FORMANN, A. K., MAZANEL, J. A., OBERHAUSER, D.C.: Numerische Klassifikationsprobleme in ,groBen' Datensatzen der demoskopischen Marktforschung: Ein empirischer Methodenvergleich von Latent-Class- und Cluster'Analyse (Pr.oblems of numerical classification in large data sets for demoscopic marketing studies: An empirical comparison between latent class analysis and cluster analysis). (In German.) Wien: Wirtschaftsverlag Dr. Anton Orac 1979. 113 pp. Arbeitspapiere des Instituts flir Werbewissenschaft und Marktforschung Wien 12/1979.

1981 ◽  
Vol 8 (1) ◽  
pp. 41-42
Keyword(s):  
Cluster Analysis ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Class Analysis ◽  
Numerical Classification ◽  
Empirical Comparison ◽  
And Cluster Analysis

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

Stepfamily Involvement in and out of School and (Step)children’s College Preparation Behaviors: A Latent Cluster Analysis

2021 ◽  
pp. 0192513X2199387
Author(s):  
Jacqueline Bible ◽  
David T. Lardier ◽  
Frank Perrone ◽  
Brad van Eeden-Moorefield
Keyword(s):  
High School ◽  
Cluster Analysis ◽  
Longitudinal Study ◽  
Latent Class Analysis ◽  
College Preparation ◽  
Latent Class ◽  
Educational Experiences ◽  
Class Analysis ◽  
Out Of School

Using a latent class analysis (LCA) with data from a subsample of children in stepfamilies ( N = 6,637) from the 2009 High School Longitudinal Study (HSLS), this study examined how stepfamily involvement in their (step)child’s education in and outside of school influenced their (step)child’s college preparation. Stepfamily involvement in their (step)child’s education in school (e.g., help with homework) and outside of school (e.g., educational experiences such as going to a museum) may help overcome challenges associated with academic and college preparation for children in stepfamilies. Results broadly indicate students with higher stepfamily involvement in education in and out of school had (step)parents who believed that college was attainable, students engaged in more activities that would prepare them for their future, and students took more AP/IB level courses and tests. Together, findings suggest that stepfamily involvement in education both in and out of school is important for their (step)child’s college preparation behaviors.

scholarly journals MINKOWSKI FUNCTIONALS AND CLUSTER ANALYSIS FOR CMB MAPS

1999 ◽  
Vol 08 (03) ◽  
pp. 291-306 ◽  
Author(s):  
D. NOVIKOV ◽  
HUME A. FELDMAN ◽  
SERGEI F. SHANDARIN
Keyword(s):  
Large Data ◽  
Threshold Level ◽  
Distribution Functions ◽  
Large Data Sets ◽  
Cumulative Distribution ◽  
Temperature Threshold ◽  
Data Sets ◽  
Minkowski Functionals ◽  
And Cluster Analysis ◽  
Non Gaussian

We suggest novel statistics for the CMB maps that are sensitive to non-Gaussian features. These statistics are natural generalizations of the geometrical and topological methods that have been already used in cosmology such as the cumulative distribution function and genus. We compute the distribution functions of the Partial Minkowski Functionals for the excursion set above or bellow a constant temperature threshold. Minkowski Functionals are additive and are translationally and rotationally invariant. Thus, they can be used for patchy and/or incomplete coverage. The technique is highly efficient computationally (it requires only O(N) operations, where N is the number of pixels per one threshold level). Further, the procedure makes it possible to split large data sets into smaller subsets. The full advantage of these statistics can be obtained only on very large data sets. We apply it to the 4-year DMR COBE data corrected for the Galaxy contamination as an illustration of the technique.

A Common Methodology

2016 ◽  
Vol 7 (2) ◽  
pp. 74-87
Author(s):  
Sunny L. Munn
Keyword(s):  
Cluster Analysis ◽  
Organizational Culture ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Organizational Structures ◽  
Work Life ◽  
Class Analysis ◽  
Analysis Methodology ◽  
The Common ◽  
The Impact

Organizational structures are comprised of an organizational culture created by the beliefs, values, traditions, policies and processes carried out by the organization. The work-life system in which individuals use work-life initiatives to achieve a work-life balance can be influenced by the type of organizational culture within one's workplace, for example a structured, rigid culture in which employees are afraid to ask questions versus a flexible, open culture where discussion is encouraged. Grouping methodologies such as cluster analysis or latent class analysis can be used to create typologies of organizational culture. The focus of this paper is to deconstruct the common methodology of cluster analysis used to identify typologies of organizational culture in the NSCW Study and the NOS Study, which set out to identify the impact of organizational culture on the use and existence of work-life benefits for individuals and organizations, respectively (Munn, 2012). The paper discusses the cluster analysis methodology in detail as well as another grouping methodology – latent class analysis - as a means to understanding the place of organizational culture in work-life research. The theoretical contributions of using cluster analysis to create typologies of organizational culture and the implications for workforce research are discussed.

A Common Methodology

2017 ◽  
pp. 439-453
Author(s):  
Sunny L. Munn
Keyword(s):  
Cluster Analysis ◽  
Organizational Culture ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Organizational Structures ◽  
Work Life ◽  
Class Analysis ◽  
Analysis Methodology ◽  
The Common ◽  
The Impact

Organizational structures are comprised of an organizational culture created by the beliefs, values, traditions, policies and processes carried out by the organization. The work-life system in which individuals use work-life initiatives to achieve a work-life balance can be influenced by the type of organizational culture within one's workplace, for example a structured, rigid culture in which employees are afraid to ask questions versus a flexible, open culture where discussion is encouraged. Grouping methodologies such as cluster analysis or latent class analysis can be used to create typologies of organizational culture. The focus of this paper is to deconstruct the common methodology of cluster analysis used to identify typologies of organizational culture in the NSCW Study and the NOS Study, which set out to identify the impact of organizational culture on the use and existence of work-life benefits for individuals and organizations, respectively (Munn, 2012). The paper discusses the cluster analysis methodology in detail as well as another grouping methodology – latent class analysis - as a means to understanding the place of organizational culture in work-life research. The theoretical contributions of using cluster analysis to create typologies of organizational culture and the implications for workforce research are discussed.

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

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