Continue the eduKateSG Learning Node Series through the reading routes below. Return to the Learning Node reading index or the Examinations & Assessment Hub for the wider map.
Measurement, uncertainty and item evidence
- How Response Styles Work | When Rating Scales Measure How People Answer as Well as What They Believe
- How Person-Fit Analysis Works | Detect When One Response Pattern Does Not Behave Like the Measurement Model
- How Classification Consistency Works | Ask Whether Pass, Mastery or Placement Would Repeat
- How Test Information Works | Measure Where a Test Is Precise Instead of Hiding Behind One Reliability Number
- How Classification Accuracy Works | Ask Whether the Pass, Mastery or Placement Decision Is Actually Correct
- How Item Parameter Drift Works | When the Same Test Question Changes Difficulty Over Time
- How Item Calibration Works | Turn Response Data Into a Measurement Scale Without Pretending the Parameters Are Facts of Nature
- How Item Fit Statistics Work | Find the Question the Measurement Model Cannot Explain Before It Distorts the Scale
- How Test Characteristic Curves Work | Turn Latent Proficiency Into an Expected Test Score Without Confusing the Two Scales
- How Differential Test Functioning Works | Find Out Whether Item-Level DIF Changes the Test as a Whole
- How Ability Estimation Works | Turn a Response Pattern Into a Proficiency Estimate Without Pretending the Point Estimate Is Certain
- How Response-Time Modeling Works | Use Speed as Evidence Without Confusing Fast With Able
- How Multidimensional Item Response Theory Works | Measure More Than One Latent Skill Without Hiding the Profile Inside One Score
- How Common-Item Nonequivalent-Groups Linking Works | Compare Test Forms Even When the Candidate Groups Are Different
- How Subscore Added Value Works | Know When a Section Score Tells You More Than the Total Score Already Does
- How Augmented Subscores Work | Borrow Strength From the Total Score Without Erasing the Domain Profile
- How Conditional Standard Error of Measurement Works | Show Score Precision Where the Decision Is Actually Made
- How Evidence-Centered Design Works | Build the Claim, Evidence and Task Before Writing the Test
- How Concept Inventories Work | Test Conceptual Understanding Without Mistaking a Topic Quiz for a Theory of Learning
- How Curriculum-Based Measurement Works | Detect Learning Growth Before the Final Test Arrives
- How Latent Profile Analysis Works | Find Recurring Learner Profiles in Continuous Data Without Turning Them Into Fixed Types
- How Knowledge Space Theory Works | Map Feasible Learning States Before Choosing the Next Task
- How Dynamic Assessment Works | Measure What Changes When the Learner Receives Structured Help
- How Microgenetic Learning Analysis Works | Watch Strategy Change While Learning Is Actually Happening
- How Construct Underrepresentation Works | Find What the Test Never Gave the Learner a Chance to Show
- How Automated Test Assembly Works | Turn a Blueprint and Item Bank Into a Feasible Test Form
- How Item Field Testing Works | Learn How Questions Behave Before Their Scores Count
- How Assessment Process Data Works | Use Clicks, Timing and Action Sequences Without Mistaking Activity for Understanding
- How Generalisability Theory Works | Separate Person, Task, Rater and Occasion Variance Before Trusting a Score
- How Test Speededness Works | Find Out What the Clock Is Really Measuring
- How Matrix Sampling in Assessment Works | Share the Questions Without Confusing Missing With Wrong
- How Plausible-Value Analysis Works | Estimate Population Learning Without Inventing Individual Marks
Test design and adaptive assessment
- How Item Exposure Control Works | Protect an Adaptive Test Without Wasting the Item Bank
- How Adaptive Stopping Rules Work | Know When a Computerised Test Has Measured Enough
- How Content Balancing in Adaptive Testing Works | Keep Statistical Efficiency From Erasing the Test Blueprint
- How Adaptive Item Pool Sufficiency Works | Know When the Item Bank Is Large Enough in the Right Places
- How Shadow Testing Works | Assemble a Full Valid Test Behind Every Adaptive Item Choice
- How Multistage Testing Works | Adapt by Modules Instead of Choosing One Item at a Time
- How Computerized Classification Testing Works | Stop Testing When the Category Is Clear Enough
- How Testlet Design Works | Keep Shared Context Without Counting the Same Evidence Twice
Score linking, equating and scale maintenance
- How Vertical Scaling Works | Put Different Grade-Level Tests on One Growth Scale Without Inventing a Ruler
- How Anchor Item Selection Works | Build a Stable Bridge Between Test Forms Without Letting the Bridge Move
- How Scale Linking Error Works | Separate Real Score Change From Uncertainty in the Bridge Between Test Forms
- How Random-Groups Equating Works | Compare Test Forms by Making the Candidate Groups Equivalent Before the Scores Are Linked
- How Single-Group Equating Works | Let the Same Candidates Take Both Forms Without Letting Order Effects Rewrite the Link
- How Equipercentile Equating Works | Match Scores by Percentile Position When the Relationship Between Forms Is Not a Straight Line
- How Linear Equating Works | Align Means and Standard Deviations Without Pretending the Score Distributions Are Identical
- How Mean Equating Works | Correct an Average Form-Difficulty Shift Without Inventing a More Complicated Score Relationship
- How Circle-Arc Equating Works | Add Just Enough Curvature for Small Samples Without Letting Noise Draw the Score Curve
- How Kernel Equating Works | Smooth Discrete Score Distributions Before Matching Percentiles Without Hiding the Uncertainty
- How IRT True-Score Equating Works | Link Test Forms Through a Common Proficiency Scale Before Converting Expected Scores
- How IRT Observed-Score Equating Works | Carry Measurement Error Through the Model Instead of Equating Only Expected Scores
- How Preequating Works | Put a New Test Form on Scale Before Operational Scores Need to Be Reported
- How Local Equating Works | Let Score Equivalence Change With Proficiency Instead of Forcing One Global Conversion
- How Score-Scale Maintenance Works | Keep a Testing Programme on One Meaningful Metric Across Years of New Forms
- How Raw-to-Scale Score Conversion Works | Detect Gaps and Clumps Before a Reporting Scale Distorts the Measurement
Validity, reporting and responsible interpretation
- How Bias and Sensitivity Review Works | Catch Irrelevant Barriers Before an Assessment Item Goes Live
- How Automated Scoring Validation Works | Use Machine Scores Without Letting Agreement Replace Validity
- How Response Process Evidence Works | Check Whether the Task Elicits the Thinking the Score Is Supposed to Represent
- How Scoring Rubric Validation Works | Turn Performance Criteria Into Scores Without Pretending the Rubric Makes Judgement Objective
- How Distractor Analysis Works | Learn From Wrong Options Without Treating Every Error as a Misconception
- How Score Interpretation and Use Arguments Work | Build the Claim Chain Before Acting on a Test Score
- How Assessment Mode Effects Work | Check Whether Paper, Computer, Device or Interface Changed the Evidence
- How Score Reports Work | Communicate Results Without Turning Precision Into False Certainty
- How Assessment Consequence Monitoring Works | Check What Decisions Produce After Scores Leave the Test