MAP Growth

Mathematics

 

Cost

Technology, Human Resources, and Accommodations for Special Needs

Service and Support

Purpose and Other Implementation Information

Usage and Reporting

Initial Cost:

MAP Growth Mathematics annual per-student subscription fees range from $7.00–$9.50. A bundled assessment suite of Mathematics, Reading, and Language Usage tests starts at $13.50 per student. Discounts are available based on volume and other factors.

 

Replacement Cost:

Subscription renewal fees subject to change annually.

 

Included in Cost:

Annual subscription fees include the following:

  • Full Assessment Suite: MAP Growth assessments can be administered up to four times per calendar year. The abbreviated Screening Assessment may be administered once a year for placement purposes.
  • Robust Reporting: All results from MAP Growth assessments, including RIT scale scores, proficiency projections, and status and growth norms, are available in a variety of views and formats through MAP Growth’s comprehensive suite of reports.
  • Learning Continuum: Dynamic reporting of learning statements, specifically aligned to the applicable state standards, provide information on what each student is ready to learn.
  • System of Support: A full system of support is provided to enable the success of MAP Growth partners, including technical support; implementation support through the first test administration; and ongoing, dedicated account management for the duration of the partnership. 
  • NWEA Professional Learning Online: Access to this online learning portal offers on-demand tutorials, webinars, courses, and videos to supplement professional learning plans and help educators use MAP Growth to improve teaching and learning.

 

NWEA offers a portfolio of flexible, customizable professional learning and training options to meet the needs of users. Please contact NWEA via https://www.nwea.org/sales-information/ for specific details on pricing.

Technology Requirements:

  • Computer or tablet
  • Internet connection

 

Training Requirements:

  • 1–4 hours of training

 

Qualified Administrators:

Examiners should meet the same qualifications as a teaching paraprofessional; examiners should complete all necessary training related to administering an assessment.

 

Accommodations:

MAP Growth assessments incorporate universal design principles for greater accessibility. This means that all content areas are created considering universal design and accessibility standards from the start. For example, alternative text descriptions (alt-tags) for images are an important feature on a website to provide access to those using screen readers. Alt-tags provide descriptions of pictures, charts, graphs, etc., to those who may not be able to see the information. Laying this foundation promotes accessibility for students using various accommodations.

Following national standards, such as the Web Content Accessibility Guidelines (WCAG) 2.0 and Accessible Rich Internet Applications (ARIA), helps to guide the creation of MAP Growth assessments. With support from the WGBH National Center for Accessible Media (NCAM), NWEA has created detailed and thorough guidelines for describing many variations of images, charts, and graphics targeted specifically to mathematics, reading, and language usage. The guidelines review concepts such as item integrity, fairness, and the unique challenges image description writers face in the context of assessment. These guidelines result in consistent, user-friendly, and valid image descriptions that support the use of screen readers.

MAP Growth includes accessibility features and allows for students to use their own assistive technologies. Tools are made available for all students on the assessment. These tools are embedded into the user interface for each item and are at the appropriate test level. Tools are not specific to a certain population but will be available to all users whenever necessary so that students can use these tools during their testing experience.

Where to Obtain:

Website: www.nwea.org

Address: 121 NW Everett Street, Portland, OR 97209

Phone number: (503) 624-1951

Please contact NWEA via https://www.nwea.org/sales-information/ for service and support questions
Access to Technical Support:

Toll-free telephone support, online support, website knowledge base, and live chat support are available.

MAP Growth assessments are used across the country for multiple purposes, including as universal screening tools in response to intervention (RTI) programs.

MAP Growth can serve as a universal screener for identifying students at risk of poor academic outcomes in mathematics. MAP Growth assessments give educators insight into the instructional needs of all students, whether they are performing at, above, or below grade level.

These assessments include increasingly more complex items to correspond with the rigor of the standards for those grade levels.

MAP Growth assessments are computer adaptive tests with a cross-grade vertical scale that assess achievement according to standards-aligned content. Scores from repeated administrations measure achievement over time, from which users interpret academic growth. MAP Growth tests can be administered three times per school year — once each in fall, winter, and spring — with an optional summer administration.

MAP Growth assessments are scaled across grades. The Rasch model, an item response theory (IRT) model commonly employed in K–12 assessment programs, was used to create the scales for MAP Growth assessments. These scales have been named RIT scales (for Rasch Unit).

 

 

Assessment Format:

  • Direct: Computerized

 

Administration Time:

  • 45 minutes per student, per subject

 

Scoring Time:

  • Scoring is automatic

 

Scoring Method:

MAP Growth scores are not based on raw scores because they are adaptive. The difficulty of the item answered is used to derive the student’s scale score. During the assessment, a Bayesian scoring algorithm is used to inform item selection. Bayesian scoring for item selection prevents artificially dramatic fluctuations in student achievement at the beginning of the test, which can occur with other scoring algorithms. Although the Bayesian scoring works well as a procedure for selecting items during test administration, Bayesian scores are not appropriate for the calculation of final student achievement scores. This is because Bayesian scoring uses information other than the student’s responses to questions (such as past performance) to calculate the achievement estimate. Since only the student’s performance today should be used to give the student’s current score, a maximum-likelihood algorithm is used to calculate a student’s actual score at the completion of the test.

 

Scores Generated:

  • Percentile score       
  • IRT-based score       
  • Developmental benchmarks
  • Developmental cut points
  • Composite scores
  • Subscale/subtest scores

 

 

Classification Accuracy

Grade2345678
Criterion 1 FallFull bubbledFull bubbledFull bubbledFull bubbledFull bubbledFull bubbledFull bubbled
Criterion 1 WinterFull bubbledFull bubbledFull bubbledFull bubbledFull bubbledFull bubbledHalf-filled bubbled
Criterion 1 SpringFull bubbledFull bubbledFull bubbledFull bubbledFull bubbledFull bubbledFull bubbled
Criterion 2 Falldashdashdashdashdashdashdash
Criterion 2 Winterdashdashdashdashdashdashdash
Criterion 2 Springdashdashdashdashdashdashdash

Primary Sample

 

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Fall

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 168, PARCC < 700

MAP Growth < 182, PARCC < 700

MAP Growth < 193, PARCC < 700

MAP Growth < 202, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 214, PARCC < 700

MAP Growth < 219, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.08

0.09

0.11

0.08

0.09

0.08

0.16

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.13

0.12

0.11

0.13

0.13

0.14

0.10

False Negative Rate

0.23

0.15

0.19

0.21

0.14

0.14

0.31

Sensitivity

0.78

0.86

0.81

0.79

0.86

0.87

0.69

Specificity

0.87

0.88

0.89

0.87

0.87

0.86

0.91

Positive Predictive Power

0.33

0.40

0.47

0.36

0.39

0.34

0.58

Negative Predictive Power

0.98

0.98

0.97

0.98

0.98

0.99

0.94

Overall Classification Rate

0.86

0.88

0.88

0.87

0.87

0.86

0.87

Area Under the Curve (AUC)

0.91

0.94

0.93

0.92

0.94

0.94

0.91

AUC 95% Confidence Interval Lower

0.90

0.93

0.93

0.92

0.93

0.93

0.91

AUC 95% Confidence Interval Upper

0.92

0.94

0.94

0.93

0.94

0.94

0.92

At 90% Sensitivity, specificity equals

0.69

0.82

0.78

0.73

0.80

0.80

0.70

At 80% Sensitivity, specificity equals

0.87

0.93

0.93

0.91

0.93

0.93

0.87

At 70% Sensitivity, specificity equals

0.94

0.98

0.98

0.96

0.97

0.97

0.94

 

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Winter

 

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 178, PARCC < 700

MAP Growth < 188, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 206, PARCC < 700

MAP Growth < 209, PARCC < 700

MAP Growth < 214, PARCC < 700

MAP Growth < 220, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.09

0.10

0.12

0.10

0.11

0.09

0.18

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.14

0.12

0.11

0.13

0.12

0.13

0.09

False Negative Rate

0.20

0.15

0.18

0.18

0.17

0.17

0.34

Sensitivity

0.80

0.85

0.82

0.82

0.84

0.83

0.66

Specificity

0.86

0.88

0.89

0.88

0.88

0.87

0.91

Positive Predictive Power

0.35

0.45

0.49

0.41

0.46

0.40

0.63

Negative Predictive Power

0.98

0.98

0.97

0.98

0.98

0.98

0.92

Overall Classification Rate

0.86

0.88

0.88

0.87

0.88

0.87

0.87

Area Under the Curve (AUC)

0.92

0.94

0.94

0.93

0.94

0.93

0.91

AUC 95% Confidence Interval Lower

0.91

0.94

0.93

0.92

0.93

0.93

0.90

AUC 95% Confidence Interval Upper

0.93

0.95

0.94

0.93

0.94

0.94

0.91

At 90% Sensitivity, specificity equals

0.72

0.82

0.78

0.76

0.80

0.77

0.70

At 80% Sensitivity, specificity equals

0.89

0.93

0.94

0.91

0.92

0.92

0.85

At 70% Sensitivity, specificity equals

0.95

0.98

0.98

0.96

0.97

0.97

0.93

 

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Spring

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 184, PARCC < 700

MAP Growth < 195, PARCC < 700

MAP Growth < 204, PARCC < 700

MAP Growth < 211, PARCC < 700

MAP Growth < 215, PARCC < 700

MAP Growth < 220, PARCC < 700

MAP Growth < 224, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.08

0.09

0.11

0.09

0.09

0.08

0.15

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.13

0.12

0.11

0.13

0.13

0.13

0.09

False Negative Rate

0.19

0.09

0.13

0.14

0.11

0.13

0.28

Sensitivity

0.81

0.91

0.87

0.86

0.90

0.87

0.72

Specificity

0.87

0.88

0.89

0.87

0.87

0.87

0.91

Positive Predictive Power

0.36

0.42

0.49

0.39

0.42

0.36

0.59

Negative Predictive Power

0.98

0.99

0.98

0.99

0.99

0.99

0.95

Overall Classification Rate

0.87

0.88

0.89

0.87

0.88

0.87

0.88

Area Under the Curve (AUC)

0.92

0.96

0.95

0.94

0.95

0.94

0.92

AUC 95% Confidence Interval Lower

0.92

0.95

0.95

0.93

0.95

0.94

0.92

AUC 95% Confidence Interval Upper

0.93

0.96

0.96

0.94

0.95

0.95

0.93

At 90% Sensitivity, specificity equals

0.75

0.87

0.86

0.80

0.84

0.81

0.74

At 80% Sensitivity, specificity equals

0.89

0.96

0.95

0.94

0.95

0.94

0.88

At 70% Sensitivity, specificity equals

0.96

0.98

0.99

0.97

0.99

0.97

0.95

 

Additional Classification Accuracy

The following are provided for context and did not factor into the Classification Accuracy ratings.

 

Disaggregated Data

 

Subgroup: Asian or Pacific Islander

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Fall

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 168, PARCC < 700

MAP Growth < 182, PARCC < 700

MAP Growth < 193, PARCC < 700

MAP Growth < 202, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 214, PARCC < 700

MAP Growth < 219, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.02

0.02

0.03

0.02

0.03

0.02

0.05

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.05

0.05

0.04

0.06

0.05

0.05

0.03

False Negative Rate

0.33

0.04

0.14

0.16

0.07

0.17

0.33

Sensitivity

0.67

0.96

0.86

0.84

0.93

0.83

0.67

Specificity

0.95

0.95

0.96

0.94

0.95

0.95

0.97

Positive Predictive Power

0.22

0.27

0.38

0.26

0.36

0.27

0.51

Negative Predictive Power

0.99

1.00

1.00

1.00

1.00

1.00

0.98

Overall Classification Rate

0.94

0.95

0.96

0.94

0.95

0.95

0.95

Area Under the Curve (AUC)

0.94

0.98

0.98

0.97

0.98

0.97

0.95

AUC 95% Confidence Interval Lower

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

AUC 95% Confidence Interval Upper

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

At 90% Sensitivity, specificity equals

0.84

0.96

1.00

0.95

0.98

0.91

0.91

At 80% Sensitivity, specificity equals

0.96

1.00

1.00

0.97

0.98

1.00

0.93

At 70% Sensitivity, specificity equals

0.96

1.00

1.00

1.00

0.98

1.00

0.97

 

Subgroup: Asian or Pacific Islander

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Winter

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 178, PARCC < 700

MAP Growth < 188, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 206, PARCC < 700

MAP Growth < 209, PARCC < 700

MAP Growth < 214, PARCC < 700

MAP Growth < 220, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.02

0.02

0.02

0.03

0.03

0.03

0.06

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.05

0.04

0.04

0.05

0.04

0.04

0.03

False Negative Rate

0.23

0.24

0.22

0.13

0.15

0.21

0.32

Sensitivity

0.77

0.76

0.78

0.88

0.85

0.79

0.69

Specificity

0.95

0.96

0.96

0.95

0.96

0.97

0.97

Positive Predictive Power

0.26

0.29

0.33

0.33

0.46

0.40

0.57

Negative Predictive Power

1.00

0.99

1.00

1.00

1.00

0.99

0.98

Overall Classification Rate

0.95

0.95

0.96

0.95

0.96

0.96

0.95

Area Under the Curve (AUC)

0.95

0.96

0.97

0.97

0.99

0.97

0.96

AUC 95% Confidence Interval Lower

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

AUC 95% Confidence Interval Upper

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

At 90% Sensitivity, specificity equals

0.91

0.88

0.89

0.94

1.00

0.95

0.87

At 80% Sensitivity, specificity equals

0.96

1.00

1.00

1.00

1.00

1.00

0.96

At 70% Sensitivity, specificity equals

0.96

1.00

1.00

1.00

1.00

1.00

0.98

 

Subgroup: Asian or Pacific Islander

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Spring

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 184, PARCC < 700

MAP Growth < 195, PARCC < 700

MAP Growth < 204, PARCC < 700

MAP Growth < 211, PARCC < 700

MAP Growth < 215, PARCC < 700

MAP Growth < 220, PARCC < 700

MAP Growth < 224, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.02

0.02

0.03

0.03

0.03

0.03

0.05

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.05

0.04

0.04

0.06

0.04

0.04

0.03

False Negative Rate

0.36

0.17

0.15

0.11

0.04

0.26

0.30

Sensitivity

0.64

0.83

0.85

0.89

0.96

0.74

0.70

Specificity

0.95

0.96

0.97

0.94

0.96

0.96

0.97

Positive Predictive Power

0.21

0.33

0.42

0.30

0.44

0.32

0.56

Negative Predictive Power

0.99

1.00

1.00

1.00

1.00

0.99

0.98

Overall Classification Rate

0.95

0.96

0.96

0.94

0.96

0.95

0.96

Area Under the Curve (AUC)

0.95

0.97

0.98

0.98

0.99

0.97

0.96

AUC 95% Confidence Interval Lower

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

AUC 95% Confidence Interval Upper

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

At 90% Sensitivity, specificity equals

0.80

0.94

0.98

0.97

1.00

0.94

0.91

At 80% Sensitivity, specificity equals

0.95

0.97

1.00

0.98

1.00

0.97

0.95

At 70% Sensitivity, specificity equals

1.00

0.97

1.00

1.00

1.00

0.97

0.98

 

Subgroup: Black

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Fall

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 168, PARCC < 700

MAP Growth < 182, PARCC < 700

MAP Growth < 193, PARCC < 700

MAP Growth < 202, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 214, PARCC < 700

MAP Growth < 219, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.18

0.20

0.25

0.17

0.20

0.16

0.34

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.19

0.23

0.20

0.28

0.27

0.30

0.20

False Negative Rate

0.21

0.14

0.17

0.14

0.13

0.10

0.27

Sensitivity

0.79

0.86

0.84

0.86

0.87

0.90

0.73

Specificity

0.81

0.77

0.80

0.72

0.73

0.70

0.80

Positive Predictive Power

0.46

0.48

0.57

0.40

0.45

0.37

0.65

Negative Predictive Power

0.95

0.96

0.94

0.96

0.96

0.97

0.85

Overall Classification Rate

0.80

0.79

0.81

0.75

0.76

0.73

0.77

Area Under the Curve (AUC)

0.87

0.89

0.89

0.86

0.89

0.88

0.85

AUC 95% Confidence Interval Lower

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

AUC 95% Confidence Interval Upper

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

At 90% Sensitivity, specificity equals

0.50

0.63

0.66

0.54

0.69

0.56

0.59

At 80% Sensitivity, specificity equals

0.80

0.83

0.83

0.77

0.81

0.83

0.72

At 70% Sensitivity, specificity equals

0.89

0.91

0.92

0.86

0.90

0.90

0.83

 

Subgroup: Black

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Winter

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 178, PARCC < 700

MAP Growth < 188, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 206, PARCC < 700

MAP Growth < 209, PARCC < 700

MAP Growth < 214, PARCC < 700

MAP Growth < 220, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.20

0.23

0.25

0.19

0.22

0.18

0.37

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.28

0.24

0.19

0.27

0.24

0.28

0.18

False Negative Rate

0.22

0.14

0.12

0.17

0.12

0.11

0.29

Sensitivity

0.78

0.86

0.88

0.83

0.89

0.89

0.71

Specificity

0.73

0.77

0.81

0.73

0.76

0.72

0.82

Positive Predictive Power

0.41

0.52

0.61

0.42

0.51

0.41

0.70

Negative Predictive Power

0.93

0.95

0.95

0.95

0.96

0.97

0.83

Overall Classification Rate

0.74

0.79

0.83

0.75

0.79

0.75

0.78

Area Under the Curve (AUC)

0.85

0.90

0.91

0.87

0.90

0.88

0.87

AUC 95% Confidence Interval Lower

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

AUC 95% Confidence Interval Upper

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

At 90% Sensitivity, specificity equals

0.52

0.66

0.76

0.55

0.67

0.56

0.57

At 80% Sensitivity, specificity equals

0.70

0.82

0.89

0.78

0.84

0.79

0.74

At 70% Sensitivity, specificity equals

0.79

0.93

0.95

0.86

0.92

0.92

0.85

 

Subgroup: Black

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Spring

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 184, PARCC < 700

MAP Growth < 195, PARCC < 700

MAP Growth < 204, PARCC < 700

MAP Growth < 211, PARCC < 700

MAP Growth < 215, PARCC < 700

MAP Growth < 220, PARCC < 700

MAP Growth < 224, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.18

0.20

0.25

0.18

0.21

0.16

0.35

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.29

0.27

0.26

0.30

0.26

0.27

0.21

False Negative Rate

0.16

0.07

0.07

0.13

0.08

0.09

0.21

Sensitivity

0.84

0.93

0.93

0.87

0.92

0.91

0.79

Specificity

0.71

0.73

0.74

0.70

0.74

0.73

0.79

Positive Predictive Power

0.39

0.47

0.54

0.39

0.48

0.40

0.67

Negative Predictive Power

0.95

0.98

0.97

0.96

0.97

0.98

0.87

Overall Classification Rate

0.73

0.77

0.78

0.73

0.78

0.76

0.79

Area Under the Curve (AUC)

0.86

0.91

0.91

0.86

0.90

0.89

0.86

AUC 95% Confidence Interval Lower

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

AUC 95% Confidence Interval Upper

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

At 90% Sensitivity, specificity equals

0.63

0.69

0.70

0.53

0.67

0.64

0.57

At 80% Sensitivity, specificity equals

0.76

0.88

0.87

0.74

0.85

0.83

0.75

At 70% Sensitivity, specificity equals

0.84

0.97

0.94

0.86

0.94

0.93

0.85

 

Subgroup: Hispanic

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Fall

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 168, PARCC < 700

MAP Growth < 182, PARCC < 700

MAP Growth < 193, PARCC < 700

MAP Growth < 202, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 214, PARCC < 700

MAP Growth < 219, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.16

0.16

0.22

0.17

0.16

0.13

0.27

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.24

0.24

0.21

0.25

0.25

0.25

0.19

False Negative Rate

0.18

0.10

0.17

0.17

0.13

0.10

0.27

Sensitivity

0.82

0.90

0.83

0.83

0.87

0.90

0.74

Specificity

0.77

0.76

0.79

0.75

0.75

0.76

0.81

Positive Predictive Power

0.40

0.42

0.52

0.40

0.39

0.35

0.60

Negative Predictive Power

0.96

0.97

0.94

0.96

0.97

0.98

0.89

Overall Classification Rate

0.77

0.78

0.80

0.77

0.77

0.77

0.79

Area Under the Curve (AUC)

0.87

0.90

0.89

0.88

0.90

0.90

0.86

AUC 95% Confidence Interval Lower

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

AUC 95% Confidence Interval Upper

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

At 90% Sensitivity, specificity equals

0.57

0.68

0.62

0.59

0.67

0.67

0.55

At 80% Sensitivity, specificity equals

0.78

0.86

0.82

0.78

0.83

0.87

0.75

At 70% Sensitivity, specificity equals

0.88

0.92

0.91

0.90

0.91

0.93

0.85

 

Subgroup: Hispanic

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Winter

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 178, PARCC < 700

MAP Growth < 188, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 206, PARCC < 700

MAP Growth < 209, PARCC < 700

MAP Growth < 214, PARCC < 700

MAP Growth < 220, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.17

0.18

0.22

0.17

0.17

0.14

0.28

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.26

0.23

0.22

0.22

0.21

0.21

0.16

False Negative Rate

0.12

0.11

0.17

0.16

0.14

0.16

0.31

Sensitivity

0.88

0.89

0.83

0.84

0.86

0.85

0.69

Specificity

0.74

0.77

0.78

0.78

0.79

0.79

0.84

Positive Predictive Power

0.40

0.46

0.52

0.45

0.46

0.39

0.62

Negative Predictive Power

0.97

0.97

0.94

0.96

0.97

0.97

0.88

Overall Classification Rate

0.76

0.79

0.79

0.79

0.80

0.79

0.80

Area Under the Curve (AUC)

0.88

0.91

0.89

0.88

0.90

0.90

0.86

AUC 95% Confidence Interval Lower

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

AUC 95% Confidence Interval Upper

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

At 90% Sensitivity, specificity equals

0.57

0.71

0.63

0.61

0.66

0.68

0.56

At 80% Sensitivity, specificity equals

0.82

0.86

0.81

0.82

0.85

0.83

0.77

At 70% Sensitivity, specificity equals

0.91

0.94

0.90

0.91

0.93

0.91

0.85

 

Subgroup: Hispanic

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Spring

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 184, PARCC < 700

MAP Growth < 195, PARCC < 700

MAP Growth < 204, PARCC < 700

MAP Growth < 211, PARCC < 700

MAP Growth < 215, PARCC < 700

MAP Growth < 220, PARCC < 700

MAP Growth < 224, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.16

0.16

0.22

0.17

0.16

0.13

0.26

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.26

0.24

0.23

0.25

0.24

0.25

0.18

False Negative Rate

0.12

0.07

0.12

0.10

0.08

0.08

0.25

Sensitivity

0.88

0.93

0.88

0.90

0.92

0.92

0.76

Specificity

0.75

0.76

0.78

0.75

0.76

0.76

0.82

Positive Predictive Power

0.39

0.43

0.52

0.42

0.42

0.36

0.60

Negative Predictive Power

0.97

0.98

0.96

0.97

0.98

0.98

0.90

Overall Classification Rate

0.77

0.79

0.80

0.77

0.78

0.78

0.80

Area Under the Curve (AUC)

0.88

0.92

0.91

0.90

0.91

0.91

0.88

AUC 95% Confidence Interval Lower

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

AUC 95% Confidence Interval Upper

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

At 90% Sensitivity, specificity equals

0.60

0.78

0.71

0.67

0.71

0.72

0.62

At 80% Sensitivity, specificity equals

0.79

0.90

0.86

0.84

0.89

0.87

0.79

At 70% Sensitivity, specificity equals

0.89

0.95

0.94

0.93

0.94

0.94

0.87

 

Subgroup: White

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Fall

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 168, PARCC < 700

MAP Growth < 182, PARCC < 700

MAP Growth < 193, PARCC < 700

MAP Growth < 202, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 214, PARCC < 700

MAP Growth < 219, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.04

0.04

0.06

0.04

0.05

0.05

0.10

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.09

0.07

0.07

0.07

0.08

0.09

0.06

False Negative Rate

0.34

0.25

0.27

0.31

0.18

0.19

0.39

Sensitivity

0.67

0.75

0.73

0.69

0.82

0.81

0.61

Specificity

0.91

0.93

0.93

0.93

0.92

0.91

0.94

Positive Predictive Power

0.22

0.35

0.40

0.30

0.34

0.31

0.52

Negative Predictive Power

0.99

0.99

0.98

0.99

0.99

0.99

0.96

Overall Classification Rate

0.90

0.93

0.92

0.92

0.92

0.91

0.91

Area Under the Curve (AUC)

0.91

0.95

0.95

0.93

0.96

0.95

0.92

AUC 95% Confidence Interval Lower

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

AUC 95% Confidence Interval Upper

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

At 90% Sensitivity, specificity equals

0.69

0.85

0.84

0.79

0.87

0.84

0.73

At 80% Sensitivity, specificity equals

0.88

0.96

0.96

0.92

0.96

0.93

0.88

At 70% Sensitivity, specificity equals

0.94

0.98

0.99

0.96

0.99

0.98

0.94

 

Subgroup: White

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Winter

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 178, PARCC < 700

MAP Growth < 188, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 206, PARCC < 700

MAP Growth < 209, PARCC < 700

MAP Growth < 214, PARCC < 700

MAP Growth < 220, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.04

0.05

0.06

0.05

0.06

0.06

0.12

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.08

0.07

0.07

0.08

0.07

0.07

0.05

False Negative Rate

0.31

0.21

0.24

0.25

0.24

0.23

0.41

Sensitivity

0.69

0.79

0.76

0.75

0.76

0.77

0.59

Specificity

0.92

0.94

0.93

0.93

0.94

0.93

0.95

Positive Predictive Power

0.24

0.41

0.42

0.35

0.41

0.39

0.60

Negative Predictive Power

0.99

0.99

0.98

0.99

0.99

0.99

0.95

Overall Classification Rate

0.91

0.93

0.92

0.92

0.93

0.92

0.91

Area Under the Curve (AUC)

0.93

0.96

0.95

0.94

0.95

0.94

0.91

AUC 95% Confidence Interval Lower

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

AUC 95% Confidence Interval Upper

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

At 90% Sensitivity, specificity equals

0.76

0.89

0.87

0.82

0.84

0.83

0.73

At 80% Sensitivity, specificity equals

0.93

0.96

0.98

0.93

0.96

0.94

0.87

At 70% Sensitivity, specificity equals

0.96

0.99

0.99

0.95

0.99

0.97

0.93

 

Subgroup: White

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Spring

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 184, PARCC < 700

MAP Growth < 195, PARCC < 700

MAP Growth < 204, PARCC < 700

MAP Growth < 211, PARCC < 700

MAP Growth < 215, PARCC < 700

MAP Growth < 220, PARCC < 700

MAP Growth < 224, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.03

0.05

0.06

0.05

0.05

0.05

0.09

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.07

0.07

0.07

0.07

0.07

0.08

0.06

False Negative Rate

0.31

0.13

0.16

0.22

0.16

0.20

0.35

Sensitivity

0.69

0.87

0.84

0.78

0.84

0.80

0.65

Specificity

0.93

0.93

0.93

0.93

0.93

0.92

0.94

Positive Predictive Power

0.26

0.37

0.43

0.34

0.36

0.33

0.54

Negative Predictive Power

0.99

0.99

0.99

0.99

0.99

0.99

0.96

Overall Classification Rate

0.92

0.93

0.93

0.92

0.92

0.91

0.92

Area Under the Curve (AUC)

0.93

0.97

0.97

0.95

0.96

0.95

0.93

AUC 95% Confidence Interval Lower

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

AUC 95% Confidence Interval Upper

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

At 90% Sensitivity, specificity equals

0.76

0.92

0.90

0.85

0.91

0.83

0.77

At 80% Sensitivity, specificity equals

0.92

0.98

0.98

0.94

0.98

0.94

0.90

At 70% Sensitivity, specificity equals

0.97

0.99

1.00

0.98

0.99

0.97

0.94

 

Subgroup: Multi-Ethnic

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Fall

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 168, PARCC < 700

MAP Growth < 182, PARCC < 700

MAP Growth < 193, PARCC < 700

MAP Growth < 202, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 214, PARCC < 700

MAP Growth < 219, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.08

0.08

0.09

0.09

0.08

0.07

0.13

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.12

0.11

0.06

0.12

0.11

0.13

0.07

False Negative Rate

0.23

0.19

0.26

0.11

0.13

0.16

0.31

Sensitivity

0.77

0.81

0.74

0.89

0.87

0.84

0.69

Specificity

0.88

0.89

0.94

0.88

0.89

0.88

0.93

Positive Predictive Power

0.37

0.40

0.54

0.41

0.39

0.34

0.60

Negative Predictive Power

0.98

0.98

0.97

0.99

0.99

0.99

0.95

Overall Classification Rate

0.87

0.88

0.92

0.88

0.89

0.87

0.90

Area Under the Curve (AUC)

0.91

0.94

0.95

0.94

0.94

0.94

0.95

AUC 95% Confidence Interval Lower

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

AUC 95% Confidence Interval Upper

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

At 90% Sensitivity, specificity equals

0.72

0.79

0.81

0.78

0.80

0.84

0.80

At 80% Sensitivity, specificity equals

0.87

0.93

0.95

0.97

0.97

0.94

0.95

At 70% Sensitivity, specificity equals

0.97

0.96

0.97

0.97

0.97

0.98

1.00

 

Subgroup: Multi-Ethnic

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Winter

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 178, PARCC < 700

MAP Growth < 188, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 206, PARCC < 700

MAP Growth < 209, PARCC < 700

MAP Growth < 214, PARCC < 700

MAP Growth < 220, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.08

0.09

0.08

0.09

0.10

0.08

0.17

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.11

0.11

0.06

0.13

0.10

0.11

0.06

False Negative Rate

0.35

0.08

0.20

0.09

0.16

0.10

0.29

Sensitivity

0.65

0.92

0.80

0.91

0.84

0.90

0.71

Specificity

0.89

0.89

0.94

0.87

0.90

0.89

0.94

Positive Predictive Power

0.34

0.43

0.52

0.41

0.46

0.42

0.69

Negative Predictive Power

0.97

0.99

0.98

0.99

0.98

0.99

0.94

Overall Classification Rate

0.87

0.89

0.93

0.88

0.89

0.89

0.90

Area Under the Curve (AUC)

0.87

0.94

0.96

0.95

0.92

0.94

0.94

AUC 95% Confidence Interval Lower

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

AUC 95% Confidence Interval Upper

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

At 90% Sensitivity, specificity equals

0.54

0.85

0.90

0.83

0.82

0.84

0.79

At 80% Sensitivity, specificity equals

0.81

0.92

0.96

0.97

0.90

0.97

0.90

At 70% Sensitivity, specificity equals

0.89

0.94

0.97

0.97

0.97

0.97

0.97

 

Subgroup: Multi-Ethnic

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Spring

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 184, PARCC < 700

MAP Growth < 195, PARCC < 700

MAP Growth < 204, PARCC < 700

MAP Growth < 211, PARCC < 700

MAP Growth < 215, PARCC < 700

MAP Growth < 220, PARCC < 700

MAP Growth < 224, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.08

0.08

0.09

0.09

0.08

0.07

0.13

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.11

0.13

0.09

0.12

0.12

0.13

0.07

False Negative Rate

0.24

0.07

0.14

0.13

0.12

0.14

0.24

Sensitivity

0.76

0.93

0.86

0.87

0.88

0.86

0.76

Specificity

0.89

0.87

0.91

0.88

0.88

0.87

0.93

Positive Predictive Power

0.36

0.38

0.48

0.39

0.39

0.34

0.62

Negative Predictive Power

0.98

0.99

0.99

0.99

0.99

0.99

0.96

Overall Classification Rate

0.88

0.88

0.90

0.88

0.88

0.87

0.91

Area Under the Curve (AUC)

0.91

0.96

0.97

0.95

0.94

0.94

0.95

AUC 95% Confidence Interval Lower

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

AUC 95% Confidence Interval Upper

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

At 90% Sensitivity, specificity equals

0.72

0.86

0.91

0.84

0.83

0.83

0.86

At 80% Sensitivity, specificity equals

0.82

0.96

0.98

0.92

0.95

0.89

0.94

At 70% Sensitivity, specificity equals

0.95

0.98

1.00

0.97

0.95

0.94

1.00

 

Subgroup: Not Specified or Other

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Fall

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 168, PARCC < 700

MAP Growth < 182, PARCC < 700

MAP Growth < 193, PARCC < 700

MAP Growth < 202, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 214, PARCC < 700

MAP Growth < 219, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.07

0.07

0.07

0.06

0.07

0.05

0.18

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.15

0.11

0.11

0.10

0.11

0.08

0.11

False Negative Rate

0.23

0.14

0.17

0.28

0.13

0.08

0.23

Sensitivity

0.77

0.86

0.83

0.72

0.87

0.92

0.77

Specificity

0.85

0.89

0.89

0.90

0.90

0.92

0.89

Positive Predictive Power

0.29

0.38

0.36

0.33

0.39

0.37

0.62

Negative Predictive Power

0.98

0.99

0.99

0.98

0.99

1.00

0.95

Overall Classification Rate

0.85

0.89

0.89

0.89

0.89

0.92

0.87

Area Under the Curve (AUC)

0.90

0.95

0.94

0.93

0.95

0.96

0.92

AUC 95% Confidence Interval Lower

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

AUC 95% Confidence Interval Upper

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

At 90% Sensitivity, specificity equals

0.63

0.85

0.80

0.74

0.86

0.92

0.72

At 80% Sensitivity, specificity equals

0.85

0.95

0.94

0.92

0.93

0.96

0.90

At 70% Sensitivity, specificity equals

0.91

0.97

0.98

0.97

0.98

1.00

0.94

 

Subgroup: Not Specified or Other

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Winter

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 178, PARCC < 700

MAP Growth < 188, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 206, PARCC < 700

MAP Growth < 209, PARCC < 700

MAP Growth < 214, PARCC < 700

MAP Growth < 220, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.09

0.09

0.09

0.08

0.11

0.13

0.23

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.14

0.10

0.11

0.10

0.12

0.15

0.09

False Negative Rate

0.30

0.19

0.16

0.21

0.15

0.23

0.34

Sensitivity

0.71

0.81

0.84

0.79

0.85

0.77

0.66

Specificity

0.86

0.90

0.89

0.90

0.88

0.85

0.91

Positive Predictive Power

0.34

0.44

0.42

0.42

0.48

0.44

0.69

Negative Predictive Power

0.97

0.98

0.98

0.98

0.98

0.96

0.90

Overall Classification Rate

0.85

0.90

0.89

0.89

0.88

0.84

0.86

Area Under the Curve (AUC)

0.89

0.94

0.93

0.94

0.94

0.92

0.90

AUC 95% Confidence Interval Lower

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

AUC 95% Confidence Interval Upper

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

At 90% Sensitivity, specificity equals

0.57

0.81

0.79

0.80

0.81

0.71

0.68

At 80% Sensitivity, specificity equals

0.82

0.90

0.91

0.95

0.93

0.91

0.85

At 70% Sensitivity, specificity equals

0.91

0.97

0.98

0.97

0.98

0.97

0.91

 

Subgroup: Not Specified or Other

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Spring

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 184, PARCC < 700

MAP Growth < 195, PARCC < 700

MAP Growth < 204, PARCC < 700

MAP Growth < 211, PARCC < 700

MAP Growth < 215, PARCC < 700

MAP Growth < 220, PARCC < 700

MAP Growth < 224, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.09

0.07

0.07

0.06

0.07

0.05

0.17

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.10

0.09

0.09

0.09

0.09

0.08

0.08

False Negative Rate

0.23

0.12

0.15

0.15

0.14

0.18

0.32

Sensitivity

0.77

0.88

0.85

0.86

0.86

0.83

0.68

Specificity

0.90

0.91

0.91

0.91

0.91

0.92

0.92

Positive Predictive Power

0.41

0.43

0.42

0.38

0.44

0.36

0.64

Negative Predictive Power

0.98

0.99

0.99

0.99

0.99

0.99

0.93

Overall Classification Rate

0.89

0.91

0.91

0.90

0.91

0.92

0.88

Area Under the Curve (AUC)

0.92

0.96

0.95

0.95

0.96

0.96

0.92

AUC 95% Confidence Interval Lower

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

AUC 95% Confidence Interval Upper

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

At 90% Sensitivity, specificity equals

0.76

0.89

0.87

0.86

0.88

0.89

0.71

At 80% Sensitivity, specificity equals

0.89

0.97

0.95

0.96

0.98

0.97

0.82

At 70% Sensitivity, specificity equals

0.96

0.99

0.98

0.99

0.99

1.00

0.96

 

Subgroup: Female

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Fall

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 168, PARCC < 700

MAP Growth < 182, PARCC < 700

MAP Growth < 193, PARCC < 700

MAP Growth < 202, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 214, PARCC < 700

MAP Growth < 219, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.08

0.08

0.11

0.07

0.08

0.07

0.14

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.13

0.12

0.11

0.13

0.14

0.14

0.10

False Negative Rate

0.19

0.13

0.18

0.18

0.13

0.13

0.29

Sensitivity

0.81

0.87

0.82

0.82

0.87

0.88

0.71

Specificity

0.87

0.88

0.89

0.87

0.86

0.86

0.90

Positive Predictive Power

0.35

0.39

0.47

0.31

0.35

0.31

0.53

Negative Predictive Power

0.98

0.99

0.98

0.99

0.99

0.99

0.95

Overall Classification Rate

0.87

0.88

0.88

0.86

0.86

0.87

0.88

Area Under the Curve (AUC)

0.92

0.94

0.94

0.93

0.94

0.94

0.91

AUC 95% Confidence Interval Lower

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

AUC 95% Confidence Interval Upper

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

At 90% Sensitivity, specificity equals

0.75

0.83

0.80

0.75

0.81

0.82

0.71

At 80% Sensitivity, specificity equals

0.90

0.94

0.95

0.92

0.94

0.94

0.88

At 70% Sensitivity, specificity equals

0.95

0.98

0.99

0.96

0.97

0.98

0.95

 

Subgroup: Female

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Winter

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 178, PARCC < 700

MAP Growth < 188, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 206, PARCC < 700

MAP Growth < 209, PARCC < 700

MAP Growth < 214, PARCC < 700

MAP Growth < 220, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.08

0.10

0.11

0.08

0.09

0.08

0.16

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.14

0.13

0.13

0.13

0.12

0.13

0.09

False Negative Rate

0.17

0.13

0.16

0.14

0.17

0.17

0.31

Sensitivity

0.83

0.88

0.84

0.86

0.83

0.83

0.69

Specificity

0.87

0.87

0.87

0.87

0.88

0.87

0.91

Positive Predictive Power

0.35

0.42

0.46

0.35

0.41

0.37

0.59

Negative Predictive Power

0.98

0.99

0.98

0.99

0.98

0.98

0.94

Overall Classification Rate

0.86

0.87

0.87

0.87

0.87

0.87

0.87

Area Under the Curve (AUC)

0.93

0.95

0.94

0.94

0.94

0.94

0.91

AUC 95% Confidence Interval Lower

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

AUC 95% Confidence Interval Upper

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

At 90% Sensitivity, specificity equals

0.75

0.83

0.77

0.80

0.79

0.78

0.70

At 80% Sensitivity, specificity equals

0.90

0.95

0.94

0.93

0.91

0.92

0.86

At 70% Sensitivity, specificity equals

0.96

0.98

0.98

0.98

0.97

0.98

0.93

 

Subgroup: Female

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Spring

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 184, PARCC < 700

MAP Growth < 195, PARCC < 700

MAP Growth < 204, PARCC < 700

MAP Growth < 211, PARCC < 700

MAP Growth < 215, PARCC < 700

MAP Growth < 220, PARCC < 700

MAP Growth < 224, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.07

0.09

0.11

0.07

0.08

0.07

0.13

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.13

0.13

0.12

0.14

0.13

0.13

0.10

False Negative Rate

0.19

0.07

0.11

0.10

0.10

0.12

0.25

Sensitivity

0.81

0.93

0.89

0.90

0.90

0.89

0.75

Specificity

0.88

0.87

0.88

0.86

0.87

0.87

0.90

Positive Predictive Power

0.34

0.39

0.47

0.33

0.38

0.32

0.54

Negative Predictive Power

0.98

0.99

0.99

0.99

0.99

0.99

0.96

Overall Classification Rate

0.87

0.87

0.88

0.86

0.88

0.87

0.88

Area Under the Curve (AUC)

0.93

0.96

0.95

0.95

0.95

0.95

0.92

AUC 95% Confidence Interval Lower

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

AUC 95% Confidence Interval Upper

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

At 90% Sensitivity, specificity equals

0.75

0.89

0.86

0.82

0.87

0.83

0.76

At 80% Sensitivity, specificity equals

0.89

0.97

0.95

0.96

0.96

0.94

0.89

At 70% Sensitivity, specificity equals

0.96

0.99

0.99

0.98

0.99

0.98

0.95

 

Subgroup: Male

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Fall

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 168, PARCC < 700

MAP Growth < 182, PARCC < 700

MAP Growth < 193, PARCC < 700

MAP Growth < 202, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 214, PARCC < 700

MAP Growth < 219, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.08

0.09

0.11

0.10

0.10

0.09

0.18

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.14

0.12

0.11

0.12

0.13

0.14

0.09

False Negative Rate

0.26

0.16

0.21

0.22

0.15

0.14

0.33

Sensitivity

0.74

0.84

0.79

0.78

0.85

0.86

0.68

Specificity

0.86

0.88

0.89

0.88

0.87

0.86

0.91

Positive Predictive Power

0.32

0.41

0.48

0.41

0.42

0.36

0.62

Negative Predictive Power

0.97

0.98

0.97

0.97

0.98

0.99

0.93

Overall Classification Rate

0.85

0.87

0.88

0.87

0.87

0.86

0.87

Area Under the Curve (AUC)

0.90

0.94

0.93

0.92

0.94

0.93

0.91

AUC 95% Confidence Interval Lower

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

AUC 95% Confidence Interval Upper

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

At 90% Sensitivity, specificity equals

0.63

0.80

0.76

0.72

0.79

0.79

0.70

At 80% Sensitivity, specificity equals

0.84

0.93

0.91

0.92

0.93

0.92

0.87

At 70% Sensitivity, specificity equals

0.92

0.98

0.98

0.96

0.98

0.97

0.94

 

Subgroup: Male

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Winter

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 178, PARCC < 700

MAP Growth < 188, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 206, PARCC < 700

MAP Growth < 209, PARCC < 700

MAP Growth < 214, PARCC < 700

MAP Growth < 220, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.09

0.11

0.12

0.12

0.12

0.11

0.20

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.14

0.11

0.10

0.12

0.11

0.13

0.08

False Negative Rate

0.22

0.17

0.19

0.21

0.16

0.18

0.36

Sensitivity

0.78

0.83

0.81

0.79

0.84

0.82

0.64

Specificity

0.86

0.89

0.90

0.88

0.89

0.87

0.92

Positive Predictive Power

0.36

0.47

0.52

0.47

0.50

0.43

0.67

Negative Predictive Power

0.98

0.98

0.97

0.97

0.98

0.98

0.91

Overall Classification Rate

0.86

0.88

0.89

0.87

0.88

0.87

0.86

Area Under the Curve (AUC)

0.91

0.94

0.94

0.92

0.94

0.93

0.90

AUC 95% Confidence Interval Lower

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

AUC 95% Confidence Interval Upper

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

At 90% Sensitivity, specificity equals

0.70

0.80

0.80

0.76

0.81

0.76

0.71

At 80% Sensitivity, specificity equals

0.89

0.93

0.94

0.90

0.93

0.91

0.85

At 70% Sensitivity, specificity equals

0.94

0.98

0.98

0.95

0.97

0.97

0.93

 

Subgroup: Male

Criterion 1: Partnership for Assessment of Readiness for College and Careers (PARCC) Math

Time of Year: Spring

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 184, PARCC < 700

MAP Growth < 195, PARCC < 700

MAP Growth < 204, PARCC < 700

MAP Growth < 211, PARCC < 700

MAP Growth < 215, PARCC < 700

MAP Growth < 220, PARCC < 700

MAP Growth < 224, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.09

0.10

0.11

0.10

0.10

0.09

0.17

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.20

0.20

0.20

0.20

0.20

0.20

0.20

False Positive Rate

0.13

0.12

0.11

0.12

0.12

0.13

0.09

False Negative Rate

0.18

0.11

0.14

0.16

0.11

0.14

0.30

Sensitivity

0.82

0.89

0.86

0.84

0.89

0.86

0.70

Specificity

0.87

0.88

0.90

0.88

0.88

0.87

0.91

Positive Predictive Power

0.38

0.45

0.51

0.45

0.45

0.39

0.63

Negative Predictive Power

0.98

0.99

0.98

0.98

0.99

0.99

0.93

Overall Classification Rate

0.87

0.89

0.89

0.88

0.88

0.87

0.88

Area Under the Curve (AUC)

0.92

0.95

0.95

0.94

0.95

0.94

0.92

AUC 95% Confidence Interval Lower

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

AUC 95% Confidence Interval Upper

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

Not Provided

At 90% Sensitivity, specificity equals

0.74

0.86

0.85

0.80

0.83

0.80

0.74

At 80% Sensitivity, specificity equals

0.90

0.96

0.95

0.93

0.95

0.93

0.89

At 70% Sensitivity, specificity equals

0.96

0.98

0.99

0.97

0.99

0.97

0.94

 

Reliability

Grade2345678
RatingFull bubbledFull bubbledFull bubbledFull bubbledFull bubbledFull bubbledFull bubbled
  1. Justification for each type of reliability reported, given the type and purpose of the tool:

Using MAP Growth as an academic screener, the internal consistency reliability of student test scores (i.e., student RIT scores on MAP Growth) is key. However, estimating the internal consistency of an adaptive test, such as MAP Growth, is challenging because traditional methods depend on all test takers to take a common test consisting of the same items. Application of these methods to adaptive tests is statistically cumbersome and inaccurate. Fortunately, an equally valid alternative is available in the marginal reliability coefficient    that incorporates measurement error as a function of the test score. In effect, it is the result of combining measurement error estimated at different points on the achievement scale into a single index. Note that this method of calculating reliability yields results that are nearly identical to coefficient alpha, when both methods are applied to the same fixed-form test.

 

MAP Growth affords the means to screen students on multiple occasions (e.g., Fall, Winter, Spring) during the school year. Thus, test-retest reliability is also key, and we estimate test-retest reliability via the Pearson correlation between MAP Growth RIT scores of students taking MAP Growth in two terms within the school year (Fall and Winter, Fall and Spring, and Winter and Spring). Given that MAP Growth is an adaptive test, without any fixed-forms, this approach to test-retest reliability may be more accurately described as a mix between test-retest reliability and a type of parallel forms reliability. That is, MAP Growth RIT scores are obtained for students taking MAP twice, spread across several months. The second test (or retest) is not the same test. Rather, the second test is comparable to the first, by its content and structure, differing only in the difficulty level of its items. Thus, both temporally related and parallel forms of reliability are defined as the consistency of covalent measures taken across time. Green, Bock, Humphreys, Linn, and Reckase  suggested the term “stratified, randomly parallel form reliability” to characterize this form of reliability.

 

  1. Description of the sample(s), including size and characteristics, for each reliability analysis conducted:

Representation: National: New England, Middle Atlantic, East North Central, South Atlantic, Mountain.  Local representation (please describe, including number of states): The sample for the study contained student records from a total of five states (Colorado, Illinois, New Jersey, New Mexico, and Rhode Island) and one federal district (District of Columbia), and thus had representation from all four U.S. Census regions.

Date: MAP Growth data was from test administrations occurring during the Fall 2015, Winter 2016, and Spring 2016 school terms, which spanned from August 2015 through June 2016. The MAP Growth scores of the Grade 3 students from the previous academic year (i.e., Fall 2014, Winter 2015, and Spring 2015) were used as the Grade 2 MAP Growth scores.

Size:

Table 7: Number of Students Per State by Grade

 

2

3

4

5

6

7

8

Total

CO

3,228

3,228

3,201

3,107

3,864

3,726

3,196

20,322

DC

171

171

161

132

236

165

173

1,038

IL

12,165

12,165

12,131

12,186

12,819

12,572

11,030

72,903

NJ

644

644

669

610

648

552

208

3,331

NM

208

208

208

200

198

206

147

1,167

RI

209

209

199

201

218

190

142

1,159

Total

16,625

16,625

16,569

16,436

17,983

17,411

14,896

99,920

 

Table 8: Number of Students Per Region by Grade

 

2

3

4

5

6

7

8

Total

Midwest

12,165

12,165

12,131

12,186

12,819

12,572

11,030

72,903

Northeast

853

853

868

811

866

742

350

4,490

South

171

171

161

132

236

165

173

1,038

West

3,436

3,436

3,409

3,307

4,062

3,932

3,343

21,489

Total

16,625

16,625

16,569

16,436

17,983

17,411

14,896

99,920

 

Table 9: Number of Students Per Division by Grade

 

2

3

4

5

6

7

8

Total

East North Central

12,165

12,165

12,131

12,186

12,819

12,572

11,030

72,903

Middle Atlantaic

644

644

669

610

648

552

208

3,331

Mountain

3,436

3,436

3,409

3,307

4,062

3,932

3,343

21,489

New England

209

209

199

201

218

190

142

1,159

South Atlantic

171

171

161

132

236

165

173

1,038

Total

16,625

16,625

16,569

16,436

17,983

17,411

14,896

99,920

 

Male

51.11%

Female

48.88%     

Unknown

0.02%

Other SES Indicators

 

Free or reduced-price lunch

 

White, Non-Hispanic

44.99%

Black, Non-Hispanic

6.42%

Hispanic

23.84%

American Indian/Alaska Native:

1.83%

Asian/Pacific Islander:

8.47%

Multi-Ethnic

2.88%

Not Specified or Other

11.45%

 

  1. Description of the analysis procedures for each reported type of reliability:

Marginal Reliability. The approach taken for estimating marginal reliability on MAP Growth was suggested by Wright in 1999[1]. For a sample of N students, marginal reliability () is estimated by

where  is the IRT achievement level (on a standardized or scaled score metric),  is an estimate of ,  is the observed variance of  across the sample of N students,  is the squared conditional (on ) standard error of measurement (CSEM), and  is the average squared CSEM across the sample of N students.

A bootstrapping approach is used to calculate a 95% confidence interval for marginal reliability. For an initial dataset of the achievement levels and CSEMs for N students, a bootstrap 95% confidence interval for marginal reliability is obtained as follows:

  1. Draw a random sample of size N with replacement from the initial dataset.
  2. Calculate marginal reliability based on the random sample drawn in Step 1.
  3. Repeat steps 1 and 2, 1,000 times.
  4. Determine the 2.5 and 97.5 percentile points from the resulting 1,000 estimates of marginal reliability. The value of these two percentiles are the bootstrap 95% confidence interval.

Test-Retest Reliability. Test-retest reliability of MAP Growth was estimated as the Pearson correlation of student RIT scores on MAP Growth for a set of students who took MAP Growth twice. For Grades 3–8, this was either in Fall 2015 and Winter 2016, in Fall 2015 and Spring 2016, or in Winter 2016 and Spring 2016. For Grade 2, this was either in Fall 2014 and Winter 2015, in Fall 2014 and Spring 2015, or in Winter 2015 and Spring 2015. Fundamentally, the test-retest reliability coefficient is a Pearson correlation. As such, the confidence interval (CI) for the test-retest reliability coefficient was obtained using the standard CI for a Pearson correlation (i.e., via the Fisher’s z-transformation).

 

 

  1. Reliability of performance level score (e.g., model-based, internal consistency, inter-rater reliability).

 

Type of Reliability

Grade

N

Coefficient

Confidence Interval

Marginal (Fall)

2

12,655

0.95

0.95, 0.96

Marginal (Winter)

2

10,505

0.95

0.94, 0.95

Marginal (Spring)

2

13,750

0.95

0.95, 0.95

Marginal (Fall)

3

15,332

0.95

0.95, 0.96

Marginal (Winter)

3

11,829

0.95

0.95, 0.95

Marginal (Spring)

3

16,625

0.96

0.96, 0.96

Marginal (Fall)

4

15,165

0.96

0.96, 0.96

Marginal (Winter)

4

12,249

0.96

0.96, 0.96

Marginal (Spring)

4

16,569

0.97

0.97, 0.97

Marginal (Fall)

5

15,023

0.97

0.96, 0.97

Marginal (Winter)

5

12,951

0.96

0.96, 0.97

Marginal (Spring)

5

16,436

0.97

0.97, 0.97

Marginal (Fall)

6

16,651

0.97

0.97, 0.97

Marginal (Winter)

6

13,108

0.97

0.97, 0.97

Marginal (Spring)

6

17,983

0.97

0.97, 0.97

Marginal (Fall)

7

15,956

0.97

0.97, 0.97

Marginal (Winter)

7

11,836

0.97

0.97, 0.97

Marginal (Spring)

7

17,411

0.97

0.97, 0.97

Marginal (Fall)

8

13,577

0.98

0.97, 0.98

Marginal (Winter)

8

11,040

0.98

0.97, 0.98

Marginal (Spring)

8

14,896

0.98

0.98, 0.98

Test-Retest (Fall/Winter)

2

10,192

0.86

0.86, 0.87

Test-Retest (Fall/Spring)

2

12,023

0.83

0.82, 0.83

Test-Retest (Winter/Spring)

2

10,391

0.87

0.86, 0.87

Test-Retest (Fall/Winter)

3

11,286

0.89

0.88, 0.89

Test-Retest (Fall/Spring)

3

15,332

0.86

0.86, 0.87

Test-Retest (Winter/Spring)

3

11,829

0.89

0.89, 0.90

Test-Retest (Fall/Winter)

4

11,699

0.90

0.90, 0.91

Test-Retest (Fall/Spring)

4

15,165

0.88

0.88, 0.88

Test-Retest (Winter/Spring)

4

12,249

0.91

0.91, 0.91

Test-Retest (Fall/Winter)

5

12,468

0.91

0.91, 0.92

Test-Retest (Fall/Spring)

5

15,023

0.90

0.89, 0.90

Test-Retest (Winter/Spring)

5

12,951

0.91

0.91, 0.92

Test-Retest (Fall/Winter)

6

12,869

0.92

0.92, 0.93

Test-Retest (Fall/Spring)

6

16,651

0.91

0.91, 0.91

Test-Retest (Winter/Spring)

6

13,108

0.92

0.92, 0.93

Test-Retest (Fall/Winter)

7

11,622

0.93

0.93, 0.93

Test-Retest (Fall/Spring)

7

15,956

0.92

0.91, 0.92

Test-Retest (Winter/Spring)

7

11,836

0.93

0.93, 0.93

Test-Retest (Fall/Winter)

8

10,859

0.93

0.93, 0.94

Test-Retest (Fall/Spring)

8

13,577

0.93

0.92, 0.93

Test-Retest (Winter/Spring)

8

11,040

0.93

0.93, 0.93

 

Disaggregated Reliability

The following disaggregated reliability data are provided for context and did not factor into the Reliability rating.

 

Type of

Reliability

Subgroup

Grade

N

Coefficient

Confidence

Interval

Marginal (Fall)

Ethnicity: Asian or Pacific Islander

2

1,177

0.96

0.95, 0.96

Marginal (Winter)

Ethnicity: Asian or Pacific Islander

2

1,046

0.94

0.94, 0.95

Marginal (Spring)

Ethnicity: Asian or Pacific Islander

2

1,315

0.95

0.94, 0.95

Marginal (Fall)

Ethnicity: Black

2

567

0.95

0.94, 0.95

Marginal (Winter)

Ethnicity: Black

2

417

0.94

0.93, 0.95

Marginal (Spring)

Ethnicity: Black

2

742

0.95

0.94, 0.95

Marginal (Fall)

Ethnicity: Hispanic

2

2,983

0.94

0.94, 0.94

Marginal (Winter)

Ethnicity: Hispanic

2

2,610

0.93

0.93, 0.94

Marginal (Spring)

Ethnicity: Hispanic

2

3,337

0.94

0.94, 0.94

Marginal (Fall)

Ethnicity: Multi-Ethnic

2

368

0.95

0.95, 0.96

Marginal (Winter)

Ethnicity: Multi-Ethnic

2

315

0.94

0.94, 0.95

Marginal (Spring)

Ethnicity: Multi-Ethnic

2

441

0.94

0.94, 0.95

Marginal (Fall)

Ethnicity: Not Specified or Other

2

2,268

0.95

0.95, 0.96

Marginal (Winter)

Ethnicity: Not Specified or Other

2

1,447

0.94

0.94, 0.95

Marginal (Spring)

Ethnicity: Not Specified or Other

2

1,834

0.94

0.94, 0.95

Marginal (Fall)

Ethnicity: White

2

5,136

0.94

0.94, 0.95

Marginal (Winter)

Ethnicity: White

2

4,521

0.93

0.92, 0.93

Marginal (Spring)

Ethnicity: White

2

5,918

0.93

0.93, 0.93

Marginal (Fall)

Gender: Female

2

6,182

0.95

0.95, 0.95

Marginal (Winter)

Gender: Female

2

5,142

0.94

0.93, 0.94

Marginal (Spring)

Gender: Female

2

6,728

0.94

0.94, 0.94

Marginal (Fall)

Gender: Male

2

6,471

0.96

0.96, 0.96

Marginal (Winter)

Gender: Male

2

5,362

0.95

0.95, 0.95

Marginal (Spring)

Gender: Male

2

7,019

0.95

0.95, 0.95

Marginal (Fall)

Ethnicity: American Indian or Alaskan

3

295

0.94

0.93, 0.95

Marginal (Winter)

Ethnicity: American Indian or Alaskan

3

367

0.93

0.92, 0.94

Marginal (Spring)

Ethnicity: American Indian or Alaskan

3

378

0.94

0.94, 0.95

Marginal (Fall)

Ethnicity: American Indian or Alaskan

4

310

0.92

0.9, 0.93

Marginal (Winter)

Ethnicity: American Indian or Alaskan

4

323

0.92

0.91, 0.93

Marginal (Spring)

Ethnicity: American Indian or Alaskan

4

339

0.94

0.93, 0.95

Marginal (Fall)

Ethnicity: American Indian or Alaskan

5

275

0.94

0.92, 0.95

Marginal (Winter)

Ethnicity: American Indian or Alaskan

5

290

0.94

0.93, 0.95

Marginal (Spring)

Ethnicity: American Indian or Alaskan

5

302

0.95

0.94, 0.96

Marginal (Fall)

Ethnicity: American Indian or Alaskan

6

314

0.94

0.93, 0.95

Marginal (Winter)

Ethnicity: American Indian or Alaskan

6

321

0.95

0.94, 0.96

Marginal (Spring)

Ethnicity: American Indian or Alaskan

6

336

0.96

0.95, 0.97

Marginal (Fall)

Ethnicity: American Indian or Alaskan

7

329

0.96

0.95, 0.97

Marginal (Winter)

Ethnicity: American Indian or Alaskan

7

329

0.96

0.95, 0.96

Marginal (Spring)

Ethnicity: American Indian or Alaskan

7

342

0.97

0.96, 0.97

Marginal (Fall)

Ethnicity: Asian or Pacific Islander

3

1,398

0.95

0.95, 0.96

Marginal (Winter)

Ethnicity: Asian or Pacific Islander

3

1,074

0.95

0.94, 0.95

Marginal (Spring)

Ethnicity: Asian or Pacific Islander

3

1,477

0.96

0.95, 0.96

Marginal (Fall)

Ethnicity: Asian or Pacific Islander

4

1,354

0.96

0.96, 0.96

Marginal (Winter)

Ethnicity: Asian or Pacific Islander

4

1,176

0.96

0.95, 0.96

Marginal (Spring)

Ethnicity: Asian or Pacific Islander

4

1,459

0.96

0.96, 0.97

Marginal (Fall)

Ethnicity: Asian or Pacific Islander

5

1,352

0.97

0.96, 0.97

Marginal (Winter)

Ethnicity: Asian or Pacific Islander

5

1,214

0.96

0.96, 0.97

Marginal (Spring)

Ethnicity: Asian or Pacific Islander

5

1,433

0.97

0.97, 0.97

Marginal (Fall)

Ethnicity: Asian or Pacific Islander

6

1,459

0.97

0.97, 0.97

Marginal (Winter)

Ethnicity: Asian or Pacific Islander

6

1,205

0.97

0.97, 0.98

Marginal (Spring)

Ethnicity: Asian or Pacific Islander

6

1,510

0.97

0.97, 0.98

Marginal (Fall)

Ethnicity: Asian or Pacific Islander

7

1,357

0.97

0.97, 0.97

Marginal (Winter)

Ethnicity: Asian or Pacific Islander

7

1,026

0.97

0.97, 0.97

Marginal (Spring)

Ethnicity: Asian or Pacific Islander

7

1,394

0.97

0.97, 0.97

Marginal (Fall)

Ethnicity: Asian or Pacific Islander

8

1,163

0.98

0.97, 0.98

Marginal (Winter)

Ethnicity: Asian or Pacific Islander

8

956

0.97

0.97, 0.98

Marginal (Spring)

Ethnicity: Asian or Pacific Islander

8

1,192

0.98

0.98, 0.98

Marginal (Fall)

Ethnicity: Black

3

921

0.95

0.94, 0.95

Marginal (Winter)

Ethnicity: Black

3

639

0.94

0.94, 0.95

Marginal (Spring)

Ethnicity: Black

3

1,020

0.95

0.95, 0.96

Marginal (Fall)

Ethnicity: Black

4

886

0.95

0.94, 0.95

Marginal (Winter)

Ethnicity: Black

4

629

0.95

0.94, 0.95

Marginal (Spring)

Ethnicity: Black

4

1,001

0.95

0.95, 0.96

Marginal (Fall)

Ethnicity: Black

5

893

0.96

0.95, 0.96

Marginal (Winter)

Ethnicity: Black

5

823

0.95

0.95, 0.96

Marginal (Spring)

Ethnicity: Black

5

982

0.96

0.96, 0.96

Marginal (Fall)

Ethnicity: Black

6

1,143

0.96

0.95, 0.96

Marginal (Winter)

Ethnicity: Black

6

872

0.96

0.95, 0.96

Marginal (Spring)

Ethnicity: Black

6

1,222

0.96

0.96, 0.97

Marginal (Fall)

Ethnicity: Black

7

1,162

0.96

0.96, 0.97

Marginal (Winter)

Ethnicity: Black

7

873

0.96

0.96, 0.97

Marginal (Spring)

Ethnicity: Black

7

1,233

0.97

0.96, 0.97

Marginal (Fall)

Ethnicity: Black

8

900

0.97

0.96, 0.97

Marginal (Winter)

Ethnicity: Black

8

770

0.97

0.96, 0.97

Marginal (Spring)

Ethnicity: Black

8

954

0.97

0.97, 0.97

Marginal (Fall)

Ethnicity: Hispanic

3

3,618

0.94

0.94, 0.95

Marginal (Winter)

Ethnicity: Hispanic

3

2,982

0.94

0.94, 0.95

Marginal (Spring)

Ethnicity: Hispanic

3

3,890

0.95

0.95, 0.95

Marginal (Fall)

Ethnicity: Hispanic

4

3,578

0.95

0.94, 0.95

Marginal (Winter)

Ethnicity: Hispanic

4

3,068

0.95

0.94, 0.95

Marginal (Spring)

Ethnicity: Hispanic

4

3,845

0.96

0.95, 0.96

Marginal (Fall)

Ethnicity: Hispanic

5

3,437

0.96

0.95, 0.96

Marginal (Winter)

Ethnicity: Hispanic

5

3,330

0.96

0.95, 0.96

Marginal (Spring)

Ethnicity: Hispanic

5

3,779

0.96

0.96, 0.97

Marginal (Fall)

Ethnicity: Hispanic

6

4,029

0.96

0.96, 0.96

Marginal (Winter)

Ethnicity: Hispanic

6

3,577

0.96

0.96, 0.96

Marginal (Spring)

Ethnicity: Hispanic

6

4,399

0.97

0.96, 0.97

Marginal (Fall)

Ethnicity: Hispanic

7

3,854

0.96

0.96, 0.96

Marginal (Winter)

Ethnicity: Hispanic

7

3,411

0.96

0.96, 0.97

Marginal (Spring)

Ethnicity: Hispanic

7

4,273

0.97

0.97, 0.97

Marginal (Fall)

Ethnicity: Hispanic

8

3,311

0.97

0.97, 0.97

Marginal (Winter)

Ethnicity: Hispanic

8

3,124

0.97

0.96, 0.97

Marginal (Spring)

Ethnicity: Hispanic

8

3,630

0.97

0.97, 0.97

Marginal (Fall)

Ethnicity: Multi-Ethnic

3

516

0.95

0.95, 0.96

Marginal (Winter)

Ethnicity: Multi-Ethnic

3

415

0.95

0.94, 0.95

Marginal (Spring)

Ethnicity: Multi-Ethnic

3

554

0.96

0.95, 0.96

Marginal (Fall)

Ethnicity: Multi-Ethnic

4

440

0.96

0.95, 0.97

Marginal (Winter)

Ethnicity: Multi-Ethnic

4

379

0.96

0.96, 0.97

Marginal (Spring)

Ethnicity: Multi-Ethnic

4

486

0.97

0.96, 0.97

Marginal (Fall)

Ethnicity: Multi-Ethnic

5

418

0.97

0.96, 0.97

Marginal (Winter)

Ethnicity: Multi-Ethnic

5

407

0.97

0.97, 0.97

Marginal (Spring)

Ethnicity: Multi-Ethnic

5

448

0.97

0.97, 0.98

Marginal (Fall)

Ethnicity: Multi-Ethnic

6

488

0.97

0.96, 0.97

Marginal (Winter)

Ethnicity: Multi-Ethnic

6

384

0.97

0.96, 0.97

Marginal (Spring)

Ethnicity: Multi-Ethnic

6

510

0.97

0.97, 0.98

Marginal (Fall)

Ethnicity: Multi-Ethnic

7

457

0.97

0.97, 0.97

Marginal (Winter)

Ethnicity: Multi-Ethnic

7

352

0.97

0.97, 0.97

Marginal (Spring)

Ethnicity: Multi-Ethnic

7

479

0.97

0.97, 0.98

Marginal (Fall)

Ethnicity: Multi-Ethnic

8

383

0.98

0.97, 0.98

Marginal (Winter)

Ethnicity: Multi-Ethnic

8

310

0.98

0.97, 0.98

Marginal (Spring)

Ethnicity: Multi-Ethnic

8

403

0.98

0.98, 0.98

Marginal (Fall)

Ethnicity: Not Specified or Other

3

2,423

0.95

0.95, 0.95

Marginal (Winter)

Ethnicity: Not Specified or Other

3

1,389

0.95

0.94, 0.95

Marginal (Spring)

Ethnicity: Not Specified or Other

3

2,487

0.95

0.95, 0.96

Marginal (Fall)

Ethnicity: Not Specified or Other

4

2,467

0.96

0.96, 0.96

Marginal (Winter)

Ethnicity: Not Specified or Other

4

1,406

0.96

0.96, 0.96

Marginal (Spring)

Ethnicity: Not Specified or Other

4

2,545

0.96

0.96, 0.97

Marginal (Fall)

Ethnicity: Not Specified or Other

5

2,352

0.96

0.96, 0.97

Marginal (Winter)

Ethnicity: Not Specified or Other

5

1,325

0.96

0.96, 0.96

Marginal (Spring)

Ethnicity: Not Specified or Other

5

2,420

0.97

0.97, 0.97

Marginal (Fall)

Ethnicity: Not Specified or Other

6

2,245

0.97

0.97, 0.97

Marginal (Winter)

Ethnicity: Not Specified or Other

6

1,080

0.97

0.97, 0.97

Marginal (Spring)

Ethnicity: Not Specified or Other

6

2,348

0.97

0.97, 0.97

Marginal (Fall)

Ethnicity: Not Specified or Other

7

1,042

0.97

0.97, 0.97

Marginal (Winter)

Ethnicity: Not Specified or Other

7

265

0.98

0.97, 0.98

Marginal (Spring)

Ethnicity: Not Specified or Other

7

1,117

0.97

0.97, 0.98

Marginal (Fall)

Ethnicity: Not Specified or Other

8

451

0.97

0.97, 0.98

Marginal (Winter)

Ethnicity: Not Specified or Other

8

298

0.98

0.97, 0.98

Marginal (Spring)

Ethnicity: Not Specified or Other

8

525

0.98

0.97, 0.98

Marginal (Fall)

Ethnicity: White

3

6,140

0.94

0.94, 0.94

Marginal (Winter)

Ethnicity: White

3

4,947

0.94

0.94, 0.94

Marginal (Spring)

Ethnicity: White

3

6,797

0.95

0.94, 0.95

Marginal (Fall)

Ethnicity: White

4

6,113

0.95

0.95, 0.95

Marginal (Winter)

Ethnicity: White

4

5,257

0.95

0.95, 0.95

Marginal (Spring)

Ethnicity: White

4

6,875

0.96

0.96, 0.96

Marginal (Fall)

Ethnicity: White

5

6,277

0.96

0.95, 0.96

Marginal (Winter)

Ethnicity: White

5

5,543

0.96

0.95, 0.96

Marginal (Spring)

Ethnicity: White

5

7,049

0.96

0.96, 0.96

Marginal (Fall)

Ethnicity: White

6

6,957

0.96

0.96, 0.96

Marginal (Winter)

Ethnicity: White

6

5,658

0.96

0.96, 0.96

Marginal (Spring)

Ethnicity: White

6

7,641

0.96

0.96, 0.97

Marginal (Fall)

Ethnicity: White

7

7,734

0.97

0.97, 0.97

Marginal (Winter)

Ethnicity: White

7

5,566

0.97

0.97, 0.97

Marginal (Spring)

Ethnicity: White

7

8,551

0.97

0.97, 0.97

Marginal (Fall)

Ethnicity: White

8

7,233

0.97

0.97, 0.97

Marginal (Winter)

Ethnicity: White

8

5,450

0.97

0.97, 0.97

Marginal (Spring)

Ethnicity: White

8

8,044

0.97

0.97, 0.97

Marginal (Fall)

Gender: Female

3

7,539

0.95

0.94, 0.95

Marginal (Winter)

Gender: Female

3

5,785

0.95

0.94, 0.95

Marginal (Spring)

Gender: Female

3

8,153

0.95

0.95, 0.95

Marginal (Fall)

Gender: Female

4

7,416

0.95

0.95, 0.96

Marginal (Winter)

Gender: Female

4

5,982

0.96

0.95, 0.96

Marginal (Spring)

Gender: Female

4

8,086

0.96

0.96, 0.96

Marginal (Fall)

Gender: Female

5

7,446

0.96

0.96, 0.96

Marginal (Winter)

Gender: Female

5

6,441

0.96

0.96, 0.96

Marginal (Spring)

Gender: Female

5

8,137

0.97

0.97, 0.97

Marginal (Fall)

Gender: Female

6

8,056

0.96

0.96, 0.96

Marginal (Winter)

Gender: Female

6

6,298

0.97

0.96, 0.97

Marginal (Spring)

Gender: Female

6

8,708

0.97

0.97, 0.97

Marginal (Fall)

Gender: Female

7

7,712

0.97

0.97, 0.97

Marginal (Winter)

Gender: Female

7

5,706

0.97

0.97, 0.97

Marginal (Spring)

Gender: Female

7

8,401

0.97

0.97, 0.97

Marginal (Fall)

Gender: Female

8

6,685

0.97

0.97, 0.97

Marginal (Winter)

Gender: Female

8

5,428

0.97

0.97, 0.97

Marginal (Spring)

Gender: Female

8

7,353

0.98

0.97, 0.98

Marginal (Fall)

Gender: Male

3

7,791

0.96

0.96, 0.96

Marginal (Winter)

Gender: Male

3

6,042

0.96

0.96, 0.96

Marginal (Spring)

Gender: Male

3

8,468

0.96

0.96, 0.96

Marginal (Fall)

Gender: Male

4

7,748

0.96

0.96, 0.96

Marginal (Winter)

Gender: Male

4

6,267

0.96

0.96, 0.96

Marginal (Spring)

Gender: Male

4

8,482

0.97

0.97, 0.97

Marginal (Fall)

Gender: Male

5

7,576

0.97

0.97, 0.97

Marginal (Winter)

Gender: Male

5

6,510

0.97

0.97, 0.97

Marginal (Spring)

Gender: Male

5

8,297

0.97

0.97, 0.97

Marginal (Fall)

Gender: Male

6

8,593

0.97

0.97, 0.97

Marginal (Winter)

Gender: Male

6

6,808

0.97

0.97, 0.97

Marginal (Spring)

Gender: Male

6

9,272

0.97

0.97, 0.98

Marginal (Fall)

Gender: Male

7

8,240

0.97

0.97, 0.97

Marginal (Winter)

Gender: Male

7

6,126

0.98

0.97, 0.98

Marginal (Spring)

Gender: Male

7

9,006

0.98

0.98, 0.98

Marginal (Fall)

Gender: Male

8

6,890

0.98

0.98, 0.98

Marginal (Winter)

Gender: Male

8

5,610

0.98

0.98, 0.98

Marginal (Spring)

Gender: Male

8

7,541

0.98

0.98, 0.98

Test-Retest (Fall/Winter)

Ethnicity: Asian or Pacific Islander

2

1,027

0.89

0.88, 0.9

Test-Retest (Fall/Spring)

Ethnicity: Asian or Pacific Islander

2

1,162

0.88

0.87, 0.89

Test-Retest (Winter/Spring)

Ethnicity: Asian or Pacific Islander

2

1,037

0.88

0.86, 0.89

Test-Retest (Fall/Winter)

Ethnicity: Black

2

394

0.83

0.79, 0.86

Test-Retest (Fall/Spring)

Ethnicity: Black

2

549

0.80

0.77, 0.83

Test-Retest (Winter/Spring)

Ethnicity: Black

2

402

0.81

0.77, 0.84

Test-Retest (Fall/Winter)

Ethnicity: Hispanic

2

2,474

0.83

0.81, 0.84

Test-Retest (Fall/Spring)

Ethnicity: Hispanic

2

2,925

0.77

0.76, 0.79

Test-Retest (Winter/Spring)

Ethnicity: Hispanic

2

2,568

0.83

0.82, 0.84

Test-Retest (Fall/Winter)

Ethnicity: Multi-Ethnic

2

303

0.85

0.81, 0.88

Test-Retest (Fall/Spring)

Ethnicity: Multi-Ethnic

2

361

0.86

0.82, 0.88

Test-Retest (Winter/Spring)

Ethnicity: Multi-Ethnic

2

310

0.88

0.85, 0.9

Test-Retest (Fall/Winter)

Ethnicity: Not Specified or Other

2

1,427

0.83

0.81, 0.85

Test-Retest (Fall/Spring)

Ethnicity: Not Specified or Other

2

1,793

0.79

0.77, 0.81

Test-Retest (Winter/Spring)

Ethnicity: Not Specified or Other

2

1,435

0.86

0.84, 0.87

Test-Retest (Fall/Winter)

Ethnicity: White

2

4,419

0.84

0.83, 0.84

Test-Retest (Fall/Spring)

Ethnicity: White

2

5,082

0.79

0.78, 0.8

Test-Retest (Winter/Spring)

Ethnicity: White

2

4,494

0.84

0.83, 0.84

Test-Retest (Fall/Winter)

Gender: Female

2

4,999

0.86

0.85, 0.87

Test-Retest (Fall/Spring)

Gender: Female

2

5,874

0.82

0.81, 0.83

Test-Retest (Winter/Spring)

Gender: Female

2

5,095

0.86

0.85, 0.86

Test-Retest (Fall/Winter)

Gender: Male

2

5,193

0.86

0.85, 0.87

Test-Retest (Fall/Spring)

Gender: Male

2

6,147

0.83

0.82, 0.84

Test-Retest (Winter/Spring)

Gender: Male

2

5,295

0.88

0.87, 0.88

Test-Retest (Fall/Winter)

Ethnicity: American Indian or Alaskan

3

288

0.85

0.81, 0.88

Test-Retest (Fall/Spring)

Ethnicity: American Indian or Alaskan

3

295

0.81

0.76, 0.84

Test-Retest (Winter/Spring)

Ethnicity: American Indian or Alaskan

3

367

0.86

0.83, 0.89

Test-Retest (Fall/Winter)

Ethnicity: American Indian or Alaskan

4

303

0.85

0.82, 0.88

Test-Retest (Fall/Spring)

Ethnicity: American Indian or Alaskan

4

310

0.79

0.75, 0.83

Test-Retest (Winter/Spring)

Ethnicity: American Indian or Alaskan

4

323

0.83

0.8, 0.86

Test-Retest (Fall/Winter)

Ethnicity: American Indian or Alaskan

5

269

0.86

0.82, 0.89

Test-Retest (Fall/Spring)

Ethnicity: American Indian or Alaskan

5

275

0.84

0.8, 0.87

Test-Retest (Winter/Spring)

Ethnicity: American Indian or Alaskan

5

290

0.88

0.85, 0.9

Test-Retest (Fall/Winter)

Ethnicity: American Indian or Alaskan

6

308

0.85

0.82, 0.88

Test-Retest (Fall/Spring)

Ethnicity: American Indian or Alaskan

6

314

0.86

0.83, 0.88

Test-Retest (Winter/Spring)

Ethnicity: American Indian or Alaskan

6

321

0.89

0.87, 0.91

Test-Retest (Fall/Winter)

Ethnicity: American Indian or Alaskan

7

322

0.89

0.86, 0.91

Test-Retest (Fall/Spring)

Ethnicity: American Indian or Alaskan

7

329

0.88

0.85, 0.9

Test-Retest (Winter/Spring)

Ethnicity: American Indian or Alaskan

7

329

0.90

0.88, 0.92

Test-Retest (Fall/Winter)

Ethnicity: Asian or Pacific Islander

3

1,038

0.90

0.89, 0.91

Test-Retest (Fall/Spring)

Ethnicity: Asian or Pacific Islander

3

1,398

0.86

0.85, 0.88

Test-Retest (Winter/Spring)

Ethnicity: Asian or Pacific Islander

3

1,074

0.90

0.88, 0.91

Test-Retest (Fall/Winter)

Ethnicity: Asian or Pacific Islander

4

1,125

0.91

0.9, 0.92

Test-Retest (Fall/Spring)

Ethnicity: Asian or Pacific Islander

4

1,354

0.87

0.86, 0.88

Test-Retest (Winter/Spring)

Ethnicity: Asian or Pacific Islander

4

1,176

0.89

0.88, 0.91

Test-Retest (Fall/Winter)

Ethnicity: Asian or Pacific Islander

5

1,163

0.92

0.92, 0.93

Test-Retest (Fall/Spring)

Ethnicity: Asian or Pacific Islander

5

1,352

0.89

0.88, 0.9

Test-Retest (Winter/Spring)

Ethnicity: Asian or Pacific Islander

5

1,214

0.91

0.9, 0.92

Test-Retest (Fall/Winter)

Ethnicity: Asian or Pacific Islander

6

1,186

0.94

0.93, 0.95

Test-Retest (Fall/Spring)

Ethnicity: Asian or Pacific Islander

6

1,459

0.91

0.91, 0.92

Test-Retest (Winter/Spring)

Ethnicity: Asian or Pacific Islander

6

1,205

0.93

0.92, 0.94

Test-Retest (Fall/Winter)

Ethnicity: Asian or Pacific Islander

7

1,011

0.94

0.94, 0.95

Test-Retest (Fall/Spring)

Ethnicity: Asian or Pacific Islander

7

1,357

0.92

0.91, 0.93

Test-Retest (Winter/Spring)

Ethnicity: Asian or Pacific Islander

7

1,026

0.94

0.93, 0.95

Test-Retest (Fall/Winter)

Ethnicity: Asian or Pacific Islander

8

943

0.95

0.94, 0.96

Test-Retest (Fall/Spring)

Ethnicity: Asian or Pacific Islander

8

1,163

0.93

0.92, 0.94

Test-Retest (Winter/Spring)

Ethnicity: Asian or Pacific Islander

8

956

0.94

0.93, 0.94

Test-Retest (Fall/Winter)

Ethnicity: Black

3

600

0.84

0.81, 0.86

Test-Retest (Fall/Spring)

Ethnicity: Black

3

921

0.82

0.8, 0.84

Test-Retest (Winter/Spring)

Ethnicity: Black

3

639

0.86

0.83, 0.87

Test-Retest (Fall/Winter)

Ethnicity: Black

4

587

0.89

0.87, 0.91

Test-Retest (Fall/Spring)

Ethnicity: Black

4

886

0.84

0.82, 0.86

Test-Retest (Winter/Spring)

Ethnicity: Black

4

629

0.87

0.85, 0.89

Test-Retest (Fall/Winter)

Ethnicity: Black

5

786

0.87

0.86, 0.89

Test-Retest (Fall/Spring)

Ethnicity: Black

5

893

0.83

0.81, 0.85

Test-Retest (Winter/Spring)

Ethnicity: Black

5

823

0.86

0.84, 0.87

Test-Retest (Fall/Winter)

Ethnicity: Black

6

843

0.87

0.85, 0.89

Test-Retest (Fall/Spring)

Ethnicity: Black

6

1,143

0.85

0.83, 0.87

Test-Retest (Winter/Spring)

Ethnicity: Black

6

872

0.86

0.85, 0.88

Test-Retest (Fall/Winter)

Ethnicity: Black

7

851

0.87

0.86, 0.89

Test-Retest (Fall/Spring)

Ethnicity: Black

7

1,162

0.87

0.85, 0.88

Test-Retest (Winter/Spring)

Ethnicity: Black

7

873

0.88

0.86, 0.89

Test-Retest (Fall/Winter)

Ethnicity: Black

8

747

0.89

0.87, 0.9

Test-Retest (Fall/Spring)

Ethnicity: Black

8

900

0.88

0.87, 0.9

Test-Retest (Winter/Spring)

Ethnicity: Black

8

770

0.88

0.86, 0.89

Test-Retest (Fall/Winter)

Ethnicity: Hispanic

3

2,836

0.86

0.84, 0.86

Test-Retest (Fall/Spring)

Ethnicity: Hispanic

3

3,618

0.83

0.82, 0.84

Test-Retest (Winter/Spring)

Ethnicity: Hispanic

3

2,982

0.86

0.85, 0.87

Test-Retest (Fall/Winter)

Ethnicity: Hispanic

4

2,915

0.88

0.87, 0.89

Test-Retest (Fall/Spring)

Ethnicity: Hispanic

4

3,578

0.85

0.84, 0.86

Test-Retest (Winter/Spring)

Ethnicity: Hispanic

4

3,068

0.88

0.87, 0.88

Test-Retest (Fall/Winter)

Ethnicity: Hispanic

5

3,184

0.89

0.88, 0.9

Test-Retest (Fall/Spring)

Ethnicity: Hispanic

5

3,437

0.87

0.86, 0.88

Test-Retest (Winter/Spring)

Ethnicity: Hispanic

5

3,330

0.89

0.88, 0.89

Test-Retest (Fall/Winter)

Ethnicity: Hispanic

6

3,499

0.90

0.89, 0.9

Test-Retest (Fall/Spring)

Ethnicity: Hispanic

6

4,029

0.88

0.87, 0.88

Test-Retest (Winter/Spring)

Ethnicity: Hispanic

6

3,577

0.90

0.89, 0.9

Test-Retest (Fall/Winter)

Ethnicity: Hispanic

7

3,335

0.89

0.89, 0.9

Test-Retest (Fall/Spring)

Ethnicity: Hispanic

7

3,854

0.88

0.87, 0.88

Test-Retest (Winter/Spring)

Ethnicity: Hispanic

7

3,411

0.90

0.89, 0.9

Test-Retest (Fall/Winter)

Ethnicity: Hispanic

8

3,074

0.90

0.9, 0.91

Test-Retest (Fall/Spring)

Ethnicity: Hispanic

8

3,311

0.90

0.89, 0.9

Test-Retest (Winter/Spring)

Ethnicity: Hispanic

8

3,124

0.91

0.9, 0.92

Test-Retest (Fall/Winter)

Ethnicity: Multi-Ethnic

3

397

0.88

0.86, 0.9

Test-Retest (Fall/Spring)

Ethnicity: Multi-Ethnic

3

516

0.86

0.84, 0.88

Test-Retest (Winter/Spring)

Ethnicity: Multi-Ethnic

3

415

0.89

0.87, 0.91

Test-Retest (Fall/Winter)

Ethnicity: Multi-Ethnic

4

358

0.91

0.89, 0.92

Test-Retest (Fall/Spring)

Ethnicity: Multi-Ethnic

4

440

0.90

0.88, 0.91

Test-Retest (Winter/Spring)

Ethnicity: Multi-Ethnic

4

379

0.92

0.9, 0.93

Test-Retest (Fall/Winter)

Ethnicity: Multi-Ethnic

5

389

0.92

0.9, 0.93

Test-Retest (Fall/Spring)

Ethnicity: Multi-Ethnic

5

418

0.90

0.88, 0.91

Test-Retest (Winter/Spring)

Ethnicity: Multi-Ethnic

5

407

0.93

0.91, 0.94

Test-Retest (Fall/Winter)

Ethnicity: Multi-Ethnic

6

379

0.92

0.9, 0.93

Test-Retest (Fall/Spring)

Ethnicity: Multi-Ethnic

6

488

0.90

0.88, 0.92

Test-Retest (Winter/Spring)

Ethnicity: Multi-Ethnic

6

384

0.91

0.89, 0.93

Test-Retest (Fall/Winter)

Ethnicity: Multi-Ethnic

7

351

0.94

0.92, 0.95

Test-Retest (Fall/Spring)

Ethnicity: Multi-Ethnic

7

457

0.91

0.9, 0.93

Test-Retest (Winter/Spring)

Ethnicity: Multi-Ethnic

7

352

0.93

0.92, 0.94

Test-Retest (Fall/Winter)

Ethnicity: Multi-Ethnic

8

304

0.94

0.92, 0.95

Test-Retest (Fall/Spring)

Ethnicity: Multi-Ethnic

8

383

0.93

0.92, 0.95

Test-Retest (Winter/Spring)

Ethnicity: Multi-Ethnic

8

310

0.93

0.91, 0.94

Test-Retest (Fall/Winter)

Ethnicity: Not Specified or Other

3

1,360

0.87

0.86, 0.89

Test-Retest (Fall/Spring)

Ethnicity: Not Specified or Other

3

2,423

0.84

0.83, 0.85

Test-Retest (Winter/Spring)

Ethnicity: Not Specified or Other

3

1,389

0.88

0.87, 0.89

Test-Retest (Fall/Winter)

Ethnicity: Not Specified or Other

4

1,362

0.89

0.87, 0.9

Test-Retest (Fall/Spring)

Ethnicity: Not Specified or Other

4

2,467

0.86

0.85, 0.87

Test-Retest (Winter/Spring)

Ethnicity: Not Specified or Other

4

1,406

0.91

0.9, 0.92

Test-Retest (Fall/Winter)

Ethnicity: Not Specified or Other

5

1,304

0.91

0.9, 0.92

Test-Retest (Fall/Spring)

Ethnicity: Not Specified or Other

5

2,352

0.90

0.89, 0.9

Test-Retest (Winter/Spring)

Ethnicity: Not Specified or Other

5

1,325

0.91

0.9, 0.92

Test-Retest (Fall/Winter)

Ethnicity: Not Specified or Other

6

1,059

0.91

0.9, 0.92

Test-Retest (Fall/Spring)

Ethnicity: Not Specified or Other

6

2,245

0.91

0.9, 0.92

Test-Retest (Winter/Spring)

Ethnicity: Not Specified or Other

6

1,080

0.92

0.91, 0.92

Test-Retest (Fall/Winter)

Ethnicity: Not Specified or Other

7

257

0.92

0.90, 0.93

Test-Retest (Fall/Spring)

Ethnicity: Not Specified or Other

7

1,042

0.90

0.89, 0.91

Test-Retest (Winter/Spring)

Ethnicity: Not Specified or Other

7

265

0.93

0.92, 0.95

Test-Retest (Fall/Winter)

Ethnicity: Not Specified or Other

8

287

0.87

0.84, 0.90

Test-Retest (Fall/Spring)

Ethnicity: Not Specified or Other

8

451

0.87

0.85, 0.89

Test-Retest (Winter/Spring)

Ethnicity: Not Specified or Other

8

298

0.91

0.89, 0.93

Test-Retest (Fall/Winter)

Ethnicity: White

3

4,751

0.86

0.85, 0.87

Test-Retest (Fall/Spring)

Ethnicity: White

3

6,140

0.83

0.82, 0.84

Test-Retest (Winter/Spring)

Ethnicity: White

3

4,947

0.87

0.86, 0.88

Test-Retest (Fall/Winter)

Ethnicity: White

4

5,038

0.88

0.88, 0.89

Test-Retest (Fall/Spring)

Ethnicity: White

4

6,113

0.86

0.85, 0.87

Test-Retest (Winter/Spring)

Ethnicity: White

4

5,257

0.89

0.89, 0.90

Test-Retest (Fall/Winter)

Ethnicity: White

5

5,355

0.90

0.89, 0.90

Test-Retest (Fall/Spring)

Ethnicity: White

5

6,277

0.87

0.87, 0.88

Test-Retest (Winter/Spring)

Ethnicity: White

5

5,543

0.90

0.89, 0.90

Test-Retest (Fall/Winter)

Ethnicity: White

6

5,585

0.91

0.91, 0.92

Test-Retest (Fall/Spring)

Ethnicity: White

6

6,957

0.90

0.89, 0.9

Test-Retest (Winter/Spring)

Ethnicity: White

6

5,658

0.92

0.91, 0.92

Test-Retest (Fall/Winter)

Ethnicity: White

7

5,481

0.93

0.93, 0.93

Test-Retest (Fall/Spring)

Ethnicity: White

7

7,734

0.91

0.91, 0.91

Test-Retest (Winter/Spring)

Ethnicity: White

7

5,566

0.93

0.92, 0.93

Test-Retest (Fall/Winter)

Ethnicity: White

8

5,376

0.93

0.93, 0.93

Test-Retest (Fall/Spring)

Ethnicity: White

8

7,233

0.92

0.91, 0.92

Test-Retest (Winter/Spring)

Ethnicity: White

8

5,450

0.92

0.92, 0.93

Test-Retest (Fall/Winter)

Gender: Female

3

5,534

0.89

0.88, 0.89

Test-Retest (Fall/Spring)

Gender: Female

3

7,539

0.86

0.85, 0.87

Test-Retest (Winter/Spring)

Gender: Female

3

5,785

0.89

0.89, 0.9

Test-Retest (Fall/Winter)

Gender: Female

4

5,717

0.90

0.90, 0.91

Test-Retest (Fall/Spring)

Gender: Female

4

7,416

0.87

0.87, 0.88

Test-Retest (Winter/Spring)

Gender: Female

4

5,982

0.90

0.9, 0.91

Test-Retest (Fall/Winter)

Gender: Female

5

6,214

0.91

0.91, 0.92

Test-Retest (Fall/Spring)

Gender: Female

5

7,446

0.89

0.89, 0.9

Test-Retest (Winter/Spring)

Gender: Female

5

6,441

0.91

0.9, 0.91

Test-Retest (Fall/Winter)

Gender: Female

6

6,180

0.92

0.92, 0.93

Test-Retest (Fall/Spring)

Gender: Female

6

8,056

0.91

0.9, 0.91

Test-Retest (Winter/Spring)

Gender: Female

6

6,298

0.92

0.92, 0.93

Test-Retest (Fall/Winter)

Gender: Female

7

5,619

0.93

0.93, 0.94

Test-Retest (Fall/Spring)

Gender: Female

7

7,712

0.92

0.91, 0.92

Test-Retest (Winter/Spring)

Gender: Female

7

5,706

0.93

0.93, 0.93

Test-Retest (Fall/Winter)

Gender: Female

8

5,340

0.94

0.93, 0.94

Test-Retest (Fall/Spring)

Gender: Female

8

6,685

0.93

0.92, 0.93

Test-Retest (Winter/Spring)

Gender: Female

8

5,428

0.93

0.93, 0.94

Test-Retest (Fall/Winter)

Gender: Male

3

5,751

0.89

0.88, 0.89

Test-Retest (Fall/Spring)

Gender: Male

3

7,791

0.86

0.86, 0.87

Test-Retest (Winter/Spring)

Gender: Male

3

6,042

0.89

0.89, 0.9

Test-Retest (Fall/Winter)

Gender: Male

4

5,982

0.90

0.9, 0.91

Test-Retest (Fall/Spring)

Gender: Male

4

7,748

0.88

0.88, 0.89

Test-Retest (Winter/Spring)

Gender: Male

4

6,267

0.91

0.91, 0.92

Test-Retest (Fall/Winter)

Gender: Male

5

6,254

0.92

0.91, 0.92

Test-Retest (Fall/Spring)

Gender: Male

5

7,576

0.90

0.9, 0.9

Test-Retest (Winter/Spring)

Gender: Male

5

6,510

0.92

0.91, 0.92

Test-Retest (Fall/Winter)

Gender: Male

6

6,688

0.92

0.92, 0.93

Test-Retest (Fall/Spring)

Gender: Male

6

8,593

0.91

0.91, 0.91

Test-Retest (Winter/Spring)

Gender: Male

6

6,808

0.93

0.92, 0.93

Test-Retest (Fall/Winter)

Gender: Male

7

5,999

0.93

0.93, 0.93

Test-Retest (Fall/Spring)

Gender: Male

7

8,240

0.91

0.91, 0.92

Test-Retest (Winter/Spring)

Gender: Male

7

6,126

0.93

0.93, 0.93

Test-Retest (Fall/Winter)

Gender: Male

8

5,517

0.93

0.93, 0.94

Test-Retest (Fall/Spring)

Gender: Male

8

6,890

0.92

0.92, 0.93

Test-Retest (Winter/Spring)

Gender: Male

8

5,610

0.93

0.93, 0.93



[1] Wright, B. D. (1999). “Rasch Measurement Models.” In G.N. Masters and J.P. Keeves (Eds.), Advances in Measurement in Educational Research and Assessment (pp. 85-97). Oxford, UK: Elsevier Science Ltd.

 

Validity

Grade2345678
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  1. Description of each criterion measure used and explanation as to why each measure is appropriate, given the type and purpose of the tool:

In general terms, the better a test measures what it purports to measure and can support its intended uses and decision making, the stronger its validity is said to be. Within this broad statement resides a wide range of information that can be used as validity evidence. This information ranges, for example, from the adequacy and coverage of a test’s content, to its ability to yield scores that are predictive of a status in some area, to its ability to draw accurate inferences about a test taker’s status with respect to a construct, to its ability to allow generalizations from test performance within a domain to like performance in the same domain.

 

Much of the validity evidence for MAP Growth comes from the relationships of MAP Growth test scores to state content-aligned accountability test scores. These relationships include a) the concurrent performance of students on MAP Growth tests with their performance on state tests given for accountability purposes and b) the predictive relationship between students’ performance on MAP Growth tests with their performance, two testing terms later, on state accountability tests

Several important points should be noted regarding concurrent performance on MAP Growth tests with that on state accountability tests. First, these two forms of tests (i.e., interim vs. summative) are designed to serve two related but different purposes. MAP Growth tests are designed to provide estimates of achievement status with low measurement error. They are also designed to provide reasonable estimates of students’ strengths and weaknesses within the identified goal structure.

State accountability tests are commonly designed to determine student proficiency within the state performance standard structure, with the most important decision being the classification of the student as proficient or not proficient. This primary purpose of most state tests in conjunction with adopted content and curriculum standards and structures can influence the relationship of student performance between the two tests.

 

For example, one of the most common factors influencing these relationships is the use of constructed response items in state tests. In general, the greater the number of constructed response items, the weaker the relationship will appear. Another difference is in test design. Since most state accountability tests are fixed form, it is reasonable for the test to be constructed so that maximum test information is established around the proficiency cut point. This is where a state wants to be the most confident about the classification decision that the test will inform. To the extent that this strategy is reflected in the state’s operational test, the relationship in performance between MAP Growth tests and state tests will be attenuated due to a more truncated range of scores on the state test.

 

The requirement that state test content be connected to single grade level content standards is different than MAP Growth test content structure that spans grade levels. This difference is another factor that weakens the observed score relationships between tests. Finally, when focus is placed on the relationship between performance on MAP Growth tests and the assigned proficiency category from the state test, information from the state test will have been collapsed into three to five categories. The correlations between RIT scores and these category assignments will always be substantially lower than if the correlations were based on RIT scores and scale scores.

 

Concurrent validity evidence is expressed as the degree of relationship to performance on another test measuring achievement in the same domain (e.g., mathematics, reading) administered close in time. This form of validity can also be expressed in the form of a Pearson correlation coefficient between the total domain area RIT score and the total scale score of another established test. It answers the question, “How well do the scores from this test that reference this (RIT) scale in this subject area (e.g., mathematics, reading) correspond to the scores obtained from an established test that references some other scale in the same subject area?” Both tests are administered to the same students in close temporal proximity, roughly two to three weeks apart. Correlations with non-NWEA tests that include more performance test items that require subjective scoring tend to have lower correlations than when non-NWEA tests consist of exclusively multiple-choice items.

 

Predictive validity evidence is expressed as the degree of relationship to performance on another test measuring achievement in the same domain (e.g., mathematics, reading) at some later point in time. This form of validity can also be expressed in the form of a Pearson correlation coefficient between the total domain area RIT score and the total scale score of another established test. It answers the question, “How well do the scores from this test that reference this (RIT) scale in this subject area (e.g., reading) predict the scores obtained from an established test that references some other scale in the same subject area at a later point in time?” Both tests are administered to the same students several weeks apart, typically 12 to 36 weeks in evidence reported here. Strong predictive validity is indicated when the correlations are in the low 0.80s. Correlations with non-NWEA tests that include more performance test items that require subjective scoring tend to have lower correlations than when non-NWEA tests consist of exclusively multiple-choice items.

 

The criterion measure used for this series of analyses was the scaled score on the PARCC mathematics assessment, taken by students in the sample during the Spring 2016 school term.

 

In addition to concurrent and predictive validity, validity evidence for MAP Growth also comes from the degree and stability of the relationship of RIT scores across multiple and extended periods of time. This type of evidence supports the construct validity of MAP Growth and the ability underlying the RIT scale. This type of construct validity evidence is provided for Grade 2, since concurrent validity coefficients were not available for Grade 2 (i.e., Grade 2 RIT scores were from administrations during the school year prior to the administration of the PARCC assessment).

 

  1. Description of the sample(s), including size and characteristics, for each validity analysis conducted

Representation: National: New England, Middle Atlantic, East North Central, South Atlantic, Mountain.  The sample for the study contained student records from a total of five states (Colorado, Illinois, New Jersey, New Mexico, and Rhode Island) and one federal district (District of Columbia), and thus had representation from all four U.S. Census regions.

Date: MAP Growth data for Grades 3–8 came from test administrations occurring during the Fall 2015, Winter 2016, and Spring 2016 school terms, which spanned from August 2015 through June 2016. The MAP Growth scores of the Grade 3 students from the previous academic year (i.e., Fall 2014, Winter 2015, and Spring 2015) were used as the Grade 2 MAP Growth scores. The Partnership for Assessment of Readiness for College and Careers (PARCC) data was from the Spring 2016 administration of the PARCC assessment, spanning approximately from March 2016 through June 2016.

Size:

Table 10: Number of Students Per State by Grade

 

2

3

4

5

6

7

8

Total

CO

3,228

3,228

3,201

3,107

3,864

3,726

3,196

20,322

DC

171

171

161

132

236

165

173

1,038

IL

12,165

12,165

12,131

12,186

12,819

12,572

11,030

72,903

NJ

644

644

669

610

648

552

208

3,331

NM

208

208

208

200

198

206

147

1,167

RI

209

209

199

201

218

190

142

1,159

Total

16,625

16,625

16,569

16,436

17,983

17,411

14,896

99,920

 

Table 11: Number of Students Per Region by Grade

 

2

3

4

5

6

7

8

Total

Midwest

12,165

12,165

12,131

12,186

12,819

12,572

11,030

72,903

Northeast

853

853

868

811

866

742

350

4,490

South

171

171

161

132

236

165

173

1,038

West

3,436

3,436

3,409

3,307

4,062

3,932

3,343

21,489

Total

16,625

16,625

16,569

16,436

17,983

17,411

14,896

99,920

 

Table 12: Number of Students Per Division by Grade

 

2

3

4

5

6

7

8

Total

East North Central

12,165

12,165

12,131

12,186

12,819

12,572

11,030

72,903

Middle Atlantaic

644

644

669

610

648

552

208

3,331

Mountain

3,436

3,436

3,409

3,307

4,062

3,932

3,343

21,489

New England

209

209

199

201

218

190

142

1,159

South Atlantic

171

171

161

132

236

165

173

1,038

Total

16,625

16,625

16,569

16,436

17,983

17,411

14,896

99,920

 

Male

51.11%

Female

48.88%

Unknown

0.02%

Other SES Indicators

Not Provided

Free or reduced-price lunch

Not Provided

White, Non-Hispanic

44.99%

Black, Non-Hispanic

6.42%

Hispanic

23.84%

American Indian/Alaska Native:

1.83%

Asian/Pacific Islander:

8.59%

Multi-Ethnic

2.88%

Not Specified or Other

11.45%

Disability classification

Not Provided

First language

Not Provided

Language proficiency status

Not Provided

 

  1. Description of the analysis procedures for each reported type of validity:

Concurrent validity was estimated as the Pearson correlation coefficient between student RIT scores from Spring 2016 and the same students’ total scale score on the PARCC test, also administered in Spring 2016. Predictive validity was estimated as the Pearson correlation coefficient between student RIT scores from a given term (Fall 2015 or Winter 2016) and the same students’ total scale score on the PARCC test administered in Spring 2016. For Grade 2, construct validity was estimated as the Pearson correlation coefficient between student RIT scores from each term in the 2015 school year (Fall 2014, Winter 2015, and Spring 2015) versus RIT scores from each term of the 2016 school year (Fall 2015, Winter 2016, and Spring 2016). For Grade 2, predictive validity was estimated as the Pearson correlation coefficient between student RIT scores from each term in the 2015 school year (Fall 2014, Winter 2015, and Spring 2015) versus the students’ total scales on the Grade 3 PARCC test administered in Spring.

 

The 95% confidence interval for concurrent and predictive validity coefficients was based on the standard 95% confidence interval for a Pearson correlation, using the Fisher z-transformation.

 

  1. 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

Grade

Test or Criterion

n

Coefficient

Confidence

Interval

Construct (Fall)

2

MAP Growth: Math (Fall 2015)

12,037

0.84

0.83, 0.84

Construct (Fall)

2

MAP Growth: Math (Winter 2016)

9,501

0.82

0.81, 0.83

Construct (Fall)

2

MAP Growth: Math (Spring 2015)

12,023

0.83

0.82, 0.83

Construct (Fall)

2

MAP Growth: Math (Spring 2016)

12,655

0.80

0.79, 0.81

Predictive (Fall)

2

PARCC Math

12,655

0.79

0.78, 0.80

Construct (Winter)

2

MAP Growth: Math (Fall 2015)

10,056

0.86

0.85, 0.86

Construct (Winter)

2

MAP Growth: Math (Winter 2016)

9,183

0.85

0.84, 0.86

Construct (Winter)

2

MAP Growth: Math (Spring 2015)

10,391

0.87

0.86, 0.87

Construct (Winter)

2

MAP Growth: Math (Spring 2016)

10,505

0.83

0.82, 0.83

Predictive (Winter)

2

PARCC Math

10,505

0.8

0.80, 0.81

Construct (Spring)

2

MAP Growth: Math (Fall 2015)

12,930

0.86

0.86, 0.87

Construct (Spring)

2

MAP Growth: Math (Winter 2016)

10,212

0.86

0.85, 0.86

Construct (Spring)

2

MAP Growth: Math (Spring 2016)

13,750

0.84

0.83, 0.84

Predictive (Spring)

2

PARCC Math

13,750

0.81

0.80, 0.81

Predictive (Fall/Spring)

3

PARCC Math

15,332

0.84

0.83, 0.84

Predictive (Winter/Spring)

3

PARCC Math

11,829

0.87

0.86, 0.87

Concurrent (Spring/Spring)

3

PARCC Math

16,625

0.88

0.87, 0.88

Predictive (Fall/Spring)

4

PARCC Math

15,165

0.85

0.84, 0.85

Predictive (Winter/Spring)

4

PARCC Math

12,249

0.87

0.87, 0.87

Concurrent (Spring/Spring)

4

PARCC Math

16,569

0.89

0.89, 0.89

Predictive (Fall/Spring)

5

PARCC Math

15,023

0.85

0.85, 0.86

Predictive (Winter/Spring)

5

PARCC Math

12,951

0.87

0.86, 0.87

Concurrent (Spring/Spring)

5

PARCC Math

16,436

0.89

0.88, 0.89

Predictive (Fall/Spring)

6

PARCC Math

16,651

0.87

0.87, 0.87

Predictive (Winter/Spring)

6

PARCC Math

13,108

0.88

0.88, 0.89

Concurrent (Spring/Spring)

6

PARCC Math

17,983

0.90

0.89, 0.90

Predictive (Fall/Spring)

7

PARCC Math

15,956

0.87

0.87, 0.87

Predictive (Winter/Spring)

7

PARCC Math

11,836

0.88

0.88, 0.89

Concurrent (Spring/Spring)

7

PARCC Math

17,411

0.89

0.89, 0.89

Predictive (Fall/Spring)

8

PARCC Math

13,577

0.85

0.84, 0.85

Predictive (Winter/Spring)

8

PARCC Math

11,040

0.86

0.85, 0.86

Concurrent (Spring/Spring)

8

PARCC Math

14,896

0.86

0.86, 0.87

 

 

  1. Results for other forms of validity (e.g. factor analysis) not conducive to the table format:

None Provided

 

  1. Describe the degree to which the provided data support the validity of the tool:

Concurrent, predictive, and construct validity coefficients, for each grade and each time of year, were consistently in the mid to high 0.80s. This validity evidence demonstrates a strong relationship between the MAP Growth and PARCC assessments, across the grades and times of year reported, as well as a strong relationship of MAP Growth RIT scores across school years.

 

 

Disaggregated Validity

The following disaggregated validity data are provided for context and did not factor into the Validity rating.

Type of

Validity

Subgroup

Grade

Test or

Criterion

n

Coefficient

Confidence

Interval

Construct (Fall)

Ethnicity: Asian or Pacific Islander

2

MAP Growth: Math (Fall 2015)

1,146

0.87

0.85, 0.88

Construct (Fall)

Ethnicity: Asian or Pacific Islander

2

MAP Growth: Math (Winter 2016)

938

0.86

0.84, 0.88

Construct (Fall)

Ethnicity: Asian or Pacific Islander

2

MAP Growth: Math (Spring 2015)

1,162

0.88

0.87, 0.89

Construct (Fall)

Ethnicity: Asian or Pacific Islander

2

MAP Growth: Math (Spring 2016)

1,177

0.83

0.81, 0.85

Predictive (Fall)

Ethnicity: Asian or Pacific Islander

2

PARCC Math

1,177

0.80

0.77, 0.82

Construct (Winter)

Ethnicity: Asian or Pacific Islander

2

MAP Growth: Math (Fall 2015)

1,025

0.87

0.85, 0.88

Construct (Winter)

Ethnicity: Asian or Pacific Islander

2

MAP Growth: Math (Winter 2016)

932

0.86

0.84, 0.88

Construct (Winter)

Ethnicity: Asian or Pacific Islander

2

MAP Growth: Math (Spring 2015)

1,037

0.88

0.86, 0.89

Construct (Winter)

Ethnicity: Asian or Pacific Islander

2

MAP Growth: Math (Spring 2016)

1,046

0.83

0.82, 0.85

Predictive (Winter)

Ethnicity: Asian or Pacific Islander

2

PARCC Math

1,046

0.79

0.77, 0.81

Construct (Spring)

Ethnicity: Asian or Pacific Islander

2

MAP Growth: Math (Fall 2015)

1,288

0.89

0.88, 0.90

Construct (Spring)

Ethnicity: Asian or Pacific Islander

2

MAP Growth: Math (Winter 2016)

981

0.87

0.86, 0.89

Construct (Spring)

Ethnicity: Asian or Pacific Islander

2

MAP Growth: Math (Spring 2016)

1,315

0.85

0.83, 0.86

Predictive (Spring)

Ethnicity: Asian or Pacific Islander

2

PARCC Math

1,315

0.79

0.77, 0.81

Construct (Fall)

Ethnicity: Black

2

MAP Growth: Math (Fall 2015)

528

0.80

0.76, 0.82

Construct (Fall)

Ethnicity: Black

2

MAP Growth: Math (Winter 2016)

339

0.79

0.75, 0.83

Construct (Fall)

Ethnicity: Black

2

MAP Growth: Math (Spring 2015)

549

0.80

0.77, 0.83

Construct (Fall)

Ethnicity: Black

2

MAP Growth: Math (Spring 2016)

567

0.77

0.73, 0.80

Predictive (Fall)

Ethnicity: Black

2

PARCC Math

567

0.76

0.72, 0.79

Construct (Winter)

Ethnicity: Black

2

MAP Growth: Math (Fall 2015)

375

0.79

0.75, 0.83

Construct (Winter)

Ethnicity: Black

2

MAP Growth: Math (Winter 2016)

333

0.82

0.78, 0.85

Construct (Winter)

Ethnicity: Black

2

MAP Growth: Math (Spring 2015)

402

0.81

0.77, 0.84

Construct (Winter)

Ethnicity: Black

2

MAP Growth: Math (Spring 2016)

417

0.78

0.74, 0.81

Predictive (Winter)

Ethnicity: Black

2

PARCC Math

417

0.76

0.71, 0.79

Construct (Spring)

Ethnicity: Black

2

MAP Growth: Math (Fall 2015)

687

0.82

0.79, 0.84

Construct (Spring)

Ethnicity: Black

2

MAP Growth: Math (Winter 2016)

450

0.81

0.77, 0.84

Construct (Spring)

Ethnicity: Black

2

MAP Growth: Math (Spring 2016)

742

0.80

0.77, 0.82

Predictive (Spring)

Ethnicity: Black

2

PARCC Math

742

0.77

0.74, 0.80

Construct (Fall)

Ethnicity: Hispanic

2

MAP Growth: Math (Fall 2015)

2,830

0.80

0.78, 0.81

Construct (Fall)

Ethnicity: Hispanic

2

MAP Growth: Math (Winter 2016)

2,492

0.77

0.75, 0.79

Construct (Fall)

Ethnicity: Hispanic

2

MAP Growth: Math (Spring 2015)

2,925

0.77

0.76, 0.79

Construct (Fall)

Ethnicity: Hispanic

2

MAP Growth: Math (Spring 2016)

2,983

0.75

0.73, 0.77

Predictive (Fall)

Ethnicity: Hispanic

2

PARCC Math

2,983

0.74

0.72, 0.76

Construct (Winter)

Ethnicity: Hispanic

2

MAP Growth: Math (Fall 2015)

2,471

0.82

0.81, 0.83

Construct (Winter)

Ethnicity: Hispanic

2

MAP Growth: Math (Winter 2016)

2,422

0.80

0.78, 0.81

Construct (Winter)

Ethnicity: Hispanic

2

MAP Growth: Math (Spring 2015)

2,568

0.83

0.82, 0.84

Construct (Winter)

Ethnicity: Hispanic

2

MAP Growth: Math (Spring 2016)

2,610

0.77

0.75, 0.78

Predictive (Winter)

Ethnicity: Hispanic

2

PARCC Math

2,610

0.75

0.73, 0.77

Construct (Spring)

Ethnicity: Hispanic

2

MAP Growth: Math (Fall 2015)

3,171

0.82

0.81, 0.83

Construct (Spring)

Ethnicity: Hispanic

2

MAP Growth: Math (Winter 2016)

2,721

0.80

0.79, 0.82

Construct (Spring)

Ethnicity: Hispanic

2

MAP Growth: Math (Spring 2016)

3,337

0.78

0.76, 0.79

Predictive (Spring)

Ethnicity: Hispanic

2

PARCC Math

3,337

0.75

0.73, 0.76

Construct (Fall)

Ethnicity: Multi-Ethnic

2

MAP Growth: Math (Fall 2015)

348

0.85

0.81, 0.87

Construct (Fall)

Ethnicity: Multi-Ethnic

2

MAP Growth: Math (Winter 2016)

289

0.81

0.76, 0.84

Construct (Fall)

Ethnicity: Multi-Ethnic

2

MAP Growth: Math (Spring 2015)

361

0.86

0.82, 0.88

Construct (Fall)

Ethnicity: Multi-Ethnic

2

MAP Growth: Math (Spring 2016)

368

0.82

0.78, 0.85

Predictive (Fall)

Ethnicity: Multi-Ethnic

2

PARCC Math

368

0.80

0.76, 0.83

Construct (Winter)

Ethnicity: Multi-Ethnic

2

MAP Growth: Math (Fall 2015)

295

0.85

0.82, 0.88

Construct (Winter)

Ethnicity: Multi-Ethnic

2

MAP Growth: Math (Winter 2016)

289

0.83

0.79, 0.86

Construct (Winter)

Ethnicity: Multi-Ethnic

2

MAP Growth: Math (Spring 2015)

310

0.88

0.85, 0.90

Construct (Winter)

Ethnicity: Multi-Ethnic

2

MAP Growth: Math (Spring 2016)

315

0.82

0.78, 0.85

Predictive (Winter)

Ethnicity: Multi-Ethnic

2

PARCC Math

315

0.78

0.74, 0.82

Construct (Spring)

Ethnicity: Multi-Ethnic

2

MAP Growth: Math (Fall 2015)

415

0.86

0.83, 0.88

Construct (Spring)

Ethnicity: Multi-Ethnic

2

MAP Growth: Math (Winter 2016)

324

0.86

0.82, 0.88

Construct (Spring)

Ethnicity: Multi-Ethnic

2

MAP Growth: Math (Spring 2016)

441

0.82

0.78, 0.85

Predictive (Spring)

Ethnicity: Multi-Ethnic

2

PARCC Math

441

0.80

0.77, 0.83

Construct (Fall)

Ethnicity: Not Specified or Other

2

MAP Growth: Math (Fall 2015)

2,250

0.82

0.80, 0.83

Construct (Fall)

Ethnicity: Not Specified or Other

2

MAP Growth: Math (Winter 2016)

1,266

0.78

0.76, 0.80

Construct (Fall)

Ethnicity: Not Specified or Other

2

MAP Growth: Math (Spring 2015)

1,793

0.79

0.77, 0.81

Construct (Fall)

Ethnicity: Not Specified or Other

2

MAP Growth: Math (Spring 2016)

2,268

0.78

0.76, 0.79

Predictive (Fall)

Ethnicity: Not Specified or Other

2

PARCC Math

2,268

0.76

0.74, 0.78

Construct (Winter)

Ethnicity: Not Specified or Other

2

MAP Growth: Math (Fall 2015)

1,431

0.84

0.82, 0.85

Construct (Winter)

Ethnicity: Not Specified or Other

2

MAP Growth: Math (Winter 2016)

1,133

0.84

0.82, 0.85

Construct (Winter)

Ethnicity: Not Specified or Other

2

MAP Growth: Math (Spring 2015)

1,435

0.86

0.84, 0.87

Construct (Winter)

Ethnicity: Not Specified or Other

2

MAP Growth: Math (Spring 2016)

1,447

0.81

0.79, 0.83

Predictive (Winter)

Ethnicity: Not Specified or Other

2

PARCC Math

1,447

0.77

0.75, 0.79

Construct (Spring)

Ethnicity: Not Specified or Other

2

MAP Growth: Math (Fall 2015)

1,811

0.84

0.83, 0.85

Construct (Spring)

Ethnicity: Not Specified or Other

2

MAP Growth: Math (Winter 2016)

1,276

0.84

0.83, 0.86

Construct (Spring)

Ethnicity: Not Specified or Other

2

MAP Growth: Math (Spring 2016)

1,834

0.84

0.82, 0.85

Predictive (Spring)

Ethnicity: Not Specified or Other

2

PARCC Math

1,834

0.80

0.79, 0.82

Construct (Fall)

Ethnicity: White

2

MAP Growth: Math (Fall 2015)

4,796

0.80

0.79, 0.81

Construct (Fall)

Ethnicity: White

2

MAP Growth: Math (Winter 2016)

4,030

0.78

0.77, 0.79

Construct (Fall)

Ethnicity: White

2

MAP Growth: Math (Spring 2015)

5,082

0.79

0.78, 0.80

Construct (Fall)

Ethnicity: White

2

MAP Growth: Math (Spring 2016)

5,136

0.76

0.74, 0.77

Predictive (Fall)

Ethnicity: White

2

PARCC Math

5,136

0.75

0.74, 0.76

Construct (Winter)

Ethnicity: White

2

MAP Growth: Math (Fall 2015)

4,328

0.82

0.81, 0.83

Construct (Winter)

Ethnicity: White

2

MAP Growth: Math (Winter 2016)

3,933

0.81

0.80, 0.82

Construct (Winter)

Ethnicity: White

2

MAP Growth: Math (Spring 2015)

4,494

0.84

0.83, 0.84

Construct (Winter)

Ethnicity: White

2

MAP Growth: Math (Spring 2016)

4,521

0.79

0.78, 0.80

Predictive (Winter)

Ethnicity: White

2

PARCC Math

4,521

0.77

0.76, 0.78

Construct (Spring)

Ethnicity: White

2

MAP Growth: Math (Fall 2015)

5,414

0.83

0.82, 0.84

Construct (Spring)

Ethnicity: White

2

MAP Growth: Math (Winter 2016)

4,310

0.82

0.81, 0.83

Construct (Spring)

Ethnicity: White

2

MAP Growth: Math (Spring 2016)

5,918

0.80

0.79, 0.81

Predictive (Spring)

Ethnicity: White

2

PARCC Math

5,918

0.76

0.75, 0.77

Construct (Fall)

Gender: Female

2

MAP Growth: Math (Fall 2015)

5,892

0.83

0.83, 0.84

Construct (Fall)

Gender: Female

2

MAP Growth: Math (Winter 2016)

4,627

0.82

0.81, 0.83

Construct (Fall)

Gender: Female

2

MAP Growth: Math (Spring 2015)

5,874

0.82

0.81, 0.83

Construct (Fall)

Gender: Female

2

MAP Growth: Math (Spring 2016)

6,182

0.80

0.79, 0.81

Predictive (Fall)

Gender: Female

2

PARCC Math

6,182

0.80

0.79, 0.81

Construct (Winter)

Gender: Female

2

MAP Growth: Math (Fall 2015)

4,938

0.85

0.84, 0.86

Construct (Winter)

Gender: Female

2

MAP Growth: Math (Winter 2016)

4,477

0.85

0.84, 0.85

Construct (Winter)

Gender: Female

2

MAP Growth: Math (Spring 2015)

5,095

0.86

0.85, 0.86

Construct (Winter)

Gender: Female

2

MAP Growth: Math (Spring 2016)

5,142

0.83

0.82, 0.84

Predictive (Winter)

Gender: Female

2

PARCC Math

5,142

0.81

0.80, 0.82

Construct (Spring)

Gender: Female

2

MAP Growth: Math (Fall 2015)

6,355

0.85

0.85, 0.86

Construct (Spring)

Gender: Female

2

MAP Growth: Math (Winter 2016)

4,981

0.85

0.84, 0.86

Construct (Spring)

Gender: Female

2

MAP Growth: Math (Spring 2016)

6,728

0.83

0.82, 0.83

Predictive (Spring)

Gender: Female

2

PARCC Math

6,728

0.81

0.80, 0.81

Construct (Fall)

Gender: Male

2

MAP Growth: Math (Fall 2015)

6,144

0.84

0.83, 0.85

Construct (Fall)

Gender: Male

2

MAP Growth: Math (Winter 2016)

4,874

0.82

0.81, 0.83

Construct (Fall)

Gender: Male

2

MAP Growth: Math (Spring 2015)

6,147

0.83

0.82, 0.84

Construct (Fall)

Gender: Male

2

MAP Growth: Math (Spring 2016)

6,471

0.80

0.79, 0.81

Predictive (Fall)

Gender: Male

2

PARCC Math

6,471

0.79

0.78, 0.80

Construct (Winter)

Gender: Male

2

MAP Growth: Math (Fall 2015)

5,117

0.86

0.85, 0.86

Construct (Winter)

Gender: Male

2

MAP Growth: Math (Winter 2016)

4,705

0.85

0.84, 0.86

Construct (Winter)

Gender: Male

2

MAP Growth: Math (Spring 2015)

5,295

0.88

0.87, 0.88

Construct (Winter)

Gender: Male

2

MAP Growth: Math (Spring 2016)

5,362

0.82

0.81, 0.83

Predictive (Winter)

Gender: Male

2

PARCC Math

5,362

0.80

0.79, 0.81

Construct (Spring)

Gender: Male

2

MAP Growth: Math (Fall 2015)

6,573

0.87

0.86, 0.87

Construct (Spring)

Gender: Male

2

MAP Growth: Math (Winter 2016)

5,230

0.86

0.86, 0.87

Construct (Spring)

Gender: Male

2

MAP Growth: Math (Spring 2016)

7,019

0.84

0.84, 0.85

Predictive (Spring)

Gender: Male

2

PARCC Math

7,019

0.81

0.80, 0.82

Predictive (Fall/Spring)

Ethnicity: American Indian or Alaskan

3

PARCC Math

295

0.80

0.75, 0.83

Predictive (Winter/Spring)

Ethnicity: American Indian or Alaskan

3

PARCC Math

367

0.80

0.76, 0.83

Concurrent (Spring/Spring)

Ethnicity: American Indian or Alaskan

3

PARCC Math

378

0.86

0.84, 0.89

Predictive (Fall/Spring)

Ethnicity: American Indian or Alaskan

4

PARCC Math

310

0.77

0.72, 0.81

Predictive (Winter/Spring)

Ethnicity: American Indian or Alaskan

4

PARCC Math

323

0.80

0.75, 0.83

Concurrent (Spring/Spring)

Ethnicity: American Indian or Alaskan

4

PARCC Math

339

0.84

0.80, 0.87

Predictive (Fall/Spring)

Ethnicity: American Indian or Alaskan

5

PARCC Math

275

0.76

0.70, 0.80

Predictive (Winter/Spring)

Ethnicity: American Indian or Alaskan

5

PARCC Math

290

0.78

0.73, 0.82

Concurrent (Spring/Spring)

Ethnicity: American Indian or Alaskan

5

PARCC Math

302

0.85

0.81, 0.88

Predictive (Fall/Spring)

Ethnicity: American Indian or Alaskan

6

PARCC Math

314

0.74

0.68, 0.78

Predictive (Winter/Spring)

Ethnicity: American Indian or Alaskan

6

PARCC Math

321

0.76

0.71, 0.80

Concurrent (Spring/Spring)

Ethnicity: American Indian or Alaskan

6

PARCC Math

336

0.80

0.76, 0.84

Predictive (Fall/Spring)

Ethnicity: American Indian or Alaskan

7

PARCC Math

329

0.79

0.75, 0.83

Predictive (Winter/Spring)

Ethnicity: American Indian or Alaskan

7

PARCC Math

329

0.82

0.78, 0.85

Concurrent (Spring/Spring)

Ethnicity: American Indian or Alaskan

7

PARCC Math

342

0.82

0.79, 0.86

Predictive (Fall/Spring)

Ethnicity: Asian or Pacific Islander

3

PARCC Math

1,398

0.82

0.80, 0.84

Predictive (Winter/Spring)

Ethnicity: Asian or Pacific Islander

3

PARCC Math

1,074

0.85

0.84, 0.87

Concurrent (Spring/Spring)

Ethnicity: Asian or Pacific Islander

3

PARCC Math

1,477

0.85

0.83, 0.86

Predictive (Fall/Spring)

Ethnicity: Asian or Pacific Islander

4

PARCC Math

1,354

0.83

0.81, 0.85

Predictive (Winter/Spring)

Ethnicity: Asian or Pacific Islander

4

PARCC Math

1,176

0.85

0.84, 0.87

Concurrent (Spring/Spring)

Ethnicity: Asian or Pacific Islander

4

PARCC Math

1,459

0.87

0.86, 0.88

Predictive (Fall/Spring)

Ethnicity: Asian or Pacific Islander

5

PARCC Math

1,352

0.84

0.82, 0.85

Predictive (Winter/Spring)

Ethnicity: Asian or Pacific Islander

5

PARCC Math

1,214

0.86

0.85, 0.88

Concurrent (Spring/Spring)

Ethnicity: Asian or Pacific Islander

5

PARCC Math

1,433

0.88

0.86, 0.89

Predictive (Fall/Spring)

Ethnicity: Asian or Pacific Islander

6

PARCC Math

1,459

0.87

0.86, 0.88

Predictive (Winter/Spring)

Ethnicity: Asian or Pacific Islander

6

PARCC Math

1,205

0.89

0.88, 0.90

Concurrent (Spring/Spring)

Ethnicity: Asian or Pacific Islander

6

PARCC Math

1,510

0.90

0.89, 0.91

Predictive (Fall/Spring)

Ethnicity: Asian or Pacific Islander

7

PARCC Math

1,357

0.88

0.86, 0.89

Predictive (Winter/Spring)

Ethnicity: Asian or Pacific Islander

7

PARCC Math

1,026

0.90

0.89, 0.91

Concurrent (Spring/Spring)

Ethnicity: Asian or Pacific Islander

7

PARCC Math

1,394

0.89

0.88, 0.90

Predictive (Fall/Spring)

Ethnicity: Asian or Pacific Islander

8

PARCC Math

1,163

0.88

0.86, 0.89

Predictive (Winter/Spring)

Ethnicity: Asian or Pacific Islander

8

PARCC Math

956

0.88

0.87, 0.89

Concurrent (Spring/Spring)

Ethnicity: Asian or Pacific Islander

8

PARCC Math

1,192

0.89

0.88, 0.90

Predictive (Fall/Spring)

Ethnicity: Black

3

PARCC Math

921

0.79

0.76, 0.81

Predictive (Winter/Spring)

Ethnicity: Black

3

PARCC Math

639

0.83

0.81, 0.85

Concurrent (Spring/Spring)

Ethnicity: Black

3

PARCC Math

1,020

0.85

0.83, 0.86

Predictive (Fall/Spring)

Ethnicity: Black

4

PARCC Math

886

0.80

0.78, 0.83

Predictive (Winter/Spring)

Ethnicity: Black

4

PARCC Math

629

0.83

0.80, 0.85

Concurrent (Spring/Spring)

Ethnicity: Black

4

PARCC Math

1,001

0.83

0.81, 0.85

Predictive (Fall/Spring)

Ethnicity: Black

5

PARCC Math

893

0.78

0.75, 0.80

Predictive (Winter/Spring)

Ethnicity: Black

5

PARCC Math

823

0.79

0.77, 0.82

Concurrent (Spring/Spring)

Ethnicity: Black

5

PARCC Math

982

0.81

0.78, 0.83

Predictive (Fall/Spring)

Ethnicity: Black

6

PARCC Math

1,143

0.79

0.77, 0.81

Predictive (Winter/Spring)

Ethnicity: Black

6

PARCC Math

872

0.79

0.77, 0.82

Concurrent (Spring/Spring)

Ethnicity: Black

6

PARCC Math

1,222

0.82

0.80, 0.84

Predictive (Fall/Spring)

Ethnicity: Black

7

PARCC Math

1,162

0.81

0.79, 0.83

Predictive (Winter/Spring)

Ethnicity: Black

7

PARCC Math

873

0.80

0.78, 0.82

Concurrent (Spring/Spring)

Ethnicity: Black

7

PARCC Math

1,233

0.82

0.80, 0.84

Predictive (Fall/Spring)

Ethnicity: Black

8

PARCC Math

900

0.77

0.74, 0.79

Predictive (Winter/Spring)

Ethnicity: Black

8

PARCC Math

770

0.78

0.75, 0.81

Concurrent (Spring/Spring)

Ethnicity: Black

8

PARCC Math

954

0.78

0.75, 0.80

Predictive (Fall/Spring)

Ethnicity: Hispanic

3

PARCC Math

3,618

0.79

0.78, 0.80

Predictive (Winter/Spring)

Ethnicity: Hispanic

3

PARCC Math

2,982

0.83

0.82, 0.84

Concurrent (Spring/Spring)

Ethnicity: Hispanic

3

PARCC Math

3,890

0.84

0.84, 0.85

Predictive (Fall/Spring)

Ethnicity: Hispanic

4

PARCC Math

3,578

0.79

0.78, 0.81

Predictive (Winter/Spring)

Ethnicity: Hispanic

4

PARCC Math

3,068

0.82

0.81, 0.83

Concurrent (Spring/Spring)

Ethnicity: Hispanic

4

PARCC Math

3,845

0.85

0.84, 0.86

Predictive (Fall/Spring)

Ethnicity: Hispanic

5

PARCC Math

3,437

0.80

0.79, 0.81

Predictive (Winter/Spring)

Ethnicity: Hispanic

5

PARCC Math

3,330

0.82

0.80, 0.83

Concurrent (Spring/Spring)

Ethnicity: Hispanic

5

PARCC Math

3,779

0.84

0.83, 0.85

Predictive (Fall/Spring)

Ethnicity: Hispanic

6

PARCC Math

4,029

0.82

0.81, 0.83

Predictive (Winter/Spring)

Ethnicity: Hispanic

6

PARCC Math

3,577

0.83

0.82, 0.84

Concurrent (Spring/Spring)

Ethnicity: Hispanic

6

PARCC Math

4,399

0.86

0.85, 0.87

Predictive (Fall/Spring)

Ethnicity: Hispanic

7

PARCC Math

3,854

0.82

0.81, 0.83

Predictive (Winter/Spring)

Ethnicity: Hispanic

7

PARCC Math

3,411

0.82

0.81, 0.84

Concurrent (Spring/Spring)

Ethnicity: Hispanic

7

PARCC Math

4,273

0.85

0.84, 0.86

Predictive (Fall/Spring)

Ethnicity: Hispanic

8

PARCC Math

3,311

0.79

0.78, 0.80

Predictive (Winter/Spring)

Ethnicity: Hispanic

8

PARCC Math

3,124

0.79

0.78, 0.81

Concurrent (Spring/Spring)

Ethnicity: Hispanic

8

PARCC Math

3,630

0.81

0.80, 0.83

Predictive (Fall/Spring)

Ethnicity: Multi-Ethnic

3

PARCC Math

516

0.84

0.81, 0.87

Predictive (Winter/Spring)

Ethnicity: Multi-Ethnic

3

PARCC Math

415

0.87

0.85, 0.89

Concurrent (Spring/Spring)

Ethnicity: Multi-Ethnic

3

PARCC Math

554

0.88

0.86, 0.90

Predictive (Fall/Spring)

Ethnicity: Multi-Ethnic

4

PARCC Math

440

0.85

0.82, 0.87

Predictive (Winter/Spring)

Ethnicity: Multi-Ethnic

4

PARCC Math

379

0.87

0.84, 0.89

Concurrent (Spring/Spring)

Ethnicity: Multi-Ethnic

4

PARCC Math

486

0.89

0.86, 0.90

Predictive (Fall/Spring)

Ethnicity: Multi-Ethnic

5

PARCC Math

418

0.87

0.84, 0.89

Predictive (Winter/Spring)

Ethnicity: Multi-Ethnic

5

PARCC Math

407

0.87

0.85, 0.89

Concurrent (Spring/Spring)

Ethnicity: Multi-Ethnic

5

PARCC Math

448

0.89

0.87, 0.91

Predictive (Fall/Spring)

Ethnicity: Multi-Ethnic

6

PARCC Math

488

0.86

0.84, 0.88

Predictive (Winter/Spring)

Ethnicity: Multi-Ethnic

6

PARCC Math

384

0.88

0.86, 0.90

Concurrent (Spring/Spring)

Ethnicity: Multi-Ethnic

6

PARCC Math

510

0.90

0.88, 0.91

Predictive (Fall/Spring)

Ethnicity: Multi-Ethnic

7

PARCC Math

457

0.87

0.84, 0.89

Predictive (Winter/Spring)

Ethnicity: Multi-Ethnic

7

PARCC Math

352

0.89

0.87, 0.91

Concurrent (Spring/Spring)

Ethnicity: Multi-Ethnic

7

PARCC Math

479

0.89

0.87, 0.91

Predictive (Fall/Spring)

Ethnicity: Multi-Ethnic

8

PARCC Math

383

0.88

0.85, 0.90

Predictive (Winter/Spring)

Ethnicity: Multi-Ethnic

8

PARCC Math

310

0.88

0.86, 0.91

Concurrent (Spring/Spring)

Ethnicity: Multi-Ethnic

8

PARCC Math

403

0.87

0.84, 0.89

Predictive (Fall/Spring)

Ethnicity: Not Specified or Other

3

PARCC Math

2,423

0.82

0.81, 0.83

Predictive (Winter/Spring)

Ethnicity: Not Specified or Other

3

PARCC Math

1,389

0.85

0.83, 0.86

Concurrent (Spring/Spring)

Ethnicity: Not Specified or Other

3

PARCC Math

2,487

0.87

0.86, 0.88

Predictive (Fall/Spring)

Ethnicity: Not Specified or Other

4

PARCC Math

2,467

0.82

0.80, 0.83

Predictive (Winter/Spring)

Ethnicity: Not Specified or Other

4

PARCC Math

1,406

0.87

0.86, 0.88

Concurrent (Spring/Spring)

Ethnicity: Not Specified or Other

4

PARCC Math

2,545

0.89

0.88, 0.89

Predictive (Fall/Spring)

Ethnicity: Not Specified or Other

5

PARCC Math

2,352

0.84

0.83, 0.85

Predictive (Winter/Spring)

Ethnicity: Not Specified or Other

5

PARCC Math

1,325

0.87

0.85, 0.88

Concurrent (Spring/Spring)

Ethnicity: Not Specified or Other

5

PARCC Math

2,420

0.90

0.89, 0.90

Predictive (Fall/Spring)

Ethnicity: Not Specified or Other

6

PARCC Math

2,245

0.87

0.86, 0.88

Predictive (Winter/Spring)

Ethnicity: Not Specified or Other

6

PARCC Math

1,080

0.88

0.87, 0.90

Concurrent (Spring/Spring)

Ethnicity: Not Specified or Other

6

PARCC Math

2,348

0.90

0.89, 0.90

Predictive (Fall/Spring)

Ethnicity: Not Specified or Other

7

PARCC Math

1,042

0.86

0.85, 0.88

Predictive (Winter/Spring)

Ethnicity: Not Specified or Other

7

PARCC Math

265

0.85

0.81, 0.88

Concurrent (Spring/Spring)

Ethnicity: Not Specified or Other

7

PARCC Math

1,117

0.88

0.86, 0.89

Predictive (Fall/Spring)

Ethnicity: Not Specified or Other

8

PARCC Math

451

0.83

0.80, 0.86

Predictive (Winter/Spring)

Ethnicity: Not Specified or Other

8

PARCC Math

298

0.83

0.79, 0.86

Concurrent (Spring/Spring)

Ethnicity: Not Specified or Other

8

PARCC Math

525

0.85

0.82, 0.87

Predictive (Fall/Spring)

Ethnicity: White

3

PARCC Math

6,140

0.81

0.80, 0.82

Predictive (Winter/Spring)

Ethnicity: White

3

PARCC Math

4,947

0.84

0.83, 0.85

Concurrent (Spring/Spring)

Ethnicity: White

3

PARCC Math

6,797

0.85

0.84, 0.86

Predictive (Fall/Spring)

Ethnicity: White

4

PARCC Math

6,113

0.82

0.81, 0.83

Predictive (Winter/Spring)

Ethnicity: White

4

PARCC Math

5,257

0.84

0.84, 0.85

Concurrent (Spring/Spring)

Ethnicity: White

4

PARCC Math

6,875

0.87

0.86, 0.87

Predictive (Fall/Spring)

Ethnicity: White

5

PARCC Math

6,277

0.83

0.82, 0.84

Predictive (Winter/Spring)

Ethnicity: White

5

PARCC Math

5,543

0.85

0.84, 0.86

Concurrent (Spring/Spring)

Ethnicity: White

5

PARCC Math

7,049

0.87

0.87, 0.88

Predictive (Fall/Spring)

Ethnicity: White

6

PARCC Math

6,957

0.85

0.85, 0.86

Predictive (Winter/Spring)

Ethnicity: White

6

PARCC Math

5,658

0.87

0.87, 0.88

Concurrent (Spring/Spring)

Ethnicity: White

6

PARCC Math

7,641

0.89

0.88, 0.89

Predictive (Fall/Spring)

Ethnicity: White

7

PARCC Math

7,734

0.86

0.85, 0.86

Predictive (Winter/Spring)

Ethnicity: White

7

PARCC Math

5,566

0.88

0.87, 0.88

Concurrent (Spring/Spring)

Ethnicity: White

7

PARCC Math

8,551

0.88

0.87, 0.88

Predictive (Fall/Spring)

Ethnicity: White

8

PARCC Math

7,233

0.82

0.82, 0.83

Predictive (Winter/Spring)

Ethnicity: White

8

PARCC Math

5,450

0.84

0.83, 0.85

Concurrent (Spring/Spring)

Ethnicity: White

8

PARCC Math

8,044

0.84

0.83, 0.85

Predictive (Fall/Spring)

Gender: Female

3

PARCC Math

7,539

0.84

0.84, 0.85

Predictive (Winter/Spring)

Gender: Female

3

PARCC Math

5,785

0.87

0.86, 0.88

Concurrent (Spring/Spring)

Gender: Female

3

PARCC Math

8,153

0.89

0.88, 0.89

Predictive (Fall/Spring)

Gender: Female

4

PARCC Math

7,416

0.84

0.84, 0.85

Predictive (Winter/Spring)

Gender: Female

4

PARCC Math

5,982

0.87

0.86, 0.88

Concurrent (Spring/Spring)

Gender: Female

4

PARCC Math

8,086

0.89

0.88, 0.89

Predictive (Fall/Spring)

Gender: Female

5

PARCC Math

7,446

0.85

0.85, 0.86

Predictive (Winter/Spring)

Gender: Female

5

PARCC Math

6,441

0.87

0.86, 0.87

Concurrent (Spring/Spring)

Gender: Female

5

PARCC Math

8,137

0.89

0.88, 0.89

Predictive (Fall/Spring)

Gender: Female

6

PARCC Math

8,056

0.87

0.87, 0.88

Predictive (Winter/Spring)

Gender: Female

6

PARCC Math

6,298

0.89

0.88, 0.89

Concurrent (Spring/Spring)

Gender: Female

6

PARCC Math

8,708

0.90

0.90, 0.91

Predictive (Fall/Spring)

Gender: Female

7

PARCC Math

7,712

0.87

0.87, 0.88

Predictive (Winter/Spring)

Gender: Female

7

PARCC Math

5,706

0.89

0.88, 0.89

Concurrent (Spring/Spring)

Gender: Female

7

PARCC Math

8,401

0.89

0.89, 0.90

Predictive (Fall/Spring)

Gender: Female

8

PARCC Math

6,685

0.85

0.85, 0.86

Predictive (Winter/Spring)

Gender: Female

8

PARCC Math

5,428

0.86

0.86, 0.87

Concurrent (Spring/Spring)

Gender: Female

8

PARCC Math

7,353

0.87

0.86, 0.87

Predictive (Fall/Spring)

Gender: Male

3

PARCC Math

7,791

0.84

0.83, 0.84

Predictive (Winter/Spring)

Gender: Male

3

PARCC Math

6,042

0.87

0.86, 0.87

Concurrent (Spring/Spring)

Gender: Male

3

PARCC Math

8,468

0.87

0.87, 0.88

Predictive (Fall/Spring)

Gender: Male

4

PARCC Math

7,748

0.85

0.84, 0.86

Predictive (Winter/Spring)

Gender: Male

4

PARCC Math

6,267

0.87

0.87, 0.88

Concurrent (Spring/Spring)

Gender: Male

4

PARCC Math

8,482

0.89

0.89, 0.90

Predictive (Fall/Spring)

Gender: Male

5

PARCC Math

7,576

0.86

0.85, 0.86

Predictive (Winter/Spring)

Gender: Male

5

PARCC Math

6,510

0.87

0.87, 0.88

Concurrent (Spring/Spring)

Gender: Male

5

PARCC Math

8,297

0.89

0.89, 0.89

Predictive (Fall/Spring)

Gender: Male

6

PARCC Math

8,593

0.87

0.87, 0.88

Predictive (Winter/Spring)

Gender: Male

6

PARCC Math

6,808

0.88

0.88, 0.89

Concurrent (Spring/Spring)

Gender: Male

6

PARCC Math

9,272

0.90

0.89, 0.90

Predictive (Fall/Spring)

Gender: Male

7

PARCC Math

8,240

0.87

0.87, 0.88

Predictive (Winter/Spring)

Gender: Male

7

PARCC Math

6,126

0.88

0.88, 0.89

Concurrent (Spring/Spring)

Gender: Male

7

PARCC Math

9,006

0.89

0.88, 0.89

Predictive (Fall/Spring)

Gender: Male

8

PARCC Math

6,890

0.85

0.84, 0.85

Predictive (Winter/Spring)

Gender: Male

8

PARCC Math

5,610

0.85

0.85, 0.86

Concurrent (Spring/Spring)

Gender: Male

8

PARCC Math

7,541

0.86

0.86, 0.87

 

 

Results for other forms of disaggregated validity (e.g. factor analysis) not conducive to the table format:

None Provided

Sample Representativeness

Grade2345678
RatingHalf-filled bubbleHalf-filled bubbleHalf-filled bubbleHalf-filled bubbleHalf-filled bubbleHalf-filled bubbleHalf-filled bubble

Primary Classification Accuracy Sample

Representation: National: New England, Middle Atlantic, East North Central, South Atlantic, East South Central, Mountain.  The sample for the study contained student records from a total of five states (Colorado, Illinois, New Jersey, New Mexico, and Rhode Island) and one federal district (District of Columbia), and thus had representation from all four U.S. Census regions.

Date: MAP Growth data for Grades 3–8 came from test administrations occurring during the Fall 2015, Winter 2016, and Spring 2016 school terms, which spanned from August 2015 through June 2016. The MAP Growth scores of the Grade 3 students from the previous academic year (i.e., Fall 2014, Winter 2015, and Spring 2015) were used as the Grade 2 MAP Growth scores. The Partnership for Assessment of Readiness for College and Careers (PARCC) data was from the Spring 2016 administration of the PARCC assessment, spanning approximately from March 2016 through June 2016.

Size:

Table 3: Number of Students Per State by Grade

 

2

3

4

5

6

7

8

Total

CO

3,228

3,228

3,201

3,107

3,864

3,726

3,196

20,322

DC

171

171

161

132

236

165

173

1,038

IL

12,165

12,165

12,131

12,186

12,819

12,572

11,030

72,903

NJ

644

644

669

610

648

552

208

3,331

NM

208

208

208

200

198

206

147

1,167

RI

209

209

199

201

218

190

142

1,159

Total

16,625

16,625

16,569

16,436

17,983

17,411

14,896

99,920

 

Table 4: Number of Students Per Region by Grade

 

2

3

4

5

6

7

8

Total

Midwest

12,165

12,165

12,131

12,186

12,819

12,572

11,030

72,903

Northeast

853

853

868

811

866

742

350

4,490

South

171

171

161

132

236

165

173

1,038

West

3,436

3,436

3,409

3,307

4,062

3,932

3,343

21,489

Total

16,625

16,625

16,569

16,436

17,983

17,411

14,896

99,920

 

Table 5: Number of Students Per Division by Grade

 

2

3

4

5

6

7

8

Total

East North Central

12,165

12,165

12,131

12,186

12,819

12,572

11,030

72,903

Middle Atlantic

644

644

669

610

648

552

208

3,331

Mountain

3,436

3,436

3,409

3,307

4,062

3,932

3,343

21,489

New England

209

209

199

201

218

190

142

1,159

South Atlantic

171

171

161

132

236

165

173

1,038

Total

16,625

16,625

16,569

16,436

17,983

17,411

14,896

99,920

 

Male

51.11%

Female

48.88%

Unknown

0.02%

Other SES Indicators

Not Provided

Free or reduced-price lunch

Not Provided

White, Non-Hispanic

44.99%

Black, Non-Hispanic

6.42%

Hispanic

23.84%

American Indian/Alaska Native

1.83%

Asian/Pacific Islander

8.59%

Multi-Ethnic

2.88%

Not Specified or Other

11.45%

Disability classification

Not Provided

First language

Not Provided

Language proficiency status

Not Provided

 

Table 6: Percent of Students in the Sample, as a Function of Grade and PARCC Performance Level

 

2

3

4

5

6

7

8

Level 1: Did not yet meet expectations

N/A

9.1%

15.5%

24.9%

40.6%

12.2%

10.5%

Level 2: Partially met expectations

N/A

20.8%

25.6%

39.3%

6.2%

7.7%

19.1%

Level 3: Approached expectations

N/A

26.5%

36.3%

9.0%

10.3%

20.5%

29.8%

Level 4: Met expectations

N/A

36.7%

8.6%

8.6%

21.9%

30.4%

34.2%

Level 5: Exceeded expectations

N/A

6.9%

14.0%

18.2%

21.1%

29.1%

6.3%

Total

N/A

16,548

16,349

15,669

15,878

18,287

17,189

 

Bias Analysis Conducted

Grade2345678
RatingYesYesYesYesYesYesYes
  1. Description of the method used to determine the presence or absence of bias:

Once tests have been administered and results collected, analysis to detect differential item function (DIF) may be conducted. The method used to detect DIF for NWEA is based on the work of Linacre and Wright[1], implemented by Linacre[2]. When executed as part of a Winsteps[3] analysis, this method entails:

  1. Carrying out a joint Rasch analysis of all person-group classifications that anchors all student abilities and item difficulties to a common (theta) scale.
  2. Carrying out a calibration analysis for the Reference group keeping the student ability estimates and scale structure anchored to produce Reference group item difficulty estimates.
  3. Carrying out a calibration analysis for the Focal group keeping the student ability estimates and scale structure anchored to produce Focal group item difficulty estimates.
  4. Computing pair-wise item difficulty differences (Focal group difficulty minus Reference group difficulty). The calibration analyses in steps b and c are computed for each item, as though all items, except the item currently targeted, are anchored at the joint calibration run (step a).

Ideally, analyzing items for DIF would be incorporated within the item calibration process. This can prove to be a useful initial screen to identify items that should be subjected to heightened surveillance for DIF. However, the number of responses to an item by members of demographic groups of interest may well be insufficient to yield stable calibration estimates at the group level. This can introduce statistical artifacts as well as Type I errors into DIF analyses. To avoid this, data for analyses are taken from responses to operational tests.

 

  1. Description of the subgroups for which bias analyses were conducted:

None Provided

 

  1. Description of the results of the bias analyses conducted, including data and interpretative statements:

None Provided



[1] Linacre, J. M. & Wright, B. D. (1989). Mantel-Haenszel DIF and PROX are Equivalent. Rasch Measurement Transactions, 1989, 3, (2), 52-53.

[2] Linacre, J. M. (2012).  Winsteps-Ministep: Rasch Model Computer Programs, Version 3.75.0, www.winsteps.com.

[3] Ibid.

 

Administration Format

Grade2345678
Data
  • Individual
  • Individual
  • Individual
  • Individual
  • Individual
  • Individual
  • Individual
  • Administration & Scoring Time

    Grade2345678
    Data
  • 45 minutes
  • 45 minutes
  • 45 minutes
  • 45 minutes
  • 45 minutes
  • 45 mintutes
  • 45 minutes
  • Scoring Format

    Grade2345678
    Data
  • Automatic
  • Automatic
  • Automatic
  • Automatic
  • Automatic
  • Automatic
  • Automatic
  • Types of Decision Rules

    Grade2345678
    Data
  • None
  • None
  • None
  • None
  • None
  • None
  • None
  • Evidence Available for Multiple Decision Rules

    Grade2345678
    Data
  • No
  • No
  • No
  • No
  • No
  • No
  • No