Spring Math
Mathematics
Cost 
Technology, Human Resources, and Accommodations for Special Needs 
Service and Support 
Purpose and Other Implementation Information 
Usage and Reporting 
Initial Cost: $9$15 per student depending upon enrollment.
Replacement Cost: $9$15 per student depending upon enrollment. Annual license renewal fee subject to change.
Included in Cost: Spring Math provides extensive implementation support at no additional cost through a support portal to which all users have access. Support materials include howto videos, brief howto documents, access to all assessments and acquisition lesson plans for 130 skills, and live and archived webinars. In addition to the support portal, sites that wish to purchase additional coaching support can do so by accessing our network of trained coaches who have expertise in RtI/MTSS leadership and specific training in Spring Math; see http://www.springmath.com/trainingsupport.
Sites must have access to one computer per teacher, internet connection, and the ability to print in black and white.

Technology Requirements:
Training Requirements:
Qualified Administrators:
Accommodations: Assessments are standardized, but very brief in duration. If a student requires accomodations, intervention allows for oral and written responding, the use of individual rewards for “beating the last best score,” a range of concrete, representational, and abstract understanding activities, and individualized modeling with immediate corrective feedback.

Where to Obtain: Website: www.springmath.com Address: 2340 Energy Park Drive, Suite 200, St. Paul, MN 55108 Phone: (651) 9996100
Support materials are provided organized by userrole (teacher, coach, data administrator) via the online support portal under the dropdown menu below the login icon. Spring Math provides free webinars throughout the year for users and host free training institutes at least annually. Spring Math provides a systematic onboarding process for new users to help them get underway. If users encounter technical difficulties, they can submit a request for help directly from their account, which generates a support ticket with the tech support team. Support tickets are monitored during business hours and are responded to the same day.

Spring Math is a comprehensive RtI system that includes screening, progress monitoring, classwide and individual math intervention, and implementation and decisionmaking support. Assessments are generated within the tool when needed and Spring Math uses student data to customize classwide and individual intervention plans for students. Clear and easytounderstand graphs and reports are provided within the teacher and coach dashboards. Spring Math uses gated screening that involves CBMs administered to the class as a whole followed by classwide intervention to identify students in need of intensive intervention.
Spring Math assesses 130 skills in Grades K8 and addresses gaps in learning for grades K12. The skills offer comprehensive but strategic coverage of the Common Core State Standards. Spring Math assesses mastery of number operations, prealgebraic thinking, and mathematical logic. It also measures understanding of “tool skills.” Tool skills provide the foundation a child needs to question, speculate, reason, solve, and explain realworld problems. Spring Math emphasizes tool skills across grades with gradeappropriate techniques and materials. 
Assessment Format:
Administration Time:
Scoring Time:
Scoring Method:
Scores Generated:

Classification Accuracy
Grade  K  1  3  5  7 

Criterion 1 Fall  
Criterion 1 Winter  
Criterion 1 Spring  
Criterion 2 Fall  
Criterion 2 Winter  
Criterion 2 Spring 
Primary Sample
Criterion 1, Fall
Grade 
K 
1 
3 
5 
7 
Criterion 
Winter Composite Score 
Winter Composite Score 
YearEnd State Mathematics Assessment in AZ 
YearEnd State Mathematics Assessment in AZ 
YearEnd State Mathematics Assessment in AZ 
Cut points: Percentile rank on criterion measure 
20th 
20th 
20th 
20th 
20th 
Cut points: Performance score (numeric) on criterion measure 
17 
59 
3515 
3582 
3633 
Cut points: Corresponding performance score (numeric) on screener measure 
12 
39 
24 
35 
13 
Base rate in the sample for children requiring intensive intervention 
0.20 
0.20 
0.20 
0.20 
0.20 
False Positive Rate 
0.17 
0.15 
0.19 
0.21 
0.18 
False Negative Rate 
0.30 
0.29 
0.18 
0.27 
0.23 
Sensitivity 
0.83 
0.71 
0.82 
0.73 
0.78 
Specificity 
0.70 
0.85 
0.81 
0.79 
0.82 
Positive Predictive Power 
0.56 
0.58 
0.39 
0.42 
0.51 
Negative Predictive Power 
0.90 
0.91 
0.97 
0.94 
0.94 
Overall Classification Rate 
0.80 
0.82 
0.81 
0.78 
0.81 
Area Under the Curve (AUC) 
0.85 
0.85 
0.86 
0.77 
0.91 
AUC 95% Confidence Interval Lower Bound 
0.76 
0.76 
0.73 
0.63 
0.87 
AUC 95% Confidence Interval Upper Bound 
0.94 
0.93 
0.99 
0.91 
0.96 
Criterion 1, Winter
Grade 
K 
1 
3 
5 
7 
Criterion 
Spring Composite Score 
Spring Composite Score 
YearEnd State Mathematics Assessment in AZ 
YearEnd State Mathematics Assessment in AZ 
YearEnd State Mathematics Assessment in AZ 
Cut points: Percentile rank on criterion measure 
20th 
20th 
20th 
20th 
20th 
Cut points: Performance score (numeric) on criterion measure 
29 
37 
3515 
3582 
3633 
Cut points: Corresponding performance score (numeric) on screener measure 
21 
26 
31 
31 
26 
Base rate in the sample for children requiring intensive intervention 
0.20 
0.20 
0.20 
0.20 
0.20 
False Positive Rate 
0.18 
0.19 
0.28 
0.47 
0.19 
False Negative Rate 
0.24 
0.14 
0.28 
0.26 
0.19 
Sensitivity 
0.76 
0.86 
0.72 
0.74 
0.81 
Specificity 
0.82 
0.81 
0.72 
0.53 
0.81 
Positive Predictive Power 
0.48 
0.55 
0.37 
0.29 
0.51 
Negative Predictive Power 
0.94 
0.94 
0.92 
0.89 
0.95 
Overall Classification Rate 
0.81 
0.82 
0.72 
0.57 
0.81 
Area Under the Curve (AUC) 
0.87 
0.91 
0.79 
0.74 
0.91 
AUC 95% Confidence Interval Lower Bound 
0.79 
0.86 
0.67 
0.62 
0.87 
AUC 95% Confidence Interval Upper Bound 
0.95 
0.97 
0.91 
0.86 
0.95 
Criterion 1, Spring
Grade 
K 
1 
3 
5 
7 
Criterion 
Spring Composite Score 
Spring Composite Score 
YearEnd State Mathematics Assessment in AZ 
YearEnd State Mathematics Assessment in AZ 
Not Provided 
Cut points: Percentile rank on criterion measure 
20th 
20th 
20th 
20th 
Not Provided 
Cut points: Performance score (numeric) on criterion measure 
29 
37 
3515 
3582 
Not Provided 
Cut points: Corresponding performance score (numeric) on screener measure 
0.06 
0.08 
0.11 
0.05 
Not Provided 
Base rate in the sample for children requiring intensive intervention 
0.20 
0.20 
0.20 
0.20 
Not Provided 
False Positive Rate 
0.35 
0.14 
0.20 
0.14 
Not Provided 
False Negative Rate 
0.18 
0.14 
0.20 
0.23 
Not Provided 
Sensitivity 
0.82 
0.90 
0.80 
0.77 
Not Provided 
Specificity 
0.65 
0.86 
0.80 
0.86 
Not Provided 
Positive Predictive Power 
0.33 
0.63 
0.50 
0.61 
Not Provided 
Negative Predictive Power 
0.94 
0.96 
0.94 
0.93 
Not Provided 
Overall Classification Rate 
0.68 
0.87 
0.80 
0.84 
Not Provided 
Area Under the Curve (AUC) 
0.79 
0.95 
0.86 
0.85 
Not Provided 
AUC 95% Confidence Interval Lower Bound 
0.67 
0.90 
0.78 
0.78 
Not Provided 
AUC 95% Confidence Interval Upper Bound 
0.91 
0.99 
0.96 
0.93 
Not Provided 
Reliability
Grade  K  1  3  5  7 

Rating 
 Justification for each type of reliability reported, given the type and purpose of the tool: Probes are generated following a set of programmed parameters that were built and tested in a development phase. To determine measure equivalence, problem sets were generated, and each problem within a problem set was scored for possible digits correct. The digits correct metric comes from the curriculumbased measurement literature (Deno & Mirkin, 1977) and allows for sensitive measurement of child responding. Typically, each digit that appears in the correct place value position to arrive at the correct final answer is counted as a digit correct. Generally, digits correct work is counted for all the work that occurs below the problem (in the answer) but does not include any work that may appear above the problem in composing or decomposing hundreds or tens, for example, when regrouping.
A standard response format was selected for all measures, which reflected the relevant responses in steps to arrive at a correct and complete answer. Potential digits correct was the unit of analysis that we used to test the equivalence of generated problem sets. For example, in scoring adding and subtracting fractions with unlike denominators, all digits correct in generating fractions with equivalent denominators, then the digits correct in combining or taking the fraction quantity, and digits correct in simplifying the final fraction were counted. The number of problems generated depended upon the task difficulty of the measure. If the measure assessed an easier skill (defined as having fewer potential digits correct), then the number of problems generated was greater than the number of problems that were generated and tested for harder skills for which the possible digits correct scores were much higher. Problems generated for equivalence testing ranged from 80 problems to 480 problems per measure.
A total of 46,022 problems were generated and scored for possible digits correct to test the equivalence of generated problem sets. Problem sets ranged from 848 problems. Most problem sets contained 30 problems. For each round of testing, 10 problem sets were generated per measure. The mean possible digits correct per problem was computed for each problem set for each measure. The standard deviation of possible digits correct across the ten generated problem sets was computed and was required to be less than 10% of the mean possible digits correct to establish equivalence.
Spring Math has 130 measures. Thirtyeight measures were not tested for equivalence because there was no variation in possible digits correct per problem type. These measures were all singledigit answers and included measures like Sums to 6, Subtraction 05, and Number Names. Eightythree measures met equivalence standards on the first round of testing, with a standard deviation of possible digits correct per problem per problem set that was on average 4% of the mean possible digits correct per problem. Seven measures required revision and a second round of testing. These measures included Mixed Fraction Operations, Multiply Fractions, Convert Improper to Mixed, Solve 2Step Equations, Solve Equations with Percentages, Convert Fractions to Decimals, and Collect Like Terms. After revision and retesting, the average percent of the mean that the standard deviation represented was 4%. One measure required a third round of revision and retesting. This measure was Order of Operations. On the third round, it met the equivalence criterion with the standard deviation representing on average 10% of the mean possible digits correct per problem across generated problem sets. In this section of the application, we report the results from a yearlong study in Louisiana during which screening measures were generated and administered to classes of children with a 1week interval of time between assessment occasions. Measures were administered by researchers with rigorous integrity and interreliability controls in place.
 Description of the sample(s), including size and characteristics, for each reliability analysis conducted: Reliability data were collected in three schools in southeastern Louisiana with appropriate procedural controls. Researchers administered the screening measures for the reliability study following an administration script. On 25% of testing occasions balanced across times (time1 and time2), grades, and classrooms, a second trained observer documented the percentage of correctly completed steps during screening administration. Average integrity (percentage of steps correctly conducted) was 99.36% with a less than perfect integrity score on only 4 occasions (one missed the sentence in the protocol telling students not to skip around, one exceeded the 2min timing interval for one measure by 5 seconds, and on two occasions, students turned their papers over before being told to do so). Demographic data are provided in the table below for the reliability sample.

n 
Ethnicity 
Sex 
Percent Students with Disabilities 
Kindergarten Fall 
86 
76% White 20% African American 5% Hispanic 
50% Male 
17% 
Kindergarten Winter 
79 
71% White 25% African American 4% Hispanic 
51% Male 
14% 
Grade 1 Fall 
79 
67% White 27% Black 5% Hispanic 1 % Native American 
49% Male 
22% 
Grade 1 Winter 
75 
75% White 19% African American 5% Hispanic 1% Native American 
45% Male 
19% 
Grade 3 Fall 
93 
68% White 29% African American 3% Hispanic 
51% Male 
8% 
Grade 3 Winter 
91 
66% White 30% African American 4% Hispanic 
43% Male 
11% 
Grade 5 Fall 
48 
98% White 2% African American 
62% Male 
4% 
Grade 5 Winter 
48 
93% White 4% African American 3% Hispanic 
62% Male 
4% 
Grade 7 Fall 
41 
98% White 2% Hispanic 
61% Male 
15% 
Grade 7 Winter 
38 
100% White 
63% Male 
13% 
 Description of the analysis procedures for each reported type of reliability: Spring Math uses 34 timed measures per screening occasions. The initial risk decision and subsequent classwide intervention risk decision is based on the set of measures as a whole and the subsequent risk during classwide intervention. For these reliability analyses, we combine the measures at each testing occasion (fall & winter) to yield a composite score for analysis. We report the Pearson r correlation coefficient (with 95% CI) for time 1 and time 2 composite scores for the generated (i.e., alternate form) measures administered 1 week apart. Because the Spring Math screening measures are rigorous by design (i.e., sensitivity is emphasized), a restricted range of scores was not unexpected and certainly impacted the individual CBM reliability values. To test that possibility, for the measures for which we had two consecutive weeks of classwide intervention scores for classes of students within a school in Arizona where we collected validity data, we examined the Pearson r values. Pearson r values were much higher once score ranges increased with only one week of intervention. For example, in Grade 3, the subtest for Division 09 t1 – t2 correlation increased from r = .61 to r = .92, Multiply 1digit by 23 digits with and without regrouping increased from r = .45 to r = .91. This pattern was replicated across grade levels with stronger correlation values collected in a setting in which range restriction was not an issue. We did not provide these data in the table below because not all of the subtests were accessed during the classwide intervention within the school (i.e., some subtests would have had missing data).
 Reliability of performance level score (e.g., modelbased, internal consistency, interrater reliability).
Type of Reliability 
Age or Grade 
n 
Coefficient 
95% Confidence Interval: Lower Bound 
95% Confidence Interval: Upper Bound 

Alternate Form 
K Fall 
86 
0.79 
0.69 
0.86 
Alternate Form 
K Winter 
79 
0.80 
0.70 
0.86 
Alternate Form 
Grade 1 Fall 
79 
0.85 
0.78 
0.90 
Alternate Form 
Grade 1 Winter 
75 
0.86 
0.78 
0.91 
Alternate Form 
Grade 3 Fall 
93 
0.82 
0.74 
0.88 
Alternate Form 
Grade 3 Winter 
91 
0.84 
0.77 
0.89 
Alternate Form 
Grade 5 Fall 
48 
0.77 
0.62 
0.86 
Alternate Form 
Grade 5 Winter 
45 
0.87 
0.77 
0.93 
Alternate Form 
Grade 7 Fall 
41 
0.80 
0.66 
0.89 
Alternate Form 
Grade 7 Winter 
38 
0.88 
0.78 
0.94 
Disaggregated Reliability
The following disaggregated reliability data are provided for context and did not factor into the Reliability rating.
Type of Reliability 
Subgroup 
Age or Grade 
n 
Coefficient 
95% Confidence Interval: Lower Bound 
95% Confidence Interval: Upper Bound 

None 






Validity
Grade  K  1  3  5  7 

Rating 
 Description of each criterion measure used and explanation as to why each measure is appropriate, given the type and purpose of the tool: The validity measure in Grades 3, 5 and 7 was AzMERIT, the statewide achievement test in Arizona.
 Description of the sample(s), including size and characteristics, for each validity analysis conducted: The sample size is included in the table. The demographics are similar to those described in the reliability section.
 Description of the analysis procedures for each reported type of validity: We have reported the Pearson r correlation for theoretically anticipated convergent measures and theoretically anticipated discriminant measures.
 Validity for the performance level score (e.g., concurrent, predictive, 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), and the criterion measures.
Type of Validity 
Age or Grade 
Test or Criterion 
n 
Coefficient 
95% Confidence Interval: Lower Bound 
95% Confidence Interval: Upper Bound 

Predictive Convergent 
Fall K 
Winter Composite 
85 
0.64 
0.49 
0.75 
Predictive Convergent 
Win K 
Spring Composite 
97 
0.72 
0.61 
0.81 
Predictive Convergent 
Fall 1^{st} 
Winter Composite 
94 
0.65 
0.52 
0.76 
Predictive Convergent 
Win 1^{st} 
Spring Composite 
102 
0.80 
0.72 
0.86 
Predictive Convergent 
Fall 3^{rd} 
State YearEnd Math Score 
86 
0.65 
0.51 
0.76 
Predictive Discriminant 
Fall 3^{rd} 
State YearEnd Reading Score 
86 
0.57 
0.41 
0.70 
Predictive Convergent 
Win 3^{rd} 
State YearEnd Math Score 
96 
0.58 
0.43 
0.70 
Predictive Discriminant 
Win 3^{rd} 
State YearEnd Reading Score 
96 
0.52 
0.36 
0.65 
Predictive Convergent 
Fall 5^{th} 
State YearEnd Math Score 
88 
0.66 
0.53 
0.77 
Predictive Discriminant 
Fall 5^{th} 
State YearEnd Reading Score 
88 
0.38 
0.19 
0.55 
Predictive Convergent 
Win 5^{th} 
State YearEnd Math Score 
94 
0.63 
0.49 
0.74 
Predictive Discriminant 
Win 5^{th} 
State YearEnd Reading Score 
94 
0.38 
0.19 
0.54 
Predictive Convergent 
Fall 7^{th} 
State YearEnd Math Score 
48 
0.73 
0.56 
0.84 
Predictive Discriminant 
Fall 7^{th} 
State YearEnd Reading Score 
48 
0.59 
0.36 
0.75 
Predictive Convergent 
Win 7^{th} 
State YearEnd Math Score 
49 
0.67 
0.48 
0.80 
Predictive Discriminant 
Win 7^{th} 
State YearEnd Reading Score 
49 
0.57 
0.34 
0.73 
 Results for other forms of validity (e.g. factor analysis) not conducive to the table format: Not provided.
 Describe the degree to which the provided data support the validity of the tool: We see a pattern of correlations that supports multitrait, multimethod logic (Campbell & Fiske, 1959).
Disaggregated Validity
The following disaggregated validity data are provided for context and did not factor into the Validity rating.
Type of Validity 
Subgroup 
Age or Grade 
Test or Criterion 
n 
Coefficient 
95% Confidence Interval: Lower Bound 
95% Confidence Interval: Upper Bound 

None 







Sample Representativeness
Grade  K  1  3  5  7 

Data 
Primary Classification Accuracy Sample
Criterion 1, Fall
Grade 
K 
1 
3 
5 
7 
Criterion 
Winter Composite 
Winter Composite 
YearEnd State Test in AZ 
YearEnd State Test in AZ 
YearEnd State Test in AZ 
National/Local Representation 
AZ 
AZ 
AZ 
AZ 
AZ 
Date 
9/1/17 
9/1/17 
9/1/17 
9/1/17 
9/1/17 
Sample Size 
85 
95 
86 
88 
210 
Male 
58% 
56% 
57% 
51% 
54% 
Female 
35% 
44% 
43% 
49% 
46% 
Gender Unknown 
7% 
0% 
0% 
0% 
0% 
Free or Reducedprice Lunch Eligible 
18% 
34% 
28% 
39% 
22% 
White, NonHispanic 
44% 
50% 
52% 
49% 
70% 
Black, NonHispanic 
4% 
4% 
0% 
0% 
0% 
Hispanic 
33% 
40% 
41% 
43% 
27% 
American Indian/Alaska Native 
0% 
1% 
3.5% 
0% 
0.9% 
Other 
13% 
4% 
3.5% 
8% 
2.6% 
Race/Ethnicity Unknown 
7% 
1% 
0% 
0% 
0% 
Disability Classification 
8% 
9% 
7% 
13% 
9% 
First Language 
1% 
1% 
0% 
2% 
0.5% 
Language Proficiency Status 
N/A 
N/A 
N/A 
N/A 
N/A 
Criterion 2, Winter
Grade 
K 
1 
3 
5 
7 
Criterion 
Spring Composite 
Spring Composite 
YearEnd State Test in AZ 
YearEnd State Test in AZ 
YearEnd State Test in AZ 
National/Local Representation 
AZ 
AZ 
AZ 
AZ 
AZ 
Date 
1/5/18 
1/5/18 
1/5/18 
1/5/18 
1/5/18 
Sample Size 
96 
101 
96 
94 
215 
Male 
55% 
54% 
53% 
53% 
53% 
Female 
38% 
45% 
47% 
47% 
47% 
Gender Unknown 
7% 
2% 
0% 
0% 
0% 
Free or Reducedprice Lunch Eligible 
19% 
36% 
32% 
38% 
22% 
White, NonHispanic 
45% 
49% 
52% 
50% 
70% 
Black, NonHispanic 
3% 
5% 
0% 
0% 
0% 
Hispanic 
34% 
38% 
42% 
42% 
27% 
American Indian/Alaska Native 
0% 
1% 
3% 
0% 
0.5% 
Other 
12% 
5% 
3% 
8% 
3% 
Race/Ethnicity Unknown 
7% 
3% 
0% 
0% 
0% 
Disability Classification 
7% 
9% 
8% 
13% 
9% 
First Language 
1% 
1% 
0% 
3% 
0.5% 
Language Proficiency Status 
N/A 
N/A 
N/A 
N/A 
N/A 
Bias Analysis Conducted
Grade  K  1  3  5  7 

Rating  Yes  Yes  Yes  Yes  Yes 
 Description of the method used to determine the presence or absence of bias: We conducted a series of binary logistic regression analyses using Stata. Scoring below the 20th percentile on the Arizona yearend state test was the outcome criterion. The interaction term for each subgroup and the fall composite screening score, winter composite screening score, and classwide intervention risk is provided in the table below.
 Description of the subgroups for which bias analyses were conducted: Gender, Students with Disabilities, Ethnicity, and SES.
 Description of the results of the bias analyses conducted, including data and interpretative statements: None of the interactions were significant. Thus, screening accuracy did not differ across subgroups in a way that was statistically significant.
Grade 
Interaction Tested 
Fall Composite 
Winter Composite 
Classwide Intervention Risk 
K 
Gender 
0.201 p = 0.252 n = 79 
 0.134 p = 0.340 n = 89 
 18.06 p = 0.172 n = 89 

Students with Disabilities 
 5.22 p = 0.995 n = 79 
model doesn’t converge 
 55.30 p = 0.524 n = 89 

Ethnicity 
0.039 p = 0.426 n = 79 
 0.190 p = 0.853 n = 89 
2.912 p = 0.602 n = 89 

SES 
0.140 p = 0.265 n = 79 
0.174 p = 0.415 n = 89 
 40.39 p = 0.171 n = 89 
1 
Gender 
0.012 p = 0.777 n = 95 
 0.003 p = 0.907 n = 99 
 7.29 p = 0.627 n = 99 

Students with Disabilities 
model doesn’t converge 
0.007 p = 0.851 n = 99 
 411.70 p = 0.993 n = 99 

Ethnicity 
0.722 p = 0.230 n = 95 
 1.04 p = 0.269 n = 99 
2.08 p = 0.723 n= 99 

SES 
 0.102 p = 0.259 n = 95 
 0.457 p = 0.820 n = 99 
 21.60 p = 0.265 n = 99 
3rd 
Gender x 
7.838 p = 0.994 n = 86 
0.065 p = 0.140 n = 96 
9.822 p = 0.166 n =99 

Students with Disabilities x 
 2.838 p = 0.993 n = 86 
0.010 p = 0.785 n = 96 
2.51 p = 0.815 n = 99 

Ethnicity x 
 0.008 p = 0.814 n = 86 
0.006 p = 0.729 n = 96 
4.68 p = 0.850 n = 99 

SES x 
0.124 p = 0.297 n = 88 
0.126 p =0.061 n = 94 
5.96 p = 0.404 n = 101 
5th 
Gender x 
0.04 p = 0.257 n = 88 
0.06 p = 0.150 n = 94 
1.46 p = 0.883 n = 101 

Students w Disabilities x 
0.037 p = 0.601 n =88 
0.023 p = 0.216 n = 94 
6.91 p = 0.560 n = 101 

Ethnicity x 
0.007 p = 0.640 n = 88 
0.023 p = 0.216 n = 94 
2.88 p = 0.463 n = 101 

SES x 
 0.015 p = 698 n = 86 
 0.027 p = 0.535 n = 96 
 15.431 p = 0.208 n = 99 
7^{th} 
Gender x 
0.045 p = 0.734 n = 210 
 0.014 p = 0.809 n = 215 
 12.293 p = 0.336 n = 50 

Students w Disabilities x 
0.026 p = 0.908 n = 210 
0.045 p = 0.686 n = 215 
3.01 p = 0.839 n = 50 

Ethnicity x 
 0.021 p = 0.664 n = 210 
0.013 p = 0.648 n = 215 
 .106 p = 0.989 n = 50 

SES x 
 0.445 p = 0.124 n = 210 
0.006 p = 0.926 n = 215 
16.678 p = 0.181 n = 50 
Administration Format
Grade  K  1  3  5  7 

Data 
Administration & Scoring Time
Grade  K  1  3  5  7 

Data 
Scoring Format
Grade  K  1  3  5  7 

Data 
Types of Decision Rules
Grade  K  1  3  5  7 

Data 
Evidence Available for Multiple Decision Rules
Grade  K  1  3  5  7 

Data 