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Oral Reading Fluency
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edSpring: Contact for cost details. Edcheckup: Each license (or “set”) allows you to access materials, enter data, and generate reports for up to 35 students. The cost per set is as follows:

Computer and Internet access are required for full use of product services. Testers will require less than 1 hour of training. Paraprofessionals can administer the test. 
Where to obtain:
edSpring
TIES
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St. Paul, MN 55108
Website: www.edspring.org
Edcheckup
Edcheckup, LLC
7701 York Ave S, Ste 250
Edina, MN 55435
Website: www.edcheckup.com
edSpring / Edcheckup: Fieldtested training manuals are included and should provide all implementation information.
Ongoing technical support is available.

Edcheckup Standard Reading Passages consist of 20 passages at the same difficulty level for use for repeated 1 minute samples of reading aloud from text. Testers follow along on a duplicate passage while the student reads aloud, and mark incorrectly read works and coding errors if desired. Testers are encouraged to rate the degree of prosody evidence in the student’s oral reading of the passage. Raw scores are entered into the Edcheckup online system. 
Edcheckup is individually administered and requires 2 minutes for administration and 1 additional minute for scoring. Raw scores are available. 20 alternate forms are available. 
Reliability of the Performance Level Score
Grade  1  2  3  4  5  6 

Rating 
Type of Reliability  Age or Grade  n (range)  Coefficient  SEM  Information / Subjects  
range  median  
Cronbach’s Alpha  1  89  0.94340.9495  0.9446  Median = 4.04  Alpha = 0.9554, Standardized Item Alpha = 0.9582 
Cronbach’s Alpha  2  216  0.89900.9164  0.9016  Median = 2.56  Alpha = 0.9554 Standardized Item Alpha = 0.9582, 17.9% Students of Color, 7.6% Students of Poverty, 13.4% Students with Disabilities 
Cronbach’s Alpha  3  227  0.90560.9152  0.9152  Median = 2.57  Alpha = 0.9554, Standardized Item Alpha = 0.9582, 19.7% Students of Color, 7.5% Students of Poverty, 15.1% Students with Disabilities 
Cronbach’s Alpha  4  206  0.90480.9303  0.9204  Median = 2.53  Alpha = 0.9554, Standardized Item Alpha = 0.9582, 23.8% Students of Color, 6.0% Students of Poverty, 13.1% Students with Disabilities 
Cronbach’s Alpha  5  255  0.87030.8965  0.8860  Median = 2.44  Alpha = 0.9554, Standardized Item Alpha = 0.9582, 21.8% Students of Color, 5.6% Students of Poverty, 11.6% Students with Disabilities 
Cronbach’s Alpha  6  78  0.89300.9229  0.9046  Median = 4.61  Alpha = 0.9554, Standardized Item Alpha = 0.9582, 33.3% Students of Color, 16.7% Students of Poverty, 11.5% Students with Disabilities 
Alternate Form  2  216  0.8990.916  0.902  17.9% Students of Color, 7.6% Students of Poverty, 13.4% Students with Disabilities  
Alternate Form  3  229  0.9060.915  0.914  19.7% Students of Color, 7.5% Students of Poverty, 15.1% Students with Disabilities  
Alternate Form  4  206  0.9050.930  0.920  23.8% Students of Color, 6.0% Students of Poverty, 13.1% Students with Disabilities  
Alternate Form  5  255  0.8700.897  0.886  21.8% Students of Color, 5.6% Students of Poverty, 11.6% Students with Disabilities  
Alternate Form  6  78  0.8930.923  0.905  33.3% Students of Color, 16.7% Students of Poverty, 11.5% Students with Disabilities 
Reliability of the Slope
Grade  1  2  3  4  5  6 

Rating 
In our reliability of slope analysis we used Latent Growth Modeling (LGM) to estimate the reliability of longitudinal weekly growth data as demonstrated by Yeo, Kim, BranumMartin, Wayman, and Espin (2011). These researchers point out, "The feature of LGM that treats time as an independent variable at each occasion makes it possible to estimate the reliability of longititudinal data such as CBM data" (p. 276). An important conclusion of their study was, “ a crucial advantage of LGM is that reliability estimated by LGM takes into account the developmental trajectories at the individual level of analysis (Tisak & Tisak, 2000)” (p. 287).
The reliabilities for 9 weekly assessments at each grade level, grades 16, are presented in the table below. For each grade level (and the entire sample) we report sample sizes, means, modelimplied observed score variance, measurement error variance, observed variance  error variance, SEM, reliability for each assessment, and the median reliability for each grade level. The range of median reliability coefficients for weekly progress monitoring probes across the six grades levels was 0.73 to 0.89 with a median of 0.81.
Type of Reliability 
Grade 
Assessment 
n 
Mean 
Modelimplied observed score variance 
Measurement error variance 
Observed variance  error variance 
SEM 
Reliability 
Latent Growth Modeling 
all 
Assess1 
2029 
74.44 
1966.10 
219.73 
1746.37 
14.82 
0.89 
Latent Growth Modeling 
all 
Assess2 
2029 
76.06 
1907.83 
195.29 
1712.54 
13.97 
0.90 
Latent Growth Modeling 
all 
Assess3 
2029 
78.09 
1951.90 
234.21 
1717.69 
15.30 
0.88 
Latent Growth Modeling 
all 
Assess4 
2029 
79.66 
1927.61 
213.60 
1714.01 
14.62 
0.89 
Latent Growth Modeling 
all 
Assess5 
2029 
81.43 
1913.37 
204.53 
1708.84 
14.30 
0.89 
Latent Growth Modeling 
all 
Assess6 
2029 
83.23 
1924.56 
206.26 
1718.30 
14.36 
0.89 
Latent Growth Modeling 
all 
Assess7 
2029 
85.21 
1995.32 
194.37 
1800.95 
13.94 
0.90 
Latent Growth Modeling 
all 
Assess8 
2029 
86.21 
1926.07 
206.16 
1719.92 
14.36 
0.89 
Latent Growth Modeling 
all 
Assess9 
2029 
87.58 
1915.85 
205.17 
1710.67 
14.32 
0.89 
Latent Growth Modeling 
Median 







0.89 










Latent Growth Modeling 
1 
Assess1 
267 
23.98 
302.22 
72.28 
229.94 
8.50 
0.76 
Latent Growth Modeling 
1 
Assess2 
267 
25.19 
325.45 
69.83 
255.62 
8.36 
0.79 
Latent Growth Modeling 
1 
Assess3 
267 
27.26 
356.97 
72.74 
284.23 
8.53 
0.80 
Latent Growth Modeling 
1 
Assess4 
267 
29.66 
416.99 
72.34 
344.65 
8.51 
0.83 
Latent Growth Modeling 
1 
Assess5 
267 
31.50 
442.08 
87.55 
354.53 
9.36 
0.80 
Latent Growth Modeling 
1 
Assess6 
267 
33.23 
459.59 
71.49 
388.10 
8.46 
0.84 
Latent Growth Modeling 
1 
Assess7 
267 
35.24 
497.72 
81.67 
416.05 
9.04 
0.84 
Latent Growth Modeling 
1 
Assess8 
267 
36.63 
525.61 
99.11 
426.50 
9.96 
0.81 
Latent Growth Modeling 
1 
Assess9 
267 
39.96 
674.19 
113.48 
560.71 
10.65 
0.83 
Latent Growth Modeling 
Median 







0.81 










Latent Growth Modeling 
2 
Assess1 
412 
41.77 
818.61 
215.78 
602.83 
14.69 
0.74 
Latent Growth Modeling 
2 
Assess2 
412 
43.77 
809.76 
126.18 
683.58 
11.23 
0.84 
Latent Growth Modeling 
2 
Assess3 
412 
46.20 
836.64 
130.46 
706.17 
11.42 
0.84 
Latent Growth Modeling 
2 
Assess4 
412 
47.94 
752.42 
127.84 
624.58 
11.31 
0.83 
Latent Growth Modeling 
2 
Assess5 
412 
49.13 
846.01 
129.77 
716.24 
11.39 
0.85 
Latent Growth Modeling 
2 
Assess6 
412 
51.47 
813.24 
123.38 
689.87 
11.11 
0.85 
Latent Growth Modeling 
2 
Assess7 
412 
53.57 
906.92 
101.97 
804.95 
10.10 
0.89 
Latent Growth Modeling 
2 
Assess8 
412 
55.70 
1004.91 
202.09 
802.83 
14.22 
0.80 
Latent Growth Modeling 
2 
Assess9 
412 
56.52 
896.76 
144.52 
752.25 
12.02 
0.84 
Latent Growth Modeling 
Median 







0.84 










Latent Growth Modeling 
3 
Assess1 
491 
77.50 
1202.54 
138.87 
1063.67 
11.78 
0.88 
Latent Growth Modeling 
3 
Assess2 
491 
80.83 
1188.67 
133.77 
1054.90 
11.57 
0.89 
Latent Growth Modeling 
3 
Assess3 
491 
83.36 
1324.91 
224.67 
1100.24 
14.99 
0.83 
Latent Growth Modeling 
3 
Assess4 
491 
86.53 
1383.40 
311.23 
1072.18 
17.64 
0.78 
Latent Growth Modeling 
3 
Assess5 
491 
87.33 
1204.38 
141.96 
1062.43 
11.91 
0.88 
Latent Growth Modeling 
3 
Assess6 
491 
88.90 
1270.41 
133.28 
1137.13 
11.54 
0.90 
Latent Growth Modeling 
3 
Assess7 
491 
91.46 
1459.82 
224.46 
1235.35 
14.98 
0.85 
Latent Growth Modeling 
3 
Assess8 
491 
92.66 
1273.58 
137.24 
1136.34 
11.72 
0.89 
Latent Growth Modeling 
Median 
Assess9 
491 
93.27 
1261.91 
162.22 
1099.70 
12.74 
0.87 









0.88 










Latent Growth Modeling 
4 
Assess1 
352 
98.46 
1365.94 
481.32 
884.63 
21.94 
0.65 
Latent Growth Modeling 
4 
Assess2 
352 
98.76 
1087.14 
273.45 
813.69 
16.54 
0.75 
Latent Growth Modeling 
4 
Assess3 
352 
100.56 
1056.35 
257.96 
798.39 
16.06 
0.76 
Latent Growth Modeling 
4 
Assess4 
352 
100.13 
1087.95 
198.58 
889.37 
14.09 
0.82 
Latent Growth Modeling 
4 
Assess5 
352 
103.21 
1022.30 
207.64 
814.66 
14.41 
0.80 
Latent Growth Modeling 
4 
Assess6 
352 
105.81 
1205.94 
276.01 
929.93 
16.61 
0.77 
Latent Growth Modeling 
4 
Assess7 
352 
106.20 
1161.25 
165.32 
995.93 
12.86 
0.86 
Latent Growth Modeling 
4 
Assess8 
352 
107.14 
1091.65 
232.15 
859.50 
15.24 
0.79 
Latent Growth Modeling 
4 
Assess9 
352 
107.82 
1207.99 
207.29 
1000.70 
14.40 
0.83 
Latent Growth Modeling 
Median 







0.79 










Latent Growth Modeling 
5 
Assess1 
329 
103.12 
1227.87 
301.66 
926.21 
17.37 
0.75 
Latent Growth Modeling 
5 
Assess2 
329 
104.56 
1165.03 
305.33 
859.71 
17.47 
0.74 
Latent Growth Modeling 
5 
Assess3 
329 
107.04 
1301.86 
451.24 
850.62 
21.24 
0.65 
Latent Growth Modeling 
5 
Assess4 
329 
106.05 
1279.40 
300.96 
978.44 
17.35 
0.76 
Latent Growth Modeling 
5 
Assess5 
329 
106.95 
1153.05 
285.32 
867.73 
16.89 
0.75 
Latent Growth Modeling 
5 
Assess6 
329 
110.37 
1179.15 
319.27 
859.88 
17.87 
0.73 
Latent Growth Modeling 
5 
Assess7 
329 
113.45 
1177.76 
318.17 
859.59 
17.84 
0.73 
Latent Growth Modeling 
5 
Assess8 
329 
111.49 
1227.17 
350.06 
877.11 
18.71 
0.71 
Latent Growth Modeling 
5 
Assess9 
329 
113.66 
1177.98 
326.42 
851.56 
18.07 
0.72 
Latent Growth Modeling 
Median 







0.73 










Latent Growth Modeling 
6 
Assess1 
178 
116.90 
1507.86 
344.73 
1163.12 
18.57 
0.77 
Latent Growth Modeling 
6 
Assess2 
178 
116.55 
1292.55 
338.50 
954.06 
18.40 
0.74 
Latent Growth Modeling 
6 
Assess3 
178 
115.99 
1342.37 
280.82 
1061.55 
16.76 
0.79 
Latent Growth Modeling 
6 
Assess4 
178 
120.14 
1317.25 
242.36 
1074.89 
15.57 
0.82 
Latent Growth Modeling 
6 
Assess5 
178 
123.16 
1317.93 
279.80 
1038.14 
16.73 
0.79 
Latent Growth Modeling 
6 
Assess6 
178 
121.14 
1327.09 
454.85 
872.24 
21.33 
0.66 
Latent Growth Modeling 
6 
Assess7 
178 
124.65 
1318.15 
314.48 
1003.67 
17.73 
0.76 
Latent Growth Modeling 
6 
Assess8 
178 
125.46 
1376.98 
251.33 
1125.65 
15.85 
0.82 
Latent Growth Modeling 
6 
Assess9 
178 
127.28 
1327.66 
355.22 
972.44 
18.85 
0.73 
Latent Growth Modeling 
Median 







0.77 
Information (including normative data) / Subjects:
Total students in subject sample was 2,029. The percentage of students that were American Indian was 5.3%, for African American was 29.7%, for Asian American was 9.9%, for Hispanic American was 14.4%, and for White American was 40.1%. 47% of the sample was female. 18.8% of the sample were students with disabilities.
Validity of the Performance Level Score
Grade  1  2  3  4  5  6 

Rating 
Type of Validity  Age or Grade  Test or Criterion  n (range)  Coefficient  Information / Subjects  

range  median  
Construct  2  Measure of Academic Progress  215  0.756  16.6% Students of Color, 7.1%, Students of Poverty, 13.3% Students with Disabilities  
Construct  3  Measure of Academic Progress  229  0.626  19.5% Students of Color, 7.1%, Students of Poverty, 14.2% Students with Disabilities  
Construct  4  Measure of Academic Progress  206  0.694  22.3% Students of Color, 5.9%, Students of Poverty, 11.9% Students with Disabilities  
Construct  5  Measure of Academic Progress  256  0.670  20.4% Students of Color, 5.4%, Students of Poverty, 11.2% Students with Disabilities  
Construct  6  Measure of Academic Progress  78  0.760  34.7% Students of Color, 17.3%, Students of Poverty, 10.7% Students with Disabilities  
Predictive  2  Measure of Academic Progress  204  0.764  16.4% Students of Color, 5.8%, Students of Poverty, 12.6% Students with Disabilities  
Predictive  3  Measure of Academic Progress  219  0.669  18.7% Students of Color, 5.9%, Students of Poverty, 14.2% Students with Disabilities  
Predictive  4  Measure of Academic Progress  195  0.661  22.1% Students of Color, 5.1%, Students of Poverty, 11.3% Students with Disabilities  
Predictive  5  Measure of Academic Progress  157  0.583  17.2% Students of Color, 3.2%, Students of Poverty, 8.3% Students with Disabilities  
Predictive 
1  Maze Comprehension  245  0.68  
Predictive 
6  Maze Comprehension  158  0.61 
Predictive Validity of the Slope of Improvement
Grade  1  2  3  4  5  6 

Rating 
Type of Validity  Age or Grade  Test or Criterion  n (range)  Coefficient  Information (including normative data)/Subjects  

range  median  
Predictive  K, 1, 3  NAEPMAP  37  0.57  Heirarchical multiple regression analysis using Oral Reading growth from Spring of Kindergarten to Fall of 1st grade to predict end of 3rd grade performance on NAEPMAP where (AF (1,33) = 8.48, p < 0.01) and accounted for 11% more of the variance.  
Predictive  K, 1, 3  NAEPMAP  37  0.68  Heirarchical multiple regression analysis using Oral Reading growth from Spring of Kindergarten to Spring of 1st grade to predict end of 3rd grade performance on NAEPMAP where (AF (1,33) = 22.51, p < 0.001) and accounted for 22% more of the variance.  
Predictive  1, 3  NAEPMAP  37  0.69  Heirarchical multiple regression analysis using Oral Reading growth from Fall of 1st grade to Spring of 1st grade to predict end of 3rd grade performance on NAEPMAP where (AF (1,33) = 13.15, p < 0.01) and accounted for 13% more of the variance. 
Two studies are described below that provide evidence bearing on this GOM.
Study 1
A special type of the structural equation modeling approach, latent growth modeling (LGM) was used to the proposed growth model. A conditional LGM with MAP scores was employed to investigate whether CBM reading aloud slope predicts performance on MAP scores, after controlling for initial status on CBM reading aloud (See figure 1).
Figure 1. Latent growth modeling with three time points
In evaluating the overall goodness of fit, the comparative fit index (CFI), normed fit index (NFI), and incremental fit index (IFI) were used in the study. Models leading to a CFI, NFI, and IFI higher 0.90 were considered a reasonable good fit. In the table below we present sample means and standard deviations for the study variables.
Kindergarten  Fall  Spring  MAP  

Mean  26.7  41.2  90.2  210.0 
S.D  29.7  35.0  44.1  10.8 
Pearson correlations were calculated to provide information on the rankordered reliability and validity of CBM data and shown in the table below.
Kindergarten  Fall  Spring  

Kindergarten  1  
Fall  0.94  1  
Spring  0.77  0.83  1 
MAP  0.68  0.75  0.82 
To examine the value of the CBM growth rate as a significant predictor of improved MAP reading scores, the conditional LGM with MAP was used in this study. All fit measures, including IFI, CFI, and NFI indicated a fit within the acceptable to good range. The parameter estimates (slope and intercept) are presented in the table below.
Estimate  SE  

Slope  63.41***  4.80 
Intercept  26.83***  5.24 
*** p<0.001
The estimated slope was 63.41 WRCM/month and the estimated initial status was 26.84 WRCM. The table below provides the direct effect coefficient of intercept and slope on MAP.
Intercept> MAP  Slope> MAP  

Estimate  0.22***  0.19*** 
SE  0.04  0.05 
*** p<0.001
According to the results the direct effect of the CBM slope was significant, meaning that increasing growth rate predicted performance on MAP over time, after controlling for initial status on CBM reading aloud (Kindergarten). In addition, the intercept (initial status) of CBM reading aloud contributed statistically to the prediction of MAP performance, after controlling for the slope of CBM reading aloud. In conclusion, both the intercept and slope of CBM reading aloud were significant predictors of performance on MAP scores.
Study 2 (HLM)
For these analyses the OR dataset included 528 students with 10 or more measures for OR where at least 14 days had passed between the fourth to last and the third to last measures.
With this sample, HLM was used to estimate individual student slopes across all but the last three measures. A separate HLM was run for each grade. These slopes were then used in a regression model to predict the average of the last three time points, called AVG, again by grade. Since fewer than 30 students met the criteria in Grades 5 & 6, the regression model was not used at those grade levels. Results from the regression analyses are included in the table below.
OR Regression Results for Slopes predicting AVG by Grade
Grade  N  R  F  pvalue 

1  221  0.81  431.50  0.000 
2  137  0.62  85.72  0.000 
3  92  0.21  4.24  0.042 
4  57  0.91  267.50  0.000 
5  16       
6  5       
Bias Analysis Conducted
Grade  1  2  3  4  5  6 

Rating  Yes  Yes  Yes  Yes  Yes  Yes 
Have you conducted additional analyses related to the extent to which your tool is or is not biased against subgroups (e.g., race/ethnicity, gender, socioeconomic status, students with disabilities, English language learners)?
Bias Analysis Method: Calculated Comparative Fit Indices, TuckerLewisIndices, Root Mean Square Error of Approximation
Subgroups: White, African American, Asian, Hispanic, American Indian
Bias Analysis Results: See below
Test bias (measurement invariance) analysis results using invariance testing in multiplegroup confirmatory factor models.
1. Model fit indices for each ethnic group
Ethnic group (n) 
Chisquare^{*} (df) 
CFI 
TLI 
RMSEA (90% CI) 
White (648) 
96.541 (27) 
0.993 
0.991 
0.063 (0.0500.077) 
African American (646) 
99.024 (27) 
0.993 
0.990 
0.064 (0.0510.078) 
Asian (192) 
39.131 (27) 
0.996 
0.995 
0.048 (0.0000.080) 
Hispanic (50) 
38.578 (27) 
0.986 
0.981 
0.093 (0.0000.154) 
American Indian (53) 
42.681 (27) 
0.981 
0.975 
0.105 (0.0350.162) 
^{*} Chisquare values are influenced by sample size.
Overall, every ethnic group’s modelfit well. All relative fit indices (CFI, TLI) were above 0.98, which indicates very good model fit, given that a value over 0.90 is suggested to be a reasonably good fit for CFI and TLI (Hu & Bentler, 1998). In addition, the 90 percent confidence interval for RMSEA indicates very good model fit, given that a value below or at 0.05 is considered a very close approximate fit (Browne & Cudeck, 1993). These results show that all CBMR assessments appeared to function equally well as one factor for all ethnic groups.
Below are results for the measurement invariance (test bias) among different ethnic groups.
2. Model fit statistics for tests of structural/measurement invariance between White and other groups
(a) White versus African American
Model 
Chisquare 
df 
Delta Chisquare 
Delta df 
Critical value (α= .05) 
CFI 
RMSEA (90% CI) 
Configural model (equal structure; baseline) 
195.565 
54 
 
 
 
0.993 
0.064 (0.0540.073) 
Full metric model (equal loadings) 
212.670 
62 
17.105 
8 
15.507 
0.992 
0.061 (0.0520.070) 
Partial metric model^{*} (equal loadings but one loading) 
204.222 
61 
8.657 
7 
14.067 
0.993 
0.060 (0.0510.069) 
Full scalar model (equal intercepts) 
222.125 
70 
17.903 
9 
16.919 
0.992 
0.058 (0.0490.067) 
Partial scalar model (equal intercepts but one intercept) 
212.284 
68 
8.062 
7 
14.067 
0.993 
0.057 (0.0490.066) 
^{*} Partial metric/scalar models were applied based on the model modification indices.
(b) White versus Asian
Model 
Chisquare 
df 
Delta Chisquare 
Delta df 
Critical value 
CFI 
RMSEA (90% CI) 
Configural model (equal structure; baseline) 
135.672 
54 
 
 
 
0.994 
0.060 (0.0470.073) 
Full metric model (equal loadings) 
151.275 
62 
15.603 
8 
15.507 
0.993 
0.059 (0.0470.070) 
Partial metric model^{*} (equal loadings but one loading) 
144.606 
61 
8.934 
7 
14.067 
0.994 
0.057 (0.0450.069) 
Full scalar model (equal intercepts) 
165.097 
70 
20.491 
9 
16.919 
0.993 
0.057 (0.0460.068) 
Partial scalar model (equal intercepts but one intercept) 
155.250 
68 
10.644 
7 
14.067 
0.993 
0.055 (0.0440.067) 
(c) White versus Hispanic
Model 
Chisquare 
df 
Delta Chisquare 
Delta df 
Critical value 
CFI 
RMSEA (90% CI) 
Configural model (equal structure; baseline) 
135.119 
54 
 
 
 
0.992 
0.066 (0.0520.080) 
Full metric model (equal loadings) 
154.734 
62 
19.615 
8 
15.507 
0.991 
0.065 (0.0530.078) 
Partial metric model^{*} (equal loadings but one loading) 
137.729 
60 
2.61 
6 
12.592 
0.993 
0.061 (0.0480.074) 
Full scalar model (equal intercepts) 
165.019 
70 
27.29 
10 
18.307 
0.991 
0.062 (0.0500.075) 
Partial scalar model (equal intercepts but one intercept) 
145.049 
66 
7.32 
6 
12.592 
0.993 
0.059 (0.0460.072) 
(d) White versus American Indian
Model 
Chisquare 
df 
Delta Chisquare 
Delta df 
Critical value 
CFI 
RMSEA (90% CI) 
Configural model (equal structure; baseline) 
139.222 
54 
 
 
 
0.992 
0.067 (0.0530.081) 
Full metric model (equal loadings) 
146.153 
62 
6.931 
8 
15.507 
0.992 
0.062 (0.0490.075) 
Partial metric model^{*} (equal loadings but one loading) 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
Full scalar model (equal intercepts) 
155.698 
70 
9.545 
8 
15.507 
0.992 
0.059 (0.0470.072) 
Partial scalar model (equal intercepts but one intercept) 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
For the first three comparisons (White vs. African American/Asian/Hispanic), the configural (equal structure) model had a good fit, and thus a series of model constraints were then applied in successive models to examine potential decrease in model fit. Two nested models were compared in model fit based on chisquare and degree of freedom differences at α= 0.05. The full metric invariance model fit well; but resulted in a marginally significant decrease in fit (chisquare difference) relative to the configural model. The partial metric invariance model (freeing just one loading), however, did not result in a significant decrease in fit, which indicates that CBMR measures seem to be related to the factor equivalently between White and other groups. The full scalar model fit well; but also resulted in a marginally significant decrease in fit relative to the partial metric model. However, the partial scalar model (freeing just one intercept) did not result in a significant decrease in fit, which means that both ethnic groups (with the same level of reading) are expected to perform on CBMR assessments in a very similar way. In case of the comparison between White and American Indian, both the full metric invariance model and the full scalar model did not result in a significant decrease in fit, which indicates that the same latent factor was being measured for the two groups and they have exactly the same expected performance on CBMR if their levels are the same.
* Comparison between White and Hawaiian was not analyzed due to too small sample size of Hawaiian (n=4).
3. Conclusion
These results suggest that the same structure (configural invariance) and the same latent factor (full/partial metric invariance) were being measured in each ethnic group. In addition, the fact that the partial or full scalar invariance held indicates that White and other ethnic groups (with the same level of reading) have almost the same expected performance on CBMR. In conclusion, there was no evidence on structural/measurement invariance (i.e., test bias) between White and other ethnic groups, meaning that CBMR appears to be an unbiased measure among different ethnic groups.
Disaggregated Reliability and Validity Data
Grade  1  2  3  4  5  6 

Rating  No  Yes  Yes  Yes  Yes  Yes 
Disaggregated Reliability of the Performance Level Score
Type of Reliability  Age or Grade  n (range)  Coefficient  SEM  Information / Subjects  
range  median  
Cronbach’s Alpha  2  19  0.95540.9658  0.9633 
African American Alpha = 0.9844 Standardized item Alpha = 0.9868 

Cronbach’s Alpha  3  20  0.81620.8811  0.8173 
African American Alpha = 0.9382 Standardized item Alpha = 0.9396 

Cronbach’s Alpha  4  25  0.87200.9018  0.8989 
African American Alpha = 0.9543 Standardized item Alpha = 0.9608 

Cronbach’s Alpha  5  25  0.90110.9289  0.9241 
African American Alpha = 0.9671 Standardized item Alpha = 0.9711 

Cronbach’s Alpha  6  9  0.97490.9899  0.9825 
African American Alpha = 0.9929 Standardized item Alpha = 0.9941 

Alternate Form 
2  19  0.9550.966  0.963  African American students in grade 2  
Alternate Form 
3  20  0.8160.881  0.817  African American students in grade 3  
Alternate Form 
4  25  0.8720.902  0.899  African American students in grade 4  
Alternate Form 
5  25  0.9010.929  0.924  African American students in grade 5  
Alternate Form 
6  9  0.9750.990  0.983  African American students in grade 6 
Disaggregated Validity of the Performance Level Score
Type of Validity  Age or Grade  Test or Criterion  n (range)  Coefficient  Information / Subjects  
range  median  
Construct  2  Measure of Academic Progress  18  0.854  African American students  
Construct  3  Measure of Academic Progress  20  0.774  African American students  
Construct  4  Measure of Academic Progress  25  0.688  African American students  
Construct  5  Measure of Academic Progress  25  0.580  African American students  
Predictive  2  Measure of Academic Progress  16  0.831  African American students  
Predictive  3  Measure of Academic Progress  20  0.665  African American students  
Predictive  4  Measure of Academic Progress  24  0.689  African American students  
Predictive  5  Measure of Academic Progress  15  0.775  African American students 
Alternate Forms
Grade  1  2  3  4  5  6 

Rating 
1. Evidence that alternate forms are of equal and controlled difficulty or, if IRT based, evidence of item or ability invariance:
Evidence of controlled difficulty for the passages used for progress monitoring at each grade was determined through use of the FleschKinkaid Grade Level readability formula and the Lexile Framework for Reading (Metametrics, 2002). Both analyses indicate the passages were placed at appropriate grade levels. At grade 1 the passages had an average Flesch readability of 1.6 and Lexile of 272. At grade 2 the average Flesch readability was 2.4 and Lexile of 397. At grade 3 the average Flesch score was 3.3 and Flesch was 511. At grade 4 the average readability was 4.2 and Lexile was 656. The grade 5 Flesch readability was 5.8 and Lexile score was 759. Grade 6 Flesch readability was 6.9 and Lexile was 850.
Readability estimates for each probe (20) at each grade level (6). For each passage we list below the Lexile rating, as scored by Metametrics, and the Flesch readability score.
Grade  Passage  Lexile  Flesch  Grade  Passage  Lexile  Flesch  

1  1  140  1.3  2  1  360  2.9  
1  2  180  2.5  2  2  250  2.7  
1  3  190  0.4  2  3  220  3.2  
1  4  200  2.4  2  4  260  3.0  
1  5  210  1.1  2  5  280  2.9  
1  6  220  2.2  2  6  290  2.9  
1  7  250  2.4  2  7  310  2.9  
1  8  260  2.5  2  8  310  2.6  
1  9  270  2.1  2  9  320  2.9  
1  10  300  2.1  2  10  330  2.9  
1  11  320  2.5  2  11  340  3.2  
1  12  330  2.4  2  12  350  2.9  
1  13  330  2.4  2  13  380  2.9  
1  14  340  2.2  2  14  380  2.9  
1  15  350  2.0  2  15  400  3.0  
1  16  360  2.1  2  16  410  3.1  
1  17  380  2.3  2  17  340  3.0  
1  18  390  1.9  2  18  470  2.9  
1  19  430  1.0  2  19  400  2.6  
1  20  280  0.8  2  20  370  2.9 
Grade  Passage  Lexile  Flesch  Grade  Passage  Lexile  Flesch  

3  1  400  3.3  4  1  410  4.4  
3  2  420  3.3  4  2  580  3.9  
2  3  320  3.3  4  3  760  6.1  
3  4  340  3.9  4  4  530  4.3  
3  5  420  3.9  4  5  510  4.2  
3  6  460  3.5  4  6  530  4.3  
3  7  480  3.8  4  7  600  4.7  
3  8  490  3.7  4  8  640  4.3  
3  9  520  3.3  4  9  640  4.7  
3  10  520  3.8  4  10  650  4.7  
3  11  530  3.5  4  11  680  4.2  
3  12  540  4.0  4  12  680  4.2  
3  13  550  4.0  4  13  710  5.4  
3  14  560  3.7  4  14  740  4.7  
3  15  580  3.8  4  15  760  4.6  
3  16  600  4.0  4  16  780  4.6  
3  17  610  3.5  4  17  390  4.8  
3  18  630  4.0  4  18  580  4.8  
3  19  720  3.7  4  19  360  4.8  
3  20  760  3.1  4  20  470  4.8 
Grade  Passage  Lexile  Flesch  Grade  Passage  Lexile  Flesch  

5  1  570  5.3  6  1  810  7.7  
5  2  620  5.5  6  2  790  7.5  
5  3  630  5.3  6  3  800  7.5  
5  4  670  5.2  6  4  850  7.0  
5  5  670  5.7  6  5  880  6.9  
5  6  470  5.1  6  6  910  7.0  
5  7  690  6.3  6  7  750  6.4  
5  8  700  5.1  6  8  920  7.5  
5  9  750  5.6  6  9  920  7.4  
5  10  790  6.0  6  10  950  6.8  
5  11  790  5.9  6  11  950  6.4  
5  12  850  6.3  6  12  990  6.5  
5  13  860  5.9  6  13  1060  7.5  
5  14  670  5.2  6  14  770  6.1  
5  15  970  5.7  6  15  470  6.5  
5  16  1020  6.5  6  16  550  6.9  
5  17  810  5.2  6  17  610  6.1  
5  18  520  5.2  6  18  600  6.2  
5  19  630  5.3  6  19  640  6.0  
5  20  580  5.1  6  20  950  7.5 
2. Number of alternate forms of equal and controlled difficulty:
20
Rates of Improvement Specified
Grade  1  2  3  4  5  6 

Rating 
Is minimum acceptable growth (slope of improvement or average weekly increase in score by grade level) specified in your manual or published materials?
Yes
Specify the growth standards:
Our growth standards are derived from a sample of 2007 students enrolled in grades 1 to 6 who were progress monitored on a weekly basis during the school year. The sample included 228 students in grade 1; 418 students in grade 2; 493 students in grades 3; 353 students in grade 4; 332 students in grade 5; and 183 students in grade 6. The average number of progress monitoring passages administered to these students was 16.5 (SD=6.3).
To specify "rates of improvement" for Words Read Correctly we calculated the slope for each student. We then developed percentiles for Words Read Correctly for each grade level. In the table below we provide the 25th, 50, and 75th percentiles at each grade, 1 through 6 (see table below). Slopes associated with the 75^{th} percentile are 1.6 at grade 1; 1.6 at grade 2; 1.4 at grade 3; 1.2 at grade 4; 1.4 at grade 5; and 1.3 at grade 6.
Grade 
Percentile 
Slope 
Grade 1 N=228 
25^{th} 
0.109 

50^{th} 
0.647 

75^{th} 
1.607 
Grade 2 N=415 
25^{th} 
0.616 

50^{th} 
1.025 

75^{th} 
1.570 
Grade 3 N=493 
25^{th} 
0.565 

50^{th} 
0.948 

75^{th} 
1.445 
Grade 4 N=353 
25^{th} 
0.336 

50^{th} 
0.800 

75^{th} 
1.194 
Grade 5 N=332 
25^{th} 
0.260 

50^{th} 
0.833 

75^{th} 
1.437 
Grade 6 N=183 
25^{th} 
0.218 

50^{th} 
0.688 

75^{th} 
1.310 
What is the basis for specifying minimum acceptable growth?
NormReferenced
Basis for specifying minimum acceptable growth:
The Progress Monitoring system is structured so that teachers can choose from among 3 different growth rates at each grade level to set appropriate goals for their students. Those growth rates (termed “Modest,” “Reasonable”, and “Ambitious”) allow the teachers to choose growth rates for individual students that are consistent with their screening levels of performance and with the teacher’s knowledge of the student. The choice of growth rate then becomes a multiplier for individual goal setting and for setting up a progress graph that becomes a realistic basis for subsequent instructional decision making and program modifications. Once the student’s progress graph has been created, feedback on the degree of acceptability of student progress is provided by displaying the student’s scores in 3 colorcoded ranges of risk exceeding desired growth, at desired growth, below desired growth. Developers used growth rates derived from a range of empirical research articles as a basis for establishing the desired growth rates for each grade level including the following:
Deno, S. L. & Marston, D. E. (2006) Curriculumbased Measurement of oral reading growth: An approach to measuring “fluency?” In Jay Samuels & Alan Dershwitz (Eds) Fluency International Reading Association: Newark, DE.
Deno, S. L., Fuchs, L. S., Marston, D.B & Shin, J., (2001). Using curriculumbased measurement to establish growth standards for students with learning disabilities. School Psycholgy Review,30(4), 507524.
Fuchs, L. S., Fuchs, D., Hamlett, C. L., Walz, L., & Germann, G. (1993). Formative evaluation of academic progress: How much growth can we expect? School Psychology Review, 22(1), 2748.
Marston, D. & Magnusson, D. (1988) Curriculumbased measurement: District level implementation. In J. Graden, J. Zins, & M. Curtis (Eds.) Alternative educational delivery systems: Enhancing instructional options for all students. (pp 137172)Washington, D.C.: National Association of School Psychologists.
Representation: Local
Date: 201415 school year
Number of States: One state: Minnesota
Size: 2007 students
Gender: 53.1% Male; 46.9% Female
SES Indicators: 72.2 % of sample receives free or reduced lunch (FRL)
Race/Ethnicity: 40.1% White, NonHispanic; 29.7% Black, NonHispanic; 5.3% American Indian/Alaska Native; 9.9% Asian/Pacific Islander; 14.4% Hispanic
Disability classification: 18.8% students with disabilities
EndofYear Benchmarks
Grade  1  2  3  4  5  6 

Rating 
1. Are benchmarks for minimum acceptable endofyear performance specified in your manual or published materials?
Yes.
a. Specify the endofyear performance standards:
b. Basis for specifying minimum acceptable endofyear performance:
Normreferenced/Criterionreferenced
c. Specify the benchmarks:
The following year end Benchmarks are recommended in the Edcheckup manual.
Grade 1: 60 Words Read Correctly
Grade 2: 90 Words Read Correctly
Grade 3: 120 Words Read Correctly
Grade 4: 130 Words Read Correctly
Grade 5: 140 Words Read Correctly
Grade 6: 150 Words Read Correctly
d. Basis for specifying these benchmarks?
We think these are supportable benchmarks based on two separate studies with our passages. Our first study consists of data already analyzed for GOM 2. In that study the 50th percentile at each grade level was consistent with the benchmarks reported above. Grade 1: Median = 58.6, SD = 35.8, N = 746; Grade 2: Median = 100.0, SD = 37.5, N = 525; Grade 3: Median = 116.0, SD = 37.8, N = 637; Grade 4: Median = 119.0, SD = 37.2, N = 478; Grade 5: Median = 134.8, SD = 42.8, N = 382; Grade 6: Median = 149.0, SD = 50.0, N = 164.
In addition to the data in this analysis we provided, the following means were obtained in of our Edcheckup data when submitting the Standard Protocol for screening purposes. Grade 1: Mean = 73.9, SD = 37.2, N =90; Grade 2: Mean = 121.5, SD = 36.3, N = 216; Grade 3: Mean = 134.0, SD = 38.1, N = 229; Grade 4: Mean = 132.5, SD = 35.2, N = 207; Grade 5: Mean = 154.4, SD = 38.2, N = 256; Grade 6: Mean = 146.8, SD = 40.7, N = 78.
In selecting these benchmarks we not only used results from these two studies but also the large literature on oral reading scores that is available.
Sensitive to Student Improvement
Grade  1  2  3  4  5  6 

Rating 
1. Describe evidence that the monitoring system produces data that are sensitive to student improvement (i.e., when student learning actually occurs, student performance on the monitoring tool increases on average).
Evidence of the sensitivity to student improvement is proved by the weekly growth rates for Edcheckup oral reading passages for grades 1 to 6 is below. These growth rates are comparable to those currently available in the literature on this topic.
Grade  Sample Size  Weekly Grown Rate 
1  N = 421  1.52 
2  N = 482  1.47 
3  N = 573  1.36 
4  N = 421  1.02 
5  N = 349  1.02 
6  N = 126  0.76 
Decision Rules for Changing Instruction
Grade  1  2  3  4  5  6 

Rating 
Decision Rules for Increasing Goals
Grade  1  2  3  4  5  6 

Rating 
Improved Student Achievement
Grade  1  2  3  4  5  6 

Rating 
Improved Teacher Planning
Grade  1  2  3  4  5  6 

Rating 