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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 14 hours of training. Paraprofessionals and professionals can administer the test. Accommodations: 
Behavioral Research and Teaching Phone: 5413463535 A fieldtested training manual is available and provides all needed implementation information. 
In grades K8, 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 K3. Raw and percentile scores are provided. Raw scores are the number of items correct. 
Reliability of the Performance Level Score
Grade  K  1  2  3 

Rating 
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 Retest 
1 
48 & 52 
0.94 and 0.95 


n and SEM values are from two sessions 
Test Retest 
3 
48 
0.92 and 0.94 


n and SEM values are from two sessions 
Reliability of the Slope
Grade  K  1  2  3 

Rating 
Type of Reliability  Age or Grade  n (range)  Coefficient  SEM  Information / Subjects  

range  median  
Parallel Processing Model**  Grade 1  87501  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  79354  0.95  0.088  
Parallel Processing Model  Grade 3  1067  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 
**Modelbased reliability by parallelprocess structural equation growth modeling. Values represent SpearmanBrown 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 SpearmanBrown 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 SpearmanBrown formula. Our procedure was exactly the same as VanDerHyden and Burns’ fourstep 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 biweekly segments were evenly split into two parallel processes in the following manner. The first biweekly 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 biweekly 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 biweekly segment (average of weeks 5 and 6) was labeled 2A and assigned to Process A, while the fourth biweekly segment (average of weeks 7 and 8) was labeled 2B and assigned to Process B. This pattern continued for the entire available biweekly segments, totaling 20 time segments, 1A – 10B, across 38 weeks of the school year. However, in many grades there were zero or nearzero 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 nearzero 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
Grade  K  1  2  3 

Rating 
Type of Validity  Age or Grade  Test or Criterion  n (range)  R^{2}  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  8621,449  0.9970.999/ 0.9920.997  0.0280.047  
Construct  1  CFA  412876  0.9780.993/ 0.9440.982  0.0840.136  
Construct  2  CFA  1,6851,973  0.9950.998/ 0.9970.999  0.0190.035  
Construct  3  CFA  1,8302,046  0.9710.977/ 0.9840.987  0.0230.027 
Type of Validity  Age or Grade  Test or Criterion  Zeroorder 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 
0.9970.999/ 0.9920.997 
Construct  1  easyCBM 
0.9780.993/ 0.9440.982 
Construct  2  easyCBM 
0.9950.998/ 0.9970.999 
Construct  3  easyCBM 
0.9710.977/ 0.9840.987 
Predictive Validity of the Slope of Improvement
Grade  K  1  2  3 

Rating 
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% 
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
Grade  K  1  2  3 

Rating  No  No  No  No 
Disaggregated Reliability and Validity Data
Grade  K  1  2  3 

Rating  No  No  No  No 
Disaggregated Reliability of the Performance Level Score (PDF)
Alternate Forms
Grade  K  1  2  3 

Rating 
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 gradelevel 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 testretest 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 1PL 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 K3: 3 forms are used for screening and 17 forms are available to progress monitor.
Rates of Improvement Specified
Grade  K  1  2  3 

Rating 
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 K1. 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:
Normreferenced
Normative profile:
Representation: National/Local
Date:
Number of States:
Size:
Gender:
SES:
Race/Ethnicity:
ELL:
Disability classification:
EndofYear Benchmarks
Grade  K  1  2  3 

Rating 
1. Are benchmarks for minimum acceptable endofyear performance specified in your manual or published materials?
Yes.
a. Specify the endofyear 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 endofyear performance:
Normreferenced.
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 endofyear 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?
Normreferenced
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
Grade  K  1  2  3 

Rating 
Decision Rules for Changing Instruction
Grade  K  1  2  3 

Rating 
Decision Rules for Increasing Goals
Grade  K  1  2  3 

Rating 
Improved Student Achievement
Grade  K  1  2  3 

Rating 
Improved Teacher Planning
Grade  K  1  2  3 

Rating 