Journal articles: 'Class analysis'

1

Breen, Richard, and David Rottman. "Class Analysis and Class Theory." Sociology 29, no. 3 (August 1995): 453–73. http://dx.doi.org/10.1177/0038038595029003005.

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2

Croon, Marcel. "Latent class analysis with ordered latent classe." British Journal of Mathematical and Statistical Psychology 43, no. 2 (November 1990): 171–92. http://dx.doi.org/10.1111/j.2044-8317.1990.tb00934.x.

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3

So, Alvin Y. "Class Struggle Analysis: A Critique of Class Structure Analysis." Sociological Perspectives 34, no. 1 (March 1991): 39–59. http://dx.doi.org/10.2307/1389142.

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4

McKinnon, Christopher. "Dual-Class Capital Structures: A Legal, Theoretical & Empirical Buy-Side Analysis." Michigan Business & Entrepreneurial Law Review, no. 5.1 (2015): 81. http://dx.doi.org/10.36639/mbelr.5.1.dual-class.

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“The advantage of a dual-class share structure is that it protects entrepreneurial management from the demands of ordinary shareholders. The disadvantage of a dual-class share structure is that it protects entrepreneurial management from the demands of shareholders.” Issuing dual classes of stock has become hotly debated since two major events transpired in 2014: (1) Facebook acquired WhatsApp for $19 billion and (2) Alibaba chose to list its shares on the New York Stock Exchange (NYSE) instead of the Hong Kong Exchange. Because dual-class managers, like those at Facebook and Alibaba, retain a controlling voting block, their decisions are immune from activist investors or others who disagree with corporate actions. This protection allowed Mark Zuckerberg to acquire WhatsApp at an enormous price that stockholders may have resisted, and it is why Alibaba chose to list on the NYSE even though its stockholders may have found the Hong Kong Exchange to be a more natural fit. This Comment seeks to determine whether the one-man decisional structures at Facebook and Alibaba—accomplished through dual classes of stock—allow such managers to undertake, what the market perceives to be, value-destroying transactions more often than their single-class counterparts.

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Mindrila, Diana. "Latent Class Analysis." International Journal for Cross-Disciplinary Subjects in Education 11, no. 2 (December 31, 2020): 4323–28. http://dx.doi.org/10.20533/ijcdse.2042.6364.2020.0529.

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Nakhaie, Reza, Rosemary Crompton, Fiona Devine, Mike Savage, and John Scott. "Renewing Class Analysis." Canadian Journal of Sociology / Cahiers canadiens de sociologie 28, no. 2 (2003): 251. http://dx.doi.org/10.2307/3341463.

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7

Magidson, Jay, and Allan L. McCutcheon. "Latent Class Analysis." Journal of Marketing Research 26, no. 1 (February 1989): 125. http://dx.doi.org/10.2307/3172676.

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8

Cockcroft, James D. "Gendered Class Analysis." Latin American Perspectives 25, no. 6 (November 1998): 42–46. http://dx.doi.org/10.1177/0094582x9802500608.

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9

Resnick, Stephen, and Richard Wolff. "Empireand Class Analysis." Rethinking Marxism 13, no. 3-4 (September 2001): 61–69. http://dx.doi.org/10.1080/089356901101241992.

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10

Featherstone, Liza. "Class Analysis Online." New Labor Forum 22, no. 3 (July 30, 2013): 90–92. http://dx.doi.org/10.1177/1095796013496984.

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11

Woods, A. J., and Allan L. McCutcheon. "Latent Class Analysis." Statistician 38, no. 1 (1989): 84. http://dx.doi.org/10.2307/2349031.

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12

Neuhaus, Valentin, and David C. Ring. "Latent Class Analysis." Journal of Hand Surgery 38, no. 5 (May 2013): 1018–20. http://dx.doi.org/10.1016/j.jhsa.2013.01.024.

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13

Devine, Fiona, and Erik Olin Wright. "Class Counts: Comparative Studies in Class Analysis." British Journal of Sociology 48, no. 4 (December 1997): 713. http://dx.doi.org/10.2307/591617.

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Crompton, Rosemary, Ira Katznelson, and Erik Olin Wright. "Class Counts: Comparative Studies in Class Analysis." Contemporary Sociology 26, no. 6 (November 1997): 694. http://dx.doi.org/10.2307/2654622.

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15

Gubbay, Jon. "Class Counts: Comparative Studies in Class Analysis." Historical Materialism 5, no. 1 (1999): 281–300. http://dx.doi.org/10.1163/156920699794750975.

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16

Cullenberg, Stephen. "The Politics of Class Analysis versus the Class Analysis of Politics." Review of Radical Political Economics 20, no. 2-3 (June 1988): 12–17. http://dx.doi.org/10.1177/048661348802000204.

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17

Rossi, Alessandro, Maria Marconi, Stefania Mannarini, India Minelli, Chiara Fusi, Monica Anderboni, Chiara Rossini, Giuseppe Di Lucca, and Claudio Verusio. "Profiling cancer-related distress and problems: A latent class analysis approach." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e23191-e23191. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e23191.

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e23191 Background: Psychological distresses, emotional troubles, social difficulties as well as both physical and practical issues are all among oppressive problems that oncological patients have to face almost every day. On one hand, these problems seem to have a strong impact on quality of life; and to the other hand, these issues seem to be related to specific cancer-related variables ( e.g.: type and localization of the tumor). Thus, the aim of the study was to use a Latent Class Analysis (LCA) approach to profile a latent structure accounting for the covariance between psychological distress and everyday problems due to cancer. A one-factor model with three classes was hypothesized, which comprised distress and cancer-related problems as indicators and age, type of medical treatment as well as type and localization of tumor as external variables that moderate the latent structure. Methods: Patients ( N = 264, 62.3% female, mean age = 65.3, SD = 12.4) were enrolled at the Oncology Day Hospital, “Presidio Ospedaliero” of Saronno, ASST Valle Olona, Italy. Using the standardized Distress Thermometer and Problem List (TD&PL; NCCN, 2015) patients were tested for: (A) distress; (B) practical problems (Cronbach’s α = .63); (C) social difficulties (Cronbach’s α = .65); (D) emotional issues (Cronbach’s α = .86) and (E) physical problems (Cronbach’s α = .77). Results: First of all, a CFA was performed to test the original factorial structure of the DT&PL. The original five factor solution was supported by adequate fit indices: RMSEA = .063; CFI = .924. Then, the LCA (10000 bootstrap resampling) shows the goodness-of-model fit [χ2 = 10.01; p = .11; LRχ2 = 9.30; p = .09] and the goodness-of-classification quality [Entropy = .80 ( > .7); Average-Latent-Class-Assignment-Probability: .975, .900, and .902 for Class1, Class2, and Class3, respectively]. The LCA identified a latent variable with three classes (VLMR = 193.38; p < .001; Class1 = .49%; Class2 = .23%, and Class3 = .28%). In addition, interactions with the latent variable were found for age ( β = .12; p = .039), type of medical treatment ( β = .29; p = .009), as well as type and localization of tumor ( β = .20; p = .025). Conclusions: These results provided a better understand of psychological distress and cancer-related issues: each class represents a specific “profile” – moderated by age, type of medical treatment, and type and localization of tumor. These results recommend paying more attention to the specific profile expressed by the patient suggesting new ways to improve their quality of life.

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18

Devine, Fiona. "Class Analysis and the Stability of Class Relations." Sociology 32, no. 1 (February 1998): 23–42. http://dx.doi.org/10.1177/0038038598032001003.

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19

Andersen, Robert, Jacques A. Hagenaars, and Allan L. McCutcheon. "Applied Latent Class Analysis." Canadian Journal of Sociology / Cahiers canadiens de sociologie 28, no. 4 (2003): 584. http://dx.doi.org/10.2307/3341848.

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20

Gubbay, Jon, Guglielmo Carchedi, Stephen A. Resnick, and Richard D. Wolff. "Dialectics and Class Analysis." Contemporary Sociology 18, no. 1 (January 1989): 144. http://dx.doi.org/10.2307/2072014.

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21

Davidson, Mark, and Elvin Wyly. "Class analysis for whom?" City 17, no. 3 (June 2013): 299–311. http://dx.doi.org/10.1080/13604813.2013.795327.

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22

Schmiege, Sarah J., Katherine E. Masyn, and Angela D. Bryan. "Confirmatory Latent Class Analysis." Organizational Research Methods 21, no. 4 (December 18, 2017): 983–1001. http://dx.doi.org/10.1177/1094428117747689.

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Most applications of person-centered methodologies have relied on data-driven approaches to class enumeration. As person-centered analyses grow in popularity within organizational research, confirmatory approaches may be sought to provide more stringent theoretical tests and to formalize replication efforts. Confirmatory latent class analysis (LCA) is achieved through placement of modeling constraints, yet there is variation in the types of potential constraints and a lack of standardization in evaluating model fit in published work. This article provides a comprehensive framework for operationalizing model constraints and demonstrates confirmatory LCA via two illustrations: (a) a dual sample approach ( n = 1,366 and n = 1,367 in exploratory and validation samples, respectively) and (b) confirmatory testing of a hypothesized latent class structure ( n = 1,483). We depict operationalization of threshold boundary and/or equality constraints under both illustrations to generate a confirmatory latent class structure, and explain methods of model evaluation and comparison to alternative models. The confirmatory model was well supported under the dual sample approach, and partially supported under the hypothesis-driven approach. We discuss decision making at various points of model estimation and end with future methodological developments.

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23

Crompton, Rosemary. "Consumption and Class Analysis." Sociological Review 44, no. 1_suppl (May 1997): 113–32. http://dx.doi.org/10.1111/j.1467-954x.1996.tb03438.x.

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The growing significance of ‘consumption’ is one of the factors which has contributed to sociological debates relating to the ‘end of class’. In this chapter, the different strands of consumption-related critiques are identified, and their implications explored. It is argued that one of the most prominent disputes in the field—Saunders on ‘consumption classes’—in fact engaged with but one strand of ‘class analysis’, the ‘employment-aggregate’ approach. Subsequently, the chapter explores discussions relating to the impact of consumption growth and developments on class processes, particularly those arguments concerning the significance of the experience of employment for the development of (‘class’) identities. It is suggested that recent changes in the structuring and organization of employment might, indeed, have significant consequences for the shaping of identities.

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24

Devine, Fiona, and Mike Savage. "Conclusion: Renewing Class Analysis." Sociological Review 47, no. 2_suppl (October 1999): 184–99. http://dx.doi.org/10.1111/j.1467-954x.1999.tb03501.x.

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25

Asparouhov, Tihomir, Ellen L. Hamaker, and Bengt Muthén. "Dynamic Latent Class Analysis." Structural Equation Modeling: A Multidisciplinary Journal 24, no. 2 (December 21, 2016): 257–69. http://dx.doi.org/10.1080/10705511.2016.1253479.

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26

McCarthy, John E. "Topologies on the Smirnov class." Journal of Functional Analysis 104, no. 1 (February 1992): 229–41. http://dx.doi.org/10.1016/0022-1236(92)90096-2.

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27

Armah, Gabriel Kofi, Guangchun Luo, and Ke Qin. "A Deep Analysis of the Precision Formula for Imbalanced Class Distribution." International Journal of Machine Learning and Computing 4, no. 5 (2014): 417–22. http://dx.doi.org/10.7763/ijmlc.2014.v4.447.

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28

NOWAKOWSKA, Marzena, and Michał PAJĘCKI. "Applying latent class analysis in the identification of occupational accident patterns." Scientific Papers of Silesian University of Technology. Organization and Management Series 2020, no. 146 (2020): 339–55. http://dx.doi.org/10.29119/1641-3466.2020.146.25.

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Purpose: The objective of the study is to use selected data mining techniques to discover patterns of certain recurring mechanisms related to the occurrence of occupational accidents in relation to production processes. Design/methodology/approach: The latent class analysis (LCA) method was employed in the investigation. This statistical modeling technique enables discovering mutually exclusive homogenous classes of objects in a multivariate data set on the basis of observable qualitative variables, defining the class homogeneity in terms of probabilities. Due to a bilateral agreement, Statistics Poland provided individual record-level real data for the research. Then the data were preprocessed to enable the LCA model identification. Pilot studies were conducted in relation to occupational accidents registered in production plants in 2008-2017 in the Wielkopolskie voivodeship. Findings: Three severe accident patterns and two light accident patterns represented by latent classes were obtained. The classes were subjected to descriptive characteristics and labeling, using interpretable results presented in the form of probabilities classifying categories of observable variables, symptomatic for a given latent class. Research limitations/implications: The results from the pilot studies indicate the necessity to continue the research based on a larger data set along with the analysis development, particularly as regards selecting indicators for the latent class model characterization. Practical implications: The identification of occupational accident patterns related to the production process can play a vital role in the elaboration of efficient safety countermeasures that can help to improve the prevention and outcome mitigation of such accidents among workers. Social implications: Creating a safe work environment comprises the quality of life of workers, their families, thus affirming the enterprises' principles and values in the area of corporate social responsibility. Originality/value: The investigation showed that latent class analysis is a promising tool supporting the scientific research in discovering the patterns of occupational accidents. The proposed investigation approach indicates the importance for the research both in terms of the availability of non-aggregated occupational accident data as well as the type of value aggregation of the variables taken for the analysis.

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29

Souter, Amie L., Lori L. Pollock, and Dixie Hisley. "Inter-class def-use analysis with partial class representations." ACM SIGSOFT Software Engineering Notes 24, no. 5 (September 1999): 47–56. http://dx.doi.org/10.1145/381788.316178.

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30

An, Li-Xiang, and Xing-Gang He. "A class of spectral Moran measures." Journal of Functional Analysis 266, no. 1 (January 2014): 343–54. http://dx.doi.org/10.1016/j.jfa.2013.08.031.

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31

Coppel, W. A. "A simple class of quadratic systems." Journal of Differential Equations 64, no. 3 (September 1986): 275–82. http://dx.doi.org/10.1016/0022-0396(86)90075-6.

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32

Coppel, W. A. "A new class of quadratic systems." Journal of Differential Equations 92, no. 2 (August 1991): 360–72. http://dx.doi.org/10.1016/0022-0396(91)90054-d.

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33

So, Alvin Y., and Suwarsono. "Class Theory or Class Analysis? A Reexamination of Marx's Unfinished Chapter on Class." Critical Sociology 17, no. 2 (July 1990): 35–55. http://dx.doi.org/10.1177/089692059001700202.

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34

Dow, Geoff, and George Lafferty. "From Class Analysis to Class Politics: A Critique of Sociological Interpretations of Class." Australian and New Zealand Journal of Sociology 26, no. 1 (March 1990): 3–35. http://dx.doi.org/10.1177/144078339002600101.

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35

Shlyopkin, A. K., and A. G. Rubashkin. "A class of periodic groups." Algebra and Logic 44, no. 1 (January 2005): 65–71. http://dx.doi.org/10.1007/s10469-005-0008-x.

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36

Sukhanov, E. V., and A. M. Shur. "A class of formal languages." Algebra and Logic 37, no. 4 (July 1998): 270–77. http://dx.doi.org/10.1007/bf02671630.

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37

Crompton, Rosemary. "The Fragmentation of Class Analysis." British Journal of Sociology 47, no. 1 (March 1996): 56. http://dx.doi.org/10.2307/591116.

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38

Cepić, Dražen, Karin Doolan, and Danijela Dolenec. "Class Analysis as Systemic Critique." Communist and Post-Communist Studies 55, no. 2 (June 1, 2022): 104–19. http://dx.doi.org/10.1525/j.postcomstud.2022.55.2.104.

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This article focuses on the role of class analysis in envisioning a better world, in both the past and the present. It critically reflects on class research conducted in the second half of the 20th century in Yugoslavia, and contemporary class research from selected countries of former Yugoslavia, in order to explore the place that class analysis as systemic critique occupied and occupies in a socialist and capitalist context. This approach is informed by Wright’s (2015) evaluation of different forms of class analysis through the game metaphor. According to Wright, whereas Marxist class analysis questions “what game to play,” Weberian class analysis engages with “the rules of the game” and Durkheimian class analysis examines “moves in the game.” Our historical case study of Yugoslav scholarship on class during state socialism illustrates that, despite its role in sanctifying the status quo, class analysis also drew on both Marxism and Weberian inspired life-chances research as tools for systemic critique. On the other hand, our review of post-Yugoslav class research suggests that, currently, class analysis as an instrument for the critique of capitalism is not prominent. Indeed, in contrast to the late Yugoslav period in which sociology engaged class analysis in order to question what game should be played, the post-socialist 1990s and 2000s brought a silencing of Marxist left critique, while sociologists transformed their research into what Wright (2015) would describe as struggles over the rule of the game: problematizing the variety of capitalism that emerged in post-socialism rather than capitalism itself.

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39

Wright, Erik Olin. "The Class Analysis of Poverty." International Journal of Health Services 25, no. 1 (January 1995): 85–100. http://dx.doi.org/10.2190/wyrm-630n-8m6v-7851.

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To understand more fully the nature of poverty it must be viewed as the result, in part, of inherent features of the social system. The author describes four general approaches to explaining poverty: poverty as a result of inherent individual attributes, as the by-product of contingent individual characteristics, as a by-product of social causes, and as a result of inherent properties of the social system. He then elaborates a class exploitation analysis of poverty by explaining how economic oppression, economic exploitation, and class generate a social system in which poverty plays a crucial functional role. The general problem of poverty must be broken down into two subproblems: poverty generated inside exploitative relations (the working poor) and poverty generated by nonexploitative oppression (the underclass). A class analysis of poverty argues that significant numbers of privileged people have a strong, positive material interest in maintaining poverty. Poverty can be reduced in the United States only through popular mobilization of pressure that challenges the power of the dominant classes.

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40

Payne, Geoff. "Investing in Class Analysis Futures." Sociology 30, no. 2 (May 1996): 339–54. http://dx.doi.org/10.1177/0038038596030002008.

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41

Iosifidis, Alexandros. "Probabilistic Class-Specific Discriminant Analysis." IEEE Access 8 (2020): 183847–55. http://dx.doi.org/10.1109/access.2020.3029514.

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42

Dunn, Marvin G. "Liberation Theology and Class Analysis." Latin American Perspectives 13, no. 3 (July 1986): 59–70. http://dx.doi.org/10.1177/0094582x8601300304.

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43

Grusky, David B., and Jesper B. Sørensen. "Can Class Analysis Be Salvaged?" American Journal of Sociology 103, no. 5 (March 1998): 1187–234. http://dx.doi.org/10.1086/231351.

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44

YANG, MIIN-SHEN, and HWEI-MING CHEN. "FUZZY CLASS LOGISTIC REGRESSION ANALYSIS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12, no. 06 (December 2004): 761–80. http://dx.doi.org/10.1142/s0218488504003193.

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Distribution mixtures are used as models to analyze grouped data. The estimation of parameters is an important step for mixture distributions. The latent class model is generally used as the analysis of mixture distributions for discrete data. In this paper, we consider the parameter estimation for a mixture of logistic regression models. We know that the expectation maximization (EM) algorithm was most used for estimating the parameters of logistic regression mixture models. In this paper, we propose a new type of fuzzy class model and then derive an algorithm for the parameter estimation of a fuzzy class logistic regression model. The effects of the explanatory variables on the response variables are described. The focus is on binary responses for the logistic regression mixture analysis with a fuzzy class model. An algorithm, called a fuzzy classification maximum likelihood (FCML), is then created. The mean squared error (MSE) based accuracy criterion for the FCML and EM algorithms to the parameter estimation of logistic regression mixture models are compared using the samples drawn from logistic regression mixtures of two classes. Numerical results show that the proposed FCML algorithm presents good accuracy and is recommended as a new tool for the parameter estimation of the logistic regression mixture models.

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45

Burris, Val. "New Directions in Class Analysis." Critical Sociology 15, no. 1 (April 1988): 57–66. http://dx.doi.org/10.1177/089692058801500104.

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46

Diskin, Jonathan. "Focusing and expanding class analysis." Rethinking Marxism 17, no. 1 (January 2005): 1–8. http://dx.doi.org/10.1080/0893569052000312827.

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47

Resnick, Stephen. "Class Analysis and Economic Development." Rethinking Marxism 19, no. 1 (January 2007): 24–29. http://dx.doi.org/10.1080/08935690601054449.

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48

Safri, Maliha. "Worker Cooperatives: A Class Analysis." Rethinking Marxism 23, no. 3 (July 2011): 329–33. http://dx.doi.org/10.1080/08935696.2011.583001.

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49

Resnick, Stephen, and Richard Wolff. "China Today and Class Analysis." Rethinking Marxism 3, no. 1 (March 1990): 157–64. http://dx.doi.org/10.1080/08935699008657906.

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50

Coughlin, L. B., J. R. Mcguigan, and N. G. Haddad. "Social class and semen analysis." Journal of Obstetrics and Gynaecology 23, no. 3 (January 1, 2003): 276–77. http://dx.doi.org/10.1080/01443610310000106136.

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Journal articles on the topic 'Class analysis'

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