easyCBM

Reading - Comprehension

Cost Technology, Human Resources, and Accommodations for Special Needs Service and Support Purpose and Other Implementation Information Usage and Reporting

The Teacher Version is free and can be obtained at http://easycbm.com. The Teacher version includes progress monitoring information only.

The District Version is $1 per student and includes unlimited access to a separate easyCBM website created for that district. The District Version includes screening and progress monitoring.

Testers will require 1-4 hours of training.

Paraprofessionals and professionals can administer the test.

Accommodations:
All measures were developed following Universal Design for Assessment guidelines to reduce the need for accommodations. However, districts are directed to develop their own practices for accommodations as needed.

Behavioral Research and Teaching
5262 University of Oregon – 175 Education
Eugene, OR 97403-5262

Phone: 541-346-3535

http://easycbm.com

A field-tested training manual is available and provides all needed implementation information.

In grades K-8, easyCBM provides 3 forms of a screening measure to be used locally for establishing benchmarks and multiple forms to be used to monitor progress. All the measures have been developed with reference to specific content in reading and developed using Item Response Theory (IRT).

Student reads a narrative story then answers selected response questions.

The tool provides information on student performance in English.

MCRC is a group administered computer-based test. It takes 20 minutes to administer in 2nd grade and 40 minutes in grades 3-8.

20 alternate forms are available for grades 2-5; 11 for grade 6; 17 for grade 7; and 15 are available for grade 8.

Raw and percentile scores are provided. Raw scores are the number of items correct.

 

Reliability of the Performance Level Score

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Type of Reliability Age or Grade n (range) Coefficient Information (including normative data)/Subjects
range median

Cronbach’s α

2

1,696 - 2,039

0.68 - 0.77

0.75

 

Cronbach’s α

3

2,105 - 2,271

0.55 - 0.78

0.69

 

Cronbach’s α

4

2,100 - 2,286

0.73 - 0.78

0.78

 

Cronbach’s α

5

2,251 - 2,383

0.70 - 0.75

0.70

 

Cronbach’s α

6

1,156 - 2,275

0.63 - 0.67

0.66

 

Cronbach’s α

7

2,013 - 3,163

0.59 - 0.67

0.65

 

Cronbach’s α

8

2,112 - 3,224

0.59 - 0.66

0.66

 

Cronbach’s Split-Half

2

1,696 - 2,039

0.50 - 0.60

0.58

 

Cronbach’s Split-Half

3

2,105 - 2,271

0.39 - 0.64

0.50

 

Cronbach’s Split-Half

4

2,100 - 2,286

0.56 - 0.63

0.60

 

Cronbach’s Split-Half

5

2,251 - 2,383

0.49 - 0.59

0.53

 

Cronbach’s Split-Half

6

1,156 - 2,275

0.47 - 0.52

0.48

 

Cronbach’s Split-Half

7

2,013 - 3,163

0.37 - 0.51

0.47

 

Cronbach’s Split-Half

8

2,112 - 3,224

0.37 - 0.48

0.43

 

 

Reliability of the Slope

Grade345678
RatingEmpty bubbleEmpty bubbleEmpty bubbleEmpty bubbleEmpty bubbleEmpty bubble
Type of Reliability Age or Grade n (range) Coefficient Information (including normative data)/Subjects
  2 554
693
-
398
0.48
0.62
-
0.14
Quartile 1
Quartile 2
Quartile 3
Quartile 4
  3 660
517
632
399
0.59
0.66
0.34
0.07
Quartile 1
Quartile 2
Quartile 3
Quartile 4
  4 630
456
686
409
0.33
0.63
0.61
0.43
Quartile 1
Quartile 2
Quartile 3
Quartile 4
  5 660
513
-
445
0.13
0.41
-
0.39
Quartile 1
Quartile 2
Quartile 3
Quartile 4
  6 313
407
186
271
0.16
0.59
0.61
0.37
Quartile 1
Quartile 2
Quartile 3
Quartile 4
  7 568
657
510
440
0.05
0.35
0.48
0.52
Quartile 1
Quartile 2
Quartile 3
Quartile 4
  8 662
492
614
502
0.19
0.70
0.61
0.43
Quartile 1
Quartile 2
Quartile 3
Quartile 4

 

Validity of the Performance Level Score

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RatingEmpty bubbleEmpty bubbleEmpty bubbleEmpty bubbleEmpty bubbleEmpty bubble
Type of Validity Age or Grade Test or Criterion n (range) R2 β (SE) Information (including normative data)/Subjects
Concurrent 2 Regression 205 0.001 0.01 (0.02) not sig  
Concurrent 3 Regression 2,314 0.37 1.51 (0.04)  
Concurrent 4 Regression 2,304 0.36 1.45 (0.04)  
Concurrent 5 Regression 2,395 0.30 1.54 (0.05)  
Concurrent 6 Regression 2,206 0.31 1.51 (0.05)  
Concurrent 7 Regression 3,231 0.36 1.94 (0.05)  
Concurrent 8 Regression 3,338 0.37 1.61 (0.04)  
Predictive
F → SAT10
2 Regression 205 0.01 0.01 (0.01) not sig  
Predictive
W → SAT10
2 Regression 205 0.01 -0.02 (0.02) not sig  
Predictive
F → OAKS
3 Regression 2,252 0.33 1.52 (0.05)  
Predictive
W → OAKS
3 Regression 2,391 0.29 1.69 (0.05)  
Predictive
F → OAKS
4 Regression 2,244 0.45 1.56 (0.04)  
Predictive
W → OAKS
4 Regression 2,288 0.30 1.31 (0.04)  
Predictive
F → OAKS
5 Regression 2,410 0.34 1.39 (0.04)  
Predictive
W → OAKS
5 Regression 2,428 0.28 1.20 (0.04)  
Predictive
F → OAKS
6 Regression 2,299 0.30 1.45 (0.05)  
Predictive
W → OAKS
6 Regression 1,211 0.19 0.98 (.006)  
Predictive
F → OAKS
7 Regression 3,191 0.42 1.84 (0.04)  
Predictive
W → OAKS
7 Regression 2,036 0.37 1.74 (0.05)  
Predictive
F → OAKS
8 Regression 3,325 0.34 1.57 (0.04)  
Predictive
W → OAKS
8 Regression 2,089 0.39 1.81 (0.05)  

Construct Validity

Type of Validity Age or Grade Test or Criterion n (range) FIT STATISTICS Information (including normative data)/Subjects
CFI/ TLI RMSEA
Construct 3 CFA 1,865-1,839 0.971-0.977/
0.984-0.987
0.022-0.026  
Construct 4 CFA 1,820-2,046 0.971-0.977/
0.984-0.987
0.023-0.027  
Construct 5 CFA 1,962-2,119 0.972-0.973
0.985
0.023-0.025  
Construct 6 CFA 2,271-2,366 0.952-0.964/ 0.976-0.966 0.023-0.025  
Construct 7 CFA 3,406-3,493 0.955- 0.968/0.966-0.976 0.020-0.022  
Construct 8 CFA 3,548 0.954/0.967 0.024  

Other forms of validity: ______________________________________________________________________

 

Manual cites other published validity studies:	   X yes		□	no

 

*Provide citations for additional published studies. *** Validity information may also include: evidence based on response processes, evidence based on internal structure, evidence based on relations to other variables, and/or evidence based on consequences of testing.

Jamgochian, E.M., Park, B.J., Nese, J.F.T., Lai, C.F., Sáez, L., Anderson, D., Alonzo, J., & Tindal, G. (2010) Technical adequacy of the easyCBM grade 2 reading measures, 2009-2010 version. (Technical Report #1004). Eugene, OR: Behavioral Research and Teaching.

Sáez, L , Park, B.J., Nese, J.F.T, Jamgochian, E.M., Lai, C.F., Anderson, D., Alonzo, J., & Tindal, G. (2010) Technical adequacy of the easyCBM reading measures (Grades 3-8), 2009-2010 Version. (Technical Report #1005). Eugene, OR: Behavioral Research and Teaching.

Predictive Validity of the Slope of Improvement

Grade345678
RatingEmpty bubbleEmpty bubbleEmpty bubbleEmpty bubbleEmpty bubbleEmpty bubble
Type of Validity Age or Grade n (range) Coefficient Information (including normative data)/Subjects
Predictive
Validity
2 568
696
-
400
0.68
0.67
-
0.18
Quartile 1
Quartile 2
Quartile 3
Quartile 4
Predictive
Validity
3 666
517
634
399
0.56
0.58
0.58
0.46
Quartile 1
Quartile 2
Quartile 3
Quartile 4
Predictive
Validity
4 641
472
696
412
0.53
0.54
0.48
0.48
Quartile 1
Quartile 2
Quartile 3
Quartile 4
Predictive
Validity
5 682
527
-
458
0.61
0.52
-
0.45
Quartile 1
Quartile 2
Quartile 3
Quartile 4
Predictive
Validity
6 333
430
191
281
0.60
0.58
0.46
0.42
Quartile 1
Quartile 2
Quartile 3
Quartile 4
Predictive
Validity
7 576
664
517
453
0.63
0.54
0.50
0.46
Quartile 1
Quartile 2
Quartile 3
Quartile 4
Predictive
Validity
8 677
501
629
507
0.64
0.56
0.53
0.44
Quartile 1
Quartile 2
Quartile 3
Quartile 4

 

Bias Analysis Conducted

Grade345678
RatingNoNoNoNoNoNo

Disaggregated Reliability and Validity Data

Grade345678
RatingNoNoNoNoNoNo

Disaggregated Reliability of the Performance Level Score (PDF)

Alternate Forms

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RatingEmpty bubbleEmpty bubbleEmpty bubbleEmpty bubbleEmpty bubbleEmpty bubble

1. Evidence that alternate forms are of equal and controlled difficulty or, if IRT based, evidence of item or ability invariance:

Initially, items were piloted using a common person / common item design to create an item bank with known item parameters (measure, mean square outfit, standard error, etc.). Using this data, we then analyzed the distribution of items of varying difficultly across the multiple forms (3 screening forms to be administered in the fall, winter, and spring and 17 progress monitoring) to have approximately equal item measure estimates and comparable ranges. The comparability of each of the alternate forms was tested with grade-level students, using repeated measures ANOVA to test for form differences.

Computer administered. Number of items in the item bank for each grade level: In grade 2, n = 12, items per form = 240 items. In grades 3-5, n = 20, items per form = 400 items. In grades 6-8, n = 20, items per form = 180-400 items, without replacement.

Direct evidence that alternate forms are of equal and controlled difficulty is presented in the following published technical reports:

Alonzo, J., Liu, K., & Tindal, G. (2008). Examining the technical adequacy of reading comprehension measures in a progress monitoring assessment system (Technical Report No. 41). Eugene, OR: Behavioral Research and Teaching, University of Oregon.

Alonzo, J., Liu, K., & Tindal, G. (2008). Examining the technical adequacy of second-grade reading comprehension measures in a progress monitoring assessment system (Technical Report No. 0808). Eugene, OR: Behavioral Research and Teaching, University of Oregon.

Alonzo, J., & Tindal, G. (2008). Examining the technical adequacy of fifth-grade reading comprehension measures in a progress monitoring assessment system (Technical Report No. 0807). Eugene, OR: Behavioral Research and Teaching, University of Oregon.

These technical reports describe the process of initial instrument development, where we used a repeated measures design in the development of multiple alternate forms of comparable difficulty per grade level for use in Grades 2-8.

2. Number of alternate forms of equal and controlled difficulty:

20 forms are available in reading: 3 forms are used for screening and 17 forms are available to progress monitor.

Rates of Improvement Specified

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1. Is minimum acceptable growth (slope of improvement or average weekly increase in score by grade level) specified in manual or published materials?

Yes.

a. Specify the growth standards:

Optimal easyCBM® MCRC Yearly Growth by Fall Performance Quartile and its Sensitivity/Specificity

Grade Mean Growth Yearly growth cut score
Quartile 1 Quartile 2 Quartile 3 Quartile 4
3 1.59 1.52
(0.72, 0.72)
1.56
(0.72,0.70)
1.56
(0.90, 0.90)
1.57
(0.91, 0.89)
4 0.86 1.22
(0.43, 0.43)
0.97
(0.45, 0.42)
0.74
(0.34, 0.33)
0.76
(1.00, 0.00)
5 0.35 0.55
(0.27, 0.27)
0.38
(0.25, 0.24)
0.30
(0.23, 0.23)
0.23
(0.18, 0.13)
6 0.15 0.20
(0.26, 0.26)
0.16
(0.28, 0.28)
0.13
(0.35, 0.33)
0.12
(0.21, 0.25)
7 -0.61 -0.29
(0.26, 0.25)
-0.55
(0.27, 0.27)
-0.70
(0.26, 0.21)
-0.75
(0.14, 0.14)
8 -0.60 -0.45
(0.62, 0.61)
-0.58
(0.70, 0.70)
-0.68
(0.72, 0.71)
-0.71
(0.62, 0.62)

Note. Values in parenthesis indicate Sensitivity and Specificity associated with the determined, optimal growth. The state test cut scores for passing in grades 3-8 respectively are 204, 211, 218, 222, 227, and 231.

Student growth on easyCBM multiple-choice reading comprehension (MCRC) was estimated using Hierarchical Linear Modeling (HLM) based on their scores of the fall, winter, and spring easyCBM MCRC measures gathered during the 2009-2010 school year. We split the sample into quartiles of normative achievement on the fall easyCBM MCRC scores and conducted Receiver Operating Characteristic (ROC) curve analyses by grade based to determine the adequate growth to pass the Oregon State assessment (OAKS). Student growth estimated from HLM analyses were entered as a test variable and student performance on the Oregon state test (meet/exceeds or does not meet) was entered as a state variable. Growth cut scores that were associated with maximum sensitivity and specificity values were selected as an optimal growth cut scores by each quartile for grades 3 to 8.

b. Basis for specifying minimum acceptable growth:

Criterion-referenced

Normative profile:

Representation: Local
Date: 2009-2010
Number of States: 1
Size: 1,252-3,545
Gender: 50% Male, 50% Female
SES: The percentage of students receiving free or reduced price lunch ranged from 40.0% to 46.1% by grade in District 1, and 57.2% to 66.3% by grade in District 2. District 3 did not provide these data.

Race/Ethnicity:

  • 61.0-65.0% White
  • 2.0-2.5% Black, Non-Hispanic
  • 1.0-1.6% American Indian/Alaska Native
  • 4.6-5.7% Asian/Pacific Islander
  • 2.2-3.5% Other
  • 1.7-3.4% Unknown

ELL: 4.8%-5.8
Disability classification: 13.2-18.0%

End-of-Year Benchmarks

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1. Are benchmarks for minimum acceptable end-of-year performance specified in your manual or published materials?

Yes.

a. Specify the end-of-year performance standards:

Grade Fall Winter Spring
3 9 10 12
4 9 12 12
5 13 15 14
6 14 13 14
7 13 14 12
8 14 13 13

We developed equivalent, alternate forms of easyCBM® in reading (n=20 forms) with different skills reflective of the National Reading Panel (NRP) report. We developed three forms for use as screening measures in the fall, winter, and spring so educators could identify students at risk of failure and establish benchmarks. Using the 2009 fall measure, we present normative data for grades 1-8 in Tindal, Alonzo, and Anderson (2009). These data reflect the results from several districts in the Pacific Northwest and are reported for all districts and disaggregated for each district. Although we allow educators to make decisions on minimum acceptable growth and do not specify any standards in our user manual, we do provide normative information to aid in their decision-making.

Tindal, G., Alonzo, J., & Anderson, D. (2009) Local normative data on easyCBM® reading and mathematics: Fall 2009 (Technical Report No. 0918). Eugene, OR: Behavioral Research and Teaching, University of Oregon.

b. Basis for specifying minimum acceptable end-of-year performance:

Criterion-referenced

c. Specify the benchmarks:

See table above. Receiver Operating Characteristic (ROC) curve analyses were conducted for easyCBM reading measures (passage reading fluency, multiple-choice reading comprehension, and vocabulary) at each grade level to determine the optimal cut scores to predict student performance on the Oregon state test (meet/exceeds or does not meet). At each grade, the cut score associated with maximum sensitivity and specificity was selected for each season of each measure. Although sensitivity and specificity were the primary statistics used to determine cut points, the positive and negative predictive power, and the overall correct classification rate were also computed for additional diagnostic accuracy information.

d. Basis for specifying these benchmarks?

Criterion-referenced.

Normative profile:

Representation: Local
Date: 2009-2010
Number of States: 1
Size: 1,252-3,545
Gender: 50% Male, 50% Female
SES: The percentage of students receiving free or reduced price lunch ranged from 40.0% to 46.1% by grade in District 1, and 57.2% to 66.3% by grade in District 2. District 3 did not provide these data.

Race/Ethnicity:

  • 61.0-65.0% White
  • 2.0-2.5% Black, Non-Hispanic
  • 1.0-1.6% American Indian/Alaska Native
  • 4.6-5.7% Asian/Pacific Islander
  • 2.2-3.5% Other
  • 1.7-3.4% Unknown

ELL: 4.8%-5.8%
Disability classification: 13.2-18.0%

Sensitive to Student Improvement

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Decision Rules for Changing Instruction

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Decision Rules for Increasing Goals

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Improved Student Achievement

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Improved Teacher Planning

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