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Journal articles on the topic 'Data collection Methods'

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Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

Harwood, Eileen M., and Pakou Vang. "Data Collection Methods Series." Journal of Wound, Ostomy and Continence Nursing 36, no. 1 (January 2009): 15–20. http://dx.doi.org/10.1097/01.won.0000345172.28246.7a.

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2

Harwood, Eileen M., and Elizabeth Hutchinson. "Data Collection Methods Series." Journal of Wound, Ostomy and Continence Nursing 36, no. 2 (March 2009): 129–35. http://dx.doi.org/10.1097/01.won.0000347654.49228.44.

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3

Hinojosa, Renee, Mary Erickson Megel, and Judith A. Heermann. "Methods of Data Collection." Plastic Surgical Nursing 14, no. 2 (1994): 109–10. http://dx.doi.org/10.1097/00006527-199406000-00014.

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4

Wilcox, Adam B., Kathleen D. Gallagher, Bernadette Boden-Albala, and Suzanne R. Bakken. "Research Data Collection Methods." Medical Care 50 (July 2012): S68—S73. http://dx.doi.org/10.1097/mlr.0b013e318259c1e7.

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5

Harwood, Eileen M. "Data Collection Methods Series." Journal of Wound, Ostomy and Continence Nursing 36, no. 3 (May 2009): 246–50. http://dx.doi.org/10.1097/won.0b013e3181a1a4d3.

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6

Hutchinson, Elizabeth, and Eileen M. Harwood. "Data Collection Methods Series." Journal of Wound, Ostomy and Continence Nursing 36, no. 4 (July 2009): 371–75. http://dx.doi.org/10.1097/won.0b013e3181aaf65e.

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7

Harwood, Eileen M., and Elizabeth Hutchinson. "Data Collection Methods Series." Journal of Wound, Ostomy and Continence Nursing 36, no. 6 (November 2009): 592–99. http://dx.doi.org/10.1097/won.0b013e3181bd7f77.

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8

Aishima, J., P. Gibbons, R. Fearn, A. Ashton, and K. Levik. "UsingGDAfor novel data-collection methods." Acta Crystallographica Section A Foundations of Crystallography 67, a1 (August 22, 2011): C277. http://dx.doi.org/10.1107/s0108767311093068.

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9

Frick, Kevin D. "Microcosting Quantity Data Collection Methods." Medical Care 47, Supplement (July 2009): S76—S81. http://dx.doi.org/10.1097/mlr.0b013e31819bc064.

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10

Bill, Ralf. "Unmanned Aerial Systems (UAS) - Attractive Extensions to Spatial Data Collection Methods." Photogrammetrie - Fernerkundung - Geoinformation 2014, no. 4 (August 1, 2014): 225–26. http://dx.doi.org/10.1127/1432-8364/2014/0238.

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11

Paradis, Elise, Bridget O'Brien, Laura Nimmon, Glen Bandiera, and Maria Athina (Tina) Martimianakis. "Design: Selection of Data Collection Methods." Journal of Graduate Medical Education 8, no. 2 (May 1, 2016): 263–64. http://dx.doi.org/10.4300/jgme-d-16-00098.1.

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12

Harwoodn, Eileen M., and Elizabeth Hutchinson. "Data Collection Methods Series Part 5." Journal of Wound, Ostomy and Continence Nursing 36, no. 5 (September 2009): 476–81. http://dx.doi.org/10.1097/won.0b013e3181b35248.

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13

Jasperse, Dorice M., and Susan W. Ahmed. "Comparison of two data collection methods." Controlled Clinical Trials 6, no. 3 (September 1985): 241–42. http://dx.doi.org/10.1016/0197-2456(85)90081-9.

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14

Smith, Eric E., Janet Warner, Megan Johnston, Kristin Atwood, Ruth Hall, Jean K. Mah, Colleen Maxwell, et al. "Neurological Registry Data Collection Methods and Configuration." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 40, S2 (July 2013): S27—S31. http://dx.doi.org/10.1017/s0317167100017133.

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15

Shaw, M. C. "Standardised methods of data collection are required." BMJ 310, no. 6993 (June 10, 1995): 1534–35. http://dx.doi.org/10.1136/bmj.310.6993.1534e.

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16

Stewart, Marissa C., Lara E. McCormick, Jesse R. Goliath, Paul W. Sciulli, and Sam D. Stout. "A Comparison of Histomorphometric Data Collection Methods*." Journal of Forensic Sciences 58, no. 1 (May 23, 2012): 109–13. http://dx.doi.org/10.1111/j.1556-4029.2012.02195.x.

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17

Bowles, K. A., E. H. Skinner, D. Mitchell, R. Haas, M. Ho, K. Salter, K. May, et al. "Data Collection Methods in Health Services Research." Applied Clinical Informatics 06, no. 01 (2015): 96–109. http://dx.doi.org/10.4338/aci-2014-10-ra-0097.

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Summary Background: Hospital length of stay and discharge destination are important outcome measures in evaluating effectiveness and efficiency of health services. Although hospital administrative data are readily used as a data collection source in health services research, no research has assessed this data collection method against other commonly used methods. Objective: Determine if administrative data from electronic patient management programs are an effective data collection method for key hospital outcome measures when compared with alternative hospital data collection methods. Method: Prospective observational study comparing the completeness of data capture and level of agreement between three data collection methods; manual data collection from ward-based sources, administrative data from an electronic patient management program (i.PM), and inpatient medical record review (gold standard) for hospital length of stay and discharge destination. Results: Manual data collection from ward-based sources captured only 376 (69%) of the 542 in-patient episodes captured from the hospital administrative electronic patient management program. Administrative data from the electronic patient management program had the highest levels of agreement with inpatient medical record review for both length of stay (93.4%) and discharge destination (91%) data. Conclusion: This is the first paper to demonstrate differences between data collection methods for hospital length of stay and discharge destination. Administrative data from an electronic patient management program showed the highest level of completeness of capture and level of agreement with the gold standard of inpatient medical record review for both length of stay and discharge destination, and therefore may be an acceptable data collection method for these measures. Citation: Sarkies MN, Bowles K-A, Skinner EH, Mitchell D, Haas R, Ho M, Salter K, May K, Markham D, O’Brien L, Plumb S, Haines T.P. Data collection methods in health services research – hospital length of stay and discharge destination. Appl Clin Inf 2015; 6: 96–109http://dx.doi.org/10.4338/ACI-2014-10-RA-0097

18

Robinson, Andrew L., Carolyn G. Emden, Tobias D. Croft, Gillian C. Vosper, Jean A. Elder, Christine Stirling, and James C. Vickers. "Mixed Methods Data Collection in Dementia Research." Journal of Mixed Methods Research 5, no. 4 (August 29, 2011): 330–44. http://dx.doi.org/10.1177/1558689811416940.

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19

A., Botchkaryov. "STRUCTURAL ADAPTATION OF DATA COLLECTION PROCESSES IN AUTONOMOUS DISTRIBUTED SYSTEMS USING REINFORCEMENT LEARNING METHODS." Computer systems and network 2, no. 1 (March 23, 2017): 13–26. http://dx.doi.org/10.23939/csn2020.01.013.

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A method of structural adaptation of data collection processes has been developed based on reinforcement learning of the decision block on the choice of actions at the structural and functional level subordinated to it, which provides a more efficient distribution of measuring and computing resources, higher reliability and survivability of information collection subsystems of an autonomous distributed system compared to methods of parametric adaptation. In particular, according to the results of experimental studies, the average amount of information collected in one step using the method of structural adaptation is 23.2% more than in the case of using the methods of parametric adaptation. At the same time, the amount of computational costs for the work of the structural adaptation method is on average 42.3% more than for the work of parametric adaptation methods. The reliability of the work of the method of structural adaptation was studied using the efficiency preservation coefficient for different values of the failure rate of data collection processes. Using the recovery rate coefficient for various values of relative simultaneous sudden failures, the survivability of a set of data collection processes organized by the method of structural adaptation has been investigated. In terms of reliability, the structural adaptation method exceeds the parametric adaptation methods by an average of 21.1%. The average survivability rate for the method of structural adaptation is greater than for methods of parametric adaptation by 18.4%. Key words: autonomous distributed system, data collection process, structural adaptation, reinforcement learning

20

A., Botchkaryov. "FUNCTIONAL COORDINATION OF METHODS OF ORGANIZATION OF ADAPTIVE DATA COLLECTION PROCESSES AND METHODS OF SPATIAL SELF-ORGANIZATION OF MOBILE AGENTS." Computer systems and network 2, no. 1 (March 23, 2017): 27–32. http://dx.doi.org/10.23939/csn2020.01.027.

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The way of functional coordination of methods of organization adaptive data collection processes and methods of spatial self-organization of mobile agents by parallel execution of the corresponding data collection processes and the process of motion control of a mobile agent using the proposed protocol of their interaction and the algorithm of parallel execution planning is proposed. The method allows to speed up the calculations in the decision block of the mobile agent by an average of 40.6%. Key words: functional coordination, adaptive data collection process, spatial self-organization, mobile agents

21

Laing, David K., Andrew W. Hartshorne, Roger Cook, and Geoffrey Robinson. "A Fiber Data Collection for Forensic Scientists—Collection and Examination Methods." Journal of Forensic Sciences 32, no. 2 (March 1, 1987): 11139J. http://dx.doi.org/10.1520/jfs11139j.

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22

Rice, Eric, Ian W. Holloway, Anamika Barman-Adhikari, Dahlia Fuentes, C. Hendricks Brown, and Lawrence A. Palinkas. "A Mixed Methods Approach to Network Data Collection." Field Methods 26, no. 3 (January 27, 2014): 252–68. http://dx.doi.org/10.1177/1525822x13518168.

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23

Al Rashdan, Ahmad, and Shawn St. Germain. "Methods of Data Collection in Nuclear Power Plants." Nuclear Technology 205, no. 8 (May 31, 2019): 1062–74. http://dx.doi.org/10.1080/00295450.2019.1610637.

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24

Morrison, Diane M., Barbara C. Leigh, and Mary Rogers Gillmore. "Daily data collection: A comparison of three methods." Journal of Sex Research 36, no. 1 (February 1999): 76–81. http://dx.doi.org/10.1080/00224499909551970.

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25

Lestari, Utami Puji, and David Firna Setiawan. "Data Collection Methods on Learning Outcome, Student Achievement and Academic Achievement." GUIDENA: Jurnal Ilmu Pendidikan, Psikologi, Bimbingan dan Konseling 7, no. 2 (November 10, 2017): 164. http://dx.doi.org/10.24127/gdn.v7i2.984.

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Learning outcome, student achievement and academic achievement are some of the variables that get a lot of attention in educational research. Nevertheless, from several national studies collected there are inconsistencies in particular with regard to English terminology as well as inconsistencies in methods and instruments of data collection. Therefore, this study aims to explain the different methods of data collection research on learning outcome, achievement learning, and academic achievement. This study is a study desk study using literature review to explain the different methods of data collection on learning outcome, student achievement, and academic achievement. Based on the results of research can be concluded that the method of data collection of learning outcome, can be done through three ways, namely, perception survey, interview, and forum group discussion (FGD). While the instruments that can be used to collect data learning results are questionnaires, interview guides and minutes. Methods of collecting research data about student achievement, can be done through two ways namely, (1) test and (2) non test. Data collection methods of academic achievement can be done through documentation.

26

Jiang, Ling Yun, and Zhi Biao Wang. "Data Collection and Model Construction Methods for Reverse Engineering." Advanced Materials Research 102-104 (March 2010): 189–93. http://dx.doi.org/10.4028/www.scientific.net/amr.102-104.189.

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The process of creating a CAD model from an object is mainly made up of two steps: the data collection through digital measurement and the construction of parameterized and revisable model. This paper discusses the measuring process and technical problems of the Coordinate Measuring Machine (CMM) and non-contact sensor. Through comparative analysis, we determine the application scope of those approaches in measuring different dimensions of the same objects considering the time efficiency and tolerance requirement. This paper divide the objects into two categories: freeform feature objects and regular feature objects. As for the freeform feature objects, people could fit wrap-around B-spline surfaces to construct the model. Regular feature objects for mass produce contain machined surfaces which should be precisely measured and modeled. The model of regular feature object should be constructed by three-dimensional modeling software, so that it is parametric and revisable for changing and improving the original design. Sizes and position of important surfaces of the model are acquired from CMM, and those of non-important features are fitted though point cloud processing. Some profile can’t be measured directly from CMM but should be precise, so this paper proposed two methods to construct profile line and analyze error by comparing it with point cloud.

27

Pucken, Valerie-Beau, Michèle Bodmer, Benjamin Lovis, Julie Pont, Giulia Savioli, Filipe Maximiano Sousa, and Gertraud Schüpbach-Regula. "Antimicrobial consumption: Comparison of three different data collection methods." Preventive Veterinary Medicine 186 (January 2021): 105221. http://dx.doi.org/10.1016/j.prevetmed.2020.105221.

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28

Grack Nelson, Amy, and Sarah Cohn. "Data Collection Methods for Evaluating Museum Programs and Exhibitions." Journal of Museum Education 40, no. 1 (March 2015): 27–36. http://dx.doi.org/10.1080/10598650.2015.11510830.

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29

Strelluf, Christopher. "Book Review: Data Collection in Sociolinguistics: Methods and Applications." Journal of English Linguistics 41, no. 4 (December 2013): 380–84. http://dx.doi.org/10.1177/0075424213506012.

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30

Meurer, W. J., S. M. Frederiksen, J. J. Majersik, L. Zhang, A. Sandretto, and P. A. Scott. "Qualitative Data Collection and Analysis Methods: The INSTINCT Trial." Academic Emergency Medicine 14, no. 11 (November 1, 2007): 1064–71. http://dx.doi.org/10.1197/j.aem.2007.05.005.

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31

Garner, J. B., and J. Uren. "THE USE OF PHOTOGRAPHIC METHODS FOR TRAFFIC DATA COLLECTION." Photogrammetric Record 7, no. 41 (August 26, 2006): 555–67. http://dx.doi.org/10.1111/j.1477-9730.1973.tb01189.x.

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32

Rice, Michelle C., and Jane Fellows. "COMPARING TWO METHODS OF PREVALENCE AND INCIDENCE DATA COLLECTION." Journal of Wound, Ostomy and Continence Nursing 31, Supplement (May 2004): S19—S20. http://dx.doi.org/10.1097/00152192-200405001-00055.

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33

Goneppanavar, Umesh, Zulfiqar Ali, SBala Bhaskar, and JigeeshuV Divatia. "Types of data, methods of collection, handling and distribution." Airway 2, no. 1 (2019): 36. http://dx.doi.org/10.4103/arwy.arwy_11_19.

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34

Quandt, Sara A., and Thomas A. Arcury. "Qualitative methods in arthritis research: Overview and data collection." Arthritis Care & Research 10, no. 4 (August 1997): 273–81. http://dx.doi.org/10.1002/art.1790100409.

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35

Campbell, N. J., P. Youngs, and I. Anderson. "A UK survey of obstetric anaesthetic data collection methods." International Journal of Obstetric Anesthesia 22, no. 2 (April 2013): 167–68. http://dx.doi.org/10.1016/j.ijoa.2013.01.009.

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36

darovsky, Alex. "Workshop on strategy and methods for AD data collection." Synchrotron Radiation News 9, no. 5 (September 1996): 8. http://dx.doi.org/10.1080/08940889608602895.

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37

Grove, Stephen J., and Raymond P. Fisk. "Observational data collection methods for services marketing: An overview." Journal of the Academy of Marketing Science 20, no. 3 (June 1992): 217–24. http://dx.doi.org/10.1007/bf02723408.

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38

Morgan, Karen J., S. R. Johnson, Robert L. Rizek, Robert Reese, and Gary L. Stampley. "Collection of food intake data: An evaluation of methods." Journal of the American Dietetic Association 87, no. 7 (July 1987): 888–96. http://dx.doi.org/10.1016/s0002-8223(21)03211-9.

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39

Sharp, Kayeleigh, and Melissa Litschi. "Maximizing E-Data Collection." Advances in Archaeological Practice 2, no. 2 (May 2014): 104–22. http://dx.doi.org/10.7183/2326-3768.2.2.104.

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AbstractWhile tablet devices are becoming commonplace in archaeological research, the transfer of data for analysis is a matter of critical concern. As geographic information systems (GIS) are increasingly incorporated into our archaeological research, there is a need not only to streamline data acquisition, but also to collect data that can be used in conjunction with high-level spatial analytical tools, such as those available in ESRI’s ArcGIS. During a short lab season, various methods of paper-free data collection and cross-platform transfer using these user-friendly devices were tested. It was found that both iPad and Android-based systems present great advantages in terms of portability and electronic data acquisition. However, file compatibility and transfer of data across multiple platforms present a number of important drawbacks. The present case study focuses on the development of a data collection protocol implemented by the Proyecto de Investigación Arqueológica Zaña Valley (PIAZ), Peru, that moves beyond these limitations. The ultimate incorporation of data collected using tablet devices demonstrates the potential for this tool in archaeological research, along with the pitfalls for projects that rely heavily on GIS. Significant findings regarding the advantages, as well as the limitations, of e-data collection, transfer, and storage are discussed.

40

Balińska, Agata. "Data collection methods in rural tourism in the eyes of respondents." Studia Periegetica 29, no. 1 (March 22, 2020): 115–26. http://dx.doi.org/10.5604/01.3001.0014.1234.

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The aim of this study was to verify the reliability and user-friendliness of some of the most popular ways of collecting information from respondents. The article provides a synthetic review of the use of various research methods and techniques, which is supplemented by results of the author’s own survey of 280 people who visited guest farms in rural areas of Mazowieckie province in 2019. The respondents found online and paper-based surveys the most user-friendly method while they rated telephone interviews as the least user-friendly. Asked to assess the reliability of data collection techniques, the respondents considered covert observation and mystery shopping to be the most reliable, while the telephone interview was regarded as the least trustworthy. The assessment of the user-friendliness and reliability of different data collection methods and techniques varied by gender, age and the level of education. It can be expected that insights from the study can help to improve the methodology of rural tourism research.

41

Davis, Deborah Winders. "Data Collection and Study Implementation." Neonatal Network 24, no. 1 (January 2005): 59–62. http://dx.doi.org/10.1891/0730-0832.24.1.59.

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IN PREVIOUS COLUMNS, I HAVE covered the following topics: types of study designs (September/October 2003), study reliability and validity (January/February 2004), instrument reliability and validity (May/June 2004), and selection and use of research instruments (September/ October 2004). The focus of this column is issues and methods related to data collection and study implementation. The confines of a column make an exhaustive treatment of this subject impossible. Suggestions for additional reading are provided in the bibliography for those desiring more extensive coverage.

42

SIVAK, E. D., and J. GOCHBERG. "Diagnostic Methods in Critical Care: Automated Data Collection and Interpretation." Cleveland Clinic Journal of Medicine 55, no. 6 (November 1, 1988): 566. http://dx.doi.org/10.3949/ccjm.55.6.566.

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43

Doerzaph, Zachary, Thomas A. Dingus, and Jon Hankey. "Improving Driver Safety through Naturalistic Data Collection and Analysis Methods." SAE International Journal of Passenger Cars - Electronic and Electrical Systems 3, no. 2 (October 19, 2010): 162–69. http://dx.doi.org/10.4271/2010-01-2333.

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44

Roos, Marko. "Methods of Internet data collection and implications for recruiting respondents." Statistical Journal of the United Nations Economic Commission for Europe 19, no. 3 (December 10, 2002): 175–86. http://dx.doi.org/10.3233/sju-2002-19307.

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45

Shin, Kwang-Ho, Tae-Hwa Um, Dam-Sub Lim, and Jin Ahn. "Data Collection Methods to Standardize Protocols of Safety Supervision System." Journal of the Korean society for railway 19, no. 2 (April 30, 2016): 159–69. http://dx.doi.org/10.7782/jksr.2016.19.2.159.

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46

Lohaus, Arnold. "Standardizations in methods of data collection: Adverse effects on reliability?" Australian Psychologist 21, no. 2 (July 1986): 241–51. http://dx.doi.org/10.1080/00050068608256184.

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47

Mola-Yudego, Blas, David Gritten, and Cristóbal Delgado-Matas. "Quantitative investigation of forest conflicts using different data collection methods." Scandinavian Journal of Forest Research 27, no. 2 (March 2012): 130–42. http://dx.doi.org/10.1080/02827581.2011.635081.

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48

Montaquila, J. M., J. M. Brick, D. Williams, K. Kim, and D. Han. "A Study of Two-Phase Mail Survey Data Collection Methods." Journal of Survey Statistics and Methodology 1, no. 1 (May 1, 2013): 66–87. http://dx.doi.org/10.1093/jssam/smt004.

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49

Newman, Leah, and Pascale Carayon. "Community Ergonomics: Data Collection Methods and Analysis of Human Characteristics." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 38, no. 12 (October 1994): 739–43. http://dx.doi.org/10.1177/154193129403801205.

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This paper presents methods that can be used in community ergonomics to examine the Community-Environment fit. An example is discussed that illustrates the use of survey questionnaire in assessing the fit between the community and its banking environment.

50

Preskill, Hallie. "A comparison of data collection methods for assessing training needs." Human Resource Development Quarterly 2, no. 2 (1991): 143–55. http://dx.doi.org/10.1002/hrdq.3920020207.

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