scholarly journals Sociometric research in pedagogy

2013 ◽  
Vol 45 (1) ◽  
pp. 24-41 ◽  
Author(s):  
Marina Ilic
Keyword(s):  
Cluster Analysis ◽  
Rating Scale ◽  
Group Classification ◽  
Two Dimensional ◽  
Classification Methods ◽  
Scale Method ◽  
Traditional Classification ◽  
Peer Nominations ◽  
Group Member ◽  
Selection Of

The use of sociometric research in pedagogy implies consideration of methodological demands pertaining to the selection and formulation of sociometric criteria, the selection of sociometric techniques, instruments and sociometric group classification methods. Various authors agree that sociometric criteria are basic relevant activities of the group they refer to and that they should be meaningful, understandable to every group member and clearly and precisely formulated. The selection of the appropriate sociometric technique and instrument has to be adjusted with the goal of sociometric research, along with the consideration of the advantages and short?comings of different sociometric techniques and instruments. The existing research provides very divergent results on the adequacy of different classification methods in identifying stable sociometric groups. Still, the majority of studies have confirmed that the two-dimensional rating scale method yields more stable classifications than the classification methods based on peer nominations, as well as that it is methodologically more justifiable to use cluster analysis in identifying stable sociometric status groups than the traditional classification methods.

EMERGING CLUSTER ANALYSIS OF SCI JOURNALS AND ITS EFFICIENCY

2011 ◽  
Vol 22 (11) ◽  
pp. 1247-1256 ◽  
Author(s):  
YUNFENG CHANG ◽  
YUAN PING
Keyword(s):  
Cluster Analysis ◽  
Time Complexity ◽  
Classification Methods ◽  
Clustering Method ◽  
Citation Pattern ◽  
Subjective Analysis ◽  
Knowledge Domains ◽  
Characteristic Numbers ◽  
Traditional Classification ◽  
Process Characteristic

Cluster analysis is an important way to ascertain whether or not a complex system consists of sub-clusters with different properties. On the basis of journal citation pattern, we clustered 1896 SCI journals in an emerging way (emerging clustering) in this paper. By this emerging clustering, the data to be checked for clustering is reduced from [Formula: see text] to O(NJ) (NJ is the total number of journals), this reduction could reduce the time complexity of clustering to a certain extent. During the clustering process, characteristic numbers of clusters are obtained, which correspond to various resolution scales for viewing the journals and might be helpful in understanding the mutual interactions among various knowledge domains. Its efficiency is further checked by comparisons between emerging clustering and hierarchical clustering with different inter-cluster linkage techniques. Statistical properties and comparisons of the clustering results show that this is an efficient clustering method for journal system. It could be a helpful improvement for traditional classification methods based on subjective analysis.

Cluster Analysis of Antigenic Profiles of Tumors: Selection of Number of Clusters Using Akaike’s Information Criterion

1990 ◽  
Vol 29 (03) ◽  
pp. 200-204 ◽  
Author(s):  
J. A. Koziol
Keyword(s):  
Cluster Analysis ◽  
Basic Problem ◽  
Information Criterion ◽  
Akaike's Information Criterion ◽  
Cell Surface Antigens ◽  
Number Of Clusters ◽  
Akaike’S Information Criterion ◽  
Multinomial Data ◽  
Tumor Types ◽  
Selection Of

AbstractA basic problem of cluster analysis is the determination or selection of the number of clusters evinced in any set of data. We address this issue with multinomial data using Akaike’s information criterion and demonstrate its utility in identifying an appropriate number of clusters of tumor types with similar profiles of cell surface antigens.

Bridging the Gap between Quantitative and Qualitative Historical Research: an application of multiple regression analysis and homogeneity analysis with alternating least squares

1996 ◽  
Vol 8 (3) ◽  
pp. 133-144 ◽  
Author(s):  
María del Mar del Pozo Andrés ◽  
Jacques F A Braster
Keyword(s):  
Least Squares ◽  
Multiple Regression ◽  
Historical Research ◽  
Alternating Least Squares ◽  
Categorical Variables ◽  
Two Dimensional ◽  
Homogeneity Analysis ◽  
Regression Approach ◽  
Dimensional Picture ◽  
Selection Of

In this article we propose two research techniques that can bridge the gap between quantitative and qualitative historical research. These are: (1) a multiple regression approach that gives information about general patterns between numerical variables and the selection of outliers for qualitative analysis; (2) a homogeneity analysis with alternating least squares that results in a two-dimensional picture in which the relationships between categorical variables are graphically presented.

Using clusterization principles for the selection of repair sections of main oil pipelines based on diagnostic data

2011 ◽  
Vol 8 (1) ◽  
pp. 201-210
Author(s):  
R.M. Bogdanov
Keyword(s):  
Cluster Analysis ◽  
Defect Density ◽  
Density Variation ◽  
Distance Functions ◽  
Oil Pipeline ◽  
Oil Pipelines ◽  
Classification Of Images ◽  
Main Oil Pipeline ◽  
Selection Of

The problem of determining the repair sections of the main oil pipeline is solved, basing on the classification of images using distance functions and the clustering principle, The criteria characterizing the cluster are determined by certain given values, based on a comparison with which the defect is assigned to a given cluster, procedures for the redistribution of defects in cluster zones are provided, and the cluster zones parameters are being changed. Calculations are demonstrating the range of defect density variation depending on pipeline sections and the universal capabilities of linear objects configuration with arbitrary density, provided by cluster analysis.

Adolescent Popularity as a Nonlinear, Two-Dimensional Construct: Convergent Results From Three Large Samples

2021 ◽  
pp. 027243162110160
Author(s):  
Peter E. L. Marks ◽  
Ben Babcock ◽  
Yvonne H. M. van den Berg ◽  
Rob Gommans ◽  
Antonius H. N. Cillessen
Keyword(s):  
Early Adolescents ◽  
Composite Score ◽  
Two Dimensional ◽  
Behavioral Correlates ◽  
Large Samples ◽  
Three Samples ◽  
Peer Nominations

The goal of this study was to advance the conceptualization and measurement of adolescent popularity by exploring the commonly used composite score (popularity minus unpopularity). We used standardized peer nominations from 4,414 early adolescents (ages ≈ 12-14 years) from three samples collected in two countries. Popularity and unpopularity were strongly related, but not linearly; scatterplots of the two variables resembled an L-shaped right angle. Subsequent analyses indicated that either including popularity as a curvilinear term or including both popularity and unpopularity as separate terms explained significantly more variance in social and behavioral correlates than linear, bivariate analyses using popularity, unpopularity, or composite popularity. These results suggest that researchers studying adolescent popularity should either separate popularity and unpopularity or treat composite popularity as curvilinear.

scholarly journals Semantics of Voids within Data: Ignorance-Aware Machine Learning

2021 ◽  
Vol 10 (4) ◽  
pp. 246
Author(s):  
Vagan Terziyan ◽  
Anton Nikulin
Keyword(s):  
Spatial Data ◽  
Information Science ◽  
Nearest Neighbor ◽  
Geographical Information ◽  
Two Dimensional ◽  
Prototype Selection ◽  
Data Space ◽  
Important Concern ◽  
Traditional Classification ◽  
Nearest Neighbor Classifiers

Operating with ignorance is an important concern of geographical information science when the objective is to discover knowledge from the imperfect spatial data. Data mining (driven by knowledge discovery tools) is about processing available (observed, known, and understood) samples of data aiming to build a model (e.g., a classifier) to handle data samples that are not yet observed, known, or understood. These tools traditionally take semantically labeled samples of the available data (known facts) as an input for learning. We want to challenge the indispensability of this approach, and we suggest considering the things the other way around. What if the task would be as follows: how to build a model based on the semantics of our ignorance, i.e., by processing the shape of “voids” within the available data space? Can we improve traditional classification by also modeling the ignorance? In this paper, we provide some algorithms for the discovery and visualization of the ignorance zones in two-dimensional data spaces and design two ignorance-aware smart prototype selection techniques (incremental and adversarial) to improve the performance of the nearest neighbor classifiers. We present experiments with artificial and real datasets to test the concept of the usefulness of ignorance semantics discovery.

Using Cluster Analysis to Improve Marketing Experiments

1971 ◽  
Vol 8 (3) ◽  
pp. 340-347 ◽  
Author(s):  
George S. Day ◽  
Roger M. Heeler
Keyword(s):  
Cluster Analysis ◽  
Space Representation ◽  
Reduced Space ◽  
High Degree ◽  
Degree Of Similarity ◽  
Sensitive Experiment ◽  
Selection Of

When the selection of a sample of stores or cities requires a high degree of similarity among the test units in order to ensure a sensitive experiment, the sample may no longer represent the market. These conflicting requirements can be satisfied by choosing the sample from clusters displayed in a reduced space representation of the market.

Two-dimensional gel selection of protein binding sites on DNA

1993 ◽  
Vol 14 (1) ◽  
pp. 259-265 ◽  
Author(s):  
Andrea Boffini ◽  
Pierre Prentki
Keyword(s):  
Protein Binding ◽  
Binding Sites ◽  
Two Dimensional ◽  
Protein Binding Sites ◽  
Selection Of

Exact analysis of the bending of wide beams by a modified elastica approach

2011 ◽  
Vol 225 (11) ◽  
pp. 2759-2764 ◽  
Author(s):  
L F Campanile ◽  
R Jähne ◽  
A Hasse
Keyword(s):  
Finite Element ◽  
Substantial Reduction ◽  
Short Review ◽  
Computational Effort ◽  
Two Dimensional ◽  
Finite Element Calculations ◽  
Dimensional Finite Element ◽  
Wide Beams ◽  
Insight Into ◽  
Selection Of

Classical beam models do not account for partial restraint of anticlastic bending and are therefore inherently inaccurate. This article proposes a modification of the exact Bernoulli–Euler equation which allows for an exact prediction of the beam's deflection without the need of two-dimensional finite element calculations. This approach offers a substantial reduction in the computational effort, especially when coupled with a fast-solving schema like the circle-arc method. Besides the description of the new method and its validation, this article offers an insight into the somewhat disregarded topic of anticlastic bending by a short review of the published theories and a selection of representative numerical results.

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