item analysis notess, Summaries of Psychology

item dificulty and item discrsiminaiblity

Typology: Summaries

2024/2025

Uploaded on 04/18/2026

chub-jabb
chub-jabb ๐Ÿ‡จ๐Ÿ‡ฆ

22 documents

1 / 21

Toggle sidebar

This page cannot be seen from the preview

Don't miss anything!

bg1
Item Analysis
PSYC 3200: Tests and Measurement
1
pf3
pf4
pf5
pf8
pf9
pfa
pfd
pfe
pff
pf12
pf13
pf14
pf15

Partial preview of the text

Download item analysis notess and more Summaries Psychology in PDF only on Docsity!

Item Analysis

PSYC 3200: Tests and Measurement

Learning Objectives

  • Understand Item Analysis Approaches
    • Examining Descriptives
    • Item Difficulty
    • Item Discriminability
  • Be able to interpret statistics from an Item Analysis
  • Be familiar with Item Response Theory and Adaptive Testing

IRT Difficulty

IRT Discriminability

Item Difficulty (Ease)

  • Proportion of people โ€œpassingโ€ item
    • Number passing / total attempting
  • Optimum Item Difficulty
  • Ideal Range
  • Relationship to instrument sensitivity

Item Difficulty p diff N Item difficulty, diff :^ =

chance

p optimum diff

= Example Optimum d, with four response options: ๐‘œ๐‘๐‘ก๐‘–๐‘š๐‘ข๐‘š ๐‘‘๐‘–๐‘“๐‘“ =

=. 625

Distribution of Item Difficulty [0.313, 0.384] (0.384, 0.456] (0.456, 0.528] (0.528, 0.600] (0.600, 0.672] (0.672, 0.744] (0.744, 0.816] (0.816, 0.888] 0 1 2 3 4 5 6 7

Item Difficulty Histogram

Item Discriminability

  • What is it?
  • Extreme Group method
  • Point-Biserial Method

1

x

pbis

y x

Y Y P r s P ๏ƒฉ ๏ƒน โˆ’ = ๏ƒช ๏ƒบ โˆ’ ๏ƒช๏ƒซ ๏ƒบ๏ƒป

i t b

d = P โˆ’ P Problems with rpbis in small tests

The Item Characteristic Curve 0

1 < 50 50-59 60-69 70-79 80-89 90- ICC For Two Questions Q 24 Q 7

Picking best items based on discriminability & difficulty

0.000 0.100 0.200 0.300 0.400 0.500 0.600 0.700 0.800 0.900 1. Difficulty Discrimination Scatter Plot for Discriminability vs. Difficulty

Several mathematical manifestations

  • Most popular is the logistic approach
  • Logistic models are used to predict dichotomous outcomes
    • Event = 1, Non-event = 0
  • Three (+1) versions
    • 1PL Model
      • Rasch Model
    • 2PL Model
    • 3PL Model

IRT Allows for Adaptive Testing

  • Instead of a fixed set of questions administered to everyone, emphasize finding the right set of questions to ask people that characterize the underlying trait.
  • Uses a search algorithm to determine the difficulty level of questions participant can answer.
  • Samples around that to obtain precise estimates of s.e.m.
  • The emphasis is on reliably estimating the threshold level of a trait.

Varying Difficulty and Discriminability

3PL Model