Part One: The Context Of Educational Research Part Two: Planning Educational Research Part Three: Styles Of Educational Research Part Four: Strategies And Instruments For Data Collection And Researching Part Five: Data Analysis

Research Methods in Education

Similarity breeds connection. This principle—the homophily principle—structures network ties of every type, including marriage, friendship, work, advice, support, information transfer, exchange, comembership, and other types of relationship. The result is that people's personal networks are homogeneous with regard to many sociodemographic, behavioral, and intrapersonal characteristics. Homophily limits people's social worlds in a way that has powerful implications for the information they receive, the attitudes they form, and the interactions they experience. Homophily in race and ethnicity creates the strongest divides in our personal environments, with age, religion, education, occupation, and gender following in roughly that order. Geographic propinquity, families, organizations, and isomorphic positions in social systems all create contexts in which homophilous relations form. Ties between nonsimilar individuals also dissolve at a higher rate, which sets the stage for the formation of niches (localize...

/pdf/birds-of-a-feather-homophily-in-social-networks-3kjmeyr23w.pdf

Birds of a Feather: Homophily in Social Networks

Examines whether the positive association between social support and well-being is attributable more to an overall beneficial effect of support (main- or direct-effect model) or to a process of support protecting persons from potentially adverse effects of stressful events (buffering model). The review of studies is organized according to (1) whether a measure assesses support structure (the existence of relationships) or function (the extent to which one's interpersonal relationships provide particular resources) and (2) the degree of specificity (vs globality) of the scale. Special attention is given to methodological characteristics that are requisite for a fair comparison of the models. It is concluded that there is evidence consistent with both models. Evidence for the buffering model is found when the social support measure assesses the perceived availability of interpersonal resources that are responsive to the needs elicited by stressful events. Evidence for a main effect model is found when the support measure assesses a person's degree of integration in a large social network. Both conceptualizations of social support are correct in some respects, but each represents a different process through which social support may affect well-being. Implications for theories of social support processes and for the design of preventive interventions are discussed.

/pdf/stress-social-support-and-the-buffering-hypothesis-37b7abjbst.pdf

Stress, social support, and the buffering hypothesis.

This paper reviews the origins and definitions of social capital in the writings of Bourdieu, Loury, and Coleman, among other authors. It distinguishes four sources of social capital and examines their dynamics. Applications of the concept in the sociological literature emphasize its role in social control, in family support, and in benefits mediated by extrafamilial networks. I provide examples of each of these positive functions. Negative consequences of the same processes also deserve attention for a balanced picture of the forces at play. I review four such consequences and illustrate them with relevant examples. Recent writings on social capital have extended the concept from an individual asset to a feature of communities and even nations. The final sections describe this conceptual stretch and examine its limitations. I argue that, as shorthand for the positive consequences of sociability, social capital has a definite place in sociological theory. However, excessive extensions of the concept may j...

/pdf/social-capital-its-origins-and-applications-in-modern-1u65djoshv.pdf

Social Capital: Its Origins and Applications in Modern Sociology

世界経済・社会統計 = World development indicators

Purpose Metastatic relapse of triple-negative breast cancer (TNBC) within 2 years of diagnosis is associated with particularly aggressive disease and a distinct clinical course relative to TNBCs that relapse beyond 2 years. We hypothesized that rapid relapse TNBCs (rrTNBC; metastatic relapse or death 2 years). Patients and Methods We identified 453 primary TNBCs from three publicly-available datasets and characterized each as rrTNBc, lrTNBC, or ‘no relapse’ (nrTNBC: no relapse/death with at least 5 years follow-up). We compiled primary tumor clinical and multi-omic data, including transcriptome (n=453), copy number alterations (CNAs; n=317), and mutations in 171 cancer-related genes (n=317), then calculated published gene expression and immune signatures. Results Patients with rrTNBC were higher stage at diagnosis (Chi-square p 0.75. Conclusions We provide a new approach to define TNBCs based on timing of relapse. We identify distinct clinical and genomic features that can be incorporated into machine learning models to predict rapid relapse of TNBC.

/pdf/machine-learning-predicts-rapid-relapse-of-triple-negative-58yyrl8gia.pdf

Machine learning predicts rapid relapse of triple negative breast cancer

Objectives To discover a more powerful diagnostic tool for the detection of hepatocellular carcinoma (HCC). Methods 16 extracellularly located candidates were selected by analyzing the expression array datasets in GEO. 10 of them were validated in clinical samples by ELISA. Differences of each variable were compared by one-way ANOVA or Kruskal-Wallis test. CCL20 and LCN2 were determined in all samples (HCC, 167; liver cirrhosis, 106; and healthy control, 106) and finally chosen for the construction of the combination model by binary logistic regression. The models were first built using a comprehensive control, including both liver cirrhosis (LC) and healthy donors. Then, the models were rebuilt by using the LC group alone as a control. ROC analysis was performed to compare the diagnostic efficiency of each indicator. Results Levels of CCL20 and LCN2 in HCC sera were significantly higher than those in all controls. Using the comprehensive control, ROC curves showed that the optimum diagnostic cutoff of the CCL20 and LCN2 combination was 0.443 (area under curve (AUC) of 0.927 (95% CI 0.896-0.951), sensitivity of 0.808, specificity of 0.892, and accuracy of 0.859). For detection of HCC from LC control, the optimum diagnostic cutoff was 0.590 (AUC of 0.919 (95% CI 0.880-0.948), sensitivity of 0.814, specificity of 0.868, and accuracy of 0.834). Furthermore, the model maintained diagnostic accuracy for patients with HCC in the early stage, with the sensitivity and specificity of 0.75 and 0.77 from LC control, yet the AFP only reached 0.5 and 0.67, respectively. Conclusion A combination model composed of CCL20 and LCN2 may serve as a more efficient tool for distinguishing HCC from nonmalignant liver diseases.

https://downloads.hindawi.com/journals/dm/2022/7758735.pdf

Identification of CCL20 and LCN2 as Efficient Serological Tools for Detection of Hepatocellular Carcinoma

The discussions of the structure, interaction, and action aspects of social capital described in the previous three chapters have laid the groundwork for specifying propositions to guide research. This chapter will summarize the major principles presented so far and will then present the theory's principal propositions. The Theory of Social Capital The theory of social capital focuses on the resources embedded in one's social network and how access to and use of such resources benefit the individual's actions. Resources are defined as valued goods in a society, however consensually determined, the possession of which maintains and promotes an individual's self-interest for survival and preservation. The values are normative judgments rendered on these goods. For most societies, they correspond to wealth, reputation, and power. The theory focuses on those actions that are taken for the purpose of either maintaining or gaining valued resources. Resources can be either ascribed or acquired. Ascribed resources are those one is born with, such as gender and race. Other resources are prescribed by inheritance, such as caste and sometimes religion, and may include parental resources. Resources can also be acquired, such as education, or prestigious or authoritative jobs. When resources are being invested for expected returns in the marketplace, they become social capital.

The Theory and Theoretical Propositions

In order to improve the quality and efficiency of music teaching, we try to automate the teaching of music notation. With the addition of computer vision technology and note recognition algorithms, we improve the generative adversarial network to enhance the recognition accuracy and efficiency of music short scores. We adopt an embedded matching structure based on adversarial neural networks, starting from generators and discriminators, respectively, to unify generators and discriminators from the note input side. Each network layer is then laid out according to a cascade structure to preserve the different layers of note features in each convolutional layer. Residual blocks are then inserted in some network layers to break the symmetry of the network structure and enhance the ability of the adversarial network to acquire note features. To verify the efficiency of our method, we select monophonic spectrum, polyphonic spectrum, and miscellaneous spectrum datasets for validation. The experimental results demonstrate that our method has the best recognition accuracy in the monophonic spectrum and the miscellaneous spectrum, which is better than the machine learning method. In the recognition efficiency of note detail information, our method is more efficient in recognition and outperforms other deep learning methods.

https://downloads.hindawi.com/journals/cin/2022/8724688.pdf

Generative Adversarial Network for Musical Notation Recognition during Music Teaching

Community detection in network analysis aims at partitioning nodes in a network into $K$ disjoint communities. Most currently available algorithms assume that $K$ is known, but choosing a correct $K$ is generally very difficult for real networks. In addition, many real networks contain outlier nodes not belonging to any community, but currently very few algorithm can handle networks with outliers. In this paper, we propose a novel model free tightness criterion and an efficient algorithm to maximize this criterion for community detection. This tightness criterion is closely related with the graph Laplacian with $L_0$ penalty. Unlike most community detection methods, our method does not require a known $K$ and can properly detect communities in networks with outliers. 
Both theoretical and numerical properties of the method are analyzed. The theoretical result guarantees that, under the degree corrected stochastic block model, even for networks with outliers, the maximizer of the tightness criterion can extract communities with small misclassification rates even when the number of communities grows to infinity as the network size grows. Simulation study shows that the proposed method can recover true communities more accurately than other methods. Applications to a college football data and a yeast protein-protein interaction data also reveal that the proposed method performs significantly better.

Nan Lin

Papers

Machine learning predicts rapid relapse of triple negative breast cancer

Identification of CCL20 and LCN2 as Efficient Serological Tools for Detection of Hepatocellular Carcinoma

The Theory and Theoretical Propositions

Generative Adversarial Network for Musical Notation Recognition during Music Teaching

Community Detection by $L_0$-penalized Graph Laplacian