based on the statistical results derived from survey data. Three dimensions of environmental consciousness and an integrated theoretical framework which involves both social structural and social psychological variables are proposed. Based on the proposed theoretical framework and examined data analyses, the inner causes and externally influencing factors of environmental consciousness were clarified. / 博士(文化情報学) / Doctor of Culture and Information Science / 同志社大学 / Doshisha University

本論文では大規模カテゴリカルデータのデータ解釈の場面で生じる問題を考えた．データが大規模な場合，クラスター分析や視覚化などで，データの潜在的な構造を調べる方法が有用とされるが，対象ごとにカテゴリの解釈が異なったり，同じ属性でも回答傾向が異なったりすると解釈が複雑になる．本論文ではそのように既存手法をシンプルに適用するのでは解釈が難しいようなデータに対して，よりわかりやすい解釈をするための手法を開発した． / Large-scale categorical data are often obtained in various fields. As an interpretation of large-scale data tends to be complicated, methods to capture the latent structure in data, such as a cluster analysis and a visualization method are often used to make data more interpretable. However, there are some situations where these methods failed to capture the latent structure that is interpretable (e.g., interpretation of categories by each respondent is different). Therefore in this paper, two problems that often occur in large-scale categorical data analysis is considered, and new methods to address these issues are proposed. / 博士(文化情報学) / Doctor of Culture and Information Science / 同志社大学 / Doshisha University

The purpose of this study was to show how hiring standards of evidence for graphic designers in Utah County compared with the national standards of evidence. The four major national standards of evidence for hiring graphic designers, identified by American Institute of Graphic Arts (AIGA) and Goldfarb, in order of importance are portfolio, recommendations, personality, and education. The data from this study revealed that Utah County employer's standards of evidence matched up closely to national standards of evidence, but the order of importance was slightly different because personality was ranked ahead of recommendations and education.

Computer crime is a large problem (CSI, 2004; Kabay, 2001a; Kabay, 2001b). Security managers have a variety of tools at their disposal -- firewalls, Intrusion Detection Systems (IDSs), encryption, authentication, and other hardware and software solutions to combat computer crime. Many IDS variants exist which allow security managers and engineers to identify attack network packets primarily through the use of signature detection; i.e., the IDS recognizes attack packets due to their well-known "fingerprints" or signatures as those packets cross the network's gateway threshold. On the other hand, anomaly-based ID systems determine what is normal traffic within a network and reports abnormal traffic behavior. This paper will describe a methodology towards developing a more-robust Intrusion Detection System through the use of data-mining techniques and anomaly detection. These data-mining techniques will dynamically model what a normal network should look like and reduce the false positive and false negative alarm rates in the process. We will use classification-tree techniques to accurately predict probable attack sessions. Overall, our goal is to model network traffic into network sessions and identify those network sessions that have a high-probability of being an attack and can be labeled as a "suspect session." Subsequently, we will use these techniques inclusive of signature detection methods, as they will be used in concert with known signatures and patterns in order to present a better model for detection and protection of networks and systems.