Slide 1

Transcript Slide 1

Using CogAT

David Lohman Institute for Research and Policy on Acceleration Belin-Blank Center & Iowa Testing Programs University of Iowa http://faculty.education.uiowa.edu/dlohman/



Topics

Distinguishing between ability & achievement  Overview of CogAT  Comparing CogAT with other ability tests  Interpreting CogAT scores  General issues in selection  Identification of talent in special populations  Combining Achievement, Ability, & Teacher ratings: the Lohman – Renzulli matrix

Distinguishing between ability and achievement

Puzzlements for common interpretations of ability & achievement

   Is ability more biologically based?

 Most studies show same heritability for IQ (Gf) and achievement tests (Gc) Lower relative achievement than ability = underachievement  But there are an equal number of “overachievers” Status scores (IQ, PR) show good stability   But one must keep getting better to retain that IQ Between 9 – 17 r(True IQ) = .75.  60% in top 3% at 9 NOT in top 3% at age 17

 Fluid abilities invested in experience to produce particular constellations of crystallized abilities?

 Only for very young children  Thereafter, crystallized abilities -> fluid

Level 1. Nominalism (Most people here) “ability” and “achievement” are separate (Jangle fallacy –T. Kelley, 1927) Ability Achievement

Level 2. Oh, Oh – there’s more overlap than uniqueness here!

  

Its all ‘g’ (any indicant will do) Its all just a product of experience Preserve stage 1 beliefs –



Purge ability of visible achievement (e.g. measure “process” or use only “nonverbal” measures)

Ability Achievement

Level 3. Island kingdoms –Things get even more complicated ( most scholars of human abilities)

 Effects of language, culture, and experience on the development of ability (“All abilities are developed” Anastasi)  Experience alters the structure of the brain  Mental processes do not exist independently of knowledge.

Example of Flynn Effect 105 100 95 90 85 80 75 70 1910 1920 1930 1940 1950

Year

1960 1970 1980 1990 2000

Gains in Wechsler-Binet IQ for the U.S. White population.

Sources

J. Horgan (1995) and D. Schildlovsky.

Proportion of variance in WISC Full Scale IQ at age 7 accounted for by genetic factors as a function of socioeconomic status (SES) Low High

Turkheimer et al. (2003)

Psychological Science, 14

43% White, 54% Black. Most families poor.

(6). N= 319 twin pairs.

Fluid-Crystallized Continuum (1)

Fluid Physical skills General physical fitness Crystallized Basketball Swimming Wrestling Football Field hockey Volleyball Cycling

Fluid Cognitive abilities General fluid ability (Gf) Physical skills General physical fitness

Fluid-Crystallized Continuum (1)

Crystallized Science achievement Math achievement Social studies achievement Knowledge of literature Specific factual knowledge Basketball Swimming Wrestling Football Field hockey Volleyball Cycling

Fluid Cognitive abilities General fluid ability (Gf) Physical skills General physical fitness

Fluid-Crystallized Continuum (2)

Crystallized Science achievement Social studies achievement Math achievement Knowledge of literature Specific factual knowledge Basketball Swimming Wrestling Football Field hockey Volleyball Cycling

A common ability-achievement space

Level 4. Systems theories

(A handful) Aptitude Theory (Richard Snow)

 Sidesteps the issue of defining intelligence;  starts with expertise & the contexts in which it is developed & displayed,  readiness to learn in those contexts

Overview of CogAT

Some History

 Lorge -Thorndike Intelligence test  Cognitive Abilities Test  Form 1 1974  Forms 2 – 3 (no Composite score)  Forms 4 – Thorndike & Hagen – Comp score  Form 5 – Hagen  Form 6 – Lohman & Hagen  Co-normed with the ITBS & ITED

Primary uses of CogAT

 To guide efforts to adapt instruction to the needs and abilities of students  To provide an alternative measure of cognitive development  To identify students whose predicted levels of achievement differ markedly from their observed levels of achievement

Primary Battery (K-2)

Verbal Reasoning .....

General Reasoning Ability Quantitative Reasoning .....

Nonverbal Reasoning .....

Oral Vocabulary Verbal Reasoning Relational Concepts Quantitative Concepts Figure Classification Matrices No reading Tests untimed (paced by teacher) Mark directly in booklet

Multilevel Battery (gr. 3-12)

Verbal Reasoning ......

General Reasoning Ability Quantitative Reasoning .....

Nonverbal Reasoning .....

Verbal Classification Sentence Completion Verbal Analogies Quantitative Relations Number Series Equation Building Tests timed Separate Answer sheet Common Directions Figure Classification Figure Analogies Figure Analysis

3 Separate Test Batteries

(Not one)

Scores

 Raw score = number correct  Scale score – USS  Within level - map number correct on to a scale whose intervals are approximately the same size  Between levels – maps number correct on different levels of the test on to a single, common, developmental scale

A B C D etc

USS Scale

Relationships among Stanines, Percentile Ranks, and Standard Age Scores

134 - 150

Composites

 Composite scores  Partial VQ, VN, QN  Full – VQN or C [do NOT use for screening]  Primary Battery  V or (VQ) versus N  Multilevel Battery  V versus QN

Consequential Validity: Score warnings

        Age out of range Age unusual for coded grade Estimated test level Level unusual for coded grade Targeted score Too few items attempted to score Many items omitted (slow and accurate) Extremely variable responses

Personal Confidence Intervals

   Pattern of item responses aberrant?

Inconsistent across subtests within a battery?

Personal Standard Error of Measurement (PSEM) SAS PR 1 25 50 75 99 V 120 89 Q 116 84 N 125 94

Score Profiles

CogAT 6 ‘ABC’ Profile system

Measuring the pattern 

“A” profiles:

Confidence bands overlap for all three scores. Scores are at roughly the

sAme

level 

“B” profiles:

One score is

aBove

Below

other two scores, which do not differ the 

“C” profiles:

Two scores

Contrast



“E” profiles:

Extreme B or C profiles (>=24)

“A” Profile

SAS PR 1 25 50 75 99 V 120 89 Q 116 84 N 125 94

“B” Profiles

SAS PR 1 25 50 75 99 V 120 89 Q 116 84 N 100 50 N SAS PR 1 25 50 75 99 V 95 38 Q 92 31 N 110 73 N+

“C” Profile

SAS PR 1 25 50 75 99 V 120 89 Q 110 73 N 100 50 V+ N-

Extreme “C” Profile

SAS PR V 120 89 Q 107 67 N 92 31 1 25 50 75 99 SAS Max – SAS Min = 28 E (V+ N-)

Profile Level

 Median (middle) age stanine

6 5 8 2

A B (V+) C (Q+ V-) E (N+ V-)

CogAT6 Profile frequencies for students in K-12 population A Profile B

B+ B -

Percent in K-12 population 33 42

( 21) (22)

(4) (3)

CogAT6 Profile frequencies for students in K-12 pop. and for students with two stanine scores of 9 Profile A B E

B+ B B+ B -

Percent in K-12 population 33 42

( 21)

(22) 4 3

Percent in Stanine=9 group 37 27

( 6) ( 21)

( 3) ( 16)

37%

Comparing CogAT with other tests

Reliability

  Many estimates for a given test  Sources of error Correlation versus standard error of measurement (SEM)  Correlations depend on sample variability  Easily misinterpreted  SEM  Typical SD of distribution of test scores if each student could be tested many times  Person-level estimate – Only on CogAT

SEM for SAS scores

Standard Errors of Measurement for Individual & Group Tests

WISC -IV SB-V Verbal Nonverbal 3.9

3.6

4.2

3.9

Quantitative Comp/Full Scale 4.5

2.8

5.3

2.8

CogAT 6 OLSAT 8 Inview Raven NNAT 3.4

3.7

5.7

5.8

5.3

4.5

3.0

6.1

3.3

2.2

5.7

3.5

Standard Errors of Measurement for Individual & Group Tests

WISC -IV SB-V Verbal Nonverbal 3.9

3.6

4.2

3.9

Quantitative Comp/Full Scale 4.5

2.8

5.3

2.8

CogAT 6 OLSAT 8 Inview Raven NNAT

3.4

3.7

3.3

2.2

5.7

5.8

5.7

5.3

4.5

3.5

3.0

6.1

CogAT is more reliable than

 Individually-administered tests:  SB-V  WISC-IV  Group-administered tests:  Inview  Otis-Lenon  NNAT

Conditional Standard Error of Meas.

Cogat 6 Verbal Battery: Level A

20.00

15.00

10.00

5.00

0.00

0 5 10 15 20 25 30 35 40 45 50 55 60 65

Number Correct

USS Score Raw Score

Conditional SEM's for CogAT6 Verbal USS scores, by test level

Verbal USS K

191-195 196-200 201-205 206-210 211-215 216-220 221-225 226-230 231-235 236-240 241-245 246-250 251-255 256-260 261-265 266-270 15.9

17.0

11.5

12.0

12.5

13.0

9.9

11.4

12.5

13.0

13.4

13.9

14.5

15.0

6.5

7.5

8.5

10.5

13.1

14.8

16.9

95th PR 99th PR B

5.3

5.9

7.0

7.4

8.9

10.4

13.2

14.8

4.8

5.2

5.4

5.9

6.9

8.4

10.9

13.3

14.3

4.3

4.5

4.8

5.2

5.6

6.2

7.4

8.5

10.8

13.3

. . .

16.5

14.8

15.4

16.4

Out of level testing?

   SAS or PR scores?

Primary Battery – Multilevel Battery?

   Requires individual testing Assumes child can use machine-readable answer sheet Quant battery assumes familiarity with numerical operations Level A – H?

 Common time limits & directions

Validity

   Construct  Representation --- all three aspects of fluid reasoning ability Predictive  Excellent for predicting current and future academic achievement  Predictions the same for all ethnic groups Consequential  No other test comes close

Validity: Construct Representation

Carroll’s Three-Stratum Theory of Human Abilities Gf Fluid Reasoning Abilities

Carroll’s Three-Stratum Theory of Human Abilities Verbal Sequential Reasoning Quantitative Reasoning Figural Inferential Reasoning

Correlation between WISC Full Scale Score and CogAT Composite = .79

   

Predictive Validity

Correlations with current and subsequent achievement Within Battery predictions strong    Verbal with Reading, Soc Studies (r =.4 - .8) Quant with Mathematics (r = .4 - .8) Figural–Nonverbal with Math (r = .4 - .7)  Negative for verbal ach. after controlling g Across batteries 

Multiple correlations – typically R = .8

 Often better than prior achievement in the domain  V and QN partial composite especially useful Within ethnic-group correlations the same  Implications for TALENT identification

Consequential Validity: Advice on score interpretation?

  Early 20 th century theory of ‘culture-fair measure of g’ 21 st century theory of reasoning abilities  Evidence from research on human abilities  Evidence from predictions of academic achievement   Evidence from ATI research Evidence from cognitive psychology

Consequential Validity: Score use

     Does every child (teacher) receive potentially useful information?

Specific suggestions for how to use the level and profile of scores to    Assist the child in learning by adapting instruction better to meet his/her learning style Build on cognitive strengths Shore up weaknesses Interpretive Guide for Teachers & Counselors Short Guide for teachers (free online) Profile interpretation system (free online)

Norms

 Flynn Effect (next slide)  Shaunessy et al. (2004)  Cattell Culture Fair test 17.8 IQ points higher than NNAT  Project Bright Horizon in Phoenix  2000 K-6 children, about ½ ELL  CogAT, Raven, NNAT  Raven 10 SAS points higher than CogAT or NNAT

Example of Flynn Effect 105 100 95 90 85 80 75 70 1910 1920 1930 1940 1950

Year

1960 1970 1980 1990 2000

Gains in Wechsler-Binet IQ for the U.S. White population.

Sources

J. Horgan (1995) and D. Schildlovsky.

Mistakes in norming NNAT

NNAT SD's by Test Level

25 20 15 10 5 0 A B C D

Test Level

E F G George (2001) Naglieri & Ronning (2000) Bright Horizon

Level A B C D E F G True Versus Reported NAI Scores by NNAT Test Level 100

100 100 100 100 100 100 100

True NAI Score 115

121 119 119 117 115

130

142 139 137 134 130 116 116 132 132

145

163 158 156 151 145 149 148

Level A B C D E F G True Versus Reported NAI Scores by NNAT Test Level 100

100 100 100 100 100 100 100

True NAI Score 115

121 119 119 117 115

130

142 139 137 134 130 116 116 132 132

145

163 158 156 151 145 149 148

Over-identification Rates for the Number of Students with NAI Scores Above 115, 130, and 145 Level A B C D E F G 115

1.5

1.4

1.3

1.2

1.0

1.1

True NAI Score 130

3.4

2.6

2.3

1.7

1.0

1.4

145

11.9

7.3

5.8

2.9

1.0

2.0

1.9

Over-identification Rates for the Number of Students with NAI Scores Above 115, 130, and 145 Level A B C D E F G 115

1.5

1.4

1.3

1.2

1.0

1.1

True NAI Score 130

3.4

2.6

2.3

1.7

1.0

1.4

145

11.9

7.3

5.8

2.9

1.0

2.0

1.9

Interpreting CogAT scores

Primary uses of CogAT

  

To guide efforts to adapt instruction to the needs and abilities of students

To provide an alternative measure of cognitive development To identify students whose predicted levels of achievement differ markedly from their observed levels of achievement

Myths about adapting instruction

 All students are pretty much alike

400

Reading Vocab Across Grades

350 V O C A B U L A R Y 300 250 200 150 100 K 1 2 3 4 5 6

Grade

7 8 9 10 11 12 99th %-tile 80th %-tile 50th %-tile 20th %-tile 1st %-tile

Reading Vocab Across Grades

400 350 V O C A B U L A R Y 300 250 200 150 100 K 1 2 3 4 5 6

Grade

7 8 9 10 11 12 99th %-tile 80th %-tile 50th %-tile 20th %-tile 1st %-tile

Myths about adapting instruction

 All students are pretty much alike  Every student is unique

Myths about adapting instruction

 All students are pretty much alike  Every student is unique  Adaptations should be based on self-reported learning styles

Myths about adapting instruction

 All students are pretty much alike  Every student is unique  Adaptations should be based on self-reported learning styles  If the method is right, the outcome will be good

Examples of correlations

Predictor and criterion Aspirin and reduced risk of death by heart attack a General batting skill as a Major League baseball player and hit success on a given instance at bat a Calcium intake and bone mass in premenopausal women a Effect of nonsteroidal anti-inflammatory drugs (e.g., ibuprofen) on pain reduction a Weight and height for U.S. adults a .02

.06

.08

.14

.44

22,071 — 2,493 8,488 16,948

Myths about adapting instruction

     All students are pretty much alike Every student is unique Adaptations should be based on self-reported learning styles If the method is right, the outcome will be good Individualization requires separate learning tasks

Important Characteristics of Students

   Cognition (knowing)  Domain knowledge & skill 

Reasoning abilities in the symbol systems used to communicate knowledge (Verbal, Quant., Spatial)

Affection (feeling)  anxiety, interests, working alone/with others Conation (willing)  persistence, impulsivity

Important Characteristics of Classrooms

 Structure  Novelty/Complexity/Abstractness  Dominant symbol system  Opportunities for working alone or with others

General Principles of Instructional Adaptation

 Build on Strength  Focus on working memory  Scaffold wisely  Emphasize strategies  When grouping, aim for diversity

Case Study: Naomi

Verbal Quantitative Nonverbal Composite PR V 67 Q 17 N 71 No. of Items 40 40 40 Number Raw Age Scores Attempted Score USS SAS PR S 39 31 148 107 67 6 38 40 18 30 109 85 147 109 135 100 17 3 71 6 50 5 Grade Scores PR 59 11 S 5 2 60 38 6 4 1 25 50 75 99 Profile 6E (Q-)

Primary uses of CogAT

   To guide efforts to adapt instruction to the needs and abilities of students

To provide an alternative measure of cognitive development

To identify students whose predicted levels of achievement differ markedly from their observed levels of achievement

ITBS – CogAT correlation

High Low Low ITBS High

ITBS – CogAT correlation

High Low Low ITBS High

ITBS – CogAT correlation

High Low Low ITBS High

ITBS – CogAT correlation

CogAT only High Both ITBS only Low Low ITBS High

Proportion of students identified by one test also identified by the second test

Cut score Top 1% Top 2% Top 3% 0.50

Correlation between tests 0.60

0.70

0.80 0.90

0.13

0.17

0.20

0.19

0.23

0.26

0.27

0.31

0.35

0.38

0.54

0.42

0.58

0.45

0.60

“Do not use the Composite score to screen children for academic giftedness”

 Thorndike & Hagen (1984) (CogAT4)  Thorndike & Hagen (1992) (CogAT5)  Lohman & Hagen (2000) (CogAT6)

Generally good news for low achieving students

 The lower the student’s score on an achievement test  The greater the likelihood that CogAT scores will be higher  Especially for nonverbal battery

Primary uses of CogAT

   To guide efforts to adapt instruction to the needs and abilities of students To provide an alternative measure of cognitive development To identify students whose predicted levels of achievement differ markedly from their observed levels of achievement

Predicting Achievement from Ability

Predicted Achievement Score A c h i e v e t m e n Hig h Avg Distribution of Achievement for SAS of 110 70 80 90 100 110 120 130 Standard Age Score

Moderate Correlation

Unexpectedly High Ach.

Expected Level of Ach.

Unexpectedly Low Ach.

A Ability

Predicting Ach vrs Flagging Ach Ability discrepancies

 Who are the students (at any ach level) who are most likely to improve if given new motivation or instructional resources?

Reasoning Ability > Achievement

• Underachievement poor effort, instruction, etc.

• Well developed ability to transfer knowledge & skills to novel situations evidence for practice in varied contexts

Achievement > Reasoning Ability

• Overachievement unusual effort, good instruction 2.

• Difficulty in applying knowledge/skills in unfamiliar contexts need for integration, cross-course transfer

General issues in selection

Golden Rules of selection

 Identification criteria must be logically and psychologically tied to the requirements of the day-to-day activities that students will pursue.

 Mathematics?

 Literary arts?

 Visual Arts?

 Differentiated selection implies differentiated instruction

100 90 80 70 60 50 40 30 20 10 0 0 10 20 30 40 50 60

Nonverbal Reasoning

70 80 90 100

100 90 80 70 60 50 40 30 20 10 0 0 10 20 30 40 50 60

Nonverbal Reasoning

70 80 90 100

100 90 80 70 60 50 40 30 20 10 0 0 10 20 30 40 50 60

Nonverbal Reasoning

70 80 90 100

Example r = .6

Imprecision of even high correlations

 Given r = .8

 What is the likelihood that a student who scores in 60-70 th PR at Time 1 will scores in the 60-70 th PR at Time 2?

Lohman, D. F. (2003). Tables of prediction efficiencies .

Proportion of students identified by both tests

Cut score Top 1% Top 2% Top 3% 0.50

Correlation between tests 0.60

0.70

0.80 0.90

0.13

0.17

0.20

0.19

0.23

0.26

0.27

0.31

0.35

0.38

0.54

0.42

0.58

0.45

0.60

Regression to the mean

 The tendency of students with high scores to obtain somewhat lower scores upon retest  0 at the mean  Increases with distance from the mean  Easily predicted from correlation  Y pred = Mean + r (Y – mean)

Causes of Regression to the Mean

 “Errors” of measurement  Often much larger for high scoring students  Differential growth rates  Changes in the abilities measured by the tests at time 1 and time 2 (esp achievement tests)  Changes in the norming population  school sample or national age sample

Reducing Regression

    Use the most reliable tests available  (judge by SEM on reported score scale) Avoid accepting the highest score as the best estimate of ability Average scores   Ability and Achievement test scores  Within domain (e.g., math ach & CogAT Q or QN) Achievement at T1 and T2 Revolving door policies

Combining scores

“And,” “or” or “Average”

"And" "Or" "Average" Test 1 and Test 2 Test 1 or Test 2 Average of Test 1 and Test 2 Figure 5. Plots of the effects of three rules: (a) high scores on test 1 and test 2; (b) high scores on test 1 or test 2; and (c) high scores on the average of test 1 and test 2.

Screening tests

    You administer a screening test to reduce the number who must be administered the admissions test Assume a correlation of r = .6 between the two tests Assume students must score at the 95 th higher on the admissions test PR or What cut score on the screening test will include all of those who would meet this criterion?

Proportion of students in top X percent of screening test who exceed the same or a more stringent cut score on follow up test

r = .6

Admissions test Top x % 30% 25% 20% 15% 10% 5% 3% 5% 0.80

0.75

0.68

0.59

0.48

0.31

0.22

3% 0.84

0.80

0.73

0.65

0.54

0.36

0.26

1% 0.91

0.87

0.82

0.75

0.65

0.48

0.36

Proportion of students in top X percent of screening test who exceed the same or a more stringent cut score on follow up test

r = .6

Admissions test Top x % 30% 25% 20% 15% 10% 5% 3% 5% 0.80

0.75

0.68

0.59

0.48

0.31

0.22

3% 0.84

0.80

0.73

0.65

0.54

0.36

0.26

1% 0.91

0.87

0.82

0.75

0.65

0.48

0.36

Screening might make sense

 When admissions test is expensive to administer  When the correlation between the admissions & screening test is very high  When there are many more applicants than places in the program  When the false rejection rate is not an issue

Local versus National Norms

   Except for regional or national talent searches, the PRIMARY reference group is not the nation or even the state but the school or school district.

The need for special instruction depends on the discrepancy between the child’s level of cognitive and academic development and that of his or her classmates.

Multiple perspectives: Nation, the local population, opportunity-to-learn subgroups within the local population

Identification of Talent in Special Populations

ELL children

Identifying academic

talent

Not giftedness

Tradeoff

Measuring the right things approximately for ELL students or the wrong things with greater accuracy

Inference of Aptitude?

    When someone learns in a few trials what others learn in many trials Opportunity to learn is critical Common norms appropriate only if experiences are similar Placement by achievement

Multiple Perspectives

   The need for special programming depends most importantly on the discrepancy between a child’s achievements & abilities and that of his or her

classmates

Except for regional talent searches, summer programs that draw from different schools, etc… Make better use of local norms!

For ELL students in grade 3, compare scores to:    Other ELL students in grade 3 Other students in grade 3 in the district/school Other grade 3 students in the nation

Multiple Programming Options

    Current level of achievement is primary guide Programming goal: to improve the achievement at a rate faster than would otherwise occur For on- and below-grade-level achievement options include: tutors, after-school or weekend classes/clubs, etc. Motivational component critical.

For achievement well in advance of peers, consider single-subject acceleration

Combining ITBS and CogAT

    Grades K – 2  Average CogAT V and ITBS Reading Total   Average CogAT Q and ITBS Math total CogAT NV stands alone Grades 3 – 12   Average CogAT V and ITBS Reading Total Average CogAT QN and ITBS Math Total Use NCE scores – they can be averaged Then sort by grade and OTL group

Integrating ability, achievement, and teacher ratings

 See Lohman, D. F. & Renzulli, J. (2007). A simple procedure for combining ability test scores, achievement test scores, and teacher ratings to identify academically talented children.

Teacher Rating (Renzulli Scales) on Learning ability, Motivation, or Creativity (NOMINATED students only)

Verbal Ability Or Quant/NV Ability (ALL Students) Teacher Rating (Renzulli Scales) on Learning ability, Motivation, or Creativity

Teacher Rating (Renzulli Scales) on Learning ability, Motivation, or Creativity

Below Avg.

Above Avg.

Verbal Ability Or

>97 th PR

Quant/NV Ability

>80 th PR

Teacher Rating (Renzulli Scales) on Learning ability, Motivation, or Creativity

Below Avg.

Above Avg.

Verbal Ability Or

>97 th PR

Quant/NV Ability

>80 th PR II IV I III

Teacher Rating (Renzulli Scales) on Learning ability, Motivation, or Creativity

Below Avg.

Above Avg.

Verbal Ability Or

>97 th PR

Quant/NV Ability

>80 th PR II IV I admit III

Teacher Rating (Renzulli Scales) on Learning ability, Motivation, or Creativity

Below Avg.

Above Avg.

Verbal Ability Or

>97 th PR

Quant/NV Ability

>80 th PR II Admit but watch IV I III

Teacher Rating (Renzulli Scales) on Learning ability, Motivation, or Creativity

Below Avg.

Above Avg.

Verbal Ability Or

>97 th PR

Quant/NV Ability

>80 th PR II IV I III Enrichment

Teacher Rating (Renzulli Scales) on Learning ability, Motivation, or Creativity

Below Avg.

Above Avg.

Verbal Ability Or

>97 th PR

Quant/NV Ability

>80 th PR II IV Try next year I III

Teacher Rating (Renzulli Scales) on Learning ability, Motivation, or Creativity

Below Avg.

Above Avg.

Verbal Ability Or

>97 th PR

Quant/NV Ability

>80 th PR II Admit but watch IV Try next year I admit III Enrichment

Final Thoughts: Using CogAT

     Examine warnings and confidence intervals on score reports Do not screen using Composite score   Use V and QN instead (at grade 3 +) Combine with Reading Total and Math Total Average measures of the same construct; Use “or” for measures of different constructs To identify talent, measure the right aptitudes but then compare scores to the proper norm group(s) Emphasize local norms for in-school programs

ELL

    Compare the performance of the ELL 3 rd grader with that of other ELL 3 rd graders Be wary of national norms that you can purchase– esp on nonverbal tests (Raven, NNAT,…) Nonverbal tests have a role to play, but should never stand alone Emphasize the identification of talent rather than the identification of giftedness

General

 It is unwise to accept the highest score as the best estimate of ability  Combine ability and achievement test scores in principled ways  Teacher ratings are only as good as teacher training in making ratings  Do not simply add teacher ratings and similar measures to ability/achievement scores

 There is no way to measure innate ability; all abilities are developed  Measures of achievement and ability differ in degree – not kind  Future expertise is built on the base of current knowledge in a domain, reasoning abilities needed for new learning in that domain, interest in the domain, and the ability to persist in the pursuit of excellence  All of which depend on opportunity and circumstance

The End

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http://faculty.education.uiowa.edu/dlohman

NCE Scores

   Get from the publisher for CogAT Table look up (Table 32 in CogAT Norms Manual) Convert PR’s to NCE scores  In Excel NCE = NORMINV (PR/100, 50, 21.06)  If SAS > 135 NCE = 21.06 * [(SAS – 100)/16] + 50