easyCBM

Reading - Word 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).

Students read individual sight and decodable words presented in chart format on a single sheet of paper.

The tool provides information on student performance in English.

Word 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 K-3.

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

 

Reliability of the Performance Level Score

GradeK123
RatingdashFull bubbledashEmpty bubble
Type of Reliability Age or Grade n (range) Coefficient SEM Information (including normative data)/Subjects
range median

Alternate Form

1

48 & 52

0.95 - 0.96

0.95

2.87 & 2.85

n and SEM values are from two sessions

Alternate Form

3

48

0.87 - 0.93

0.91

3.94 & 4.00

n and SEM values are from two sessions

Test Re-test

1

48 & 52

0.94 and 0.95

 

 

n and SEM values are from two sessions

Test Re-test

3

48

0.92 and 0.94

 

 

n and SEM values are from two sessions

 

Reliability of the Slope

GradeK123
RatingdashFull bubbleFull bubbleFull bubble
Type of Reliability Age or Grade n (range) Coefficient SEM Information / Subjects
range median
Parallel Processing Model** Grade 1 87-501 0.99   0.017 Coefficient represents the correlation between processes and it thus not a range but a single estimate. SEM represents the standard error of the correlation coefficient.
Parallel Processing Model Grade 2 79-354 0.95   0.088
Parallel Processing Model Grade 3 10-67 0.81   0.189
Parallel Processing Model** Grade 1 937 0.81     Coefficient represents the correlation between processes and is thus not a range but a single estimate.
Parallel Processing Model Grade 2 665 0.88    
Parallel Processing Model Grade 3 122 0.85    

**Model-based reliability by parallel-process structural equation growth modeling. Values represent Spearman-Brown corrected correlation coefficient between each half of the parallel process model.

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

GradeK123
RatingEmpty bubbleEmpty bubbleEmpty bubbleEmpty bubble
Type of Validity Age or Grade Test or Criterion n (range) R2 β (SE) Information (including normative data)/Subjects

Concurrent

K Regression 189      

Concurrent

2 Regression 205 0.002 0.004 (0.007) not sig  

Concurrent

3 Regression 953 0.28 0.19 (0.10)  
Predictive
F → SAT10
1 Regression 161 0.45 1.47 (0.13)  
Predictive
W → SAT10
1 Regression 177 0.48 1.24 (0.10)  
Predictive
F → SAT10
2 Regression 205 0.02 0.03 (0.01)  
Predictive
W → SAT10
2 Regression 205 0.02 0.05 (0.02)  
Predictive
F → OAKS
3 Regression 821 0.34 0.25 (0.01)  
Predictive
W → OAKS
3 Regression 932 0.37 0.28 (0.01)  

 

Type of Validity Age or Grade Test or Criterion n (range) FIT STATISTICS Information (including normative data)/Subjects
CFI/TLI RMSEA
Construct K CFA 862-1,449 0.997-0.999/ 0.992-0.997 0.028-0.047  
Construct 1 CFA 412-876 0.978-0.993/ 0.944-0.982 0.084-0.136  
Construct 2 CFA 1,685-1,973 0.995-0.998/ 0.997-0.999 0.019-0.035  
Construct 3 CFA 1,830-2,046 0.971-0.977/ 0.984-0.987 0.023-0.027  

 

Type of Validity Age or Grade Test or Criterion Zero-order correlations

Concurrent

K easyCBM *r =0.80

Concurrent

1 easyCBM *r =0.75

Concurrent

2 easyCBM *r =0.60

Concurrent

3 easyCBM *r =0.53
Predictive
F → SAT10
K
1
easyCBM

*r =0.47

*r =0.67
Predictive
W → SAT10
K
1
easyCBM

*r =0.67

*r =0.69
Predictive
F → SAT10
2 easyCBM *r=0.66
Predictive
W → SAT10
2 easyCBM *r =0.62
Predictive
F → OAKS
3 easyCBM *r =.60
Predictive
W → OAKS
3 easyCBM *r =0.61
Construct K easyCBM

0.997-0.999/

0.992-0.997
Construct 1 easyCBM

0.978-0.993/

0.944-0.982
Construct 2 easyCBM

0.995-0.998/

0.997-0.999
Construct 3 easyCBM 0.971-0.977/
0.984-0.987

 

Predictive Validity of the Slope of Improvement

GradeK123
RatingFull bubbleFull bubbleEmpty bubbleEmpty bubble
Type of Validity Age or Grade n (range) Coefficient Information (including normative data)/Subjects
Predictive Validity K 904
815
0.80
0.82

Lower 50%
Upper 50%

Predictive Validity 1 636
482
519
542
0.65
0.51
0.39
-0.51
Quartile 1
Quartile 2
Quartile 3
Quartile 4
Predictive Validity 2 563
544
547
547
0.23
0.31
0.11
-0.12
Quartile 1
Quartile 2
Quartile 3
Quartile 4
Predictive Validity 3 218
218
206
207
0.50
0.07
-0.07
0.18
Quartile 1
Quartile 2
Quartile 3
Quartile 4

 

Bias Analysis Conducted

GradeK123
RatingNoNoNoNo

Disaggregated Reliability and Validity Data

GradeK123
RatingNoNoNoNo

Disaggregated Reliability of the Performance Level Score (PDF)

Alternate Forms

GradeK123
RatingEmpty 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 distributed items 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. Results of these studies are reported in the technical reports documenting the development of the measures:

Alonzo, J., & Tindal, G. (2007). The development of word and passage reading fluency measures in a progress monitoring assessment system (Technical Report No. 40). Eugene, OR: Behavioral Research and Teaching, University of Oregon.

Alonzo, J., & Tindal, G. (2009). Alternate form and test-retest reliability of easyCBM® reading measures (Technical Report No. 0906). Eugene, OR: Behavioral Research and Teaching, University of Oregon.

The first technical report describes the process of initial instrument development, where we used a 1-PL Rasch model to estimate item difficulty for each letter sound in its lower case and capital form and then used this information to construct 20 alternate forms of comparable difficulty for use in Kindergarten through Grade 3. Across all alternate forms, the mean measure of items in each row is within 0.02 of the mean measure of items in the same row on every other form. In the second technical report, evidence is presented that the process we used in measurement development did, in fact, result in alternate forms of equal and controlled difficulty. In a study of the alternate form reliability of the Word Reading Fluency measure, we found correlations ranged from 0.95 to 0.96.

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

20 forms are available in each of grades K-3: 3 forms are used for screening and 17 forms are available to progress monitor.

Rates of Improvement Specified

GradeK123
RatingEmpty bubbleEmpty bubbleEmpty bubbleEmpty bubble

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:

In the following table, we present means and standard deviations for WRF gains in grades K-1. Weekly rates of improvement were estimated from the benchmark administrations. The mean gain was divided by 32 to obtain an estimate of expected weekly growth.

Group Mean Gain SD Approximate Weekly Rate of Improvement SD
Kindergarten        
Full Sample 15.99 14.21 0.50 0.44
Low 50th %ile 11.97 9.22 0.37 0.29
Upper 50th %ile 22.70 18.05 0.71 0.56
Grade 1        
Full Sample 32.58 17.33 1.02 0.54
Quartile 1 26.12 15.53 0.82 0.49
Quartile 2 33.34 16.50 1.04 0.52
Quartile 3 41.42 15.95 1.29 0.50
Quartile 4 30.60 17.85 0.96 0.56

b. Basis for specifying minimum acceptable growth:

Norm-referenced

Normative profile:

Representation: National/Local
Date:
Number of States:
Size:
Gender: 
SES: 
Race/Ethnicity: 
ELL:
Disability classification:

End-of-Year Benchmarks

GradeK123
RatingFull bubbleFull bubbleFull bubbleFull bubble

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:

Kindergarten: 50th percentile = 11 words read correctly per minute in the spring; Grade 1 = 45; Grade 2 = 65; and Grade 3 = 65.

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

Norm-referenced.

We used a large sample of students from school districts that had agreed to administer the fall, winter, and spring benchmark screener tests to all students in their districts to calculate these end-of-year benchmark performance goals. We selected the score that corresponded with the 50th percentile rank because that score can be roughly interpreted as indicative of 'on grade level' performance for students in that grade at that time of the year.

c. Specify the benchmarks:

Percentile Word Read Correctly Per Minute
Kindergarten First Grade Second Grade
10th 3 16 29
20th 5 23 43
50th 11 45 65
75th 17 66 80
90th 30 82 93

d. Basis for specifying these benchmarks?

Norm-referenced

Normative profile:

Representation: Local
Date: 2009 – 2010
Number of States: 2
Size: approximately 2,000 per grade
Gender: 50% male, 50% female
SES: approximately 50% Title 1
Disability classification: approximately 16%

Sensitive to Student Improvement

GradeK123
Ratingdashdashdashdash

Decision Rules for Changing Instruction

GradeK123
Ratingdashdashdashdash

Decision Rules for Increasing Goals

GradeK123
Ratingdashdashdashdash

Improved Student Achievement

GradeK123
Ratingdashdashdashdash

Improved Teacher Planning

GradeK123
Ratingdashdashdashdash