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Journal articles on the topic 'Bias'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 September 2024

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1

Bailey, R. A. "Bias bias." New Scientist 199, no. 2664 (July 2008): 22–23. http://dx.doi.org/10.1016/s0262-4079(08)61727-3.

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2

Tandoc, Edson C., Bruno Takahashi, and Ryan J. Thomas. "Bias vs. Bias." Journalism Practice 12, no. 7 (July 13, 2017): 834–49. http://dx.doi.org/10.1080/17512786.2017.1343095.

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3

Raine, Rosalind. "Bias measuring bias." Journal of Health Services Research & Policy 7, no. 1 (January 1, 2002): 65–67. http://dx.doi.org/10.1258/1355819021927584.

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The British National Health Service and other publicly funded health systems operate on the principle that health care should be provided solely on the basis of need. Yet the literature abounds with reports of bias in health care use. In order to defend such a charge, two conditions must be met. The first condition is that treatment decisions must be shown to be unfair in that they are not made solely on the basis of need. This paper demonstrates the importance of considering the fair distribution of health care from two, related, perspectives. The first is that people with equal needs should be treated the same (equal use for equal need). This is referred to as the achievement of horizontal equity. The alternative perspective is that people with greater needs should have more treatment than those with lesser needs (unequal use for unequal need). This is referred to as the achievement of vertical equity. Although these perspectives are logically linked, demonstration of equal use for equal need does not necessarily indicate unequal use for unequal need. This is because it cannot be assumed that equal use occurs at every level of need. The second condition that must be met is that clinical judgement must be shown to be influenced by prejudicial notions about patients. Such research is fraught with methodological difficulties, and the charge of biased clinical decision-making is usually made as a result of a process of exclusion. Methods that could be used to examine the extent to which inequalities in health care use are due to bias are described.
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4

Brighton, Henry, and Gerd Gigerenzer. "The bias bias." Journal of Business Research 68, no. 8 (August 2015): 1772–84. http://dx.doi.org/10.1016/j.jbusres.2015.01.061.

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5

Harcum, E. Rae, and Ellen Rosen. "Bias, bias, who doesn't have the bias?" Contemporary Psychology: A Journal of Reviews 40, no. 6 (June 1995): 607. http://dx.doi.org/10.1037/005052.

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6

Gibson, John P. "Bias in naming bias." Nature Reviews Genetics 3, no. 1 (January 2002): 80. http://dx.doi.org/10.1038/nrg700-c1.

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7

Farkas, Donka F. "Bias and anti-bias." Approaches to Hungarian 18 2, no. 1 (June 19, 2023): 96–126. http://dx.doi.org/10.1075/jul.00016.far.

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Abstract This paper proposes an account of the interpretive effects of two discourse particles in Hungarian, talán and vajon, within the view of context and context change developed in Farkas & Roelofsen (2017), and shows that the restrictions on their distribution follow from their interpretive properties. Building on Gyuris (2022), talán will be treated as signaling epistemic bias in both declaratives and interrogatives. Following Farkas (2022), vajon will be treated as a non-intrusive question marker, which, in the account proposed, is incompatible with bias markers. The restrictions on the sentence types in which these particles occur, as well as the fact that there are restrictions on their co-occurence, will be derived from their interpretive contribution.
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8

G.S., Vidya, and Vijaygeetha M. "Bias in Epidemiology." Indian Journal of Preventive Medicine 4, no. 2 (2016): 75–81. http://dx.doi.org/10.21088/ijpm.2321.5917.4216.4.

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9

Baumgartner, Renate, and Sarah Kuhn. "Bias does not equal bias." TATuP - Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis 30, no. 2 (July 26, 2021): 69–70. http://dx.doi.org/10.14512/tatup.30.2.69.

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10

Faillie, Jean-Luc. "Indication bias or protopathic bias?" British Journal of Clinical Pharmacology 80, no. 4 (September 16, 2015): 779–80. http://dx.doi.org/10.1111/bcp.12705.

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11

Sedgwick, P. "Selection bias versus allocation bias." BMJ 346, may24 4 (May 24, 2013): f3345. http://dx.doi.org/10.1136/bmj.f3345.

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12

Nunan, David, Carl Heneghan, and Elizabeth A. Spencer. "Catalogue of bias: allocation bias." BMJ Evidence-Based Medicine 23, no. 1 (January 24, 2018): 20–21. http://dx.doi.org/10.1136/ebmed-2017-110882.

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This article is part of a series of articles featuring the Catalogue of Bias introduced in this volume of BMJ Evidence-Based Medicine that describes allocation bias and outlines its potential impact on research studies and the preventive steps to minimise its risk. Allocation bias is a type of selection bias and is relevant to clinical trials of interventions. Knowledge of interventions prior to group allocation can result in systematic differences in important characteristics that could influence study findings. Allocation bias can overestimate effect size by up to 30%–40%. Sequentially numbered, opaque, sealed envelopes; containers; pharmacy-controlled randomisation and central computer randomisation are methods to minimise allocation bias.
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13

Nunan, David, Jeffrey Aronson, and Clare Bankhead. "Catalogue of bias: attrition bias." BMJ Evidence-Based Medicine 23, no. 1 (January 24, 2018): 21–22. http://dx.doi.org/10.1136/ebmed-2017-110883.

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This article is part of a series of articles featuring the Catalogue of Bias introduced in this volume of BMJ Evidence-Based Medicine that describes attrition bias and outlines its potential impact on research studies and the preventive steps to minimise its risk. Attrition bias is a type of selection bias due to systematic differences between study groups in the number and the way participants are lost from a study. Differences between people who leave a study and those who continue, particularly between study groups, can be the reason for any observed effect and not the intervention itself. Associations for mortality in trials of tranexamic acid and upper gastrointestinal bleeding were no longer apparent after studies with high or unclear risk of attrition bias were removed. Over-recruitment can help prevent important attrition bias. Sampling weights and tailored replenishment samples can help to compensate for the effects of attrition bias when present.
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14

Mahtani, Kamal, Elizabeth A. Spencer, Jon Brassey, and Carl Heneghan. "Catalogue of bias: observer bias." BMJ Evidence-Based Medicine 23, no. 1 (January 24, 2018): 23–24. http://dx.doi.org/10.1136/ebmed-2017-110884.

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This article is part of a series featured from the Catalogue of Bias introduced in this volume of BMJ Evidence-Based Medicine that describes biases and outlines their potential impact in research studies. Observer bias is systematic discrepancy from the truth during the process of observing and recording information for a study. Many healthcare observations are at risk of this bias. Evidence shows that treatment effect estimates can be exaggerated by a third to two-thirds in the presence of observer bias in outcome assessment. Preventing observer bias involves proper masking in intervention studies including the use of matched placebo interventions where appropriate and training of observers to make assessment consistent and reduce biases resulting from conscious or unconscious prejudices. Where observers are involved in a research study, it is probably not possible for the study to be entirely free of observer biases.
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15

Chapman, Jens. "Eliminating bias/living with bias?" Evidence-Based Spine-Care Journal 3, no. 03 (January 9, 2013): 5–6. http://dx.doi.org/10.1055/s-0032-1327802.

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16

DeVito, Nicholas J., and Ben Goldacre. "Catalogue of bias: publication bias." BMJ Evidence-Based Medicine 24, no. 2 (December 6, 2018): 53–54. http://dx.doi.org/10.1136/bmjebm-2018-111107.

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17

Middleton, Joel A., Marc A. Scott, Ronli Diakow, and Jennifer L. Hill. "Bias Amplification and Bias Unmasking." Political Analysis 24, no. 3 (2016): 307–23. http://dx.doi.org/10.1093/pan/mpw015.

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In the analysis of causal effects in non-experimental studies, conditioning on observable covariates is one way to try to reduce unobserved confounder bias. However, a developing literature has shown that conditioning on certain covariates may increase bias, and the mechanisms underlying this phenomenon have not been fully explored. We add to the literature on bias-increasing covariates by first introducing a way to decompose omitted variable bias into three constituent parts: bias due to an unobserved confounder, bias due toexcludingobserved covariates, and bias due to amplification. This leads to two important findings. Although instruments have been the primary focus of the bias amplification literature to date, we identify the fact that the popular approach of adding group fixed effects can lead to bias amplification as well. This is an important finding because many practitioners think that fixed effects are a convenient way to account for any and all group-level confounding and are at worst harmless. The second finding introduces the concept of biasunmaskingand shows how it can be even more insidious than bias amplification in some cases. After introducing these new results analytically, we use constructed observational placebo studies to illustrate bias amplification and bias unmasking with real data. Finally, we propose a way to add bias decomposition information to graphical displays for sensitivity analysis to help practitioners think through the potential for bias amplification and bias unmasking in actual applications.
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18

Martínez-Frías, María Luisa. "Interviewer bias and maternal bias." Teratology 47, no. 6 (June 1993): 531–32. http://dx.doi.org/10.1002/tera.1420470604.

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19

Olson, Kristen. "Survey Participation, Nonresponse Bias, Measurement Error Bias, and Total Bias." Public Opinion Quarterly 70, no. 5 (2006): 737–58. http://dx.doi.org/10.1093/poq/nfl038.

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20

Devi Susilayani, Nasrullah Idris, Sarina, Maswati,. "Pengembangan Alat Ukur Indeks Bias Menggunakan Prisma Berongga dari Lembaran Kaca Komersial Biasa dan Laser He-Ne untuk Pengujian Kualitas Minyak Goreng." Risalah Fisika 1, no. 2 (July 31, 2017): 39–46. http://dx.doi.org/10.35895/rf.v1i2.45.

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Prisma berongga telah dibuat dari lembaran kaca komersial biasa sebagai instrumen optik sederhana dan murah untuk penentuan secara cepat kualitas minyak goreng dengan pengukuran indeks biasnya. Dimensi lembaran kaca komersial tersebut yang dijadikan sisi-sisi prisma tersebut adalah 10 cm × 10 cm dengan ketebalan 5 cm. Pengukuran indeks bias minyak goreng dilakukan dengan memasukkan sampel minyak goreng ke dalam rongga prisma tersebut, kemudian dilewatkan berkas cahaya helium neon (He-Ne) dan diukur sudut deviasi berkas laser tersebut setelah lewat melalui prisma berongga tersebut. Indeks bias minyak goreng kemudian dihitung menggunakan besarnya sudut deviasi hasil pengukuran dan sudut apit prisma tersebut. Sampel yang digunakan dalam penelitian ini ada 2 jenis, yaitu minyak goreng kualitas baik (minyak baru) dan minyak goreng kualitas rendah (minyak bekas pakai). Ditemukan bahwa indeks bias minyak goreng kualitas bagus (minyak goreng baru) adalah 1,5054. Sebagai pembanding, pengukuran indeks bias juga dilakukan untuk sampel air terdistilasi (aquades) dan ditemukan bahwa hasil pengukuran menggunakan prisma berongga yang dibuat dari kaca komersial biasa ini sangat dekat dengan hasil pengukuran menggunakan refraktometer Abbe. Ini menunjukkan bahwa indeks bias minyak goreng dapat diukur menggunakan prisma berongga yang dibuat dari kaca komersial biasa dengan akurasi yang relatif tinggi. Selanjutnya, telah ditemukan juga bahwa indeks bias minyak goreng kualitas rendah (bekas pakai) meningkat seiring meningkatnya frekuensi pemakaiannya, di mana minyak goreng yang telah dipakai tiga kali indeks biasnya adalah 1,5402. Hasil pengukuran indeks bias ini memperlihatkan bahwa indeks bias minyak goreng kualitas bagus (minyak baru) lebih rendah dibandingkan indeks bias minyak goreng kualitas rendah (minyak bekas pakai). Hal ini menyiratkan bahwa indeks bias minyak goreng semakin meningkat seiring penurunan kualitas minyak goreng sehingga dapat dikatakan bahwa indeks bias adalah sifat optik yang representatif untuk menyatakan kualitas minyak goreng. Hasil penerapan awal ini membuktikan prisma berongga yang dibuat dari lembaran kaca komersial biasa ini dapat digunakan sebagai instrumen optik sederhana untuk pengukuran secara cepat, murah, dan akurat indeks bias minyak goreng guna penentuan kualitasnya. Kata kunci: prisma berongga, kaca komersial, indeks bias, kualitas minyak goreng, laser He-Ne
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21

Stout, Holly. "Bias." Judicial Review 16, no. 4 (December 2011): 458–82. http://dx.doi.org/10.5235/108546811799320862.

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22

Delgado-Rodriguez, M. "Bias." Journal of Epidemiology & Community Health 58, no. 8 (August 1, 2004): 635–41. http://dx.doi.org/10.1136/jech.2003.008466.

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23

Muir, D. C. "Bias." Occupational and Environmental Medicine 50, no. 12 (December 1, 1993): 1122–23. http://dx.doi.org/10.1136/oem.50.12.1122.

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24

Berlet, Gregory C. "Bias." Foot & Ankle Specialist 4, no. 2 (April 2011): 76. http://dx.doi.org/10.1177/1938640011402007.

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25

Sitthi-amorn, C., and V. Poshyachinda. "Bias." Lancet 342, no. 8866 (July 1993): 286–88. http://dx.doi.org/10.1016/0140-6736(93)91823-5.

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26

Fearn, Tom. "Bias." NIR news 9, no. 6 (December 1998): 4–5. http://dx.doi.org/10.1255/nirn.491.

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27

Neher, Jon O. "Bias." Evidence-Based Practice 23, no. 1 (January 2020): 2. http://dx.doi.org/10.1097/ebp.0000000000000768.

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28

Gigerenzer, Gerd. "The Bias Bias in Behavioral Economics." Review of Behavioral Economics 5, no. 3-4 (December 31, 2018): 303–36. http://dx.doi.org/10.1561/105.00000092.

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29

Gawronski, Bertram, Alison Ledgerwood, and Paul W. Eastwick. "Implicit Bias ≠ Bias on Implicit Measures." Psychological Inquiry 33, no. 3 (July 3, 2022): 139–55. http://dx.doi.org/10.1080/1047840x.2022.2106750.

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30

Sedgwick, P. "Non-response bias versus response bias." BMJ 348, apr09 1 (April 9, 2014): g2573. http://dx.doi.org/10.1136/bmj.g2573.

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31

Hedden, Brian. "Hindsight bias is not a bias." Analysis 79, no. 1 (June 4, 2018): 43–52. http://dx.doi.org/10.1093/analys/any023.

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32

Norman, Geoff. "The Bias in researching cognitive bias." Advances in Health Sciences Education 19, no. 3 (June 6, 2014): 291–95. http://dx.doi.org/10.1007/s10459-014-9517-5.

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33

Cislak, Aleksandra, Magdalena Formanowicz, and Tamar Saguy. "Bias against research on gender bias." Scientometrics 115, no. 1 (February 17, 2018): 189–200. http://dx.doi.org/10.1007/s11192-018-2667-0.

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34

Garb, Howard N. "Race Bias, Social Class Bias, and Gender Bias in Clinical Judgment." Clinical Psychology: Science and Practice 4, no. 2 (June 1997): 99–120. http://dx.doi.org/10.1111/j.1468-2850.1997.tb00104.x.

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35

Pearce, N., and L. Richiardi. "Commentary: Three worlds collide: Berkson's bias, selection bias and collider bias." International Journal of Epidemiology 43, no. 2 (February 28, 2014): 521–24. http://dx.doi.org/10.1093/ije/dyu025.

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36

A., Subramaniam V. "Overconfidence Bias in Thirukkural." International Journal of Trend in Scientific Research and Development Volume-3, Issue-2 (February 28, 2019): 113–15. http://dx.doi.org/10.31142/ijtsrd20296.

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37

Daniel, Sharon, Gideon Koren, Eitan Lunenfeld, and Amalia Levy. "Reply to ‘Indication bias or protopathic bias?’." British Journal of Clinical Pharmacology 80, no. 4 (September 16, 2015): 781. http://dx.doi.org/10.1111/bcp.12708.

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38

Westreich, Daniel. "Berksonʼs Bias, Selection Bias, and Missing Data." Epidemiology 23, no. 1 (January 2012): 159–64. http://dx.doi.org/10.1097/ede.0b013e31823b6296.

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39

Daniels, Craig E., Victor M. Montori, and Denise M. Dupras. "Effect of Publication Bias on Retrieval Bias." Academic Medicine 77, no. 3 (March 2002): 266. http://dx.doi.org/10.1097/00001888-200203000-00019.

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40

Dehn, Doris M., and Edgar Erdfelder. "What kind of bias is hindsight bias?" Psychological Research 61, no. 2 (June 17, 1998): 135–46. http://dx.doi.org/10.1007/s004260050020.

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41

Mitchell, Gregory. "Measuring Situational Bias or Creating Situational Bias?" Psychological Inquiry 28, no. 4 (October 2, 2017): 292–96. http://dx.doi.org/10.1080/1047840x.2017.1373553.

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42

Frusciante, Noemi, and Ravi K. Sheth. "Lagrangian bias in the local bias model." Journal of Cosmology and Astroparticle Physics 2012, no. 11 (November 8, 2012): 016. http://dx.doi.org/10.1088/1475-7516/2012/11/016.

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43

Przepiorka, Donna, and Elihu Estey. "When is selection bias not selection bias?" American Journal of Hematology 52, no. 4 (August 1996): 330–31. http://dx.doi.org/10.1002/(sici)1096-8652(199608)52:4<330::aid-ajh20>3.0.co;2-g.

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44

Sun, Xiaohan. "Decision bias: Consumer behaviour influenced by bias." Journal of Education, Humanities and Social Sciences 23 (December 13, 2023): 527–31. http://dx.doi.org/10.54097/ehss.v23i.13111.

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This essay will explain the impact of decision bias on everyday consumer behaviour. Firstly, the meaning of decision bias will be explained and then examples of the behavioural processes by which consumer behaviour is affected by it will be presented. Next, the processes by which consumer behaviour is affected will be specifically analysed by introducing four types of bias, confirmation bias, valuation bias, loss aversion, and overconfidence. Finally, the complexity and importance of the study of decision bias in practical decision making is further discussed using the example of consumers buying and firms selling financial products. This article will be used to illustrate how businesses use the impact of biases on consumers to gain additional benefits by gaining a deeper understanding of their target audience, and how consumers can identify and avoid falling into decision-making traps once they are aware of these biases. Businesses and consumers make better economic decisions that lead to more beneficial and sustainable economic interactions.
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45

Afriani, Dila, and Halmawati Halmawati. "Pengaruh Cognitive Dissonance Bias, Overconfidence Bias Dan Herding Bias Terhadap Pengambilan Keputusan Investasi." JURNAL EKSPLORASI AKUNTANSI 1, no. 4 (December 5, 2019): 1650–65. http://dx.doi.org/10.24036/jea.v1i4.168.

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This study aims to determine whether cognitive dissonance bias, overconfidence bias and herding bias have a significant effect on stock investment decision making partially and simultaneously. the sample in this study were students of the Faculty of Economics, Padang State University. The sampling technique was simple random sampling . A total of 133 questionnaires were returned in complete condition and processed. by using multiple linear regression techniques, the results show that cognitive dissonance bias and overconfidence bias do not affect stock investment decisions. Herding bias has a positive and significant influence on stock investment decision making.
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46

Knobloch-Westerwick, Silvia, Cornelia Mothes, and Nick Polavin. "Confirmation Bias, Ingroup Bias, and Negativity Bias in Selective Exposure to Political Information." Communication Research 47, no. 1 (July 18, 2017): 104–24. http://dx.doi.org/10.1177/0093650217719596.

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Selective reading of political online information was examined based on cognitive dissonance, social identity, and news values frameworks. Online reports were displayed to 156 Americans while selective exposure was tracked. The news articles that participants chose from were either conservative or liberal and also either positive or negative regarding American political policies. In addition, information processing styles (cognitive reflection and need-for-cognition) were measured. Results revealed confirmation and negativity biases, per cognitive dissonance and news values, but did not corroborate the hypothesis derived from social identity theory. Greater cognitive reflection, greater need-for-cognition, and worse affective state fostered the confirmation bias; stronger social comparison tendency reduced the negativity bias.
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47

Bibi binte Abdullah, Roksana. "Gender Bias in Malay Language." International Journal of Social Science and Humanity 6, no. 6 (June 2016): 456–61. http://dx.doi.org/10.7763/ijssh.2016.v6.690.

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48

Kumar, Naresh. "Confirmation Bias” and its Significance." International Journal of Research Publication and Reviews 5, no. 7 (July 2024): 2088–92. http://dx.doi.org/10.55248/gengpi.5.0724.1801.

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49

Harris, M. I. "Selection Bias." Diabetes Care 9, no. 1 (January 1, 1986): 94. http://dx.doi.org/10.2337/diacare.9.1.94a.

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50

Berlin, Leonard. "Hindsight Bias." American Journal of Roentgenology 175, no. 3 (September 2000): 597–601. http://dx.doi.org/10.2214/ajr.175.3.1750597.

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