Comprehensive Study Notes on Test Bias and Fairness

Introduction to Test Bias and Fairness

• Foundational Principle: Tests are not uniformly fair and just in their application or design.
• Educational Objective: It is essential to recognize how tests can be biased and to implement strategies to reduce such bias to ensure maximum fairness across all test-takers.
• Source Attribution: All content is derived from Salkind, Tests and Measurement 3e, SAGE Publishing (2018), unless explicitly stated otherwise.

Defining Test Bias and Fairness

• Test Bias: This occurs when test scores vary across different groups due to factors that are unrelated to the actual purpose of the test.
      * Example: If systematic differences in test scores appear as a function of gender rather than the specific trait or knowledge being measured, the test is considered biased.
• Test Fairness: This concept involves the situational and social aspects of testing. Requirements for fairness include:
      * Providing opportunities for testing in environments that are secure and controlled.
      * Ensuring there is no inherent bias built into the test itself.
      * Ensuring test-takers have full access to the testing situation.
      * Ensuring that test scores are interpreted in a fair manner.
• The Intersection of Bias and Fairness:
      * Test Fairness: Relates to the use of tests and the underlying social values that dictate that usage. It involves value-based judgments.
      * Test Bias: Relates to whether a test differentially favors one group over another as a result of statistical or content analysis.
      * Requirement: For a testing instrument to be ethically sound, it must be both fair and unbiased.

Consequential Validity and the Fair Test Movement

• Consequential Validity (Samuel Mesick):
      * Testers must be concerned with how tests are used and how the results are interpreted.
      * It is vital to consider the social and personal consequences resulting from those interpretations.
• The Fair Test Movement Principles:
      * Fairness and Validity: Assessments should be valid and provide equal opportunity to measure student knowledge and capabilities without bias.
      * Openness and Transparency: Assessments must be open. There should be public access to the tests and the associated testing data, including data regarding validity and reliability.
      * Appropriate Usage: Standardized test scores should never be the sole criteria used for making high-stakes decisions.
      * Multiple Assessments Over Time: The evaluation of students and schools should utilize multiple types of assessment conducted over a period of time.
          * One single measure is inadequate to define a person’s worth, knowledge, or academic achievement.
          * One single measure is inadequate for the evaluation of an institution.
      * Alternative Assessments: There is a need to design and implement evaluation methods that accurately and fairly identify the specific strengths and weaknesses of both students and programs.
      * Professional Development: Implementation of these assessments should include professional development to ensure that tests and data are used properly.

Models to Examine Test Bias

• Difference-Difference Bias:
      * This model examines differences between groups (e.g., comparing males and females on an intelligence test).
      * Critical Evaluation: If group differences exist, one must ask if the test is truly biased or if it is reflecting external factors. For instance, if two different ethnic groups differ in achievement test scores, the test might reflect differences in educational experiences rather than the inherent characteristic being measured.
      * Action: While noting performance differences is useful, the primary goal should be identifying the source of those differences.
• Item by Item Bias:
      * This involves examining performance on every individual item within a test.
      * Researchers look for discrepancies in how different groups perform on specific questions.
      * Tool: Item Response Theory (IRT) is a primary tool used for this level of analysis.
      * Interpretation: If males and females perform differently on a specific item, it must be determined if this is due to true ability or "other factors."
• Face Validity Bias:
      * This involves judges examining the actual content of the test for biased material.
      * Biased items are those that misrepresent age groups, ethnic groups, or gender characteristics.
      * Risk Factors: Bias often occurs in pictorial items or through the use of stereotyped language.
• The Cleary Model:
      * This model suggests that people with the same test scores should perform equally well on external criteria, regardless of their group membership.
      * Statistical Application: Use a regression model to predict outcomes (e.g., using SAT scores to predict first-year college performance). The prediction should be the same for males and females if the test is unbiased.

History and Practical Development of Unbiased Tests

• Historical Context (1960s and 1970s):
      * Reports highlighted that black children scored approximately $15$ points lower than white children on standardized intelligence tests.
      * Correction for Social Factors: When researchers account for social class and social status, this score difference is reduced to approximately $5$ to $7$ points.
      * Conclusion: It is imperative that test bias and fairness are central components of every discussion regarding testing.
• Steps for Developing Unbiased Tests:
      * Self-Awareness: Recognize your own internal biases and stereotypes.
      * Representative Review: Invite representatives from potentially biased groups to examine the test items personally.
      * Expert Consultation: Utilize experienced experts to assist in identifying potential points of bias.
      * Format Impact: Recognize that test-takers differ in various ways, and the physical form or medium of the test chosen can significantly impact performance.