Techniques to improve test items and instruction

Slides:



Advertisements
Similar presentations
Alternate Choice Test Items
Advertisements

Item Analysis.
FACULTY DEVELOPMENT PROFESSIONAL SERIES OFFICE OF MEDICAL EDUCATION TULANE UNIVERSITY SCHOOL OF MEDICINE Using Statistics to Evaluate Multiple Choice.
Using Test Item Analysis to Improve Students’ Assessment
Item Analysis: A Crash Course Lou Ann Cooper, PhD Master Educator Fellowship Program January 10, 2008.
Dr. Majed Wadi MBChB, MSc Med Edu
Test Construction Processes 1- Determining the function and the form 2- Planning( Content: table of specification) 3- Preparing( Knowledge and experience)
Item Analysis What makes a question good??? Answer options?
Middle on the Normal distribution. Z = =.1003 What is going on here? It is just an exercise in using.
Item Analysis Ursula Waln, Director of Student Learning Assessment
Lesson Seven Item Analysis. Contents Item Analysis Item Analysis Item difficulty (item facility) Item difficulty (item facility) Item difficulty Item.
Item Analysis Prof. Trevor Gibbs. Item Analysis After you have set your assessment: How can you be sure that the test items are appropriate?—Not too easy.
Lesson Nine Item Analysis.
Multiple Choice Test Item Analysis Facilitator: Sophia Scott.
ANALYZING AND USING TEST ITEM DATA
Traditional Method 2 means, σ’s known. The makers of a standardized exam have two versions of the exam: version A and version B. They believe the two.
8/15/2015Slide 1 The only legitimate mathematical operation that we can use with a variable that we treat as categorical is to count the number of cases.
FREQUENCY DISTRIBUTION. Objectives; Organize data in the form of frequency distribution Distinguish an exact limits, class mark, class intervals, cumulative.
Box and Whisker Plot 5 Number Summary for Odd Numbered Data Sets.
Mean Median Mode Range by: courtney widmer. MEAN definition: the average of a set of data steps 1 add numbers 2 count numbers 3 divide the number of numbers.
8/20/2015Slide 1 SOLVING THE PROBLEM The two-sample t-test compare the means for two groups on a single variable. the The paired t-test compares the means.
1 Item Analysis - Outline 1. Types of test items A. Selected response items B. Constructed response items 2. Parts of test items 3. Guidelines for writing.
GrowingKnowing.com © Percentile What ‘s the difference between percentile and percent? Percent measures ratio 90% percent on a test shows you.
Non-parametric Statistics EDRS 5305 Educational Research & Statistics.
Part #3 © 2014 Rollant Concepts, Inc.2 Assembling a Test #
Field Test Analysis Report: SAS Macro and Item/Distractor/DIF Analyses
Measures of Position and Outliers. z-score (standard score) = number of standard deviations that a given value is above or below the mean (Round z to.
Chapter 7 Item Analysis In constructing a new test (or shortening or lengthening an existing one), the final set of items is usually identified through.
GrowingKnowing.com © GrowingKnowing.com © 2011.
Copyright © Cengage Learning. All rights reserved. 6 Normal Probability Distributions.
Group 2: 1. Miss. Duong Sochivy 2. Miss. Im Samphy 3. Miss. Lay Sreyleap 4. Miss. Seng Puthy 1 ROYAL UNIVERSITY OF PHNOM PENH INSTITUTE OF FOREIGN LANGUAGES.
Fractions, Decimals & Percentages
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Chapter 6 Normal Probability Distributions 6-1 Review and Preview 6-2 The Standard Normal.
NRTs and CRTs Group members: Camila, Ariel, Annie, William.
Lab 5: Item Analyses. Quick Notes Load the files for Lab 5 from course website –
Standardized Testing (1) EDU 330: Educational Psychology Daniel Moos.
Administering, Analyzing, and Improving the Written Test
General Strategy. Initial Moments The survey conducted before the exam does not have any bearing on test contents Calculate the number of questions required.
6/4/2016Slide 1 The one sample t-test compares two values for the population mean of a single variable. The two-sample t-test of population means (aka.
Basic Measurement and Statistics in Testing. Outline Central Tendency and Dispersion Standardized Scores Error and Standard Error of Measurement (Sm)
Grading and Analysis Report For Clinical Portfolio 1.
GrowingKnowing.com © Frequency distribution Given a 1000 rows of data, most people cannot see any useful information, just rows and rows of data.
Thursday August 29, 2013 The Z Transformation. Today: Z-Scores First--Upper and lower real limits: Boundaries of intervals for scores that are represented.
1 Item Analysis - Outline 1. Types of test items A. Selected response items B. Constructed response items 2. Parts of test items 3. Guidelines for writing.
SW388R6 Data Analysis and Computers I Slide 1 Percentiles and Standard Scores Sample Percentile Homework Problem Solving the Percentile Problem with SPSS.
 Quartiles and Percentiles. Quartiles  A quartile divides a sorted (least to greatest) data set into 4 equal parts, so that each part represents ¼ of.
Chapter 6: Analyzing and Interpreting Quantitative Data
Summary Statistics: Measures of Location and Dispersion.
Introduction to Item Analysis Objectives: To begin to understand how to identify items that should be improved or eliminated.
Outline of Today’s Discussion 1.Displaying the Order in a Group of Numbers: 2.The Mean, Variance, Standard Deviation, & Z-Scores 3.SPSS: Data Entry, Definition,
 A standardized value  A number of standard deviations a given value, x, is above or below the mean  z = (score (x) – mean)/s (standard deviation)
Dan Thompson Oklahoma State University Center for Health Science Evaluating Assessments: Utilizing ExamSoft’s item-analysis to better understand student.
Psychometrics: Exam Analysis David Hope
Dept. of Community Medicine, PDU Government Medical College,
Making Sense of Statistics: A Conceptual Overview Sixth Edition PowerPoints by Pamela Pitman Brown, PhD, CPG Fred Pyrczak Pyrczak Publishing.
Items analysis Introduction Items can adopt different formats and assess cognitive variables (skills, performance, etc.) where there are right and.
Exam Analysis Camp Teach & Learn May 2015 Stacy Lutter, D. Ed., RN Nursing Graduate Students: Mary Jane Iosue, RN Courtney Nissley, RN Jennifer Wierworka,
Announcements Exams returned at end of class Average = 78 Standard Dev = 12 Key with explanations will be posted Don’t be discouraged: First test is often.
ARDHIAN SUSENO CHOIRUL RISA PRADANA P.
Frequency Distributions
Classroom Analytics.
Classroom Assessment Ways to improve tests.
TOPIC 4 STAGES OF TEST CONSTRUCTION
Dept. of Community Medicine, PDU Government Medical College,
Using statistics to evaluate your test Gerard Seinhorst
Lies, Damned Lies & Statistical Analysis for Language Testing
Analyzing test data using Excel Gerard Seinhorst
Day 52 – Box-and-Whisker.
Can we distinguish wet years from dry years?
Tests are given for 4 primary reasons.
Presentation transcript:

Techniques to improve test items and instruction Item Analysis Techniques to improve test items and instruction Jean-Marc Wise Assistant Director, Office of Distance Learning Assessment & Testing jwise@campus.fsu.edu, 644-3541 Item Analysis Workshop, APPS

Item Analysis in a Nutshell Check the effectiveness of test items: Score the exam and sort the results by score. Select an equal number of students from each end, e.g. top 25% (upper 1/4) and bottom 25% (lower 1/4). Compare the performance of these two groups on each of the test items.

Item Analysis in a Nutshell For any well-written item a greater portion of students in the upper group should have selected the correct answer. a greater portion of students in the lower group should have selected each of the distracter (incorrect) answers.

Item Difficulty Level: Definition The percentage of students who answered the item correctly. High (Difficult) Medium (Moderate) Low (Easy) <= 30% > 30% AND < 80% >=80% 0 10 20 30 40 50 60 70 80 90 100

Item Difficulty Level: Examples Number of students who answered each item = 50 Item No. No. Correct Answers % Correct Difficulty Level 1 15 2 25 3 35 4 45 30 High 50 Medium 70 Medium 90 Low

Item Difficulty Level: Discussion Is a test that nobody failed too easy? Is a test on which nobody got 100% too difficult? Should items that are “too easy” or “too difficult” be thrown out?

What is Item Discrimination? Generally, students who did well on the exam should select the correct answer to any given item on the exam. The Discrimination Index distinguishes for each item between the performance of students who did well on the exam and students who did poorly.

How does it work? For each item, subtract the number of students in the lower group who answered correctly from the number of students in the upper group who answered correctly. Divide the result by the number of students in one group. The Discrimination Index is listed in decimal format and ranges between -1 and 1.

What is a “good” value? For exams with a normal distribution, a discrimination of 0.3 and above is good; 0.6 and above is very good. Values close to 0 mean that most students performed the same on an item. The index should never be negative.

Item Discrimination: Examples Item No. Number of Correct Answers in Group Item Discrimination Index Upper 1/4 Lower 1/4 1 90 20 2 80 70 3 100 4 5 50 6 60 0.7 0.1 1 -0.4 Number of students per group = 100

Item Discrimination: Discussion What factors could contribute to low item discrimination between the two groups of students? What is a likely cause for a negative discrimination index?

Item Discrimination (D) Quick Reference Use the following table as a guideline to determine whether an item (or its corresponding instruction) should be considered for revision. Item Discrimination (D) Item Difficulty High Medium Low D =< 0% review 0% < D < 30% ok D >= 30%

Distracter Analysis: Definition Compare the performance of the highest- and lowest-scoring 25% of the students on the distracter options (i.e. the incorrect answers presented on the exam.) Fewer of the top performers should choose each of the distracters as their answer compared to the bottom performers.

Distracter Analysis: Examples Item 1 A* B C D E Omit % of students in upper ¼ 20 5 % of students in the middle 15 10 % of students in lower ¼ Item 2 A B C D* E Omit % of students in upper ¼ 5 15 % of students in the middle 10 20 % of students in lower ¼ (*) marks the correct answer.

Distracter Analysis: Discussion What is the purpose of a good distracter? Which distracters should you consider throwing out?

Item Analysis Report Order ID and group number percentages counts The left half shows percentages, the right half counts. The correct option is indicated in parentheses. Point Biserial is similar to the discrimination index, but is not based on fixed upper and lower groups. For each item, it compares the mean score of students who chose the correct answer to the mean score of students who chose the wrong answer.

Exercise: Interpret Item Analysis Review the sample report. Identify any exam items that may require revision. For each identified item, list your observation and a hypothesis of the nature of the problem.

Reference Oosterhof, A. (1990). Classroom Applications of Educational Measurements. Merrill, Columbus, OH.