Publication Date
In 2025 | 332 |
Since 2024 | 1806 |
Since 2021 (last 5 years) | 7172 |
Since 2016 (last 10 years) | 14076 |
Since 2006 (last 20 years) | 22114 |
Descriptor
Social Bias | 11711 |
Racial Bias | 10176 |
Foreign Countries | 8272 |
Bias | 5509 |
Females | 5178 |
Higher Education | 4903 |
Gender Bias | 4573 |
Sex Bias | 4091 |
Elementary Secondary Education | 3958 |
Student Attitudes | 3814 |
Test Bias | 3688 |
More ▼ |
Source
Author
Publication Type
Education Level
Audience
Practitioners | 1317 |
Teachers | 1169 |
Researchers | 642 |
Administrators | 364 |
Policymakers | 280 |
Students | 171 |
Counselors | 103 |
Parents | 95 |
Community | 55 |
Media Staff | 19 |
Support Staff | 11 |
More ▼ |
Location
Canada | 1054 |
Australia | 851 |
United States | 787 |
California | 685 |
United Kingdom | 661 |
United Kingdom (England) | 458 |
South Africa | 410 |
Texas | 300 |
Turkey | 298 |
Germany | 293 |
China | 292 |
More ▼ |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Meets WWC Standards without Reservations | 3 |
Meets WWC Standards with or without Reservations | 4 |
Does not meet standards | 3 |
Daniela Torres; Surya Pulukuri; Binyomin Abrams – Journal of Chemical Education, 2023
OrgoPrep, a summer preparatory program integrating multiple active-learning elements (i.e., interactive videos with problem-solving and feedback, synchronous peer-led instruction, and collaborative work), was previously shown to improve academic outcomes in organic chemistry for all students. The present study examined how OrgoPrep differentially…
Descriptors: Organic Chemistry, Science Education, Summer Science Programs, Active Learning
Weese, James D.; Turner, Ronna C.; Ames, Allison; Crawford, Brandon; Liang, Xinya – Educational and Psychological Measurement, 2022
A simulation study was conducted to investigate the heuristics of the SIBTEST procedure and how it compares with ETS classification guidelines used with the Mantel-Haenszel procedure. Prior heuristics have been used for nearly 25 years, but they are based on a simulation study that was restricted due to computer limitations and that modeled item…
Descriptors: Test Bias, Heuristics, Classification, Statistical Analysis
Dimitrov, Dimiter M.; Atanasov, Dimitar V. – Educational and Psychological Measurement, 2022
This study offers an approach to testing for differential item functioning (DIF) in a recently developed measurement framework, referred to as "D"-scoring method (DSM). Under the proposed approach, called "P-Z" method of testing for DIF, the item response functions of two groups (reference and focal) are compared by…
Descriptors: Test Bias, Methods, Test Items, Scoring
Kulinskaya, Elena; Mah, Eung Yaw – Research Synthesis Methods, 2022
To present time-varying evidence, cumulative meta-analysis (CMA) updates results of previous meta-analyses to incorporate new study results. We investigate the properties of CMA, suggest possible improvements and provide the first in-depth simulation study of the use of CMA and CUSUM methods for detection of temporal trends in random-effects…
Descriptors: Meta Analysis, Computation, Statistical Analysis, Statistical Bias
Johnson, Matthew S.; Liu, Xiang; McCaffrey, Daniel F. – Journal of Educational Measurement, 2022
With the increasing use of automated scores in operational testing settings comes the need to understand the ways in which they can yield biased and unfair results. In this paper, we provide a brief survey of some of the ways in which the predictive methods used in automated scoring can lead to biased, and thus unfair automated scores. After…
Descriptors: Psychometrics, Measurement Techniques, Bias, Automation
Yumin Zhang – ProQuest LLC, 2022
This dissertation address two significant challenges in the causal inference workflow for Big Observational Data. The first is designing Big Observational Data with high-dimensional and heterogeneous covariates. The second is performing uncertainty quantification for estimates of causal estimands that are obtained from the application of black box…
Descriptors: Computation, Observation, Data, Public Colleges
Tight, Malcolm – Higher Education Quarterly, 2023
Many forms of bias have been identified in higher education research, and in educational and social research in general. This article identifies a further form of bias, positivity bias, and places it in this broader context. Positivity bias is the tendency, in some forms of published higher education research, to only or chiefly report examples of…
Descriptors: Positive Attitudes, Bias, Educational Research, Higher Education
Scheitle, Christopher P.; Platt, Lisa F.; House-Niamke, Stephanie M. – Innovative Higher Education, 2023
Research has examined the influence of a graduate student matching their advisor's demographic characteristics on a variety of outcomes, but comparatively few studies have examined students' preferences concerning such matching. Using data from a national survey of U.S. graduate students in five natural and social science disciplines, the analyses…
Descriptors: Graduate Students, Faculty Advisers, Gender Bias, Race
Ikeda, Kenji – Metacognition and Learning, 2023
This experimental study examined whether the uninformative anchoring effect, which should be ignored, on judgments of learning (JOLs) was eliminated through the learning experience. In the experiments, the participants were asked to predict whether their performance on an upcoming test would be higher or lower than the anchor value (80% in the…
Descriptors: Metacognition, Learning Processes, Evaluative Thinking, Learning Experience
Kreiner, Hamutal; Gamliel, Eyal – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2023
"Attribute-framing bias" reflects people's tendency to evaluate objects framed positively more favorably than the same objects framed negatively. Although biased by the framing valence, evaluations are nevertheless calibrated to the magnitude of the target attribute. In three experiments that manipulated magnitudes in different ways, we…
Descriptors: Responses, Bias, Evaluation, Cognitive Processes
Randall, Jennifer – Educational Assessment, 2023
In a justice-oriented antiracist assessment process, attention to the disruption of white supremacy must occur at every stage--from construct articulation to score reporting. An important step in the assessment development process is the item review stage often referred to as Bias/Fairness and Sensitivity Review. I argue that typical approaches to…
Descriptors: Social Justice, Racism, Test Bias, Test Items
Nazari, Sanaz; Leite, Walter L.; Huggins-Manley, A. Corinne – Educational and Psychological Measurement, 2023
Social desirability bias (SDB) has been a major concern in educational and psychological assessments when measuring latent variables because it has the potential to introduce measurement error and bias in assessments. Person-fit indices can detect bias in the form of misfitted response vectors. The objective of this study was to compare the…
Descriptors: Social Desirability, Bias, Indexes, Goodness of Fit
Gardner-Neblett, Nicole; Addie, Atiya; Eddie, Anissa L.; Chapman, Sandra K.; Duke, Nell K.; Vallotton, Claire D. – Reading Teacher, 2023
During the first year of life, children begin to develop preferences for their own racial group over others. To interrupt the development of these and other biases during infancy and toddlerhood, educators can use books to promote anti-racist and anti-bias thinking and behaviors in children, while also supporting children's emergent literacy. This…
Descriptors: Racism, Bias, Infants, Toddlers
Grouling, Jennifer – Assessment Update, 2023
In this article, the author shares some tips for improving university-wide assessment conversations, focusing on meetings with faculty creating and doing assessment. The lessons are from a multi-year project where the author observed meetings and interviewed faculty about their involvement in university-wide assessment.
Descriptors: College Faculty, Faculty College Relationship, Self Evaluation (Groups), Scoring Rubrics
Ulkhaq, M. Mujiya; Pramono, Susatyo N. W.; Adyatama, Arga – Journal of Applied Research in Higher Education, 2023
Purpose: Judging bias is ironically an inherent risk in every competition, which might threaten the fairness and legitimacy of the competition. The patriotism effect represents one source of judging bias as the judge favors contestants who share the same sentiments, such as the nationalistic, racial, or cultural aspects. This study attempts to…
Descriptors: Competition, College Students, Foreign Countries, Judges