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Journal articles on the topic 'Factor Analysi'

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Published: 9 March 2023

Last updated: 14 March 2023

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1

Erawati, Teguh, and Sisilia Stefani Wuarlela. "Ukuran Perusahaan, Profitabilitas, Likuiditas, Pertumbuhan Laba Dan Kualitas Laba Pada Perusahaan Pertambangan Di Indonesia." Jurnal Literasi Akuntansi 2, no. 2 (August 5, 2022): 157–66. http://dx.doi.org/10.55587/jla.v2i2.62.

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Purpose: This study aims to prove the effect of firm size ,profitability, liquidity and earnings growth on earnings quality.the research sample is mining companies listed on the indonesia stck exchange (IDX) in 2017-2020 using purposive sampling. Method: sampling used sampling method and produced 80 sample data. Data analysi technques wit multiple linier analysis Finding: this study shows that firm size has no significant effect on earnings quality,profitability has a significant effect on earnings quality,liquidity has asignificant offect on earnings quality and earning rowth has no significant effect on earning quality. Novelty: The implications of this research are relted to earning quality.other financial statement informasion needs to the quality of earnings in the company.this shows that users of financial statements,especially investor, need to consider the liquidity factor when making decisions for invertors in affiliated companies

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2

Erawati, Teguh, and Sisilia Stefani Wuarlela. "Ukuran Perusahaan, Profitabilitas, Likuiditas, Pertumbuhan Laba Dan Kualitas Laba Pada Perusahaan Pertambangan Di Indonesia." Jurnal Literasi Akuntansi 2, no. 2 (August 5, 2022): 157–66. http://dx.doi.org/10.55587/jla.v2i2.62.

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Purpose: This study aims to prove the effect of firm size ,profitability, liquidity and earnings growth on earnings quality.the research sample is mining companies listed on the indonesia stck exchange (IDX) in 2017-2020 using purposive sampling. Method: sampling used sampling method and produced 80 sample data. Data analysi technques wit multiple linier analysis Finding: this study shows that firm size has no significant effect on earnings quality,profitability has a significant effect on earnings quality,liquidity has asignificant offect on earnings quality and earning rowth has no significant effect on earning quality. Novelty: The implications of this research are relted to earning quality.other financial statement informasion needs to the quality of earnings in the company.this shows that users of financial statements,especially investor, need to consider the liquidity factor when making decisions for invertors in affiliated companies

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3

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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4

Budi S, Surawan Setya. "PENGARUH INPUT PROSES RELATIONSHIP MARKETING TERHADAP OUTPUT PROSES RELATIONSHIP MARKETING." Kajian Bisnis STIE Widya Wiwaha 27, no. 2 (July 24, 2019): 190–206. http://dx.doi.org/10.32477/jkb.v27i2.349.

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This research use relationship marketing teory which applied 4 factor of input process understanding customer expectations, building service partnerships, empowiring employers, and total quality management, and output relationship marketing process: customer satisfaction and customer layalty. The objective of this research is to find out wich factors of input process that has more impact on the output process in relationship marketing. There are 60 trusted correspondencea from Grand Inna Malioboro Hotel customer whom at least have been stayed at the hotel three times. The method of collecting data in these research use questioners and Likert scale measuring instrument 5 points that will be tested by the instrument and analyzed by using regressision analysi the simultaneous test of variabel for the relationship marketing input s. The result of the instrument shows the items used are valid and reliable. It displays the outpout process passed the assumtions test, while the signifikacant relationship marketingto the output of relationtionship marketing, For the passive test of vareable input process relationship marketing with the ouput process relationship marketing shows all significant variables unless vareabel understands customer expectations does not have a significant effect on the process of output relationship marketing

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5

S, Surawan Setya Budi. "PENGARUH INPUT PROSES RELATIONSHIP MARKETING TERHADAP OUTPUT PROSES RELATIONSHIP MARKETING STUDI KASUS GRAND INNA MALIOBORO HOTEL & CONVENTION." Kajian Bisnis Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha 27, no. 2 (August 31, 2019): 190–206. http://dx.doi.org/10.32477/jkb.v27i2.62.

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This research use relationship marketing teory which applied 4 factor of input process understanding customer expectations, building service partnerships,empowiring employers, and total quality management, and output relationship marketing process: customer satisfaction and customer layalty. The objective of this research is to find out wich factors of input process that has more impact on the output process in relationship marketing. There are 60 trusted correspondencea from Grand Inna Malioboro Hotel customer whom at least have been stayed at the hotel three times. The method of collecting data in these research use questioners and Likert scale measuring instrument 5 points that will be tested by the instrument and analyzed by using regressision analysi the simultaneous test of variabel for the relationship marketing input s. The result of the instrument shows the items used are valid and reliable. It displays the outpout process passed the assumtions test, while the signifikacant relationship marketingto the output of relationtionship marketing, For the passive test of vareable input process relationship marketing with the ouput process relationship marketing shows all significant variables unless vareabel understands customer expectations does not have a significant effect on the process of output relationship marketing.

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6

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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7

Devica, Sadana. "PERSEPSI KONSUMEN TERHADAP FLASH SALE BELANJA ONLINE DAN PENGARUHNYA PADA KEPUTUSAN PEMBELIAN." Jurnal Bisnis Terapan 4, no. 1 (June 26, 2020): 47–56. http://dx.doi.org/10.24123/jbt.v4i1.2276.

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Penelitian ini bertujuan untuk mengetahui bagaimana persepsi konsumen terhadap program promosi dalam bentuk flash sale dan bagaimana pengaruhnya tehadap keputusan pembelian. Penelitian ini dilakukan pada 145 orang responden, sedangkan untuk teknik analisis data menggunakan confirmatory factor analysi (CFA) dan analisis regresi. Terdapat tujuh indikator persepsi konsumen yang diteliti, meliputi flash sale belanja online merupakan kesepakatan harga yang sangat menguntungkan konsumen, flash sale belanja online benar-benar menawarkan harga produk yang murah, produk yang ditawarkan pada saat flash sale belanja online tetap lebih murah meskipun di toko/ritel juga menawarkan promosi harga diskon, produk yang ditawarkan pada saat flash sale belanja online merupakan produk yang berkualitas, produk yang ditawarkan pada saat flash sale belanja online sangat beragam (bervariasi), berbelanja secara online pada saat flash sale sangat aman (dari aspek keaslian produk, metode pembayaran dan proses pengiriman), dan berbelanja secara online pada saat flash sale sangat mudah dan praktis. Hasil dari penelitian menunjukkan bahwa persepsi konsumen berpengaruh positif dan signifikan terhadap keputusan pembelian. Bahkan terkadang konsumen juga melakukan pembelian tanpa perencanaan terlebih dahulu (impulse buying) ketika mereka mengetahui terdapat promo flash sale.

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8

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, no. 10-SPECIAL ISSUE (October 25, 2019): 404–10. http://dx.doi.org/10.5373/jardcs/v11sp10/20192821.

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9

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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10

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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11

Álvarez Orozco, Dolores Guadalupe, Verónica Esperanza Ruiz Domínguez, and Nidia Isela Ramírez Manjarrez. "Codependencia laboral: análisis factorial confirmatorio." Investigación & Desarrollo 30, no. 02 (August 31, 2022): 167–90. http://dx.doi.org/10.14482/indes.30.2.158.7.

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El objetivo de esta investigación es validar un instrumento para medir la codependencia laboral, el método que se implementó fue un enfoque cuantitativo, de diseño no experimental transeccional, utilizando un análisis factorial confirmatorio basado en ecuaciones estructurales. Los resultados, mostraron que el modelo presentó un ajuste adecuado NC =1.86; ?=0.000; NFI=.979; IFI=.990; CFI=.990; MFI=.925 Y RMSEA=.68, se concluye que los factores relacionados con la falta de confianza y los comportamientos abusivos son los elementos que describen la codependencia en el ámbito laboral.

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12

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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13

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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14

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

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15

Jun Li and Dacheng Tao. "Exponential Family Factors for Bayesian Factor Analysis." IEEE Transactions on Neural Networks and Learning Systems 24, no. 6 (June 2013): 964–76. http://dx.doi.org/10.1109/tnnls.2013.2245341.

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16

Hong, Soo-Young. "Kinetics Comparison Analysis of X-Factor and O-Factor according to Long Iron Swing Motion of Golf Skilled and UnSkilled." Korean Journal of Sports Science 27, no. 6 (December 31, 2018): 1083–94. http://dx.doi.org/10.35159/kjss.2018.12.27.6.1083.

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17

Dykema, Kenneth J., Allan M. Sinclair, and Roger R. Smith. "Values of the Pukánszky invariant in free group factors and the hyperfinite factor." Journal of Functional Analysis 240, no. 2 (November 2006): 373–98. http://dx.doi.org/10.1016/j.jfa.2006.03.026.

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18

Devasena, Dr S. Valli. "Physical Evidence –A Part of Customer Satisfaction An Analysis Using Factor Analysis." Indian Journal of Applied Research 3, no. 12 (October 1, 2011): 87–90. http://dx.doi.org/10.15373/2249555x/dec2013/23.

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19

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, no. 1 (2012): 25–38. http://dx.doi.org/10.5183/jjscs.1106001_197.

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20

G., Ramesh Pandi. "Attitude of Retailers towards Green Marketing Practices: Analysis through Factor Analysis and Cluster Analysis Techniques." International Journal of Psychosocial Rehabilitation 24, no. 5 (April 20, 2020): 3595–608. http://dx.doi.org/10.37200/ijpr/v24i5/pr202068.

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21

M, Deepan, Deepa N, Rohini A, Malarkodi M, and Indumathi VM. "Factors influencing mutual fund investment decisions-factor analysis." International Journal of Chemical Studies 8, no. 1 (January 1, 2020): 2106–9. http://dx.doi.org/10.22271/chemi.2020.v8.i1ae.8578.

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22

Le, Thu, Daniel Bolt, Eric Camburn, Peter Goff, and Karl Rohe. "Latent Factors in Student–Teacher Interaction Factor Analysis." Journal of Educational and Behavioral Statistics 42, no. 2 (January 6, 2017): 115–44. http://dx.doi.org/10.3102/1076998616676407.

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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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23

Parmet, Yisrael, Edna Schechtman, and Michael Sherman. "Factor Analysis Revisited – How Many Factors are There?" Communications in Statistics - Simulation and Computation 39, no. 10 (October 29, 2010): 1893–908. http://dx.doi.org/10.1080/03610918.2010.524332.

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24

R. Menaga, R. Menaga, and CHANDRASEKARAN S. CHANDRASEKARAN. S. "Adopting Marketing Strategies of Small Traders - From Factor Analysis." International Journal of Scientific Research 3, no. 6 (June 1, 2012): 86–88. http://dx.doi.org/10.15373/22778179/june2014/32.

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25

Tao, Kunwang, Liang Wang, and Xinlin Qian. "Multi-factor Constrained Analysis Method for Geological Hazard Risk." International Journal of Engineering and Technology 8, no. 3 (March 2016): 198–201. http://dx.doi.org/10.7763/ijet.2016.v6.884.

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26

Tao, Kunwang, Liang Wang, and Xinlin Qian. "Multi-factor Constrained Analysis Method for Geological Hazard Risk." International Journal of Engineering and Technology 8, no. 3 (March 2016): 198–201. http://dx.doi.org/10.7763/ijet.2016.v8.884.

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27

Kim. "Preferences Factors Analysis for Car-sharing." Journal of the Korean Society of Civil Engineers 34, no. 4 (2014): 1241. http://dx.doi.org/10.12652/ksce.2014.34.4.1241.

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28

Das, Dr Daisy, and Dr Ratul Mahanta. "Liveability in Guwahati: A Factor Analytic Approach." Indian Journal of Applied Research 1, no. 7 (October 1, 2011): 56–58. http://dx.doi.org/10.15373/2249555x/apr2012/18.

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29

Liew, Sharifah, Rizati Hamidun, and Azhar Hamzah. "Factors that cause anger among motorcyclists: exploratory factor analysis." International Journal of Public Health Science (IJPHS) 11, no. 3 (September 1, 2022): 893. http://dx.doi.org/10.11591/ijphs.v11i3.21079.

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Anger arising while riding a motorcycle is becoming a common problem on the road and it may be caused by many factors. The main objective of this study is to examine factors that could provoke anger among motorcyclists while riding. This study was based on self-reported questionnaires and comprised of 407 motorcyclists who owned valid riding licenses. exploratory factor analysis (EFA) indicated that the sample fit was suitable for factor analysis. In total, eight factors (unsafe or inappropriate actions, rude or sluggish actions, road conditions and design, police or enforces presence, illegal actions on the road, hazards on the road, rainy condition and obstruction on the road) with eigenvalues above 1.0 and 59 variables that ranged from 0.411 to 0.803, were generated. Cronbach’s Alpha analysis indicated that all factors’ values were greater than 0.60 and mean for all factors were reliable and had strength of consistency. This study provides the preliminary findings of factors and variables that could provoke riding anger among motorcyclists while on the road in the Malaysian context. The factors causing riding anger in this study have a similarity with driving anger factors that were developed by other countries and this indicates the validity of riding anger factors to provoke anger among motorcyclists on the road.

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30

Milošević, Isidora, Ivan Mihajlović, and Anđelka Stojanović. "Dominant factors of SMEs failure: Multigroup confirmatory factor analysis." Serbian Journal of Management 14, no. 2 (2019): 345–60. http://dx.doi.org/10.5937/sjm14-23536.

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31

Kukreja, Vinay, Sakshi Sakshi@chitkara.edu.in, Amandeep Kaur, and Arun Aggarwal. "What Factors Impact Online Education? A Factor Analysis Approach." Journal of Engineering Education Transformations 34 (January 31, 2021): 365. http://dx.doi.org/10.16920/jeet/2021/v34i0/157180.

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32

Lipovetsky, Stan. "Factor analysis by limited scales: which factors to analyze?" Journal of Modern Applied Statistical Methods 16, no. 1 (May 1, 2017): 233–45. http://dx.doi.org/10.22237/jmasm/1493597520.

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33

Bandalos, Deborah L. "Factors Influencing Cross-Validation of Confirmatory Factor Analysis Models." Multivariate Behavioral Research 28, no. 3 (July 1993): 351–74. http://dx.doi.org/10.1207/s15327906mbr2803_3.

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34

Ramirez, David, Ignacio Santamaria, Louis L. Scharf, and Steven Van Vaerenbergh. "Multi-Channel Factor Analysis With Common and Unique Factors." IEEE Transactions on Signal Processing 68 (2020): 113–26. http://dx.doi.org/10.1109/tsp.2019.2955829.

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35

Tamimi, Nabil. "A second‐order factor analysis of critical TQM factors." International Journal of Quality Science 3, no. 1 (March 1998): 71–79. http://dx.doi.org/10.1108/13598539810196886.

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36

Windasari, Windasari, Karwanto Karwanto, Supriyanto Supriyanto, and Puji Setiani. "Factors Affecting Teacher Digital Competence : An Exploratory Factor Analysis." Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran 8, no. 4 (December 4, 2022): 1029. http://dx.doi.org/10.33394/jk.v8i4.6095.

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This study aims to analyze the factor affecting the teacher competence in mastering digital technology in classroom learning during face-to-face learning in elementary schools in Lamongan district. This study used a quantitative approach using exploratory factor analysis (EFA) techniques. EFA is used to determine indicators related to teacher competence in mastering digital technology. The population of this study were teachers in Lamongan district. The sample of this research is 60 respondents with a cluster sampling method. The research stages include 1) instrument development, 2) instrument validation, 3) data collection, 4) data analysis, and 5) reporting. The results showed that there were 4 factors in mastering digital technology for teachers, namely collaboration, personal development, data literacy and information literacy. Collaboration and personal development in increasing digital competence influence on teacher performance in the teaching process. Teachers have numerous teaching materials due to collaboration using digital platforms.

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37

Yang, C. H., L. Wu, and H. L. Lin. "Analysis of total-factor cultivated land efficiency in China's agriculture." Agricultural Economics (Zemědělská ekonomika) 56, No. 5 (June 1, 2010): 231–42. http://dx.doi.org/10.17221/37/2009-agricecon.

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Along with the rapid economic growth, there has been a booming demand for agricultural products in China, but the increased land usage for the manufacturing sector has caused a decrease in the total cultivated land. Through the data envelopment analysis (DEA) approach, this study first constructs the index of the total-factor cultivated land efficiency (TFCLE) by taking the ratio of the actual cultivated land input to target the cultivated land input. Next, we examine the determinants of inefficiency on the cultivated land use. Based on a province-level panel data over 1997–2006, the empirical estimates indicate that the TFCLE is moderate and fluctuates during the sample period, ranging from 0.601 to 0.713, suggesting room for efficiency improvement in Chinese farmland use. The coastal regions are found to experience a significantly higher degree of the TFCLE compared with non-coastal regions. Overall, there is no significant competition effect brought on by the WTO entry on promoting TFCLE, though this effect differs between the coastal and non-coastal regions. The second-stage regression results show that the natural and artificial disasters, flood, drought, and pollution are the the main causes of inefficiency. Innovative activity and the FDI are evidenced to have a significantly positive relation with the TFCLE in Chinese agriculture. We also find the positive TFCLE-enhancing the effects of production diversification and import competition.

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38

Mekush, G. E., A. V. Antonova, A. M. Lavrov, and V. I. Buvaltseva. "FACTOR ANALYSIS AND GROWTH PROSPECTS OF POTABLE WATER LOCAL MARKET." Food and Raw Materials 4, no. 2 (December 30, 2016): 181–89. http://dx.doi.org/10.21179/2308-4057-2016-2-181-189.

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39

Brabenec, V., and H. Nešetřilová. "On applications of the factor analysis in the agricultural research." Agricultural Economics (Zemědělská ekonomika) 53, No. 10 (January 7, 2008): 441–47. http://dx.doi.org/10.17221/925-agricecon.

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The authors give a brief overview of the outcomes of an application of the factor analysis and present results of two applications within the agricultural research. The first application is a study in which relations among 16 variables characterising production of fish in nearly 200 high production (mainly carp) fish ponds in the Czech Republic were explored using the factor analysis method. In the second case, outcome of a household questionaire survey was analysed using factor analysis to shed light upon the relations among household annual income and expenditures. The paper was supported by the research project Informational and knowledge support of strategic management MSM 6046070904.

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40

Chou, Chun-Mei, and Chien-Hua Shen. "Tertiary Students’ Entrepreneurship Learning Socialization: Factor Analysis and Structural Equation." International Journal of Psychology and Educational Studies 2, no. 3 (September 1, 2015): 32–33. http://dx.doi.org/10.17220/ijpes.2015.03.004.

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41

Edwards, Noralee R., and Diana L. Mindrila. "Improving graduation rates: Legitimate practices and gaming strategies." education policy analysis archives 27 (April 15, 2019): 41. http://dx.doi.org/10.14507/epaa.27.4222.

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Accountability pressures faced by teachers and leaders may lead well-intentioned educators to engage in strategic reporting and operational practices to increase test scores, graduation rates, and other indicators of student success. Such practices are referred to as gaming behaviors. School district personnel attending a Georgia educational conference (N=146) reported a significant prevalence of two such practices – purging data for students enrolled for a short period of time and fabricating withdrawal forms in case of audit. Exploratory factor analysis yielded three categories of strategies employed by school districts to improve reported graduation rates: a) practices that directly contradict the rules governing ethical reporting of data (Factor1); b) legitimate educational practices aiming to enhance student learning (Factor2); and c) possible gaming strategies aiming to exclude low performing students from the computation of graduation rates (Factor3). Latent profile analysis distinguished a) a group with average scores on all factors (N=120); and b) a group with significantly higher scores on Factor1 and Factor3 (N=26). The second group included a significantly larger proportion of individuals from districts with 5,000 – 10,000 students; districts of this size may have the expertise in-house to understand calculations and take strategic action with their data reporting practices.

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42

Krawczyk, Joanna, and Agnieszka Gmitrowicz. "Analiza czynników chroniących przed samobójstwem." Psychiatria i Psychologia Kliniczna 14, no. 1 (May 30, 2014): 43–49. http://dx.doi.org/10.15557/pipk.2014.0005.

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43

Liu, Shuyang, Jiaye Wu, Yue Yue, and Yingqian Zhang. "Analysis of Factors Affecting the Hidden Costs of Construction Projects Factor Analysis." OALib 06, no. 08 (2019): 1–8. http://dx.doi.org/10.4236/oalib.1105665.

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44

Buvat, I., H. Benali, and R. Di Paola. "Statistical distribution of factors and factor images in factor analysis of medical image sequences." Physics in Medicine and Biology 43, no. 6 (June 1, 1998): 1695–711. http://dx.doi.org/10.1088/0031-9155/43/6/023.

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45

YAZID, AHMAD SHUKRI. "International Tourists’ Intention to Travel to Malaysia: An Exploratory Factor Analysis." Journal of Research on the Lepidoptera 51, no. 1 (February 28, 2020): 266–82. http://dx.doi.org/10.36872/lepi/v51i1/301023.

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Yazid, Ahmad Shukri. "International Tourists’ Intention to Travel to Malaysia: A Confirmatory Factor Analysis." Journal of Advanced Research in Dynamical and Control Systems 12, no. 3 (March 20, 2020): 155–65. http://dx.doi.org/10.5373/jardcs/v12i3/20201178.

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Yazid, Ahmad Shukri. "Determinants of Family Takaful Loyalty in Malaysia: An Exploratory Factor Analysis." Journal of Advanced Research in Dynamical and Control Systems 24, no. 4 (March 31, 2020): 291–98. http://dx.doi.org/10.5373/jardcs/v12i4/20201443.

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Ismail, Wan Marina Wan. "EXPLORATORY FACTOR ANALYSIS OF SELF-CARE AMONG MALAYSIAN HEART FAILURE PATIENTS." International Journal of Psychosocial Rehabilitation 24, no. 4 (February 28, 2020): 4489–98. http://dx.doi.org/10.37200/ijpr/v24i4/pr201551.

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Stephen, Dr Valsa T. "A Comparative Risk Factor Analysis of Variants of Agerelated Macular Degeneration." Journal of Medical Science And clinical Research 05, no. 06 (June 9, 2017): 22958–63. http://dx.doi.org/10.18535/jmscr/v5i6.27.

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Reddy, Dr B. Sudhakar. "Regional Variations in Agricultural Development of Andhra Pradeshsh – A Factor Analysis." Indian Journal of Applied Research 3, no. 12 (October 1, 2011): 143–46. http://dx.doi.org/10.15373/2249555x/dec2013/40.

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