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Journal articles on the topic 'Measuring Reliability'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

Kumar, Ravinder, Kiran Khatter, and Arvind Kalia. "Measuring software reliability." ACM SIGSOFT Software Engineering Notes 36, no. 6 (2011): 1–6. http://dx.doi.org/10.1145/2047414.2047425.

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2

Pfleeger, S. L. "Measuring software reliability." IEEE Spectrum 29, no. 8 (1992): 56–60. http://dx.doi.org/10.1109/6.144538.

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3

HAYES, JOHN R., and JILL A. HATCH. "Issues in Measuring Reliability." Written Communication 16, no. 3 (1999): 354–67. http://dx.doi.org/10.1177/0741088399016003004.

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4

Hashim, Mohammad. "Measuring Reliability in Service Industries." Management Decision 25, no. 4 (1987): 46–51. http://dx.doi.org/10.1108/eb001462.

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5

Musa, J. D. "Tools for measuring software reliability." IEEE Spectrum 26, no. 2 (1989): 39–42. http://dx.doi.org/10.1109/6.17360.

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6

Szeto, W. Y., Liam O'Brien, and Margaret O'Mahony. "Measuring Network Reliability by considering Paradoxes." Transportation Research Record: Journal of the Transportation Research Board 2090, no. 1 (2009): 42–50. http://dx.doi.org/10.3141/2090-05.

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7

Morrison, Geoffrey Stewart, Julien Epps, Philp Rose, Tharmarajah Thiruvaran, and Cuiling Zhang. "Measuring reliability in forensic voice comparison." Journal of the Acoustical Society of America 128, no. 4 (2010): 2378. http://dx.doi.org/10.1121/1.3508454.

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8

de Koning, Jos J., Dionne A. Noordhof, Daan de Ridder, Ruby Otter, and Carl Foster. "The Reliability of Measuring Gross Efficiency." Medicine & Science in Sports & Exercise 42 (May 2010): 338. http://dx.doi.org/10.1249/01.mss.0000384609.25687.9f.

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9

Iacobucci, Dawn, and Adam Duhachek. "Advancing Alpha: Measuring Reliability With Confidence." Journal of Consumer Psychology 13, no. 4 (2003): 478–87. http://dx.doi.org/10.1207/s15327663jcp1304_14.

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10

Mendibil, Kepa, Trevor J. Turner, and Umit S. Bititci. "Measuring and improving business process reliability." International Journal of Business Performance Management 4, no. 1 (2002): 76. http://dx.doi.org/10.1504/ijbpm.2002.000109.

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11

Hesamamiri, Roozbeh, Mohammad Mahdavi Mazdeh, and Mostafa Jafari. "Measuring the reliability of knowledge management." Aslib Proceedings 65, no. 5 (2013): 484–502. http://dx.doi.org/10.1108/ap-04-2013-0029.

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12

Ignatkin, V. U., B. M. Belyaev, and V. I. Koplakov. "Relationships between measuring-instrument reliability parameters." Measurement Techniques 31, no. 7 (1988): 634–37. http://dx.doi.org/10.1007/bf00866650.

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13

Kang, Rui, Qingyuan Zhang, Zhiguo Zeng, Enrico Zio, and Xiaoyang Li. "Measuring reliability under epistemic uncertainty: Review on non-probabilistic reliability metrics." Chinese Journal of Aeronautics 29, no. 3 (2016): 571–79. http://dx.doi.org/10.1016/j.cja.2016.04.004.

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14

Matsueda, Ross L., and Kevin M. Drakulich. "Measuring Collective Efficacy." Sociological Methods & Research 45, no. 2 (2015): 191–230. http://dx.doi.org/10.1177/0049124115578030.

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This article specifies a multilevel measurement model for survey response when data are nested. The model includes a test–retest model of reliability, a confirmatory factor model of interitem reliability with item-specific bias effects, an individual-level model of the biasing effects due to respondent characteristics, and a neighborhood-level model of construct validity. We apply this model for measuring informal social control within collective efficacy theory. Estimating the model on 3,260 respondents nested within 123 Seattle neighborhoods, we find that measures of informal control show reasonable test–retest and interitem reliability. We find support for the hypothesis that respondents’ assessments of whether their neighbors would intervene in specific child deviant acts are related to whether they have observed such acts in the past, which is consistent with a cognitive model of survey response. Finally, we find that, when proper measurement models are not used, the effects of some neighborhood covariates on informal control are biased upward and the effect of informal social control on violence is biased downward.

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15

Hripcsak, George, and Daniel F. Heitjan. "Measuring agreement in medical informatics reliability studies." Journal of Biomedical Informatics 35, no. 2 (2002): 99–110. http://dx.doi.org/10.1016/s1532-0464(02)00500-2.

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16

Schagen, Ian, and Dougal Hutchison. "Measuring the reliability of National Curriculum assessment." Educational Research 36, no. 3 (1994): 211–21. http://dx.doi.org/10.1080/0013188940360301.

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17

Batson, C. Daniel, and Patricia A. Schoenrade. "Measuring Religion as Quest: 2) Reliability Concerns." Journal for the Scientific Study of Religion 30, no. 4 (1991): 430. http://dx.doi.org/10.2307/1387278.

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18

Bell, Michael G. H. "Measuring network reliability: A game theoretic approach." Journal of Advanced Transportation 33, no. 2 (1999): 135–46. http://dx.doi.org/10.1002/atr.5670330204.

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19

Quigley, John, and Lesley Walls. "Measuring the effectiveness of reliability growth testing." Quality and Reliability Engineering International 15, no. 2 (1999): 87–93. http://dx.doi.org/10.1002/(sici)1099-1638(199903/04)15:2<87::aid-qre234>3.0.co;2-y.

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20

Deleu, Paul-André, Thibaut Leemrijse, Ivan Birch, Bruno Vande Berg, and Bernhard Devos Bevernage. "Reliability of the Maestro Radiographic Measuring Tool." Foot & Ankle International 31, no. 10 (2010): 884–91. http://dx.doi.org/10.3113/fai.2010.0884.

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21

Norman, D. "Measuring interface pressure: validity and reliability problems." Journal of Wound Care 13, no. 2 (2004): 78–80. http://dx.doi.org/10.12968/jowc.2004.13.2.26576.

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22

Baumgarten, Keith M., James L. Carey, Joseph A. Abboud, et al. "Reliability of Determining and Measuring Acromial Enthesophytes." HSS Journal ® 7, no. 3 (2011): 218–22. http://dx.doi.org/10.1007/s11420-011-9209-0.

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23

Zeliu Ding, Wei Wei, Deke Guo, and Xueshan Luo. "Measuring the Reliability of Data Center Network." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 3, no. 7 (2011): 82–91. http://dx.doi.org/10.4156/aiss.vol3.issue7.10.

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24

Adamson, Katie Anne, та Susan Prion. "Reliability: Measuring Internal Consistency Using Cronbach's α". Clinical Simulation in Nursing 9, № 5 (2013): e179-e180. http://dx.doi.org/10.1016/j.ecns.2012.12.001.

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25

Fridman, A. �. "Theory of metrological reliability of measuring devices." Measurement Techniques 34, no. 11 (1991): 1075–91. http://dx.doi.org/10.1007/bf00979675.

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26

Herlich, Matthias, and Christian Maier. "Measuring and Monitoring Reliability of Wireless Networks." IEEE Communications Magazine 59, no. 1 (2021): 76–81. http://dx.doi.org/10.1109/mcom.001.2000250.

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27

Tapia, Martha. "Measuring Emotional Intelligence." Psychological Reports 88, no. 2 (2001): 353–64. http://dx.doi.org/10.2466/pr0.2001.88.2.353.

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The purposes of this study were (a) to develop a measure of emotional intelligence, the Emotional Intelligence Inventory and (b) to find the underlying dimensions of the inventory by testing 111 high school students at a bilingual college preparatory school. The inventory has 45 items. After excluding the four weakest items, the reliability coefficient α was .83. Subsequently, 319 junior and senior high school students at the same school were administered the 41 items. The reliability coefficient was .81. A maximum likelihood factor analysis with a varimax rotation yielded four factors of empathy, utilization of feelings, handling relationships, and self-control. Psychometric properties were sound, and the revised Emotional Intelligence Inventory can be recommended for use in the investigation of emotional intelligence.

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28

Yashin, Vladimir N. "Evaluation of metrological reliability of measuring instruments by method of generating functions." Vestnik of Samara State Technical University. Technical Sciences Series 28, no. 2 (2020): 84–96. http://dx.doi.org/10.14498/tech.2020.2.6.

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An article deals with problems related to the assessment of metrological reliability of measuring instruments by method of generating functions. Metrological reliability of measuring instruments is the most important characteristic that determines the accuracy and reliability of physical quantities measurements. In the suggested article, a confidence indicator is proposed as an evaluation of metrological reliability. The quantitative value of confidence indicator can be estimated by means of the method of generating functions. This is a scientific novelty of the work. Relevance of the problem of assessing the measuring instruments metrological reliability evaluation is substantiated in this paper since the current trend towards structural and functional complexity of measuring instruments may lead to decreasing of their reliability and, in particular, metrological reliability. The main goal of this work is to systematize the problems of reliability of measuring instruments and evaluate their metrological reliability using the method of generating functions. On the base of selected mathematical model of the evolution of error of measurement and proposed indicator of metrological reliability means of the method of generating functions allow to carry out metrological forecast of variability of the error of measurement depending on time. The model of gradual failures with a discrete change of the error over time, which is typical for a certain class of measuring instruments, for example, measuring time intervals, was chosen as a model for the evolution of the error of measuring instruments. The method of generating functions used for evaluating the metrological reliability of measuring instruments has made it possible to increase the efficiency of the algorithm for quantitative evaluation of metrological reliability of measuring instruments by simplifying the mathematical operations that underlie it.

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29

Daigneault, Pierre-Marc, Steve Jacob, and Joël Tremblay. "Measuring Stakeholder Participation in Evaluation." Evaluation Review 36, no. 4 (2012): 243–71. http://dx.doi.org/10.1177/0193841x12458103.

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Background: Stakeholder participation is an important trend in the field of program evaluation. Although a few measurement instruments have been proposed, they either have not been empirically validated or do not cover the full content of the concept. Objectives: This study consists of a first empirical validation of a measurement instrument that fully covers the content of participation, namely the Participatory Evaluation Measurement Instrument (PEMI). It specifically examines (1) the intercoder reliability of scores derived by two research assistants on published evaluation cases; (2) the convergence between the scores of coders and those of key respondents (i.e., authors); and (3) the convergence between the authors’ scores on the PEMI and the Evaluation Involvement Scale (EIS). Sample: A purposive sample of 40 cases drawn from the evaluation literature was used to assess reliability. One author per case in this sample was then invited to participate in a survey; 25 fully usable questionnaires were received. Measures: Stakeholder participation was measured on nominal and ordinal scales. Cohen’s κ, the intraclass correlation coefficient, and Spearman’s ρ were used to assess reliability and convergence. Results: Reliability results ranged from fair to excellent. Convergence between coders’ and authors’ scores ranged from poor to good. Scores derived from the PEMI and the EIS were moderately associated. Conclusions: Evidence from this study is strong in the case of intercoder reliability and ranges from weak to strong in the case of convergent validation. Globally, this suggests that the PEMI can produce scores that are both reliable and valid.

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30

Ma, Zhenliang, Luis Ferreira, and Mahmoud Mesbah. "Measuring Service Reliability Using Automatic Vehicle Location Data." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/468563.

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Bus service reliability has become a major concern for both operators and passengers. Buffer time measures are believed to be appropriate to approximate passengers' experienced reliability in the context of departure planning. Two issues with regard to buffer time estimation are addressed, namely, performance disaggregation and capturing passengers’ perspectives on reliability. A Gaussian mixture models based method is applied to disaggregate the performance data. Based on the mixture models distribution, a reliability buffer time (RBT) measure is proposed from passengers’ perspective. A set of expected reliability buffer time measures is developed for operators by using different spatial-temporal levels combinations of RBTs. The average and the latest trip duration measures are proposed for passengers that can be used to choose a service mode and determine the departure time. Using empirical data from the automatic vehicle location system in Brisbane, Australia, the existence of mixture service states is verified and the advantage of mixture distribution model in fitting travel time profile is demonstrated. Numerical experiments validate that the proposed reliability measure is capable of quantifying service reliability consistently, while the conventional ones may provide inconsistent results. Potential applications for operators and passengers are also illustrated, including reliability improvement and trip planning.

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31

ElSaid, AbdElRahman, Daniel Adjekum, John Nordlie, and Fatima El Jamiy. "A Test-Bed For Measuring UAS Servo Reliability." Aerospace 6, no. 9 (2019): 96. http://dx.doi.org/10.3390/aerospace6090096.

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Extant literature suggests minimal research on the reliability of Commercial off-the-shelf (COTS) components used in fabricating non-military Unmanned Aerial System (UAS). Stochastic failures of components during operational cycles over time poses a safety hazard to flight operations. The purpose of the study was to critically assess the operational performance standards (reliability) of a laboratory designed UAS component test-bed operated using real-world data collected from a Boeing Scan Eagle® UAS aileron servo unit via a flight data recorder. The study hypothesized that the test-bed’s reliability, in terms of a measured encoder output of commanded servo positions, will not be significantly different after double and triple periods of time for continuous operations compared to a base-line mean position. Results suggested that test-bed operated within reliability criteria for a baseline period but there were significant differences in the mean of the reliability after the operational cycles were doubled and tripled in time. This study adds to paucity of extant research on UAS COTS reliability and recommends further studies on reliability of other small UAS components within periods of time.

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32

Jangbahadur, Uttara, and Vandna Sharma. "Measuring Employee Development." Global Business Review 19, no. 2 (2017): 455–76. http://dx.doi.org/10.1177/0972150917713548.

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The aim of this study is to identify the factors of employee development (ED) and to validate those identified set of factors using confirmatory factor analysis (CFA). Data were collected personally from employees of manufacturing industries. Factor analysis was carried out to explore the factors and CFA was carried out to check the reliability, validity and the model fitness. The scale had a high degree of reliability and validity and ensured the presence of both convergent and discriminant validity. The scale developed in this study is based on only four factors of ED as identified by different authors. In future, other factors, such as knowledge management and management development programmes, can also be included in the study. The instrument developed in the study for ED provides a basis for most of the academicians and the researchers to empirically test the relationship between ED, individual performance and organizational effectiveness, which has become an important area of interest among the researchers in recent years. The study is based on identifying the measures/factors of ED and validate those factors.

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33

Gong, Bu Ju, Jin Seok Han, Chun Geun Bong, You Deog Hong, and Ji Hyung Hong. "Reliability assessment of the odor sensor measuring instrument." Journal of Odor and Indoor Environment 14, no. 4 (2015): 299–314. http://dx.doi.org/10.15250/joie.2015.14.4.299.

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34

Armstrong-Heimsoth, Amy, Jodi Thomas, Roy St. Laurent, and Ashley Sinnappan. "Creating Reliability and Fidelity in Measuring Head Control." American Journal of Occupational Therapy 73, no. 4_Supplement_1 (2019): 7311515266p1. http://dx.doi.org/10.5014/ajot.2019.73s1-po4062.

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35

Rauh, Michael A., James Boyle, Matthew J. Phillips, William M. Mihalko, Kenneth A. Krackow, and Mary Bayers-Thering. "Reliability of Measuring Long-standing Lower Extremity Radiographs." Orthopedics 30, no. 4 (2007): 299–303. http://dx.doi.org/10.3928/01477447-20070401-14.

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36

Yatsuk, Vasyl, Tetiana Bubela, Mykola Mykyychuk, and Yevhen Pokhodylo. "METROLOGICAL RELIABILITY SUPPORT OF THE DISPERSED MEASURING SYSTEM." Measuring Equipment and Metrology 79, no. 3 (2018): 71–82. http://dx.doi.org/10.23939/istcmtm2018.03.071.

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37

Chae, Sophia. "Forgotten marriages? Measuring the reliability of marriage histories." Demographic Research 34 (March 22, 2016): 525–62. http://dx.doi.org/10.4054/demres.2016.34.19.

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38

Albin, Thomas J. "Measuring the validity and reliability of ergonomic checklists." Work 43, no. 3 (2012): 381–85. http://dx.doi.org/10.3233/wor-2012-1464.

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39

Keane, John, Tae-hyoung Kim, Xiaofei Wang, and Chris H. Kim. "On-chip reliability monitors for measuring circuit degradation." Microelectronics Reliability 50, no. 8 (2010): 1039–53. http://dx.doi.org/10.1016/j.microrel.2010.04.024.

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40

Mohamad, Mimi Mohaffyza, Nor Lisa Sulaiman, Lai Chee Sern, and Kahirol Mohd Salleh. "Measuring the Validity and Reliability of Research Instruments." Procedia - Social and Behavioral Sciences 204 (August 2015): 164–71. http://dx.doi.org/10.1016/j.sbspro.2015.08.129.

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41

Koo, Hyeongmo, David W. S. Wong, and Yongwan Chun. "Measuring Global Spatial Autocorrelation with Data Reliability Information." Professional Geographer 71, no. 3 (2019): 551–65. http://dx.doi.org/10.1080/00330124.2018.1559652.

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42

Walls, Judith L., Phillip H. Phan, and Pascual Berrone. "Measuring Environmental Strategy: Construct Development, Reliability, and Validity." Business & Society 50, no. 1 (2011): 71–115. http://dx.doi.org/10.1177/0007650310394427.

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43

Sathasivam, N., B. J. Mein, D. Wye, and R. Benzie. "EP03.01: Interclass observer reliability in measuring uterocervical angle." Ultrasound in Obstetrics & Gynecology 52 (October 2018): 199–200. http://dx.doi.org/10.1002/uog.19806.

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44

Mehlsen, J., J. Bonde, C. Stadeager, M. Rehling, M. Tangø, and J. Trap-Jensen. "Reliability of impedance cardiography in measuring central haemodynamics." Clinical Physiology 11, no. 6 (1991): 579–88. http://dx.doi.org/10.1111/j.1475-097x.1991.tb00677.x.

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45

Ferrando, Pere Joan. "A graded response model for measuring person reliability." British Journal of Mathematical and Statistical Psychology 62, no. 3 (2009): 641–62. http://dx.doi.org/10.1348/000711008x377745.

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46

Alviso, Debra J., Gordon T. Dong, and Gary L. Lentell. "Intertester Reliability for Measuring Pelvic Tilt in Standing." Physical Therapy 68, no. 9 (1988): 1347–51. http://dx.doi.org/10.1093/ptj/68.9.1347.

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47

krippendorff, Klaus. "Measuring the Reliability of Qualitative Text Analysis Data." Quality & Quantity 38, no. 6 (2004): 787–800. http://dx.doi.org/10.1007/s11135-004-8107-7.

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48

Thapa, Manish, Jose Espejo-Uribe, and Evangelos Pournaras. "Measuring network reliability and repairability against cascading failures." Journal of Intelligent Information Systems 52, no. 3 (2017): 573–94. http://dx.doi.org/10.1007/s10844-017-0477-0.

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49

Koo, Hyeongmo, Yongwan Chun, and David W. S. Wong. "Measuring Local Spatial Autocorrelation with Data Reliability Information." Professional Geographer 73, no. 3 (2021): 464–80. http://dx.doi.org/10.1080/00330124.2021.1898993.

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50

Tuijthof, Gabrielle J. M., Just L. Herder, Peter E. Scholten, C. Niek van Dijk, and Peter V. Pistecky. "Measuring Alignment of the Hindfoot." Journal of Biomechanical Engineering 126, no. 3 (2004): 357–62. http://dx.doi.org/10.1115/1.1762897.

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In subtalar arthrodesis operations, correction of the hindfoot alignment is performed in about half of the cases. To improve the quality of the operation, a measurement system was developed which reliably measures the hindfoot angle pre-, per-, and postoperatively. This device was evaluated by measuring subjects in standing weightbearing position and in prone nonweightbearing position. The results were compared with hindfoot angles constructed on posterior photographic images. The results are similar to other studies (all maximum values): intratester accuracy 1.4°, intertester accuracy 2.2°, intratester reliability 0.9, and intertester reliability 0.74. The proposed device will improve the quality of correction, because it enables peroperative measurement of hindfoot alignment.

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