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Articoli di riviste sul tema "Factor analysis"

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Autore: Grafiati
Pubblicato: 4 giugno 2021
Ultima modifica: 25 maggio 2024

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1

Kratochvíl, Petr. "The determination of factors in linear models of factor analysis". Applications of Mathematics 35, n. 5 (1990): 350–55. http://dx.doi.org/10.21136/am.1990.104416.

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2

Majumdar, Dr Kakali. "Factor Analysis and Business Research". Indian Journal of Applied Research 1, n. 6 (1 ottobre 2011): 151–54. http://dx.doi.org/10.15373/2249555x/mar2012/52.

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3

Dr.G.Kalaivanan, Dr G. Kalaivanan, e B. Ussaima B.Ussaima. "Factors Attracting Children to View TV Commercials - A Study Using Factor Analysis". Indian Journal of Applied Research 4, n. 6 (1 ottobre 2011): 71–73. http://dx.doi.org/10.15373/2249555x/june2014/21.

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4

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

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Abstract (sommario):
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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5

Raghuvanshi, Monika. "Attitude Exploration Using Factor Analysis Technique". unibulletin 5, n. 1-2 (1 dicembre 2016): 13–25. http://dx.doi.org/10.22521/unibulletin.2016.512.2.

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6

Maman, Abdurrohman, e Marsus Soffan. "Factor Analysis for Slow Budget Realization". INTERNATIONAL JOURNAL OF INNOVATION AND ECONOMIC DEVELOPMENT 3, n. 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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7

Jones, Roger. "Factor Analysis". British Journal of General Practice 68, n. 674 (30 agosto 2018): 403. http://dx.doi.org/10.3399/bjgp18x698417.

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8

Armstrong, Diane. "Factor analysis". Nursing Standard 14, n. 48 (16 agosto 2000): 25. http://dx.doi.org/10.7748/ns.14.48.25.s36.

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9

Robinson, Lawrence R., Deborah E. Rubner, Patricia W. Wahl, Wilfred Y. Fujimoto e Walter C. Stolov. "FACTOR ANALYSIS". American Journal of Physical Medicine & Rehabilitation 71, n. 1 (febbraio 1992): 22–27. http://dx.doi.org/10.1097/00002060-199202000-00006.

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10

Gregson, Ken. "Factor analysis". Work Study 42, n. 1 (gennaio 1993): 10–11. http://dx.doi.org/10.1108/eum0000000002688.

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11

Stewart, David, James Barnes, Joseph Cote, Robert Cudeck e Edward Malthouse. "Factor Analysis". Journal of Consumer Psychology 10, n. 1-2 (2001): 75–82. http://dx.doi.org/10.1207/s15327663jcp1001&2_07.

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12

Fearn, Tom. "Factor Analysis". NIR news 24, n. 8 (dicembre 2013): 26–27. http://dx.doi.org/10.1255/nirn.1409.

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13

Fukunaka, Kosuke, e Hideki Toyoda. "Analysis of Unique Factor Structures in Factor Analysis". Japanese Journal of Personality 20, n. 2 (2011): 98–109. http://dx.doi.org/10.2132/personality.20.98.

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14

Soewin, E., e T. Chinda. "Factors affecting construction performance: exploratory factor analysis". IOP Conference Series: Earth and Environmental Science 140 (aprile 2018): 012102. http://dx.doi.org/10.1088/1755-1315/140/1/012102.

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15

Jun Li e Dacheng Tao. "Exponential Family Factors for Bayesian Factor Analysis". IEEE Transactions on Neural Networks and Learning Systems 24, n. 6 (giugno 2013): 964–76. http://dx.doi.org/10.1109/tnnls.2013.2245341.

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16

Devasena, Dr S. Valli. "Physical Evidence –A Part of Customer Satisfaction An Analysis Using Factor Analysis". Indian Journal of Applied Research 3, n. 12 (1 ottobre 2011): 87–90. http://dx.doi.org/10.15373/2249555x/dec2013/23.

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17

G., Ramesh Pandi. "Attitude of Retailers towards Green Marketing Practices: Analysis through Factor Analysis and Cluster Analysis Techniques". International Journal of Psychosocial Rehabilitation 24, n. 5 (20 aprile 2020): 3595–608. http://dx.doi.org/10.37200/ijpr/v24i5/pr202068.

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18

Bentler, Peter M., e Jan de Leeuw. "Factor Analysis via Components Analysis". Psychometrika 76, n. 3 (1 giugno 2011): 461–70. http://dx.doi.org/10.1007/s11336-011-9217-5.

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19

Boyanov, Petar. "IMPLEMENTATION OF FACTOR ANALYSIS IN THE OPTICAL MANIFACTURING". Journal Scientific and Applied Research 8, n. 1 (11 ottobre 2015): 16–20. http://dx.doi.org/10.46687/jsar.v8i1.173.

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Abstract (sommario):
A model of factor analysis is developed in the process of designing of complex optical systems. Factor analysis is indispensable in designing of photometric and spectrophotometric optical-electronic devices which consist of entrance-scanning system by space, lens with inner focusing, collimator objective which ensure the entering of a parallel bundle of rays over a dispersing diffraction grate over a wave length, a chamber lens and respectively, a sensor. These and similar optical systems require higher degree of elimination of chromatic aberration, etc. which ensures good quality of the optical system and of the obtained results.
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20

Pattnaik, Sudhanshu. "An Empirical Investigation into the Factors Influencing While Purchasing Car in India-A Confirmatory Factor Analysis". Journal of Advanced Research in Dynamical and Control Systems 11, n. 10-SPECIAL ISSUE (25 ottobre 2019): 404–10. http://dx.doi.org/10.5373/jardcs/v11sp10/20192821.

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21

Onatski, Alexei, e Chen Wang. "Spurious Factor Analysis". Econometrica 89, n. 2 (2021): 591–614. http://dx.doi.org/10.3982/ecta16703.

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This paper draws parallels between the principal components analysis of factorless high‐dimensional nonstationary data and the classical spurious regression. We show that a few of the principal components of such data absorb nearly all the data variation. The corresponding scree plot suggests that the data contain a few factors, which is corroborated by the standard panel information criteria. Furthermore, the Dickey–Fuller tests of the unit root hypothesis applied to the estimated “idiosyncratic terms” often reject, creating an impression that a few factors are responsible for most of the nonstationarity in the data. We warn empirical researchers of these peculiar effects and suggest to always compare the analysis in levels with that in differences.
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22

Gampp, Harald, Marcel Maeder, Charles J. Meyer e Andreas D. Zuberbuehler. "Evolving Factor Analysis". Comments on Inorganic Chemistry 6, n. 1 (febbraio 1987): 41–60. http://dx.doi.org/10.1080/02603598708081852.

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23

Schostack, K., P. Parekh, S. Patel e E. R. Malinowski. "Evolutionary factor-analysis". Journal of Research of the National Bureau of Standards 93, n. 3 (maggio 1988): 256. http://dx.doi.org/10.6028/jres.093.035.

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24

FERKETICH, SANDRA, e MARY MULLER. "Factor Analysis Revisited". Nursing Research 39, n. 1 (gennaio 1990): 59. http://dx.doi.org/10.1097/00006199-199001000-00012.

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25

Reio, Thomas G., e Brad Shuck. "Exploratory Factor Analysis". Advances in Developing Human Resources 17, n. 1 (28 novembre 2014): 12–25. http://dx.doi.org/10.1177/1523422314559804.

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26

Muthén, Bengt O. "Tobit factor analysis†". British Journal of Mathematical and Statistical Psychology 42, n. 2 (novembre 1989): 241–50. http://dx.doi.org/10.1111/j.2044-8317.1989.tb00913.x.

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27

Attias, H. "Independent Factor Analysis". Neural Computation 11, n. 4 (1 maggio 1999): 803–51. http://dx.doi.org/10.1162/089976699300016458.

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Abstract (sommario):
We introduce the independent factor analysis (IFA) method for recovering independent hidden sources from their observed mixtures. IFA generalizes and unifies ordinary factor analysis (FA), principal component analysis (PCA), and independent component analysis (ICA), and can handle not only square noiseless mixing but also the general case where the number of mixtures differs from the number of sources and the data are noisy. IFA is a two-step procedure. In the first step, the source densities, mixing matrix, and noise covariance are estimated from the observed data by maximum likelihood. For this purpose we present an expectation-maximization (EM) algorithm, which performs unsupervised learning of an associated probabilistic model of the mixing situation. Each source in our model is described by a mixture of gaussians; thus, all the probabilistic calculations can be performed analytically. In the second step, the sources are reconstructed from the observed data by an optimal nonlinear estimator. A variational approximation of this algorithm is derived for cases with a large number of sources, where the exact algorithm becomes intractable. Our IFA algorithm reduces to the one for ordinary FA when the sources become gaussian, and to an EM algorithm for PCA in the zero-noise limit. We derive an additional EM algorithm specifically for noiseless IFA. This algorithm is shown to be superior to ICA since it can learn arbitrary source densities from the data. Beyond blind separation, IFA can be used for modeling multidimensional data by a highly constrained mixture of gaussians and as a tool for nonlinear signal encoding.
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28

Tolkoff, Max R., Michael E. Alfaro, Guy Baele, Philippe Lemey e Marc A. Suchard. "Phylogenetic Factor Analysis". Systematic Biology 67, n. 3 (7 agosto 2017): 384–99. http://dx.doi.org/10.1093/sysbio/syx066.

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29

Pison, Greet, Peter J. Rousseeuw, Peter Filzmoser e Christophe Croux. "Robust factor analysis". Journal of Multivariate Analysis 84, n. 1 (gennaio 2003): 145–72. http://dx.doi.org/10.1016/s0047-259x(02)00007-6.

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30

WATSON, ROGER. "Exploring factor analysis". Journal of Psychiatric and Mental Health Nursing 3, n. 1 (febbraio 1996): 75. http://dx.doi.org/10.1111/j.1365-2850.1996.tb00198.x.

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31

Manne, Rolf, Hailin Shen e Yizeng Liang. "Subwindow factor analysis". Chemometrics and Intelligent Laboratory Systems 45, n. 1-2 (gennaio 1999): 171–76. http://dx.doi.org/10.1016/s0169-7439(98)00101-4.

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32

Mason, Caroline, Marcel Maeder e Andrew Whitson. "Resolving Factor Analysis". Analytical Chemistry 73, n. 7 (aprile 2001): 1587–94. http://dx.doi.org/10.1021/ac991141q.

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33

Lewin-Koh, S. C. "Heteroscedastic factor analysis". Biometrika 90, n. 1 (1 marzo 2003): 85–97. http://dx.doi.org/10.1093/biomet/90.1.85.

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34

Srivastava, Sanvesh, Barbara E. Engelhardt e David B. Dunson. "Expandable factor analysis". Biometrika 104, n. 3 (16 giugno 2017): 649–63. http://dx.doi.org/10.1093/biomet/asx030.

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35

Emerson, Robert Wall. "Exploratory Factor Analysis". Journal of Visual Impairment & Blindness 111, n. 3 (maggio 2017): 301–2. http://dx.doi.org/10.1177/0145482x1711100313.

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36

Klami, Arto, Seppo Virtanen, Eemeli Leppaaho e Samuel Kaski. "Group Factor Analysis". IEEE Transactions on Neural Networks and Learning Systems 26, n. 9 (settembre 2015): 2136–47. http://dx.doi.org/10.1109/tnnls.2014.2376974.

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37

Ismail, K. "Unravelling factor analysis". Evidence-Based Mental Health 11, n. 4 (1 novembre 2008): 99–102. http://dx.doi.org/10.1136/ebmh.11.4.99.

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38

Keller, H. R., e D. L. Massart. "Evolving factor analysis". Chemometrics and Intelligent Laboratory Systems 12, n. 3 (1991): 209–24. http://dx.doi.org/10.1016/0169-7439(92)80002-l.

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39

Hong, Sungjin. "Warped factor analysis". Journal of Chemometrics 23, n. 7-8 (luglio 2009): 371–84. http://dx.doi.org/10.1002/cem.1231.

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40

Kosfeld, Reinhold, e Jørgen Lauridsen. "Factor analysis regression". Statistical Papers 49, n. 4 (24 novembre 2006): 653–67. http://dx.doi.org/10.1007/s00362-006-0036-1.

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41

Nakano, Takahiro, Tsuyoshi Yamada e Takahiko Nishijima. "Factor structural analysis of condition fluctuation by dynamic factor analysis". Taiikugaku kenkyu (Japan Journal of Physical Education, Health and Sport Sciences) 48, n. 4 (2003): 369–81. http://dx.doi.org/10.5432/jjpehss.kj00003390829.

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42

Adachi, Kohei. "SOME CONTRIBUTIONS TO DATA-FITTING FACTOR ANALYSIS WITH EMPIRICAL COMPARISONS TO COVARIANCE-FITTING FACTOR ANALYSIS ". Journal of the Japanese Society of Computational Statistics 25, n. 1 (2012): 25–38. http://dx.doi.org/10.5183/jjscs.1106001_197.

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43

M, Deepan, Deepa N, Rohini A, Malarkodi M e Indumathi VM. "Factors influencing mutual fund investment decisions-factor analysis". International Journal of Chemical Studies 8, n. 1 (1 gennaio 2020): 2106–9. http://dx.doi.org/10.22271/chemi.2020.v8.i1ae.8578.

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44

Le, Thu, Daniel Bolt, Eric Camburn, Peter Goff e Karl Rohe. "Latent Factors in Student–Teacher Interaction Factor Analysis". Journal of Educational and Behavioral Statistics 42, n. 2 (6 gennaio 2017): 115–44. http://dx.doi.org/10.3102/1076998616676407.

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Abstract (sommario):
Classroom interactions between students and teachers form a two-way or dyadic network. Measurements such as days absent, test scores, student ratings, or student grades can indicate the “quality” of the interaction. Together with the underlying bipartite graph, these values create a valued student–teacher dyadic interaction network. To study the broad structure of these values, we propose using interaction factor analysis (IFA), a recently developed statistical technique that can be used to investigate the hidden factors underlying the quality of student–teacher interactions. Our empirical study indicates there are latent teacher (i.e., teaching style) and student (i.e., preference for teaching style) types that influence the quality of interactions. Students and teachers of the same type tend to have more positive interactions, and those of differing types tend to have more negative interactions. IFA has the advantage of traditional factor analysis in that the types are not presupposed; instead, the types are identified by IFA and can be interpreted in post hoc analysis. Whereas traditional factor analysis requires one to observe all interactions, IFA performs well even when only a small fraction of potential interactions are actually observed.
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45

Parmet, Yisrael, Edna Schechtman e Michael Sherman. "Factor Analysis Revisited – How Many Factors are There?" Communications in Statistics - Simulation and Computation 39, n. 10 (29 ottobre 2010): 1893–908. http://dx.doi.org/10.1080/03610918.2010.524332.

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46

Uno, Kohei, Kohei Adachi e Nickolay T. Trendafilov. "Clustered Common Factor Exploration in Factor Analysis". Psychometrika 84, n. 4 (7 marzo 2019): 1048–67. http://dx.doi.org/10.1007/s11336-019-09666-5.

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47

Dykema, Kenneth J., Allan M. Sinclair e Roger R. Smith. "Values of the Pukánszky invariant in free group factors and the hyperfinite factor". Journal of Functional Analysis 240, n. 2 (novembre 2006): 373–98. http://dx.doi.org/10.1016/j.jfa.2006.03.026.

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48

Yong, An Gie, e Sean Pearce. "A Beginner’s Guide to Factor Analysis: Focusing on Exploratory Factor Analysis". Tutorials in Quantitative Methods for Psychology 9, n. 2 (1 ottobre 2013): 79–94. http://dx.doi.org/10.20982/tqmp.09.2.p079.

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49

Kaiser, Henry F., e Gerhard Derflinger. "Some Contrasts Between Maximum Likelihood Factor Analysis and Alpha Factor Analysis". Applied Psychological Measurement 14, n. 1 (marzo 1990): 29–32. http://dx.doi.org/10.1177/014662169001400103.

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50

R. Menaga, R. Menaga, e CHANDRASEKARAN S. CHANDRASEKARAN. S. "Adopting Marketing Strategies of Small Traders - From Factor Analysis". International Journal of Scientific Research 3, n. 6 (1 giugno 2012): 86–88. http://dx.doi.org/10.15373/22778179/june2014/32.

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