easyCBM

Area: Reading - Passage Reading Fluency

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 aloud a grade-level narrative passage presented on a single sheet of paper.

The tool provides information on student performance in English.

Passage Reading Fluency takes 1 minute to administer and the scores are entered on the computer. It is individually administered.

20 alternate forms are available for grades 1-6; 17 alternate forms are available for grade 7; and 19 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: Partially Convincing Evidence

Type of Reliability Age or Grade n (range) Coefficient SEM Information (including normative data)/Subjects
range median
Alternate Form-PRF Grade 1 48 & 52 0.95 - 0.97 0.96 7.71 & 8.17 n and SEM values are from two sessions
Test retest reliability with Alternate Forms Grade 1 48 & 52 0.96 - 0.97 0.97    
Alternate Form-PRF Grade 3 48 0.94 - 0.95 0.94 9.73 & 10.28 n and SEM values are from two sessions
Test retest reliability with Alternate Forms Grade 3 48 0.93 - 0.94 0.94    
Alternate Form-PRF Grade 5 54 & 49 0.87 - 0.96 0.92 12.11 & 11.36 n and SEM values are from two sessions
Test retest reliability with Alternate Forms Grade 5 54 & 49 0.92 - 0.94 0.93    
Alternate Form-PRF Grade 8 59 & 58 0.87 - 0.92 0.92 11.14 & 10.60 n and SEM values are from two sessions
Test retest reliability with Alternate Forms Grade 8 59 & 58 0.91- 0.91 0.91    

 

Reliability of the Slope: Convincing Evidence

Type of Reliability Age or Grade n (range) Coefficient SEM Information / Subjects
range median
Parallel Processing Model** Grade 1 16-547 0.93   0.03 Coefficient represents the correlation between processes and is thus not a range but a single estimate. SEM represents the standard error of the correlation coefficient.
Parallel Processing Model Grade 2 69-244 0.94   0.02  
Parallel Processing Model Grade 3 159-620 0.94   0.05  
Parallel Processing Model Grade 4 69-572 0.87   0.05  
Parallel Processing Model Grade 5 36-604 0.94   0.10  
Parallel Processing Model Grade 6 80-165 0.96   0.18  
Parallel Processing Model Grade 7 43-175 0.91   0.19  
Parallel Processing Model Grade 8 24-129 0.93   0.09  
Parallel Processing Model** Grade 1 810 0.71     Coefficient represents the correlation between processes and is thus not a range but a single estimate.
Parallel Processing Model Grade 2 385 0.87      
Parallel Processing Model Grade 3 966 0.81      
Parallel Processing Model Grade 4 1020 0.82      
Parallel Processing Model Grade 5 1146 0.84      
Parallel Processing Model Grade 6 411 0.78      
Parallel Processing Model Grade 7 393 0.81      
Parallel Processing Model Grade 8 287 0.50      

**Model-based reliability by parallel-process structural equation growth modeling. S-B = Spearman-Brown correction to the correlation between two processes.

See Patarapichayatham, Anderson, Irvin, Kamata, Alonzo, and Tindal (2011). easyCBM® Slope Reliability: Letter Names, Word Reading Fluency, and Passage Reading Fluency (Technical Report No. 1111). Eugene, OR: Behavioral Research and Teaching, University of Oregon.  

This study aimed to estimate the reliability of the slope for three easyCBM® measures. Under a structural equation modeling (SEM) framework, a growth model with two parallel growth processes was used. Essentially, two linear growth models were simultaneously modeled. The two parallel growth processes were established by splitting the available time segments into two groups. One group of time segments was used to form one linear growth process, and another group of time segments was used to form another linear growth process. For each linear growth process, the individual slopes of growth were estimated as factor scores of the latent slope factor. Then, the correlation between individual slopes from the two parallel growth processes was computed as an estimate of the reliability of the growth slope. The Spearman-Brown formula was then used to correct the correlation coefficient.

The procedure was analogous to VanDerHeyden and Burns (2008). In order to estimate the reliability of a slope, they (1) split a series of longitudinal observations into two parallel series, (2) computed an OLS regression slope for each individual for each series, (3) computed the correlation of the individual slopes between the two parallel series, and (4) corrected the correlation by the Spearman-Brown formula.  Our procedure was exactly the same as VanDerHyden and Burns’ four-step procedure, with one exception. For step 2 VanDerHyden and Burns’s derived a direct estimate of individual slopes based only on the observed measures of each student. By contrast, our method used empirical Bayes estimates of individual slopes (e.g., Raudenbush & Bryk, 2002) that incorporated information about the estimated mean slope and the estimated variance of individual slopes from the entire sample data. 

The bi-weekly segments were evenly split into two parallel processes in the following manner. The first bi-weekly segment (average of weeks 1 and 2) was labeled 1A and assigned to a group of time segments for one linear growth process (Process A). The second bi-weekly segment (average of weeks 3 and 4) was labeled 1B and assigned to a group of time segments for another linear growth process (Process B). Similarly, the third bi-weekly segment (average of weeks 5 and 6) was labeled 2A and assigned to Process A, while the fourth bi-weekly segment (average of weeks 7 and 8) was labeled 2B and assigned to Process B. This pattern continued for the entire available bi-weekly segments, totaling 20 time segments, 1A – 10B, across 38 weeks of the school year. However, in many grades there were zero or near-zero students represented in the first two time segments (1A and 1B) and the last two time segments (10A and 10B). Also, there were other time segments with very few observations for some of the data sets. As a part of data cleaning process, descriptive statistics for each time segment for each data set were examined, and time segments with zero or near-zero students represented were deleted from the data.

In each data set, the linear growth model for two parallel processes was fit. The first linear growth model (Process A) was fit with the “A” time segments (2A, 3A, 4A, 5A, 6A, 7A, 8A, and 9A), whereas the second linear growth model (Process B) was fit with the “B” time segments (2B, 3B, 4B, 5B, 6B, 7B, 8B, and 9B).  For both growth processes, the time scores of the growth slope factor were fixed to 0, 1, 2, 3, 4, 5, 6, 7, and 8 to define a linear growth model with equal time intervals between time segments. The zero time score for the growth slope factor at time segment one defines the intercept, initial status factors. On the other hand, the coefficients of the growth intercept factors were fixed at one as part of the regular growth model parameterization. The residual variances of the outcome variables (observed test scores) were estimated but fixed to be the same across time segments. Also, it was assumed that the residuals were not correlated. On the other hand, the growth slope factors were assumed to be correlated. The correlation between the two growth slope factors from the two growth processes, was interpreted as the reliability of the slope of the growth. All parameters were estimated with the Mplus software, using the Maximum Likelihood estimator with robust standard error.

Validity of the Performance Level Score: Convincing Evidence

Content Validity

Alonzo, J., Liu, K., & Tindal, G. (2007a). Examining the Technical Adequacy of Reading Comprehension Measures in a Progress Monitoring Assessment System (Technical Report # 41). Eugene, OR: Behavioral Research and Teaching.

Type of Validity

Age or Grade

Test or Criterion

n (range)

R2

β (SE)

Information (including normative data)/Subjects

Concurrent

1

Regression

180

0.57

0.76 (0.05)

Lai, C. et al. (2010). Technical adequacy of the easyCBM primary reading measures (grades K – 1), 2009-2010 version. (Technical Report #1003). Eugene, OR: Behavioral Research and Teaching.

Concurrent

2

Regression

205

0.003

0.005 (0.001)-Not significant

Jamgochian, E. M. et al. (2010). Technical adequacy of the easyCBM grade 2 reading measures, 2009-2010 version. (Technical Report #1004). Eugene, OR: Behavioral Research and Teaching.

 

 

 

 

 

 

FOR ANALYSES BELOW:

Saez, L. et al. (2010). Technical adequacy of the easyCBM reading measures (Grades 3-8), 2009-2010 version. (Technical Report #1005). Eugene, OR: Behavioral Research and Teaching.

Concurrent

3

Regression

2,146

0.45

0.17(0.004)

 

Concurrent

4

Regression

2,194

0.43

0.16 (0.004)

 

Concurrent

5

Regression

2,368

0.42

0.15 (0.004)

 

Concurrent

6

Regression

1,154

0.44

0.12 (0.004)

 

Concurrent

7

Regression

2,375

0.48

0.16 (0.003)

 

Concurrent

8

Regression

2,357

0.37

0.15 (0.003)

 

 

Type of Validity

Age or Grade

Test or Criterion

n (range)

R2

β (SE)

Information (including normative data)/Subjects

Predictive

F → SAT10

1

Regression

159

0.40

1.10 (0.11)

Lai, C. et al. (2010). Technical adequacy of the easyCBM primary reading measures (grades K – 1), 2009-2010 version. (Technical Report #1003). Eugene, OR: Behavioral Research and Teaching.

Predictive

W → SAT10

1

Regression

177

0.47

0.77 (0.06)

(see above)

Predictive

F → SAT10

2

Regression

205

0.04

0.04 (0.01)

Jamgochian, E. M. et al. (2010). Technical adequacy of the easyCBM grade 2 reading measures, 2009-2010 version. (Technical Report #1004). Eugene, OR: Behavioral Research and Teaching.

Predictive

W → SAT10

2

Regression

205

0.05

0.08 (0.02)

(see above)

 

 

 

 

 

 

FOR ANALYSES BELOW:

Saez, L. et al. (2010). Technical adequacy of the easyCBM reading measures (Grades 3-8), 2009-2010 version. (Technical Report #1005). Eugene, OR: Behavioral Research and Teaching.

Predictive

F → OAKS

3

Regression

2,145

0.45

0.18(0.004)

 

Predictive

W → OAKS

3

Regression

2,232

0.44

0.16 (0.004)

 

Predictive

F → OAKS

4

Regression

2,211

0.45

0.19 (0.004)

 

Predictive

W → OAKS

4

Regression

2,153

0.41

0.18 (0.01)

 

Predictive

F → OAKS

5

Regression

2,331

0.45

0.15 (0.003)

 

Predictive

W → OAKS

5

Regression

2,269

0.43

0.14 (0.003)

 

Predictive

F → OAKS

6

Regression

1,134

0.42

0.15 (0.01)

 

Predictive

W → OAKS

6

Regression

1,057

0.42

0.14 (0.01)

 

Predictive

F → OAKS

7

Regression

2,255

0.44

0.17 (0.004)

 

Predictive

W → OAKS

7

Regression

2,273

0.47

0.14 (0.003)

 

Predictive

F → OAKS

8

Regression

2,269

0.46

0.14 (0.003)

 

Predictive

W → OAKS

8

Regression

2,296

0.43

0.13 (0.003)

 

 

Type of Validity

Age or Grade

Test or Criterion

n (range)

FIT STATISTICS

Information (including normative data)/Subjects

CFI/
TLI

RMSEA

Construct

1

CFA

412-876

0.978-0.993/

0.944-0.982

0.084-0.136

Lai, C. et al. (2010). Technical adequacy of the easyCBM primary reading measures (grades K – 1), 2009-2010 version. (Technical Report #1003). Eugene, OR: Behavioral Research and Teaching.

Construct

2

CFA

1,685-1,973

0.995-0.998/

0.997-0.999

0.019-0.035

Jamgochian, E. M. et al. (2010). Technical adequacy of the easyCBM grade 2 reading measures, 2009-2010 version. (Technical Report #1004). Eugene, OR: Behavioral Research and Teaching.

 

 

 

 

 

 

FOR ANALYSES BELOW:

Saez, L. et al. (2010). Technical adequacy of the easyCBM reading measures (Grades 3-8), 2009-2010 version. (Technical Report #1005). Eugene, OR: Behavioral Research and Teaching.

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

 

 

Predictive Validity of the Slope of Improvement: Convincing Evidence

Type of Validity Age or Grade n (range) Coefficient Information (including normative data)/Subjects
Predictive Validity Grade 3, Quartile 1 13 0.62 Multi-Ethnic
344 0.61 White
114 0.52 Hispanic
12 0.63 Black
9 0.15 Asian
12 0.62 American Indian/Alaskan Native
Predictive Validity Grade 3, Quartile 2 13 0.75 Multi-Ethnic
386 0.34 White
103 0.25 Hispanic
13 -0.06 Black
15 0.51 Asian
6 0.79 American Indian/Alaskan Native
Predictive Validity Grade 3, Quartile 3 18 0.13 Multi-Ethnic
400 0.31 White
65 0.05 Hispanic
8 -0.46 Black
22 0.47 Asian
10 0.27 American Indian/Alaskan Native
Predictive Validity Grade 3, Quartile 4 16 -0.57 Multi-Ethnic
- - White
50 0.32 Hispanic
7 0.77 Black
31 0.18 Asian
8 0.65 American Indian/Alaskan Native
Predictive Validity Grade 4, Quartile 1 27 0.63 Multi-Ethnic
373 0.54 White
123 0.54 Hispanic
15 0.38 Black 
6 0.84 Asian
12 0.56 American Indian/Alaskan Native
Predictive Validity Grade 4, Quartile 2 19 0.33 Multi-Ethnic
367 0.25 White
103 0.28 Hispanic
12 0.76 Black
17 0.52 Asian
13 0.64 American Indian/Alaskan Native
Predictive Validity Grade 4, Quartile 3 17 0.68 Multi-Ethnic
369 0.26 White
87 0.32 Hispanic
11 0.34 Black
29 0.05 Asian
12 0.2 American Indian/Alaskan Native
Predictive Validity Grade 4, Quartile 4 31 0.11 Multi-Ethnic
427 0.12 White
48 0.23 Hispanic
- - Black
25 -0.36 Asian
8 0.37 American Indian/Alaskan Native
Predictive Validity Grade 5, Quartile 1 15 0.45 Multi-Ethnic
365 0.48 White
124 0.45 Hispanic 
25 -0.05 Black
18 -0.07 Asian
14 0.48 American Indian/Alaskan Native
Predictive Validity Grade 5, Quartile 2 22 0.04 Multi-Ethnic
418 0.28 White
125 0.21 Hispanic
7 -0.05 Black
18 0.05 Asian
18 -0.04 American Indian/Alaskan Native
Predictive Validity Grade 5, Quartile 3 21 0.27 Multi-Ethnic
408 0.27 White
84 0.05 Hispanic
13 -0.08 Black
- - Asian
9 0.25 American Indian/Alaskan Native
Predictive Validity Grade 5, Quartile 4 31 0.19 Multi-Ethnic
442 0.3 White
4 -0.4 Hispanic
52 0.4 Black
38 0.51 Asian
7 0.16 American Indian/Alaskan Native
Predictive Validity Grade 6, Quartile 1 7 0.86 Multi-Ethnic
166 0.54 White
52 0.53 Hispanic
8 0.31 Black
2 -1 Asian
16 0.68 American Indian/Alaskan Native
Predictive Validity Grade 6, Quartile 2 11 0 Multi-Ethnic
199 0.22 White
41 0.5 Hispanic
4 0.41 Black
5 0.81 Asian
6 0.27 American Indian/Alaskan Native
Predictive Validity Grade 6, Quartile 3 14 0.29 Multi-Ethnic
182 0.21 White
34 0.52 Hispanic
7 0.5 Black
17 0.14 Asian
6 0.03 American Indian/Alaskan Native
Predictive Validity Grade 6, Quartile 4 15 -0.16 Multi-Ethnic
207 0.35 White
17 0.15 Hispanic
5 -0.59 Black
6 0.87 Asian
4 0.32 American Indian/Alaskan Native
Predictive Validity Grade 7, Quartile 1 28 0.47 Multi-Ethnic
284 0.59 White
186 0.54 Hispanic
15 0.23 Black
25 0.63 Asian
7 0.74 American Indian/Alaskan Native
Predictive Validity Grade 7, Quartile 2 19 0.38 Multi-Ethnic
340 0.38 White
152 0.29 Hispanic
12 -0.25 Black
23 0.42 Asian
5 -0.63 American Indian/Alaskan Native
Predictive Validity Grade 7, Quartile 3 11 0.21 Multi-Ethnic
354 0.29 White
139 0.33 Hispanic
8 -0.31 Black
42 0.25 Asian
4 0.97 American Indian/Alaskan Native
Predictive Validity Grade 7, Quartile 4 14 0.31 Multi-Ethnic
393 0.39 White 
84 0.27 Hispanic
11 0.26 Black 
31 0.49 Asian
2 1 American Indian/Alaskan Native
Predictive Validity Grade 8, Quartile 1 11 -0.35 Multi-Ethnic
276 0.54 White
237 0.5 Hispanic
16 -0.79 Black
20 0.49 Asian
6 -0.97 American Indian/Alaskan Native
Predictive Validity Grade 8, Quartile 2 13 0.45 Multi-Ethnic
350 0.18 White 
182 0.12 Hispanic
16 0.33 Black
21 0.78 Asian
8 -0.7 American Indian/Alaskan Native
Predictive Validity Grade 8, Quartile 3 13 0.31 Multi-Ethnic
357 0.18 White
123 0.17 Hispanic
15 0.08 Black
37 0.14 Asian
5 0.57 American Indian/Alaskan Native
Predictive Validity Grade 8, Quartile 4 9 -0.46 Multi-Ethnic
417 0.33 White
74 0.29 Hispanic
15 -0.07 Black
33 0.42 Asian
4 -0.89 American Indian/Alaskan Native

 

Disaggregated Reliability and Validity Data: Convincing Evidence

Alternate Forms: Convincing Evidence

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

In a study of the alternate form reliability of the Passage Reading Fluency measures, we found correlations ranged from 0.87 to 0.97.

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

In grades 1-8, n = 20 passages per grade.

Sensitive to Student Improvement: Data Unavailable

End-of-Year Benchmarks: Convincing Evidence

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 60 86 90
4 85 110 115
5 128 134 152
6 128 141 146
7 133 154 141
8 154 151 153

The state test cut scores for passing in grades 3-8 respectively are 204, 211, 218, 222, 227, and 231.

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

Description is of the sample used in the criterion-referenced study:

Representation: Local
Date: 2009-2010
Number of States: 2
Size: approximates 1,137 – 2,320
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

Disability classification: 13.2-18.0%

Rates of Improvement Specified: Unconvincing Evidence

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:

The growth estimate displayed below is an average change over 33.67 weeks.

Grade 1: 1.39 word/week
Grade 2: 1.15 word/week
Grade 3: 0.983 word/week
Grade 4: 0.941 word/week
Grade 5: 0.630 word/week
Grade 6: 0.724 word/week
Grade 7: 0.224 word/week
Grade 8: -0.218 word/week

b. Basis for specifying minimum acceptable growth:

Criterion-referenced

Description is of the sample used in the criterion-referenced study:

Representation: Local
Date: 2009-2010
Number of States: 2
Size: approximates 1,137 – 2,320
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

Disability classification: 13.2-18.0%

c. Procedure for specifying criterion for adequate growth:

Student growth on easyCBM passage reading fluency (PRF) was estimated using Hierarchical Linear Modeling (HLM) based on their fall, winter, and spring easyCBM PRF scores collected during the 2009-2010 school year. We divided students into quartiles of normative achievement on the fall easyCBM PRF scores and conducted Receiver Operating Characteristic (ROC) curve analyses by grade 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.

Decision Rules for Changing Instruction: Data Unavailable

Decision Rules for Increasing Goals: Data Unavailable

Improved Student Achievement: Data Unavailable

Improved Teacher Planning Data Unavailable