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Foundations of Psychometrics in Educational Assessment

Introduction to Psychometrics and Item Analysis

Assessment Data and Classical Test Theory Foundations

Preparing Item Analysis Data

Item Difficulty Analysis

Understanding Item Difficulty

Calculating and Interpreting Item Difficulty Indices

Using Item Difficulty for Test Improvement

Item Discrimination Analysis

The Purpose of Item Discrimination

Upper-Lower Group Discrimination Index

Point-Biserial Correlation

Making Decisions from Discrimination Evidence

Distractor Efficiency in Multiple-Choice Items

Multiple-Choice Item Structure and Distractor Functioning

Analyzing Distractor Selection Patterns

Improving Distractor Efficiency

Introduction to Item Response Theory

From Classical Test Theory to Item Response Theory

IRT Item Parameters

Interpreting Item Characteristic Curves

Integrating Evidence for Item Evaluation

Quiz

Question 1: Item Difficulty

Question 2: Interpreting Discrimination

Question 3: Distractor Efficiency

Question 4: Point-Biserial Correlation

Question 5: Introduction to IRT

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Course Feedback Survey

Dive into the science of educational measurement with our comprehensive course, "Psychometrics and Quantitative Item Analysis in Educational Assessment. " Whether you are an educator, instructional designer, or assessment specialist, understanding how to evaluate and improve test items is crucial for developing fair, reliable, and valid assessments.

Dive into the science of educational measurement with our comprehensive course, "Psychometrics and Quantitative Item Analysis in Educational Assessment." Whether you are an educator, instructional designer, or assessment specialist, understanding how to evaluate and improve test items is crucial for developing fair, reliable, and valid assessments. This course provides a deep dive into both Classical Test Theory (CTT) and an introduction to Item Response Theory (IRT), equipping you with the statistical tools needed to analyze examinee response data effectively.

Throughout this course, you will learn how to calculate and interpret key psychometric metrics, including item difficulty (p-values), item discrimination (point-biserial correlations), and distractor efficiency for multiple-choice questions. You will explore how to organize response matrices, identify flawed items, and make data-driven decisions to revise or retain assessment content. Moving beyond the basics, the course introduces IRT concepts such as item characteristic curves and difficulty/discrimination parameters, bridging the gap between traditional and modern measurement practices.

By the end of this program, you will possess a robust framework for conducting quantitative item analysis, ensuring your educational tests accurately reflect student learning and align with intended outcomes. Join us to master the quantitative techniques that drive high-quality educational assessment and elevate your psychometric expertise.

Psychometrics and Quantitative Item Analysis in Educational Assessment

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            <p>Educational assessment relies heavily on the rigorous evaluation of test items to ensure reliability, validity, and fairness in measuring student learning. This course provides a comprehensive exploration of the statistical techniques and theoretical frameworks used to evaluate and improve educational tests. By examining the foundational principles of psychometrics and Classical Test Theory (CTT), the curriculum equips professionals with the analytical tools necessary to transform raw examinee response data into actionable insights. The coursework emphasizes the critical role of quantitative item analysis in the development, revision, and validation of assessments, ensuring that measurement instruments accurately reflect intended learning outcomes while adhering to ethical standards of fairness, confidentiality, and appropriate data use.</p>

<p>Throughout the modules, participants will systematically navigate the core components of item analysis to build a robust methodology for test improvement. The curriculum details the calculation and interpretation of item difficulty indices and item discrimination metrics, including the upper-lower group index and point-biserial correlations. Furthermore, the course delves into the structural evaluation of multiple-choice questions, focusing on distractor efficiency to identify and rectify nonfunctioning or misleading response options. Progressing beyond classical models, the final sections introduce Item Response Theory (IRT), exploring latent traits, item parameters, and item characteristic curves. By integrating these quantitative methodologies with qualitative expert review, learners will develop an evidence-based framework for making precise decisions regarding item retention, revision, or removal, ultimately contributing to the creation of highly effective and equitable educational assessments.</p>
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