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Relevant bibliographies by topics / Factor analysis

Academic literature on the topic 'Factor analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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Journal articles on the topic "Factor analysis"

1

Kratochvíl, Petr. "The determination of factors in linear models of factor analysis." Applications of Mathematics 35, no. 5 (1990): 350–55. http://dx.doi.org/10.21136/am.1990.104416.

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Majumdar, Dr Kakali. "Factor Analysis and Business Research." Indian Journal of Applied Research 1, no. 6 (October 1, 2011): 151–54. http://dx.doi.org/10.15373/2249555x/mar2012/52.

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Dr.G.Kalaivanan, Dr G. Kalaivanan, and B. Ussaima B.Ussaima. "Factors Attracting Children to View TV Commercials - A Study Using Factor Analysis." Indian Journal of Applied Research 4, no. 6 (October 1, 2011): 71–73. http://dx.doi.org/10.15373/2249555x/june2014/21.

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Gurčík, Ľ., and V. Jančíková. "Factor analysis of owners equity effectiveness." Agricultural Economics (Zemědělská ekonomika) 48, No. 5 (February 29, 2012): 229–32. http://dx.doi.org/10.17221/5308-agricecon.

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The paper shows methodical procedure and results of factor analysis of owners equity effectiveness in form of quantification and determination of analytical indicators of their pyramidal system. The analysis is realised through comparison of aggregated values of two groups of enterprises (per 30 each group) for the period 1998–2000. First group of enterprises is in the first third of soil price groups in Slovakia (cheapest soil) and the second group in the third of price group of the most expensive soil.

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Raghuvanshi, Monika. "Attitude Exploration Using Factor Analysis Technique." unibulletin 5, no. 1-2 (December 1, 2016): 13–25. http://dx.doi.org/10.22521/unibulletin.2016.512.2.

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Maman, Abdurrohman, and Marsus Soffan. "Factor Analysis for Slow Budget Realization." INTERNATIONAL JOURNAL OF INNOVATION AND ECONOMIC DEVELOPMENT 3, no. 1 (2017): 28–50. http://dx.doi.org/10.18775/ijied.1849-7551-7020.2015.31.2002.

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The government of Indonesia has long experienced an uneven pattern of budget realization. Our budget realization is characterized by small absorption in the first three-quarters and then piled up in the last quarter. An increase in spending at the end of the year eventually led to the quality of work on the national economy, which is not considered optimal. Through factor analysis, the researchers reviewed what factors are causing slow realization of the budget, especially for spending unit in the working area of KPPN Jakarta II. Several studies have been conducted to determine the problem, including Herriyanto (2012), BKF, LPEM-UI and IBRD (2012), Siswanto and Rahayu (2010), Miliasih (2012), Widjanarko (2013), and Fitriany (2015). Based on the factor analysis that has been conducted, it was found six factors that often slow down the realization of central government expenditure, especially for spending unit in working area of KPPN Jakarta II. The six factors include coordination, organizational culture, competence, technical constraints, administrative, and document. These six factors are derived from 27 indicators that were processed through the standard factor analysis, i.e. correlation between variables Kaiser Mayer Olkin (KMO), variables distribution and rotation of factors.

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Jones, Roger. "Factor Analysis." British Journal of General Practice 68, no. 674 (August 30, 2018): 403. http://dx.doi.org/10.3399/bjgp18x698417.

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Armstrong, Diane. "Factor analysis." Nursing Standard 14, no. 48 (August 16, 2000): 25. http://dx.doi.org/10.7748/ns.14.48.25.s36.

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Robinson, Lawrence R., Deborah E. Rubner, Patricia W. Wahl, Wilfred Y. Fujimoto, and Walter C. Stolov. "FACTOR ANALYSIS." American Journal of Physical Medicine & Rehabilitation 71, no. 1 (February 1992): 22–27. http://dx.doi.org/10.1097/00002060-199202000-00006.

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Gregson, Ken. "Factor analysis." Work Study 42, no. 1 (January 1993): 10–11. http://dx.doi.org/10.1108/eum0000000002688.

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Dissertations / Theses on the topic "Factor analysis"

1

Cheng, Wei. "Factor Analysis for Stock Performance." Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-050405-180040/.

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Conti, Gabriella, Sylvia Frühwirth-Schnatter, James J. Heckman, and Rémi Piatek. "Bayesian exploratory factor analysis." Elsevier, 2014. http://dx.doi.org/10.1016/j.jeconom.2014.06.008.

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This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates from a high dimensional set of psychological measurements. (authors' abstract)

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關志威 and Chi-wai Kwan. "Influential observations in factor analysis." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B29803895.

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Kwan, Chi-wai. "Influential observations in factor analysis /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19003110.

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Cool, Deborah E. "Characterization of the human factor XII (Hageman factor) CDNA and the gene." Thesis, University of British Columbia, 1987. http://hdl.handle.net/2429/26980.

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A human liver cDNA library was screened by colony hybridization with two mixtures of synthetic oligodeoxyribonucleotides as probes. These oligonucleotides encoded regions of β-factor Xlla as predicted from the amino acid sequence. Four positive clones were isolated that contained DNA coding for most of factor XII mRNA. A second human liver cDNA library was screened by colony hybridization with ³²P-labeled cDNA clones obtained from the first screen and two identical clones were isolated. DNA sequence analysis of these overlapping clones showed that they contained DNA coding for the signal peptide sequence, the complete amino acid sequence of plasma factor XII, a TGA stop codon, a 3' untranslated region of 150 nucleotides, and a poly A⁺ tail. The cDNA sequence predicts that plasma factor XII consists of 596 amino acid residues. Within the predicted amino acid sequence of factor XII, were identified three peptide bonds that are cleaved by kallikrein during the formation of β-factor Xlla. Comparison of the structure of factor XII with other proteins revealed extensive sequence identity with regions of tissue-type plasminogen activator (the epidermal growth factor-like region and the kringle region) and fibronectin (type I and type II homologies). As the type II region of fibronectin contains a collagen-binding site, the homologous region in factor XII may be responsible for the binding of factor XII to collagen. The carboxyl-terminal region of factor XII shares considerable amino acid sequence homology with other serine proteases including trypsin and many clotting factors. A human genomic phage library was screened by using a human factor XII cDNA as ahybridization probe. Two overlapping phage clones were isolated which contain the entire human factor XII gene. DNA sequence and restriction enzyme analysis of the clones indicate that the gene is approximately 12 kbp in size and is comprised of 13 introns and 14 exons. Exons 3 through 14 are contained in a genomic region of only 4.2 kbp with introns ranging in size from 80 to 554 bp. The multiple regions found in the coding sequence of FXII that are homologous to putative domains in fibronectin and tissue-type plasminogen activator are contained on separate exons in the factor XII gene. The intron/exon gene organization is similar to the serine protease gene family of plasminogen activators and not to the clotting factor family. Analysis of the 5' flanking region of the gene shows that it does not contain the typical TATA and CAAT sequences found in other genes. This is consistent with the finding that transcription of the gene is initiated at multiple start sites.
Medicine, Faculty of
Biochemistry and Molecular Biology, Department of
Graduate

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Khosla, Nitin, and n/a. "Dimensionality Reduction Using Factor Analysis." Griffith University. School of Engineering, 2006. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20061010.151217.

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In many pattern recognition applications, a large number of features are extracted in order to ensure an accurate classification of unknown classes. One way to solve the problems of high dimensions is to first reduce the dimensionality of the data to a manageable size, keeping as much of the original information as possible and then feed the reduced-dimensional data into a pattern recognition system. In this situation, dimensionality reduction process becomes the pre-processing stage of the pattern recognition system. In addition to this, probablility density estimation, with fewer variables is a simpler approach for dimensionality reduction. Dimensionality reduction is useful in speech recognition, data compression, visualization and exploratory data analysis. Some of the techniques which can be used for dimensionality reduction are; Factor Analysis (FA), Principal Component Analysis(PCA), and Linear Discriminant Analysis(LDA). Factor Analysis can be considered as an extension of Principal Component Analysis. The EM (expectation maximization) algorithm is ideally suited to problems of this sort, in that it produces maximum-likelihood (ML) estimates of parameters when there is a many-to-one mapping from an underlying distribution to the distribution governing the observation, conditioned upon the obervations. The maximization step then provides a new estimate of the parameters. This research work compares the techniques; Factor Analysis (Expectation-Maximization algorithm based), Principal Component Analysis and Linear Discriminant Analysis for dimensionality reduction and investigates Local Factor Analysis (EM algorithm based) and Local Principal Component Analysis using Vector Quantization.

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Khosla, Nitin. "Dimensionality Reduction Using Factor Analysis." Thesis, Griffith University, 2006. http://hdl.handle.net/10072/366058.

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In many pattern recognition applications, a large number of features are extracted in order to ensure an accurate classification of unknown classes. One way to solve the problems of high dimensions is to first reduce the dimensionality of the data to a manageable size, keeping as much of the original information as possible and then feed the reduced-dimensional data into a pattern recognition system. In this situation, dimensionality reduction process becomes the pre-processing stage of the pattern recognition system. In addition to this, probablility density estimation, with fewer variables is a simpler approach for dimensionality reduction. Dimensionality reduction is useful in speech recognition, data compression, visualization and exploratory data analysis. Some of the techniques which can be used for dimensionality reduction are; Factor Analysis (FA), Principal Component Analysis(PCA), and Linear Discriminant Analysis(LDA). Factor Analysis can be considered as an extension of Principal Component Analysis. The EM (expectation maximization) algorithm is ideally suited to problems of this sort, in that it produces maximum-likelihood (ML) estimates of parameters when there is a many-to-one mapping from an underlying distribution to the distribution governing the observation, conditioned upon the obervations. The maximization step then provides a new estimate of the parameters. This research work compares the techniques; Factor Analysis (Expectation-Maximization algorithm based), Principal Component Analysis and Linear Discriminant Analysis for dimensionality reduction and investigates Local Factor Analysis (EM algorithm based) and Local Principal Component Analysis using Vector Quantization.
Thesis (Masters)
Master of Philosophy (MPhil)
School of Engineering
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8

Wang, Jing. "Analogy Between Two Approaches to Separately Identify Specific Factors in Factor Analysis." Bowling Green State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1182784851.

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Wu, Amery Dai Ling. "Pratt's importance measures in factor analysis : a new technique for interpreting oblique factor models." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2333.

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This dissertation introduces a new method, Pratt's measure matrix, for interpreting multidimensional oblique factor models in both exploratory and confirmatory contexts. Overall, my thesis, supported by empirical evidence, refutes the currently recommended and practiced methods for understanding an oblique factor model; that is, interpreting the pattern matrix or structure matrix alone or juxtaposing both without integrating the information. Chapter Two reviews the complexities of interpreting a multidimensional factor solution due to factor correlation (i.e., obliquity). Three major complexities highlighted are (1) the inconsistency between the pattern and structure coefficients, (2) the distortion of additive properties, and (3) the inappropriateness of the traditional cut-off rules as being "meaningful". Chapter Three provides the theoretical rationale for adapting Pratt's importance measures from their use in multiple regression to that of factor analysis. The new method is demonstrated and tested with both continuous and categorical data in exploratory factor analysis. The results show that Pratt's measures are applicable to factor analysis and are able to resolve three interpretational complexities arising from factor obliquity. In the context of confirmatory factor analysis, Chapter Four warns researchers that a structure coefficient could be entirely spurious due to factor obliquity as well as zero constraint on its corresponding pattern coefficient. Interpreting such structure coefficients as Graham et al. (2003) suggested can be problematic. The mathematically more justified method is to transform the pattern and structure coefficients into Pratt's measures. The last chapter describes eight novel contributions in this dissertation. The new method is the first attempt ever at ordering the importance of latent variables for multivariate data. It is also the first attempt at demonstrating and explicating the existence, mechanism, and implications of the suppression effect in factor analyses. Specifically, the new method resolves the three interpretational problems due to factor obliquity, assists in identifying a better-fitting exploratory factor model, proves that a structure coefficient in a confirmatory factor analysis with a zero pattern constraint is entirely spurious, avoids the debate over the choice of oblique and orthogonal factor rotation, and last but not least, provides a tool for consolidating the role off actors as the underlying causes.

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Lo, Siu-ming, and 盧小皿. "Factor analysis for ranking data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B30162464.

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Books on the topic "Factor analysis"

1

Bánkövi, Gy. Dynamic factor analysis. Budapest: Karl Marx University of Economics, Dept. of Mathematics and Computer Sciences, 1986.

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Theodore, Wegener Duane, ed. Exploratory factor analysis. Oxford: Oxford University Press, 2012.

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Confirmatory factor analysis. New York, N.Y: Oxford University Press, 2009.

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Foundations of factor analysis. 2nd ed. Boca Raton: CRC Press, 2010.

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Malinowski, Edmund R. Factor analysis in chemistry. 2nd ed. New York: Wiley, 1991.

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Factor analysis in chemistry. 3rd ed. New York: Wiley, 2002.

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Mulaik, Stanley A. Foundations of factor analysis. 2nd ed. Boca Raton: Chapman & Hall/CRC, 2010.

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Malinowski, Edmund R. Factor analysis in chemistry. Malabar, Fla: R.E. Krieger Pub. Co., 1989.

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Factor analysis and related methods. Hillsdale, N.J: Lawrence Erlbaum Associates, 1985.

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S, Lewis-Beck Michael, ed. Factor analysis and related techniques. London: Sage, 1994.

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Book chapters on the topic "Factor analysis"

1

Backhaus, Klaus, Bernd Erichson, Sonja Gensler, Rolf Weiber, and Thomas Weiber. "Factor Analysis." In Multivariate Analysis, 381–450. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-32589-3_7.

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Backhaus, Klaus, Bernd Erichson, Sonja Gensler, Rolf Weiber, and Thomas Weiber. "Factor Analysis." In Multivariate Analysis, 381–452. Wiesbaden: Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-40411-6_7.

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Sarstedt, Marko, and Erik Mooi. "Factor Analysis." In Springer Texts in Business and Economics, 235–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-53965-7_8.

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Cleff, Thomas. "Factor Analysis." In Applied Statistics and Multivariate Data Analysis for Business and Economics, 433–46. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17767-6_13.

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Härdle, Wolfgang Karl, and Zdeněk Hlávka. "Factor Analysis." In Multivariate Statistics, 205–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-36005-3_12.

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Acton, Ciaran, Robert Miller, John Maltby, and Deirdre Fullerton. "Factor Analysis." In SPSS for Social Scientists, 241–55. London: Macmillan Education UK, 2009. http://dx.doi.org/10.1007/978-1-137-01390-3_11.

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Miller, Robert L., Ciaran Acton, Deirdre A. Fullerton, John Maltby, and Jo Campling. "Factor Analysis." In SPSS for Social Scientists, 174–85. London: Macmillan Education UK, 2002. http://dx.doi.org/10.1007/978-0-230-62968-4_10.

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Franzen, Michael D. "Factor Analysis." In Encyclopedia of Clinical Neuropsychology, 1013. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-0-387-79948-3_1195.

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Härdle, Wolfgang, and Léopold Simar. "Factor Analysis." In Applied Multivariate Statistical Analysis, 275–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05802-2_10.

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Franzen, Michael. "Factor Analysis." In Encyclopedia of Clinical Neuropsychology, 1. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56782-2_1195-2.

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Conference papers on the topic "Factor analysis"

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Vargas, Francisco, Kamen Brestnichki, Alex Papadopoulos Korfiatis, and Nils Hammerla. "Multilingual Factor Analysis." In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/p19-1170.

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Yilmaz, Yasin, and Alfred O. Hero. "Multimodal factor analysis." In 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2015. http://dx.doi.org/10.1109/mlsp.2015.7324340.

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Thaker, Khushboo, Paulo Carvalho, and Kenneth Koedinger. "Comprehension Factor Analysis." In LAK19: The 9th International Learning Analytics & Knowledge Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3303772.3303817.

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ZENG, Huilu, Zhinong LI, and Zewen ZHOU. "Comparative Study of Complex Parallel Factor Analysis and Parallel Factor Analysis." In 2019 Prognostics and System Health Management Conference (PHM-Qingdao). IEEE, 2019. http://dx.doi.org/10.1109/phm-qingdao46334.2019.8942968.

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Melgaard, D. K., A. J. Scholand, and K. W. Larson. "Scene kinetics mitigation using factor analysis with derivative factors." In SPIE Optical Engineering + Applications, edited by Andrew G. Tescher. SPIE, 2010. http://dx.doi.org/10.1117/12.863950.

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Calvino, Aida, Palmira Aldeguer, and Josep Domingo-Ferrer. "Factor Analysis for Anonymization." In 2017 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2017. http://dx.doi.org/10.1109/icdmw.2017.139.

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Manning, Jeremy R., Rajesh Ranganath, Waitsang Keung, Nicholas B. Turk-Browne, Jonathan D. Cohen, Kenneth A. Norman, and David M. Blei. "Hierarchical topographic factor analysis." In 2014 International Workshop on Pattern Recognition in Neuroimaging (PRNI). IEEE, 2014. http://dx.doi.org/10.1109/prni.2014.6858530.

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Giannakopoulos, Xavier. "Nonlinear dynamical factor analysis." In The twentieth international workshop on bayesian inference and maximum entropy methods in science and engineering. AIP, 2001. http://dx.doi.org/10.1063/1.1381895.

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Zhang, Yizhe, Yue Zhao, Lawrence David, Ricardo Henao, and Lawrence Carin. "Dynamic Poisson Factor Analysis." In 2016 IEEE 16th International Conference on Data Mining (ICDM). IEEE, 2016. http://dx.doi.org/10.1109/icdm.2016.0186.

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Morariu, Vlad I., Ejaz Ahmed, Venkataraman Santhanam, David Harwood, and Larry S. Davis. "Composite Discriminant Factor analysis." In 2014 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2014. http://dx.doi.org/10.1109/wacv.2014.6836052.

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Reports on the topic "Factor analysis"

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Piatek, Rémi, Gabriella Conti, James Heckman, and Sylvia Frühwirth-Schnatter. Bayesian exploratory factor analysis. Cemmap, July 2014. http://dx.doi.org/10.1920/wp.cem.2014.3014.

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Electrotek Concepts. Industrial Power Factor Analysis Guidebook. Office of Scientific and Technical Information (OSTI), March 1995. http://dx.doi.org/10.2172/654078.

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Gibbons, Robert D., Donald R. Hedeker, and R. D. Bock. Full-Information Item Bi-Factor Analysis. Fort Belvoir, VA: Defense Technical Information Center, July 1990. http://dx.doi.org/10.21236/ada229346.

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M. Wasiolek. NOMINAL PERFORMANCE BIOSPHERE DOSE CONVERSION FACTOR ANALYSIS. Office of Scientific and Technical Information (OSTI), August 2005. http://dx.doi.org/10.2172/883410.

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Wasiolek, M. A. Nominal Performance Biosphere Dose Conversion Factor Analysis. Office of Scientific and Technical Information (OSTI), July 2003. http://dx.doi.org/10.2172/836521.

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Wasiolek, Maryla. Nominal Performance Biosphere Dose Conversion Factor Analysis. Office of Scientific and Technical Information (OSTI), December 2000. http://dx.doi.org/10.2172/837095.

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M. Wasiolek. Disruptive Event Biosphere Doser Conversion Factor Analysis. Office of Scientific and Technical Information (OSTI), December 2000. http://dx.doi.org/10.2172/837096.

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M. Wasiolek. Nominal Performance Biosphere Dose Conversion Factor Analysis. Office of Scientific and Technical Information (OSTI), September 2004. http://dx.doi.org/10.2172/838324.

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M. Wasiolek. Disruptive Event Biosphere Dose Conversion Factor Analysis. Office of Scientific and Technical Information (OSTI), September 2004. http://dx.doi.org/10.2172/838325.

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Nadiri, M. Ishaq, and Ingmar Prucha. Dynamic Factor Demand Models and Productivity Analysis. Cambridge, MA: National Bureau of Economic Research, April 1999. http://dx.doi.org/10.3386/w7079.

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