MAP Growth

Reading

Cost

Technology, Human Resources, and Accommodations for Special Needs

Service and Support

Purpose and Other Implementation Information

Usage and Reporting

Initial Cost:

MAP Growth Reading 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 reading. 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 bubbledFull bubbled
Criterion 1 SpringFull bubbledFull bubbledFull bubbledFull bubbledFull bubbledFull bubbledFull bubbled
Criterion 2 Falldashdashdashdashdashdashdash
Criterion 2 Winterdashdashdashdashdashdashdash
Criterion 2 Springdashdashdashdashdashdashdash

Primary Sample

 

Criterion 1: PARCC ELA

Time of Year: Fall

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 160, PARCC < 700

MAP Growth < 177, PARCC < 700

MAP Growth < 188, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 203, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 213, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.13

0.15

0.10

0.08

0.09

0.11

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

0.12

0.09

0.11

0.13

0.12

0.11

0.12

False Negative Rate

0.27

0.24

0.17

0.15

0.18

0.22

0.24

Sensitivity

0.73

0.76

0.83

0.85

0.82

0.78

0.76

Specificity

0.88

0.91

0.89

0.87

0.88

0.89

0.88

Positive Predictive Power

0.49

0.59

0.46

0.37

0.39

0.46

0.45

Negative Predictive Power

0.95

0.96

0.98

0.99

0.98

0.97

0.97

Overall Classification Rate

0.86

0.89

0.88

0.87

0.87

0.88

0.86

Area Under the Curve (AUC)

0.91

0.93

0.94

0.94

0.93

0.92

0.90

AUC 95% Confidence Interval Lower

0.90

0.93

0.93

0.92=3

0.92

0.92

0.90

AUC 95% Confidence Interval Upper

0.91

0.94

0.94

0.94

0.93

0.93

0.91

At 90% Sensitivity, specificity equals

0.69

0.77

0.80

0.80

0.77

0.76

0.71

At 80% Sensitivity, specificity equals

0.86

0.91

0.93

0.92

0.89

0.90

0.85

At 70% Sensitivity, specificity equals

0.94

0.95

0.97

0.97

0.95

0.95

0.91

 

Criterion 1: PARCC ELA

Time of Year: Winter

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 170, PARCC < 700

MAP Growth < 183, PARCC < 700

MAP Growth < 194, PARCC < 700

MAP Growth < 202, PARCC < 700

MAP Growth < 205, PARCC < 700

MAP Growth < 209, PARCC < 700

MAP Growth < 213, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.14

0.17

0.12

0.09

0.11

0.13

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

0.10

0.08

0.12

0.13

0.12

0.11

0.10

False Negative Rate

0.23

0.26

0.18

0.16

0.20

0.26

0.30

Sensitivity

0.77

0.74

0.82

0.84

0.80

0.74

0.71

Specificity

0.90

0.92

0.89

0.87

0.88

0.89

0.90

Positive Predictive Power

0.55

0.66

0.49

0.40

0.43

0.50

0.52

Negative Predictive Power

0.96

0.95

0.97

0.98

0.97

0.96

0.95

Overall Classification Rate

0.88

0.89

0.88

0.87

0.87

0.87

0.87

Area Under the Curve (AUC)

0.92

0.93

0.94

0.93

0.92

0.92

0.90

AUC 95% Confidence Interval Lower

0.92

0.93

0.93

0.93

0.92

0.91

0.89

AUC 95% Confidence Interval Upper

0.93

0.94

0.94

0.94

0.93

0.92

0.91

At 90% Sensitivity, specificity equals

0.76

0.80

0.80

0.77

0.76

0.72

0.70

At 80% Sensitivity, specificity equals

0.89

0.92

0.92

0.92

0.88

0.87

0.84

At 70% Sensitivity, specificity equals

0.95

0.96

0.97

0.97

0.94

0.94

0.91

 

Criterion 1: PARCC ELA

Time of Year: Spring

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 178, PARCC < 700

MAP Growth < 189, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 205, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 213, PARCC <

MAP Growth < 216, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.14

0.15

0.11

0.08

0.09

0.11

0.11

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

0.12

0.14

0.13

0.12

0.11

False Negative Rate

0.25

0.21

0.13

0.11

0.15

0.19

0.24

Sensitivity

0.75

0.79

0.87

0.89

0.85

0.82

0.76

Specificity

0.90

0.91

0.88

0.87

0.88

0.88

0.89

Positive Predictive Power

0.55

0.62

0.47

0.37

0.40

0.45

0.46

Negative Predictive Power

0.96

0.96

0.98

0.99

0.98

0.98

0.97

Overall Classification Rate

0.88

0.90

0.88

0.87

0.87

0.88

0.87

Area Under the Curve (AUC)

0.92

0.95

0.95

0.94

0.94

0.93

0.91

AUC 95% Confidence Interval Lower

0.92

0.94

0.94

0.94

0.93

0.93

0.91

AUC 95% Confidence Interval Upper

0.93

0.95

0.95

0.95

0.94

0.94

0.92

At 90% Sensitivity, specificity equals

0.75

0.83

0.84

0.83

0.81

0.78

0.74

At 80% Sensitivity, specificity equals

0.89

0.93

0.95

0.94

0.93

0.90

0.87

At 70% Sensitivity, specificity equals

0.95

0.98

0.98

0.97

0.97

0.96

0.93

 

Additional Classification Accuracy

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

 

Disaggregated Data

Criterion 1: PARCC ELA

Time of Year: Fall

Subgroup: Asian or Pacific Islander

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 160, PARCC < 700

MAP Growth < 177, PARCC < 700

MAP Growth < 188, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 203, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 213, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.05

0.05

0.03

0.03

0.03

0.04

0.04

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

0.07

0.06

0.04

0.05

False Negative Rate

0.55

0.27

0.15

0.18

0.16

0.20

0.32

Sensitivity

0.45

0.74

0.85

0.82

0.84

0.80

0.68

Specificity

0.96

0.96

0.96

0.94

0.94

0.96

0.95

Positive Predictive Power

0.33

0.50

0.40

0.24

0.32

0.40

0.33

Negative Predictive Power

0.97

0.99

1.00

1.00

0.99

0.99

0.99

Overall Classification Rate

0.93

0.95

0.96

0.93

0.94

0.95

0.94

Area Under the Curve (AUC)

0.92

0.96

0.98

0.95

0.96

0.97

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

0.74

0.88

0.92

0.85

0.88

0.92

0.83

At 80% Sensitivity, specificity equals

0.91

0.95

1.00

0.97

0.92

0.98

0.91

At 70% Sensitivity, specificity equals

0.96

0.99

1.00

0.97

1.00

0.98

0.96

 

Criterion 1: PARCC ELA

Time of Year: Winter

Subgroup: Asian or Pacific Islander

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 170, PARCC < 700

MAP Growth < 183, PARCC < 700

MAP Growth < 194, PARCC < 700

MAP Growth < 202, PARCC < 700

MAP Growth < 205, PARCC < 700

MAP Growth < 209, PARCC < 700

MAP Growth < 213, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.04

0.05

0.03

0.03

0.04

0.04

0.04

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

0.04

0.03

0.04

0.05

0.06

0.03

0.05

False Negative Rate

0.35

0.28

0.23

0.12

0.14

0.21

0.31

Sensitivity

0.65

0.72

0.77

0.88

0.86

0.79

0.69

Specificity

0.96

0.97

0.96

0.95

0.94

0.97

0.95

Positive Predictive Power

0.41

0.59

0.37

0.35

0.36

0.47

0.39

Negative Predictive Power

0.99

0.98

0.99

1.00

0.99

0.99

0.99

Overall Classification Rate

0.95

0.96

0.95

0.95

0.94

0.96

0.94

Area Under the Curve (AUC)

0.94

0.97

0.97

0.98

0.98

0.98

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

0.78

0.91

0.94

0.94

0.98

0.97

0.78

At 80% Sensitivity, specificity equals

0.94

0.97

0.97

1.00

1.00

1.00

0.91

At 70% Sensitivity, specificity equals

0.98

1.00

1.00

1.00

1.00

1.00

0.97

 

Criterion 1: PARCC ELA

Time of Year: Spring

Subgroup: Asian or Pacific Islander

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 178, PARCC < 700

MAP Growth < 189, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 205, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 213, PARCC < 700

MAP Growth < 216, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.05

0.05

0.04

0.03

0.04

0.04

0.04

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

0.03

0.04

0.06

0.06

0.04

0.04

False Negative Rate

0.32

0.34

0.14

0.14

0.12

0.19

0.25

Sensitivity

0.68

0.66

0.87

0.87

0.88

0.81

0.75

Specificity

0.95

0.97

0.96

0.94

0.94

0.96

0.96

Positive Predictive Power

0.44

0.57

0.48

0.28

0.39

0.43

0.40

Negative Predictive Power

0.98

0.98

1.00

1.00

1.00

0.99

0.99

Overall Classification Rate

0.94

0.96

0.96

0.94

0.94

0.95

0.95

Area Under the Curve (AUC)

0.95

0.96

0.98

0.98

0.97

0.98

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

0.89

0.88

0.96

0.97

0.90

0.98

0.90

At 80% Sensitivity, specificity equals

0.95

0.95

1.00

1.00

1.00

1.00

0.92

At 70% Sensitivity, specificity equals

1.00

0.99

1.00

1.00

1.00

1.00

0.94

 

Criterion 1: PARCC ELA

Time of Year: Fall

Subgroup: Black

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 160, PARCC < 700

MAP Growth < 177, PARCC < 700

MAP Growth < 188, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 203, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 213, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.24

0.29

0.20

0.17

0.18

0.23

0.25

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

0.13

0.18

0.21

0.21

0.19

0.22

False Negative Rate

0.36

0.29

0.18

0.20

0.16

0.23

0.23

Sensitivity

0.64

0.71

0.82

0.80

0.84

0.77

0.77

Specificity

0.90

0.87

0.82

0.79

0.79

0.81

0.78

Positive Predictive Power

0.67

0.69

0.54

0.44

0.45

0.55

0.54

Negative Predictive Power

0.89

0.88

0.95

0.95

0.96

0.92

0.91

Overall Classification Rate

0.84

0.82

0.82

0.79

0.80

0.81

0.78

Area Under the Curve (AUC)

0.88

0.89

0.90

0.87

0.88

0.88

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

0.64

0.65

0.67

0.59

0.64

0.61

0.60

At 80% Sensitivity, specificity equals

0.80

0.80

0.86

0.77

0.82

0.78

0.76

At 70% Sensitivity, specificity equals

0.90

0.89

0.91

0.88

0.87

0.89

0.83

 

Criterion 1: PARCC ELA

Time of Year: Winter

Subgroup: Black

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 170, PARCC < 700

MAP Growth < 183, PARCC < 700

MAP Growth < 194, PARCC < 700

MAP Growth < 202, PARCC < 700

MAP Growth < 205, PARCC < 700

MAP Growth < 209, PARCC < 700

MAP Growth < 213, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.27

0.30

0.22

0.19

0.20

0.27

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

0.15

0.13

0.17

0.21

0.21

0.17

0.21

False Negative Rate

0.21

0.29

0.21

0.19

0.20

0.24

0.26

Sensitivity

0.79

0.71

0.79

0.81

0.80

0.76

0.74

Specificity

0.85

0.87

0.83

0.79

0.79

0.83

0.79

Positive Predictive Power

0.66

0.70

0.56

0.48

0.49

0.62

0.56

Negative Predictive Power

0.92

0.87

0.93

0.95

0.94

0.91

0.90

Overall Classification Rate

0.84

0.82

0.82

0.79

0.79

0.81

0.78

Area Under the Curve (AUC)

0.90

0.89

0.89

0.88

0.88

0.87

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

0.69

0.66

0.67

0.61

0.62

0.66

0.55

At 80% Sensitivity, specificity equals

0.87

0.80

0.80

0.80

0.79

0.78

0.73

At 70% Sensitivity, specificity equals

0.93

0.89

0.91

0.92

0.87

0.85

0.83

 

Criterion 1: PARCC ELA

Time of Year: Spring

Subgroup: Black

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 178, PARCC < 700

MAP Growth < 189, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 205, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 213, PARCC < 700

MAP Growth < 216, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.25

0.29

0.21

0.18

0.18

0.23

0.25

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

0.16

0.23

0.25

0.24

0.20

0.23

False Negative Rate

0.25

0.23

0.10

0.11

0.14

0.17

0.22

Sensitivity

0.75

0.77

0.90

0.89

0.86

0.83

0.78

Specificity

0.87

0.84

0.77

0.75

0.77

0.80

0.77

Positive Predictive Power

0.66

0.67

0.50

0.44

0.44

0.56

0.54

Negative Predictive Power

0.91

0.90

0.97

0.97

0.96

0.94

0.91

Overall Classification Rate

0.84

0.82

0.80

0.78

0.78

0.81

0.78

Area Under the Curve (AUC)

0.90

0.90

0.91

0.90

0.90

0.89

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

0.70

0.68

0.74

0.63

0.67

0.65

0.60

At 80% Sensitivity, specificity equals

0.83

0.82

0.87

0.86

0.81

0.83

0.75

At 70% Sensitivity, specificity equals

0.90

0.91

0.93

0.91

0.92

0.90

0.84

 

Criterion 1: PARCC ELA

Time of Year: Fall

Subgroup: Hispanic

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 160, PARCC < 700

MAP Growth < 177, PARCC < 700

MAP Growth < 188, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 203, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 213, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.28

0.28

0.21

0.17

0.15

0.19

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

0.23

0.18

0.23

0.25

0.24

0.21

0.25

False Negative Rate

0.20

0.19

0.13

0.11

0.13

0.18

0.13

Sensitivity

0.80

0.82

0.87

0.89

0.88

0.82

0.87

Specificity

0.77

0.82

0.77

0.75

0.76

0.79

0.75

Positive Predictive Power

0.57

0.65

0.49

0.42

0.39

0.47

0.47

Negative Predictive Power

0.91

0.92

0.96

0.97

0.97

0.95

0.96

Overall Classification Rate

0.78

0.82

0.79

0.78

0.78

0.80

0.78

Area Under the Curve (AUC)

0.86

0.90

0.90

0.90

0.90

0.89

0.89

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

0.53

0.67

0.68

0.66

0.68

0.66

0.67

At 80% Sensitivity, specificity equals

0.75

0.84

0.82

0.85

0.84

0.81

0.81

At 70% Sensitivity, specificity equals

0.88

0.91

0.92

0.92

0.92

0.89

0.90

 

Criterion 1: PARCC ELA

Time of Year: Winter

Subgroup: Hispanic

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 170, PARCC < 700

MAP Growth < 183, PARCC < 700

MAP Growth < 194, PARCC < 700

MAP Growth < 202, PARCC < 700

MAP Growth < 205, PARCC < 700

MAP Growth < 209, PARCC < 700

MAP Growth < 213, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.28

0.30

0.22

0.18

0.16

0.20

0.21

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

0.23

0.17

0.25

0.25

0.23

0.20

0.19

False Negative Rate

0.16

0.19

0.12

0.13

0.12

0.22

0.22

Sensitivity

0.84

0.81

0.88

0.87

0.88

0.78

0.78

Specificity

0.77

0.83

0.75

0.75

0.77

0.80

0.81

Positive Predictive Power

0.59

0.68

0.49

0.43

0.43

0.50

0.52

Negative Predictive Power

0.93

0.91

0.96

0.96

0.97

0.94

0.93

Overall Classification Rate

0.79

0.83

0.78

0.77

0.79

0.80

0.80

Area Under the Curve (AUC)

0.88

0.91

0.90

0.89

0.90

0.89

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

0.62

0.72

0.65

0.66

0.68

0.67

0.64

At 80% Sensitivity, specificity equals

0.81

0.84

0.83

0.82

0.85

0.78

0.79

At 70% Sensitivity, specificity equals

0.90

0.92

0.91

0.92

0.92

0.88

0.89

 

Criterion 1: PARCC ELA

Time of Year: Spring

Subgroup: Hispanic

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 178, PARCC < 700

MAP Growth < 189, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 205, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 213, PARCC < 700

MAP Growth < 216, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.28

0.29

0.20

0.17

0.15

0.18

0.19

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

0.22

0.20

0.24

0.27

0.24

0.23

0.23

False Negative Rate

0.18

0.16

0.11

0.10

0.08

0.14

0.15

Sensitivity

0.82

0.85

0.90

0.90

0.92

0.86

0.85

Specificity

0.78

0.80

0.76

0.73

0.76

0.77

0.77

Positive Predictive Power

0.58

0.63

0.49

0.40

0.40

0.46

0.47

Negative Predictive Power

0.92

0.93

0.97

0.97

0.98

0.96

0.96

Overall Classification Rate

0.79

0.82

0.79

0.76

0.78

0.79

0.79

Area Under the Curve (AUC)

0.88

0.91

0.91

0.90

0.92

0.90

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

0.64

0.71

0.73

0.68

0.74

0.69

0.69

At 80% Sensitivity, specificity equals

0.80

0.85

0.86

0.84

0.90

0.84

0.83

At 70% Sensitivity, specificity equals

0.88

0.93

0.93

0.91

0.94

0.91

0.90

 

Criterion 1: PARCC ELA

Time of Year: Fall

Subgroup: White

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 160, PARCC < 700

MAP Growth < 177, PARCC < 700

MAP Growth < 188, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 203, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 213, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.07

0.08

0.06

0.04

0.06

0.07

0.07

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

0.06

0.07

0.07

0.06

0.07

0.07

False Negative Rate

0.40

0.34

0.26

0.20

0.29

0.29

0.39

Sensitivity

0.60

0.67

0.74

0.80

0.71

0.71

0.61

Specificity

0.92

0.94

0.93

0.93

0.94

0.93

0.93

Positive Predictive Power

0.35

0.49

0.41

0.30

0.40

0.41

0.40

Negative Predictive Power

0.97

0.97

0.98

0.99

0.98

0.98

0.97

Overall Classification Rate

0.89

0.92

0.92

0.92

0.92

0.92

0.90

Area Under the Curve (AUC)

0.90

0.93

0.94

0.95

0.93

0.92

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

0.66

0.76

0.83

0.83

0.77

0.77

0.67

At 80% Sensitivity, specificity equals

0.84

0.89

0.93

0.93

0.91

0.88

0.81

At 70% Sensitivity, specificity equals

0.94

0.96

0.96

0.97

0.97

0.93

0.87

 

Criterion 1: PARCC ELA

Time of Year: Winter

Subgroup: White

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 170, PARCC < 700

MAP Growth < 183, PARCC < 700

MAP Growth < 194, PARCC < 700

MAP Growth < 202, PARCC < 700

MAP Growth < 205, PARCC < 700

MAP Growth < 209, PARCC < 700

MAP Growth < 213, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.07

0.09

0.07

0.05

0.07

0.08

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

0.06

0.05

0.06

0.08

0.06

0.06

0.06

False Negative Rate

0.38

0.38

0.25

0.23

0.35

0.35

0.43

Sensitivity

0.62

0.63

0.75

0.77

0.66

0.65

0.57

Specificity

0.94

0.95

0.94

0.92

0.94

0.94

0.95

Positive Predictive Power

0.42

0.58

0.46

0.33

0.43

0.48

0.51

Negative Predictive Power

0.97

0.96

0.98

0.99

0.97

0.97

0.96

Overall Classification Rate

0.91

0.92

0.93

0.92

0.92

0.92

0.91

Area Under the Curve (AUC)

0.92

0.93

0.95

0.95

0.92

0.92

0.89

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

0.73

0.79

0.83

0.85

0.76

0.77

0.68

At 80% Sensitivity, specificity equals

0.89

0.92

0.93

0.94

0.87

0.88

0.82

At 70% Sensitivity, specificity equals

0.94

0.95

0.98

0.97

0.93

0.93

0.89

 

Criterion 1: PARCC ELA

Time of Year: Spring

Subgroup: White

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 178, PARCC < 700

MAP Growth < 189, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 205, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 213, PARCC < 700

MAP Growth < 216, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.07

0.08

0.06

0.04

0.05

0.06

0.07

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

0.06

0.06

0.07

0.08

0.07

0.07

0.07

False Negative Rate

0.42

0.29

0.20

0.14

0.29

0.28

0.36

Sensitivity

0.58

0.71

0.80

0.86

0.71

0.72

0.64

Specificity

0.94

0.95

0.94

0.93

0.93

0.93

0.93

Positive Predictive Power

0.43

0.54

0.44

0.32

0.38

0.41

0.42

Negative Predictive Power

0.97

0.97

0.99

0.99

0.98

0.98

0.97

Overall Classification Rate

0.92

0.93

0.93

0.92

0.92

0.92

0.91

Area Under the Curve (AUC)

0.91

0.95

0.95

0.96

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

0.71

0.84

0.87

0.89

0.81

0.77

0.72

At 80% Sensitivity, specificity equals

0.88

0.95

0.93

0.96

0.92

0.90

0.84

At 70% Sensitivity, specificity equals

0.94

0.98

0.98

0.98

0.96

0.95

0.90

 

Criterion 1: PARCC ELA

Time of Year: Fall

Subgroup: Multi-Ethnic

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 160, PARCC < 700

MAP Growth < 177, PARCC < 700

MAP Growth < 188, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 203, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 213, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.12

0.12

0.06

0.08

0.08

0.09

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

0.07

0.06

0.06

0.12

0.09

0.11

0.09

False Negative Rate

0.16

0.31

0.15

0.12

0.20

0.31

0.32

Sensitivity

0.84

0.69

0.85

0.88

0.81

0.69

0.68

Specificity

0.93

0.94

0.94

0.88

0.91

0.89

0.92

Positive Predictive Power

0.61

0.63

0.48

0.40

0.44

0.39

0.47

Negative Predictive Power

0.98

0.96

0.99

0.99

0.98

0.97

0.96

Overall Classification Rate

0.92

0.91

0.94

0.88

0.90

0.87

0.89

Area Under the Curve (AUC)

0.93

0.93

0.96

0.95

0.93

0.89

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

0.87

0.78

0.89

0.82

0.82

0.69

0.68

At 80% Sensitivity, specificity equals

0.91

0.85

0.96

1.00

0.93

0.81

0.91

At 70% Sensitivity, specificity equals

0.91

0.95

0.99

1.00

0.95

0.88

0.97

 

Criterion 1: PARCC ELA

Time of Year: Winter

Subgroup: Multi-Ethnic

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 170, PARCC < 700

MAP Growth < 183, PARCC < 700

MAP Growth < 194, PARCC < 700

MAP Growth < 202, PARCC < 700

MAP Growth < 205, PARCC < 700

MAP Growth < 209, PARCC < 700

MAP Growth < 213, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.12

0.13

0.06

0.09

0.09

0.12

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

0.07

0.05

0.06

0.12

0.08

0.12

0.05

False Negative Rate

0.30

0.33

0.23

0.09

0.29

0.60

0.33

Sensitivity

0.70

0.67

0.77

0.91

0.71

0.40

0.67

Specificity

0.93

0.95

0.94

0.88

0.92

0.88

0.95

Positive Predictive Power

0.58

0.67

0.45

0.43

0.49

0.31

0.63

Negative Predictive Power

0.96

0.95

0.99

0.99

0.97

0.92

0.96

Overall Classification Rate

0.91

0.91

0.93

0.88

0.90

0.83

0.91

Area Under the Curve (AUC)

0.93

0.94

0.94

0.95

0.91

0.84

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

0.81

0.85

0.80

0.83

0.76

0.37

0.75

At 80% Sensitivity, specificity equals

0.85

0.91

0.91

0.98

0.88

0.68

0.86

At 70% Sensitivity, specificity equals

0.97

0.95

0.91

1.00

0.91

0.90

0.91

 

Criterion 1: PARCC ELA

Time of Year: Spring

Subgroup: Multi-Ethnic

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 178, PARCC < 700

MAP Growth < 189, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 205, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 213, PARCC < 700

MAP Growth < 216, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.11

0.12

0.06

0.08

0.09

0.10

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

0.14

0.11

0.12

0.08

False Negative Rate

0.24

0.25

0.13

0.03

0.16

0.20

0.26

Sensitivity

0.76

0.75

0.87

0.97

0.84

0.80

0.74

Specificity

0.91

0.93

0.93

0.87

0.89

0.88

0.93

Positive Predictive Power

0.50

0.59

0.46

0.40

0.43

0.42

0.52

Negative Predictive Power

0.97

0.96

0.99

1.00

0.98

0.98

0.97

Overall Classification Rate

0.89

0.90

0.93

0.87

0.89

0.87

0.91

Area Under the Curve (AUC)

0.93

0.92

0.97

0.98

0.93

0.92

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

0.76

0.77

0.90

0.92

0.82

0.73

0.75

At 80% Sensitivity, specificity equals

0.88

0.91

0.97

1.00

0.95

0.96

0.92

At 70% Sensitivity, specificity equals

0.95

0.94

0.98

1.00

0.98

1.00

1.00

 

Criterion 1: PARCC ELA

Time of Year: Fall

Subgroup: Not Specified or Other

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 160, PARCC < 700

MAP Growth < 177, PARCC < 700

MAP Growth < 188, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 203, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 213, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.11

0.11

0.07

0.04

0.07

0.07

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

0.12

0.09

0.10

0.11

0.11

0.08

0.16

False Negative Rate

0.24

0.21

0.14

0.13

0.19

0.21

0.21

Sensitivity

0.77

0.79

0.86

0.87

0.81

0.79

0.79

Specificity

0.88

0.92

0.90

0.90

0.89

0.93

0.84

Positive Predictive Power

0.44

0.54

0.37

0.28

0.35

0.43

0.47

Negative Predictive Power

0.97

0.97

0.99

0.99

0.99

0.98

0.96

Overall Classification Rate

0.87

0.90

0.90

0.89

0.89

0.92

0.83

Area Under the Curve (AUC)

0.92

0.94

0.96

0.95

0.94

0.95

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

0.71

0.83

0.85

0.87

0.80

0.90

0.71

At 80% Sensitivity, specificity equals

0.88

0.95

0.98

0.95

0.91

0.96

0.82

At 70% Sensitivity, specificity equals

0.95

0.96

1.00

1.00

0.97

0.96

0.91

 

Criterion 1: PARCC ELA

Time of Year: Winter

Subgroup: Not Specified or Other

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 170, PARCC < 700

MAP Growth < 183, PARCC < 700

MAP Growth < 194, PARCC < 700

MAP Growth < 202, PARCC < 700

MAP Growth < 205, PARCC < 700

MAP Growth < 209, PARCC < 700

MAP Growth < 213, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.15

0.14

0.09

0.06

0.10

0.16

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

0.10

0.04

0.10

0.10

0.13

0.13

0.15

False Negative Rate

0.25

0.25

0.20

0.21

0.14

0.28

0.21

Sensitivity

0.76

0.75

0.80

0.79

0.86

0.72

0.79

Specificity

0.90

0.96

0.90

0.90

0.88

0.87

0.85

Positive Predictive Power

0.57

0.75

0.45

0.33

0.44

0.51

0.52

Negative Predictive Power

0.96

0.96

0.98

0.99

0.98

0.94

0.95

Overall Classification Rate

0.88

0.93

0.89

0.89

0.87

0.84

0.84

Area Under the Curve (AUC)

0.92

0.95

0.94

0.93

0.93

0.89

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

0.76

0.88

0.80

0.79

0.81

0.68

0.70

At 80% Sensitivity, specificity equals

0.90

0.94

0.94

0.92

0.91

0.84

0.82

At 70% Sensitivity, specificity equals

0.93

0.96

0.99

0.97

0.97

0.88

0.84

 

Criterion 1: PARCC ELA

Time of Year: Spring

Subgroup: Not Specified or Other

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 178, PARCC < 700

MAP Growth < 189, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 205, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 213, PARCC < 700

MAP Growth < 216, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.13

0.11

0.07

0.05

0.06

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

0.08

0.05

0.09

0.11

0.10

0.08

0.13

False Negative Rate

0.24

0.20

0.13

0.14

0.14

0.14

0.23

Sensitivity

0.76

0.80

0.87

0.87

0.86

0.86

0.77

Specificity

0.93

0.96

0.91

0.89

0.90

0.92

0.87

Positive Predictive Power

0.61

0.70

0.41

0.28

0.37

0.43

0.48

Negative Predictive Power

0.96

0.97

0.99

0.99

0.99

0.99

0.96

Overall Classification Rate

0.90

0.94

0.91

0.89

0.90

0.92

0.86

Area Under the Curve (AUC)

0.94

0.97

0.97

0.95

0.95

0.95

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

0.82

0.89

0.88

0.87

0.86

0.92

0.72

At 80% Sensitivity, specificity equals

0.92

0.96

0.99

0.93

0.96

0.93

0.82

At 70% Sensitivity, specificity equals

0.96

0.99

1.00

0.98

0.97

0.98

0.96

 

Criterion 1: PARCC ELA

Time of Year: Fall

Subgroup: Female

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 160, PARCC < 700

MAP Growth < 177, PARCC < 700

MAP Growth < 188, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 203, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 213, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.11

0.12

0.08

0.06

0.06

0.07

0.07

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

0.08

0.10

0.11

0.12

0.11

0.12

False Negative Rate

0.26

0.23

0.15

0.16

0.15

0.20

0.21

Sensitivity

0.74

0.77

0.85

0.84

0.85

0.80

0.79

Specificity

0.90

0.92

0.90

0.89

0.88

0.89

0.88

Positive Predictive Power

0.48

0.57

0.42

0.30

0.30

0.35

0.35

Negative Predictive Power

0.96

0.97

0.99

0.99

0.99

0.98

0.98

Overall Classification Rate

0.88

0.90

0.89

0.88

0.88

0.89

0.88

Area Under the Curve (AUC)

0.92

0.94

0.95

0.94

0.94

0.93

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

0.73

0.81

0.85

0.81

0.81

0.78

0.76

At 80% Sensitivity, specificity equals

0.89

0.92

0.94

0.93

0.93

0.92

0.90

At 70% Sensitivity, specificity equals

0.96

0.96

0.98

0.98

0.97

0.97

0.94

 

Criterion 1: PARCC ELA

Time of Year: Winter

Subgroup: Female

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 170, PARCC < 700

MAP Growth < 183, PARCC < 700

MAP Growth < 194, PARCC < 700

MAP Growth < 202, PARCC < 700

MAP Growth < 205, PARCC < 700

MAP Growth < 209, PARCC < 700

MAP Growth < 213, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.12

0.14

0.09

0.07

0.07

0.08

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

0.10

0.07

0.11

0.12

0.12

0.11

0.10

False Negative Rate

0.24

0.26

0.17

0.19

0.18

0.25

0.26

Sensitivity

0.76

0.74

0.83

0.81

0.82

0.75

0.74

Specificity

0.90

0.93

0.89

0.88

0.88

0.89

0.90

Positive Predictive Power

0.51

0.64

0.44

0.33

0.34

0.38

0.42

Negative Predictive Power

0.97

0.96

0.98

0.99

0.99

0.98

0.97

Overall Classification Rate

0.88

0.91

0.89

0.88

0.88

0.88

0.89

Area Under the Curve (AUC)

0.93

0.94

0.94

0.93

0.94

0.93

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

0.81

0.82

0.77

0.80

0.74

0.74

At 80% Sensitivity, specificity equals

0.91

0.92

0.93

0.91

0.92

0.91

0.88

At 70% Sensitivity, specificity equals

0.97

0.96

0.98

0.97

0.97

0.96

0.93

 

Criterion 1: PARCC ELA

Time of Year: Spring

Subgroup: Female

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 178, PARCC < 700

MAP Growth < 189, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 205, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 213, PARCC < 700

MAP Growth < 216, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.12

0.13

0.08

0.06

0.06

0.07

0.07

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

0.08

0.11

0.12

0.12

0.12

0.11

False Negative Rate

0.24

0.22

0.11

0.12

0.13

0.17

0.18

Sensitivity

0.76

0.79

0.89

0.88

0.88

0.83

0.82

Specificity

0.91

0.92

0.89

0.88

0.88

0.89

0.89

Positive Predictive Power

0.53

0.59

0.42

0.30

0.30

0.34

0.37

Negative Predictive Power

0.97

0.97

0.99

0.99

0.99

0.99

0.98

Overall Classification Rate

0.89

0.91

0.89

0.88

0.88

0.88

0.88

Area Under the Curve (AUC)

0.93

0.95

0.96

0.94

0.95

0.94

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

0.78

0.85

0.87

0.85

0.85

0.80

0.80

At 80% Sensitivity, specificity equals

0.91

0.95

0.96

0.94

0.95

0.92

0.91

At 70% Sensitivity, specificity equals

0.96

0.98

0.99

0.97

0.97

0.97

0.94

 

Criterion 1: PARCC ELA

Time of Year: Fall

Subgroup: Male

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 160, PARCC < 700

MAP Growth < 177, PARCC < 700

MAP Growth < 188, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 203, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 213, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.16

0.18

0.13

0.10

0.12

0.15

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

0.10

0.13

0.14

0.13

0.12

0.13

False Negative Rate

0.28

0.25

0.18

0.14

0.20

0.23

0.26

Sensitivity

0.72

0.75

0.82

0.86

0.80

0.78

0.74

Specificity

0.87

0.90

0.87

0.86

0.87

0.88

0.87

Positive Predictive Power

0.50

0.61

0.49

0.41

0.46

0.53

0.52

Negative Predictive Power

0.94

0.95

0.97

0.98

0.97

0.96

0.95

Overall Classification Rate

0.85

0.87

0.87

0.86

0.86

0.87

0.85

Area Under the Curve (AUC)

0.90

0.92

0.93

0.93

0.92

0.92

0.89

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

0.66

0.74

0.76

0.78

0.75

0.74

0.68

At 80% Sensitivity, specificity equals

0.84

0.89

0.92

0.91

0.87

0.88

0.82

At 70% Sensitivity, specificity equals

0.92

0.95

0.97

0.97

0.93

0.94

0.89

 

Criterion 1: PARCC ELA

Time of Year: Winter

Subgroup: Male

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 170, PARCC < 700

MAP Growth < 183, PARCC < 700

MAP Growth < 194, PARCC < 700

MAP Growth < 202, PARCC < 700

MAP Growth < 205, PARCC < 700

MAP Growth < 209, PARCC < 700

MAP Growth < 213, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.17

0.19

0.14

0.12

0.14

0.18

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

0.11

0.08

0.12

0.15

0.13

0.11

0.11

False Negative Rate

0.22

0.26

0.18

0.14

0.21

0.27

0.31

Sensitivity

0.78

0.74

0.82

0.86

0.79

0.73

0.69

Specificity

0.89

0.92

0.88

0.85

0.87

0.89

0.89

Positive Predictive Power

0.58

0.68

0.52

0.44

0.50

0.58

0.59

Negative Predictive Power

0.95

0.94

0.97

0.98

0.96

0.94

0.93

Overall Classification Rate

0.87

0.88

0.87

0.85

0.86

0.86

0.86

Area Under the Curve (AUC)

0.92

0.93

0.93

0.93

0.91

0.91

0.89

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

0.78

0.78

0.77

0.73

0.71

0.67

At 80% Sensitivity, specificity equals

0.88

0.91

0.91

0.92

0.86

0.86

0.82

At 70% Sensitivity, specificity equals

0.93

0.96

0.96

0.97

0.93

0.93

0.90

 

Criterion 1: PARCC ELA

Time of Year: Spring

Subgroup: Male

 

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

Cut points

MAP Growth < 178, PARCC < 700

MAP Growth < 189, PARCC < 700

MAP Growth < 198, PARCC < 700

MAP Growth < 205, PARCC < 700

MAP Growth < 208, PARCC < 700

MAP Growth < 213, PARCC < 700

MAP Growth < 216, PARCC < 700

Base rate in the sample for children requiring intensive intervention

0.16

0.18

0.13

0.10

0.12

0.14

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

0.11

0.10

0.12

0.15

0.13

0.12

0.11

False Negative Rate

0.25

0.21

0.14

0.11

0.17

0.19

0.26

Sensitivity

0.75

0.80

0.86

0.89

0.84

0.81

0.74

Specificity

0.89

0.91

0.88

0.85

0.87

0.88

0.89

Positive Predictive Power

0.57

0.64

0.51

0.41

0.47

0.53

0.54

Negative Predictive Power

0.95

0.95

0.98

0.99

0.98

0.97

0.95

Overall Classification Rate

0.87

0.89

0.88

0.86

0.87

0.87

0.86

Area Under the Curve (AUC)

0.92

0.94

0.94

0.94

0.93

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

0.72

0.81

0.82

0.82

0.78

0.77

0.72

At 80% Sensitivity, specificity equals

0.88

0.92

0.94

0.93

0.92

0.89

0.84

At 70% Sensitivity, specificity equals

0.94

0.98

0.97

0.98

0.96

0.95

0.92

 

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[1] [2] 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[3] 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

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 were 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.

Male

51.09%

Female

48.89%

Unknown

0.02%

Other SES Indicators

Not Provided

Free or reduced-price lunch

Not Provided

White, Non-Hispanic

45.11%

Black, Non-Hispanic

6.42%

Hispanic

23.70%

American Indian/Alaska Native:

1.84%

Asian/Pacific Islander:

8.62%

Multi-Ethnic

2.88%

Not Specified or Other

11.43%

Disability classification

Not Provided

First language

Not Provided

Language proficiency status

Not Provided

 

Table 7: Number of Students Per State by Grade

 

3

4

5

6

7

8

Total

CO

2,967

3,185

3,097

3,846

3,659

3,192

19,946

DC

167

160

140

221

165

191

1,044

IL

12,140

12,064

12,129

12,766

12,525

10,970

72,594

NJ

643

665

610

649

622

548

3,737

NM

208

205

201

197

208

186

1,205

RI

209

199

202

217

190

200

1,217

Total

16,334

16,478

16,379

17,896

17,369

15,287

99,743

 

Table 8: Number of Students Per Region by Grade

 

3

4

5

6

7

8

Total

Midwest

12,140

12,064

12,129

12,766

12,525

10,970

72,594

Northeast

852

864

812

866

812

748

4,954

South

167

160

140

221

165

191

1,044

West

3,175

3,390

3,298

4,043

3,867

3,378

21,151

Total

16,334

16,478

16,379

17,896

17,369

15,287

99,743

 

Table 9: Number of Students Per Division by Grade

 

3

4

5

6

7

8

Total

East North Central

12,140

12,064

12,129

12,766

12,525

10,970

72,594

Middle Atlantic

643

665

610

649

622

548

3,737

Mountain

3,175

3,390

3,298

4,043

3,867

3,378

21,151

New England

209

199

202

217

190

200

1,217

South Atlantic

167

160

140

221

165

191

1,044

Total

16,334

16,478

16,379

17,896

17,369

15,287

99,743

 

  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[4]. 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 in Winter 2016, in Fall 2015 and in Spring 2016, or in Winter 2016 and in 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

12,410

0.97

0.96, 0.97

Marginal (Winter)

2

10,341

0.96

0.96, 0.96

Marginal (Spring)

2

11,491

0.96

0.96, 0.96

Marginal (Fall)

3

14,812

0.96

0.96, 0.96

Marginal (Winter)

3

11,158

0.96

0.96, 0.96

Marginal (Spring)

3

16,334

0.96

0.96, 0.96

Marginal (Fall)

4

15,182

0.96

0.96, 0.96

Marginal (Winter)

4

12,130

0.95

0.95, 0.95

Marginal (Spring)

4

16,478

0.95

0.95, 0.95

Marginal (Fall)

5

15,222

0.95

0.95, 0.95

Marginal (Winter)

5

12,668

0.95

0.95, 0.95

Marginal (Spring)

5

16,379

0.95

0.95, 0.95

Marginal (Fall)

6

16,305

0.95

0.95, 0.95

Marginal (Winter)

6

12,595

0.95

0.95, 0.95

Marginal (Spring)

6

17,896

0.95

0.95, 0.95

Marginal (Fall)

7

15,852

0.95

0.95, 0.95

Marginal (Winter)

7

11,550

0.95

0.95, 0.95

Marginal (Spring)

7

17,369

0.95

0.94, 0.95

Marginal (Fall)

8

13,887

0.95

0.95, 0.95

Marginal (Winter)

8

10,730

0.95

0.95, 0.95

Marginal (Spring)

8

15,287

0.95

0.95, 0.95

Test-Retest (Fall/Winter)

2

10,079

0.87

0.87, 0.88

Test-Retest (Fall/Spring)

2

11,768

0.84

0.83, 0.84

Test-Retest (Winter/Spring)

2

10,192

0.89

0.88, 0.89

Test-Retest (Fall/Winter)

3

10,603

0.89

0.88, 0.89

Test-Retest (Fall/Spring)

3

14,812

0.86

0.86, 0.87

Test-Retest (Winter/Spring)

3

11,158

0.89

0.89, 0.89

Test-Retest (Fall/Winter)

4

11,576

0.88

0.87, 0.88

Test-Retest (Fall/Spring)

4

15,182

0.86

0.86, 0.87

Test-Retest (Winter/Spring)

4

12,130

0.88

0.88, 0.89

Test-Retest (Fall/Winter)

5

12,206

0.88

0.88, 0.89

Test-Retest (Fall/Spring)

5

15,222

0.87

0.87, 0.88

Test-Retest (Winter/Spring)

5

12,668

0.88

0.88, 0.88

Test-Retest (Fall/Winter)

6

12,388

0.88

0.88, 0.89

Test-Retest (Fall/Spring)

6

16,305

0.87

0.86, 0.87

Test-Retest (Winter/Spring)

6

12,595

0.88

0.87, 0.88

Test-Retest (Fall/Winter)

7

11,313

0.88

0.87, 0.88

Test-Retest (Fall/Spring)

7

15,852

0.86

0.86, 0.87

Test-Retest (Winter/Spring)

7

11,550

0.87

0.86, 0.87

Test-Retest (Fall/Winter)

8

10,506

0.87

0.86, 0.87

Test-Retest (Fall/Spring)

8

13,887

0.86

0.85, 0.86

Test-Retest (Winter/Spring)

8

10,730

0.86

0.86, 0.87

 

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

1,145

0.96

0.96, 0.96

Marginal (Winter)

Ethnicity: Asian or Pacific Islander

2

1,047

0.95

0.94, 0.95

Marginal (Spring)

Ethnicity: Asian or Pacific Islander

2

1,152

0.95

0.94, 0.95

Marginal (Fall)

Ethnicity: Black

2

566

0.96

0.95, 0.96

Marginal (Winter)

Ethnicity: Black

2

428

0.96

0.95, 0.96

Marginal (Spring)

Ethnicity: Black

2

539

0.96

0.95, 0.96

Marginal (Fall)

Ethnicity: Hispanic

2

2,844

0.95

0.95, 0.96

Marginal (Winter)

Ethnicity: Hispanic

2

2,577

0.96

0.96, 0.96

Marginal (Spring)

Ethnicity: Hispanic

2

2,810

0.96

0.95, 0.96

Marginal (Fall)

Ethnicity: Multi-Ethnic

2

373

0.97

0.96, 0.97

Marginal (Winter)

Ethnicity: Multi-Ethnic

2

315

0.96

0.95, 0.97

Marginal (Spring)

Ethnicity: Multi-Ethnic

2

346

0.96

0.95, 0.96

Marginal (Fall)

Ethnicity: Not Specified or Other

2

2,279

0.96

0.96, 0.97

Marginal (Winter)

Ethnicity: Not Specified or Other

2

1,391

0.96

0.96, 0.96

Marginal (Spring)

Ethnicity: Not Specified or Other

2

1,834

0.96

0.95, 0.96

Marginal (Fall)

Ethnicity: White

2

5,046

0.96

0.96, 0.96

Marginal (Winter)

Ethnicity: White

2

4,431

0.95

0.95, 0.95

Marginal (Spring)

Ethnicity: White

2

4,674

0.94

0.94, 0.95

Marginal (Fall)

Gender: Female

2

6,098

0.96

0.96, 0.97

Marginal (Winter)

Gender: Female

2

5,068

0.96

0.96, 0.96

Marginal (Spring)

Gender: Female

2

5,641

0.96

0.95, 0.96

Marginal (Fall)

Gender: Male

2

6,310

0.97

0.96, 0.97

Marginal (Winter)

Gender: Male

2

5,271

0.96

0.96, 0.97

Marginal (Spring)

Gender: Male

2

5,848

0.96

0.96, 0.96

Marginal (Fall)

Ethnicity: American Indian or Alaskan

3

294

0.95

0.95, 0.96

Marginal (Winter)

Ethnicity: American Indian or Alaskan

3

367

0.95

0.94, 0.96

Marginal (Spring)

Ethnicity: American Indian or Alaskan

3

380

0.95

0.95, 0.96

Marginal (Fall)

Ethnicity: American Indian or Alaskan

4

312

0.94

0.93, 0.95

Marginal (Winter)

Ethnicity: American Indian or Alaskan

4

328

0.94

0.93, 0.95

Marginal (Spring)

Ethnicity: American Indian or Alaskan

4

340

0.94

0.93, 0.95

Marginal (Fall)

Ethnicity: American Indian or Alaskan

5

277

0.94

0.93, 0.95

Marginal (Winter)

Ethnicity: American Indian or Alaskan

5

291

0.94

0.93, 0.95

Marginal (Spring)

Ethnicity: American Indian or Alaskan

5

303

0.94

0.93, 0.95

Marginal (Fall)

Ethnicity: American Indian or Alaskan

6

314

0.94

0.93, 0.95

Marginal (Winter)

Ethnicity: American Indian or Alaskan

6

318

0.94

0.92, 0.95

Marginal (Spring)

Ethnicity: American Indian or Alaskan

6

333

0.95

0.93, 0.96

Marginal (Fall)

Ethnicity: American Indian or Alaskan

7

330

0.95

0.94, 0.96

Marginal (Winter)

Ethnicity: American Indian or Alaskan

7

327

0.95

0.93, 0.95

Marginal (Spring)

Ethnicity: American Indian or Alaskan

7

341

0.95

0.94, 0.96

Marginal (Fall)

Ethnicity: Asian or Pacific Islander

3

1,392

0.95

0.94, 0.95

Marginal (Winter)

Ethnicity: Asian or Pacific Islander

3

1,031

0.95

0.94, 0.95

Marginal (Spring)

Ethnicity: Asian or Pacific Islander

3

1,456

0.94

0.93, 0.94

Marginal (Fall)

Ethnicity: Asian or Pacific Islander

4

1,340

0.94

0.94, 0.95

Marginal (Winter)

Ethnicity: Asian or Pacific Islander

4

1,154

0.94

0.93, 0.94

Marginal (Spring)

Ethnicity: Asian or Pacific Islander

4

1,421

0.94

0.93, 0.94

Marginal (Fall)

Ethnicity: Asian or Pacific Islander

5

1,340

0.94

0.94, 0.95

Marginal (Winter)

Ethnicity: Asian or Pacific Islander

5

1,111

0.94

0.93, 0.94

Marginal (Spring)

Ethnicity: Asian or Pacific Islander

5

1,406

0.94

0.93, 0.94

Marginal (Fall)

Ethnicity: Asian or Pacific Islander

6

1,445

0.95

0.94, 0.95

Marginal (Winter)

Ethnicity: Asian or Pacific Islander

6

1,159

0.94

0.94, 0.95

Marginal (Spring)

Ethnicity: Asian or Pacific Islander

6

1,490

0.94

0.94, 0.95

Marginal (Fall)

Ethnicity: Asian or Pacific Islander

7

1,378

0.94

0.93, 0.94

Marginal (Winter)

Ethnicity: Asian or Pacific Islander

7

1,026

0.93

0.92, 0.94

Marginal (Spring)

Ethnicity: Asian or Pacific Islander

7

1,402

0.93

0.92, 0.94

Marginal (Fall)

Ethnicity: Asian or Pacific Islander

8

1,281

0.94

0.93, 0.95

Marginal (Winter)

Ethnicity: Asian or Pacific Islander

8

927

0.94

0.93, 0.94

Marginal (Spring)

Ethnicity: Asian or Pacific Islander

8

1,306

0.93

0.92, 0.94

Marginal (Fall)

Ethnicity: Black

3

912

0.96

0.95, 0.96

Marginal (Winter)

Ethnicity: Black

3

607

0.96

0.95, 0.96

Marginal (Spring)

Ethnicity: Black

3

1,015

0.95

0.95, 0.96

Marginal (Fall)

Ethnicity: Black

4

894

0.95

0.95, 0.96

Marginal (Winter)

Ethnicity: Black

4

601

0.95

0.94, 0.96

Marginal (Spring)

Ethnicity: Black

4

1,001

0.95

0.95, 0.96

Marginal (Fall)

Ethnicity: Black

5

903

0.95

0.95, 0.96

Marginal (Winter)

Ethnicity: Black

5

788

0.95

0.94, 0.95

Marginal (Spring)

Ethnicity: Black

5

985

0.95

0.95, 0.96

Marginal (Fall)

Ethnicity: Black

6

1,120

0.95

0.94, 0.95

Marginal (Winter)

Ethnicity: Black

6

842

0.94

0.93, 0.95

Marginal (Spring)

Ethnicity: Black

6

1,211

0.95

0.94, 0.95

Marginal (Fall)

Ethnicity: Black

7

1,153

0.95

0.94, 0.95

Marginal (Winter)

Ethnicity: Black

7

830

0.94

0.94, 0.95

Marginal (Spring)

Ethnicity: Black

7

1,229

0.94

0.94, 0.95

Marginal (Fall)

Ethnicity: Black

8

880

0.95

0.94, 0.95

Marginal (Winter)

Ethnicity: Black

8

754

0.94

0.94, 0.95

Marginal (Spring)

Ethnicity: Black

8

964

0.94

0.94, 0.95

Marginal (Fall)

Ethnicity: Hispanic

3

3,389

0.96

0.96, 0.96

Marginal (Winter)

Ethnicity: Hispanic

3

2,784

0.96

0.95, 0.96

Marginal (Spring)

Ethnicity: Hispanic

3

3,743

0.96

0.95, 0.96

Marginal (Fall)

Ethnicity: Hispanic

4

3,574

0.95

0.95, 0.96

Marginal (Winter)

Ethnicity: Hispanic

4

3,083

0.95

0.95, 0.95

Marginal (Spring)

Ethnicity: Hispanic

4

3,835

0.95

0.95, 0.95

Marginal (Fall)

Ethnicity: Hispanic

5

3,506

0.95

0.95, 0.95

Marginal (Winter)

Ethnicity: Hispanic

5

3,336

0.95

0.94, 0.95

Marginal (Spring)

Ethnicity: Hispanic

5

3,782

0.95

0.95, 0.95

Marginal (Fall)

Ethnicity: Hispanic

6

3,999

0.95

0.95, 0.95

Marginal (Winter)

Ethnicity: Hispanic

6

3,480

0.95

0.95, 0.95

Marginal (Spring)

Ethnicity: Hispanic

6

4,379

0.95

0.95, 0.95

Marginal (Fall)

Ethnicity: Hispanic

7

3,839

0.95

0.95, 0.95

Marginal (Winter)

Ethnicity: Hispanic

7

3,228

0.95

0.95, 0.95

Marginal (Spring)

Ethnicity: Hispanic

7

4,265

0.95

0.94, 0.95

Marginal (Fall)

Ethnicity: Hispanic

8

3,352

0.95

0.95, 0.95

Marginal (Winter)

Ethnicity: Hispanic

8

2,932

0.95

0.95, 0.95

Marginal (Spring)

Ethnicity: Hispanic

8

3,632

0.95

0.94, 0.95

Marginal (Fall)

Ethnicity: Multi-Ethnic

3

504

0.96

0.95, 0.96

Marginal (Winter)

Ethnicity: Multi-Ethnic

3

384

0.95

0.94, 0.96

Marginal (Spring)

Ethnicity: Multi-Ethnic

3

553

0.95

0.94, 0.96

Marginal (Fall)

Ethnicity: Multi-Ethnic

4

446

0.95

0.94, 0.96

Marginal (Winter)

Ethnicity: Multi-Ethnic

4

375

0.94

0.93, 0.95

Marginal (Spring)

Ethnicity: Multi-Ethnic

4

487

0.95

0.94, 0.96

Marginal (Fall)

Ethnicity: Multi-Ethnic

5

421

0.95

0.94, 0.96

Marginal (Winter)

Ethnicity: Multi-Ethnic

5

387

0.95

0.94, 0.96

Marginal (Spring)

Ethnicity: Multi-Ethnic

5

445

0.95

0.94, 0.96

Marginal (Fall)

Ethnicity: Multi-Ethnic

6

489

0.95

0.94, 0.96

Marginal (Winter)

Ethnicity: Multi-Ethnic

6

363

0.95

0.94, 0.96

Marginal (Spring)

Ethnicity: Multi-Ethnic

6

510

0.95

0.94, 0.96

Marginal (Fall)

Ethnicity: Multi-Ethnic

7

453

0.95

0.94, 0.95

Marginal (Winter)

Ethnicity: Multi-Ethnic

7

348

0.94

0.93, 0.95

Marginal (Spring)

Ethnicity: Multi-Ethnic

7

474

0.94

0.93, 0.95

Marginal (Fall)

Ethnicity: Multi-Ethnic

8

381

0.94

0.93, 0.95

Marginal (Winter)

Ethnicity: Multi-Ethnic

8

303

0.94

0.93, 0.95

Marginal (Spring)

Ethnicity: Multi-Ethnic

8

401

0.94

0.93, 0.95

Marginal (Fall)

Ethnicity: Not Specified or Other

3

2,428

0.96

0.96, 0.96

Marginal (Winter)

Ethnicity: Not Specified or Other

3

1,314

0.95

0.95, 0.96

Marginal (Spring)

Ethnicity: Not Specified or Other

3

2,490

0.95

0.95, 0.95

Marginal (Fall)

Ethnicity: Not Specified or Other

4

2,477

0.95

0.95, 0.96

Marginal (Winter)

Ethnicity: Not Specified or Other

4

1,322

0.95

0.95, 0.95

Marginal (Spring)

Ethnicity: Not Specified or Other

4

2,533

0.95

0.94, 0.95

Marginal (Fall)

Ethnicity: Not Specified or Other

5

2,349

0.95

0.95, 0.95

Marginal (Winter)

Ethnicity: Not Specified or Other

5

1,257

0.94

0.94, 0.95

Marginal (Spring)

Ethnicity: Not Specified or Other

5

2,409

0.94

0.94, 0.95

Marginal (Fall)

Ethnicity: Not Specified or Other

6

2,059

0.95

0.95, 0.96

Marginal (Winter)

Ethnicity: Not Specified or Other

6

977

0.96

0.95, 0.96

Marginal (Spring)

Ethnicity: Not Specified or Other

6

2,327

0.95

0.94, 0.95

Marginal (Fall)

Ethnicity: Not Specified or Other

7

1,001

0.95

0.94, 0.95

Marginal (Winter)

Ethnicity: Not Specified or Other

7

308

0.95

0.94, 0.96

Marginal (Spring)

Ethnicity: Not Specified or Other

7

1,093

0.94

0.93, 0.94

Marginal (Fall)

Ethnicity: Not Specified or Other

8

445

0.96

0.95, 0.96

Marginal (Winter)

Ethnicity: Not Specified or Other

8

361

0.95

0.94, 0.96

Marginal (Spring)

Ethnicity: Not Specified or Other

8

550

0.95

0.94, 0.95

Marginal (Fall)

Ethnicity: White

3

5,873

0.95

0.95, 0.95

Marginal (Winter)

Ethnicity: White

3

4,656

0.95

0.94, 0.95

Marginal (Spring)

Ethnicity: White

3

6,675

0.94

0.94, 0.94

Marginal (Fall)

Ethnicity: White

4

6,122

0.95

0.94, 0.95

Marginal (Winter)

Ethnicity: White

4

5,258

0.94

0.94, 0.94

Marginal (Spring)

Ethnicity: White

4

6,843

0.94

0.94, 0.94

Marginal (Fall)

Ethnicity: White

5

6,407

0.94

0.94, 0.94

Marginal (Winter)

Ethnicity: White

5

5,480

0.93

0.93, 0.94

Marginal (Spring)

Ethnicity: White

5

7,026

0.93

0.93, 0.94

Marginal (Fall)

Ethnicity: White

6

6,865

0.94

0.93, 0.94

Marginal (Winter)

Ethnicity: White

6

5,444

0.93

0.93, 0.94

Marginal (Spring)

Ethnicity: White

6

7,629

0.93

0.93, 0.94

Marginal (Fall)

Ethnicity: White

7

7,678

0.94

0.94, 0.94

Marginal (Winter)

Ethnicity: White

7

5,470

0.94

0.93, 0.94

Marginal (Spring)

Ethnicity: White

7

8,543

0.93

0.93, 0.93

Marginal (Fall)

Ethnicity: White

8

7,407

0.94

0.93, 0.94

Marginal (Winter)

Ethnicity: White

8

5,320

0.93

0.93, 0.94

Marginal (Spring)

Ethnicity: White

8

8,282

0.93

0.93, 0.94

Marginal (Fall)

Gender: Female

3

7,302

0.96

0.96, 0.96

Marginal (Winter)

Gender: Female

3

5,440

0.96

0.95, 0.96

Marginal (Spring)

Gender: Female

3

8,033

0.95

0.95, 0.95

Marginal (Fall)

Gender: Female

4

7,418

0.95

0.95, 0.96

Marginal (Winter)

Gender: Female

4

5,911

0.95

0.95, 0.95

Marginal (Spring)

Gender: Female

4

8,054

0.95

0.95, 0.95

Marginal (Fall)

Gender: Female

5

7,552

0.95

0.95, 0.95

Marginal (Winter)

Gender: Female

5

6,276

0.94

0.94, 0.94

Marginal (Spring)

Gender: Female

5

8,107

0.94

0.94, 0.95

Marginal (Fall)

Gender: Female

6

7,888

0.95

0.94, 0.95

Marginal (Winter)

Gender: Female

6

6,004

0.95

0.94, 0.95

Marginal (Spring)

Gender: Female

6

8,667

0.95

0.94, 0.95

Marginal (Fall)

Gender: Female

7

7,665

0.94

0.94, 0.95

Marginal (Winter)

Gender: Female

7

5,476

0.94

0.94, 0.95

Marginal (Spring)

Gender: Female

7

8,394

0.94

0.94, 0.94

Marginal (Fall)

Gender: Female

8

6,792

0.94

0.94, 0.95

Marginal (Winter)

Gender: Female

8

5,235

0.94

0.94, 0.95

Marginal (Spring)

Gender: Female

8

7,509

0.94

0.94, 0.94

Marginal (Fall)

Gender: Male

3

7,508

0.96

0.96, 0.96

Marginal (Winter)

Gender: Male

3

5,716

0.96

0.96, 0.96

Marginal (Spring)

Gender: Male

3

8,297

0.96

0.96, 0.96

Marginal (Fall)

Gender: Male

4

7,763

0.96

0.96, 0.96

Marginal (Winter)

Gender: Male

4

6,219

0.96

0.95, 0.96

Marginal (Spring)

Gender: Male

4

8,423

0.96

0.95, 0.96

Marginal (Fall)

Gender: Male

5

7,669

0.96

0.96, 0.96

Marginal (Winter)

Gender: Male

5

6,392

0.95

0.95, 0.96

Marginal (Spring)

Gender: Male

5

8,270

0.95

0.95, 0.96

Marginal (Fall)

Gender: Male

6

8,415

0.96

0.95, 0.96

Marginal (Winter)

Gender: Male

6

6,590

0.95

0.95, 0.96

Marginal (Spring)

Gender: Male

6

9,227

0.95

0.95, 0.96

Marginal (Fall)

Gender: Male

7

8,182

0.95

0.95, 0.96

Marginal (Winter)

Gender: Male

7

6,070

0.95

0.95, 0.96

Marginal (Spring)

Gender: Male

7

8,969

0.95

0.95, 0.95

Marginal (Fall)

Gender: Male

8

7,093

0.96

0.95, 0.96

Marginal (Winter)

Gender: Male

8

5,493

0.95

0.95, 0.96

Marginal (Spring)

Gender: Male

8

7,776

0.95

0.95, 0.95

Test-Retest (Fall/Winter)

Ethnicity: Asian or Pacific Islander

2

1,027

0.87

0.86, 0.89

Test-Retest (Fall/Spring)

Ethnicity: Asian or Pacific Islander

2

1,136

0.83

0.82, 0.85

Test-Retest (Winter/Spring)

Ethnicity: Asian or Pacific Islander

2

1,043

0.87

0.85, 0.88

Test-Retest (Fall/Winter)

Ethnicity: Black

2

403

0.86

0.84, 0.89

Test-Retest (Fall/Spring)

Ethnicity: Black

2

549

0.81

0.78, 0.84

Test-Retest (Winter/Spring)

Ethnicity: Black

2

413

0.86

0.84, 0.89

Test-Retest (Fall/Winter)

Ethnicity: Hispanic

2

2,455

0.85

0.84, 0.86

Test-Retest (Fall/Spring)

Ethnicity: Hispanic

2

2,767

0.82

0.81, 0.83

Test-Retest (Winter/Spring)

Ethnicity: Hispanic

2

2,507

0.88

0.87, 0.89

Test-Retest (Fall/Winter)

Ethnicity: Multi-Ethnic

2

307

0.88

0.85, 0.90

Test-Retest (Fall/Spring)

Ethnicity: Multi-Ethnic

2

362

0.87

0.84, 0.89

Test-Retest (Winter/Spring)

Ethnicity: Multi-Ethnic

2

305

0.87

0.84, 0.89

Test-Retest (Fall/Winter)

Ethnicity: Not Specified or Other

2

1,377

0.86

0.85, 0.87

Test-Retest (Fall/Spring)

Ethnicity: Not Specified or Other

2

1,816

0.83

0.81, 0.84

Test-Retest (Winter/Spring)

Ethnicity: Not Specified or Other

2

1,382

0.87

0.86, 0.88

Test-Retest (Fall/Winter)

Ethnicity: White

2

4,359

0.85

0.84, 0.86

Test-Retest (Fall/Spring)

Ethnicity: White

2

4,987

0.80

0.79, 0.81

Test-Retest (Winter/Spring)

Ethnicity: White

2

4,395

0.85

0.84, 0.86

Test-Retest (Fall/Winter)

Gender: Female

2

4,954

0.88

0.88, 0.89

Test-Retest (Fall/Spring)

Gender: Female

2

5,793

0.84

0.83, 0.85

Test-Retest (Winter/Spring)

Gender: Female

2

5,015

0.89

0.88, 0.89

Test-Retest (Fall/Winter)

Gender: Male

2

5,124

0.87

0.86, 0.87

Test-Retest (Fall/Spring)

Gender: Male

2

5,973

0.84

0.83, 0.85

Test-Retest (Winter/Spring)

Gender: Male

2

5,175

0.89

0.88, 0.89

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

294

0.83

0.80, 0.87

Test-Retest (Winter/Spring)

Ethnicity: American Indian or Alaskan

3

367

0.87

0.84, 0.89

Test-Retest (Fall/Winter)

Ethnicity: American Indian or Alaskan

4

308

0.84

0.81, 0.87

Test-Retest (Fall/Spring)

Ethnicity: American Indian or Alaskan

4

312

0.82

0.78, 0.85

Test-Retest (Winter/Spring)

Ethnicity: American Indian or Alaskan

4

328

0.82

0.78, 0.85

Test-Retest (Fall/Winter)

Ethnicity: American Indian or Alaskan

5

270

0.83

0.79, 0.87

Test-Retest (Fall/Spring)

Ethnicity: American Indian or Alaskan

5

277

0.85

0.82, 0.88

Test-Retest (Winter/Spring)

Ethnicity: American Indian or Alaskan

5

291

0.85

0.81, 0.87

Test-Retest (Fall/Winter)

Ethnicity: American Indian or Alaskan

6

309

0.84

0.81, 0.87

Test-Retest (Fall/Spring)

Ethnicity: American Indian or Alaskan

6

314

0.80

0.76, 0.84

Test-Retest (Winter/Spring)

Ethnicity: American Indian or Alaskan

6

318

0.82

0.78, 0.85

Test-Retest (Fall/Winter)

Ethnicity: American Indian or Alaskan

7

322

0.88

0.85, 0.90

Test-Retest (Fall/Spring)

Ethnicity: American Indian or Alaskan

7

330

0.87

0.84, 0.89

Test-Retest (Winter/Spring)

Ethnicity: American Indian or Alaskan

7

327

0.86

0.83, 0.89

Test-Retest (Fall/Winter)

Ethnicity: Asian or Pacific Islander

3

998

0.88

0.86, 0.89

Test-Retest (Fall/Spring)

Ethnicity: Asian or Pacific Islander

3

1,392

0.85

0.83, 0.86

Test-Retest (Winter/Spring)

Ethnicity: Asian or Pacific Islander

3

1,031

0.88

0.87, 0.89

Test-Retest (Fall/Winter)

Ethnicity: Asian or Pacific Islander

4

1,112

0.86

0.84, 0.87

Test-Retest (Fall/Spring)

Ethnicity: Asian or Pacific Islander

4

1,340

0.86

0.84, 0.87

Test-Retest (Winter/Spring)

Ethnicity: Asian or Pacific Islander

4

1,154

0.86

0.84, 0.87

Test-Retest (Fall/Winter)

Ethnicity: Asian or Pacific Islander

5

1,062

0.88

0.87, 0.89

Test-Retest (Fall/Spring)

Ethnicity: Asian or Pacific Islander

5

1,340

0.87

0.86, 0.88

Test-Retest (Winter/Spring)

Ethnicity: Asian or Pacific Islander

5

1,111

0.88

0.86, 0.89

Test-Retest (Fall/Winter)

Ethnicity: Asian or Pacific Islander

6

1,146

0.89

0.88, 0.90

Test-Retest (Fall/Spring)

Ethnicity: Asian or Pacific Islander

6

1,445

0.88

0.87, 0.89

Test-Retest (Winter/Spring)

Ethnicity: Asian or Pacific Islander

6

1,159

0.90

0.88, 0.91

Test-Retest (Fall/Winter)

Ethnicity: Asian or Pacific Islander

7

1,015

0.89

0.87, 0.90

Test-Retest (Fall/Spring)

Ethnicity: Asian or Pacific Islander

7

1,378

0.86

0.85, 0.87

Test-Retest (Winter/Spring)

Ethnicity: Asian or Pacific Islander

7

1,026

0.87

0.86, 0.89

Test-Retest (Fall/Winter)

Ethnicity: Asian or Pacific Islander

8

920

0.88

0.86, 0.89

Test-Retest (Fall/Spring)

Ethnicity: Asian or Pacific Islander

8

1,281

0.88

0.86, 0.89

Test-Retest (Winter/Spring)

Ethnicity: Asian or Pacific Islander

8

927

0.87

0.86, 0.89

Test-Retest (Fall/Winter)

Ethnicity: Black

3

565

0.86

0.84, 0.88

Test-Retest (Fall/Spring)

Ethnicity: Black

3

912

0.84

0.82, 0.85

Test-Retest (Winter/Spring)

Ethnicity: Black

3

607

0.84

0.81, 0.86

Test-Retest (Fall/Winter)

Ethnicity: Black

4

559

0.84

0.81, 0.86

Test-Retest (Fall/Spring)

Ethnicity: Black

4

894

0.81

0.78, 0.83

Test-Retest (Winter/Spring)

Ethnicity: Black

4

601

0.84

0.81, 0.86

Test-Retest (Fall/Winter)

Ethnicity: Black

5

752

0.84

0.82, 0.86

Test-Retest (Fall/Spring)

Ethnicity: Black

5

903

0.82

0.79, 0.84

Test-Retest (Winter/Spring)

Ethnicity: Black

5

788

0.83

0.81, 0.85

Test-Retest (Fall/Winter)

Ethnicity: Black

6

805

0.83

0.81, 0.85

Test-Retest (Fall/Spring)

Ethnicity: Black

6

1,120

0.82

0.80, 0.84

Test-Retest (Winter/Spring)

Ethnicity: Black

6

842

0.81

0.78, 0.83

Test-Retest (Fall/Winter)

Ethnicity: Black

7

799

0.82

0.80, 0.84

Test-Retest (Fall/Spring)

Ethnicity: Black

7

1,153

0.82

0.80, 0.84

Test-Retest (Winter/Spring)

Ethnicity: Black

7

830

0.83

0.81, 0.85

Test-Retest (Fall/Winter)

Ethnicity: Black

8

709

0.81

0.78, 0.83

Test-Retest (Fall/Spring)

Ethnicity: Black

8

880

0.77

0.74, 0.80

Test-Retest (Winter/Spring)

Ethnicity: Black

8

754

0.79

0.76, 0.81

Test-Retest (Fall/Winter)

Ethnicity: Hispanic

3

2,630

0.88

0.87, 0.89

Test-Retest (Fall/Spring)

Ethnicity: Hispanic

3

3,389

0.85

0.84, 0.86

Test-Retest (Winter/Spring)

Ethnicity: Hispanic

3

2,784

0.87

0.87, 0.88

Test-Retest (Fall/Winter)

Ethnicity: Hispanic

4

2,925

0.85

0.83, 0.86

Test-Retest (Fall/Spring)

Ethnicity: Hispanic

4

3,574

0.83

0.82, 0.84

Test-Retest (Winter/Spring)

Ethnicity: Hispanic

4

3,083

0.86

0.85, 0.87

Test-Retest (Fall/Winter)

Ethnicity: Hispanic

5

3,194

0.86

0.86, 0.87

Test-Retest (Fall/Spring)

Ethnicity: Hispanic

5

3,506

0.85

0.84, 0.86

Test-Retest (Winter/Spring)

Ethnicity: Hispanic

5

3,336

0.86

0.85, 0.87

Test-Retest (Fall/Winter)

Ethnicity: Hispanic

6

3,417

0.85

0.84, 0.86

Test-Retest (Fall/Spring)

Ethnicity: Hispanic

6

3,999

0.85

0.84, 0.86

Test-Retest (Winter/Spring)

Ethnicity: Hispanic

6

3,480

0.85

0.84, 0.86

Test-Retest (Fall/Winter)

Ethnicity: Hispanic

7

3,134

0.84

0.83, 0.85

Test-Retest (Fall/Spring)

Ethnicity: Hispanic

7

3,839

0.83

0.82, 0.84

Test-Retest (Winter/Spring)

Ethnicity: Hispanic

7

3,228

0.83

0.82, 0.84

Test-Retest (Fall/Winter)

Ethnicity: Hispanic

8

2,866

0.85

0.84, 0.86

Test-Retest (Fall/Spring)

Ethnicity: Hispanic

8

3,352

0.84

0.83, 0.85

Test-Retest (Winter/Spring)

Ethnicity: Hispanic

8

2,932

0.84

0.83, 0.85

Test-Retest (Fall/Winter)

Ethnicity: Multi-Ethnic

3

365

0.89

0.86, 0.91

Test-Retest (Fall/Spring)

Ethnicity: Multi-Ethnic

3

504

0.86

0.83, 0.88

Test-Retest (Winter/Spring)

Ethnicity: Multi-Ethnic

3

384

0.88

0.85, 0.90

Test-Retest (Fall/Winter)

Ethnicity: Multi-Ethnic

4

353

0.88

0.86, 0.91

Test-Retest (Fall/Spring)

Ethnicity: Multi-Ethnic

4

446

0.88

0.85, 0.90

Test-Retest (Winter/Spring)

Ethnicity: Multi-Ethnic

4

375

0.88

0.85, 0.90

Test-Retest (Fall/Winter)

Ethnicity: Multi-Ethnic

5

369

0.88

0.86, 0.90

Test-Retest (Fall/Spring)

Ethnicity: Multi-Ethnic

5

421

0.88

0.85, 0.90

Test-Retest (Winter/Spring)

Ethnicity: Multi-Ethnic

5

387

0.89

0.86, 0.91

Test-Retest (Fall/Winter)

Ethnicity: Multi-Ethnic

6

359

0.88

0.85, 0.90

Test-Retest (Fall/Spring)

Ethnicity: Multi-Ethnic

6

489

0.87

0.85, 0.89

Test-Retest (Winter/Spring)

Ethnicity: Multi-Ethnic

6

363

0.87

0.84, 0.89

Test-Retest (Fall/Winter)

Ethnicity: Multi-Ethnic

7

346

0.84

0.81, 0.87

Test-Retest (Fall/Spring)

Ethnicity: Multi-Ethnic

7

453

0.84

0.81, 0.87

Test-Retest (Winter/Spring)

Ethnicity: Multi-Ethnic

7

348

0.83

0.79, 0.86

Test-Retest (Fall/Winter)

Ethnicity: Multi-Ethnic

8

296

0.85

0.81, 0.88

Test-Retest (Fall/Spring)

Ethnicity: Multi-Ethnic

8

381

0.82

0.79, 0.85

Test-Retest (Winter/Spring)

Ethnicity: Multi-Ethnic

8

303

0.84

0.80, 0.87

Test-Retest (Fall/Winter)

Ethnicity: Not Specified or Other

3

1,291

0.87

0.85, 0.88

Test-Retest (Fall/Spring)

Ethnicity: Not Specified or Other

3

2,428

0.84

0.83, 0.85

Test-Retest (Winter/Spring)

Ethnicity: Not Specified or Other

3

1,314

0.88

0.86, 0.89

Test-Retest (Fall/Winter)

Ethnicity: Not Specified or Other

4

1,297

0.86

0.84, 0.87

Test-Retest (Fall/Spring)

Ethnicity: Not Specified or Other

4

2,477

0.85

0.84, 0.86

Test-Retest (Winter/Spring)

Ethnicity: Not Specified or Other

4

1,322

0.87

0.85, 0.88

Test-Retest (Fall/Winter)

Ethnicity: Not Specified or Other

5

1,237

0.86

0.85, 0.88

Test-Retest (Fall/Spring)

Ethnicity: Not Specified or Other

5

2,349

0.85

0.84, 0.86

Test-Retest (Winter/Spring)

Ethnicity: Not Specified or Other

5

1,257

0.86

0.85, 0.88

Test-Retest (Fall/Winter)

Ethnicity: Not Specified or Other

6

955

0.88

0.86, 0.89

Test-Retest (Fall/Spring)

Ethnicity: Not Specified or Other

6

2,059

0.86

0.85, 0.87

Test-Retest (Winter/Spring)

Ethnicity: Not Specified or Other

6

977

0.88

0.86, 0.89

Test-Retest (Fall/Winter)

Ethnicity: Not Specified or Other

7

298

0.89

0.86, 0.91

Test-Retest (Fall/Spring)

Ethnicity: Not Specified or Other

7

1,001

0.85

0.83, 0.87

Test-Retest (Winter/Spring)

Ethnicity: Not Specified or Other

7

308

0.86

0.82, 0.88

Test-Retest (Fall/Winter)

Ethnicity: Not Specified or Other

8

348

0.82

0.78, 0.85

Test-Retest (Fall/Spring)

Ethnicity: Not Specified or Other

8

445

0.82

0.79, 0.85

Test-Retest (Winter/Spring)

Ethnicity: Not Specified or Other

8

361

0.85

0.81, 0.87

Test-Retest (Fall/Winter)

Ethnicity: White

3

4,451

0.85

0.84, 0.86

Test-Retest (Fall/Spring)

Ethnicity: White

3

5,873

0.83

0.82, 0.84

Test-Retest (Winter/Spring)

Ethnicity: White

3

4,656

0.86

0.85, 0.87

Test-Retest (Fall/Winter)

Ethnicity: White

4

5,013

0.85

0.84, 0.86

Test-Retest (Fall/Spring)

Ethnicity: White

4

6,122

0.84

0.83, 0.85

Test-Retest (Winter/Spring)

Ethnicity: White

4

5,258

0.86

0.85, 0.86

Test-Retest (Fall/Winter)

Ethnicity: White

5

5,305

0.87

0.86, 0.87

Test-Retest (Fall/Spring)

Ethnicity: White

5

6,407

0.85

0.84, 0.85

Test-Retest (Winter/Spring)

Ethnicity: White

5

5,480

0.86

0.85, 0.86

Test-Retest (Fall/Winter)

Ethnicity: White

6

5,386

0.86

0.86, 0.87

Test-Retest (Fall/Spring)

Ethnicity: White

6

6,865

0.84

0.83, 0.84

Test-Retest (Winter/Spring)

Ethnicity: White

6

5,444

0.85

0.85, 0.86

Test-Retest (Fall/Winter)

Ethnicity: White

7

5,386

0.87

0.86, 0.87

Test-Retest (Fall/Spring)

Ethnicity: White

7

7,678

0.84

0.84, 0.85

Test-Retest (Winter/Spring)

Ethnicity: White

7

5,470

0.86

0.85, 0.86

Test-Retest (Fall/Winter)

Ethnicity: White

8

5,237

0.85

0.84, 0.85

Test-Retest (Fall/Spring)

Ethnicity: White

8

7,407

0.83

0.82, 0.84

Test-Retest (Winter/Spring)

Ethnicity: White

8

5,320

0.85

0.84, 0.85

Test-Retest (Fall/Winter)

Gender: Female

3

5,185

0.88

0.88, 0.89

Test-Retest (Fall/Spring)

Gender: Female

3

7,302

0.86

0.86, 0.87

Test-Retest (Winter/Spring)

Gender: Female

3

5,440

0.89

0.88, 0.89

Test-Retest (Fall/Winter)

Gender: Female

4

5,641

0.88

0.88, 0.89

Test-Retest (Fall/Spring)

Gender: Female

4

7,418

0.87

0.86, 0.87

Test-Retest (Winter/Spring)

Gender: Female

4

5,911

0.88

0.88, 0.89

Test-Retest (Fall/Winter)

Gender: Female

5

6,053

0.88

0.88, 0.89

Test-Retest (Fall/Spring)

Gender: Female

5

7,552

0.87

0.86, 0.87

Test-Retest (Winter/Spring)

Gender: Female

5

6,276

0.87

0.87, 0.88

Test-Retest (Fall/Winter)

Gender: Female

6

5,906

0.88

0.88, 0.89

Test-Retest (Fall/Spring)

Gender: Female

6

7,888

0.87

0.86, 0.87

Test-Retest (Winter/Spring)

Gender: Female

6

6,004

0.88

0.87, 0.88

Test-Retest (Fall/Winter)

Gender: Female

7

5,387

0.88

0.88, 0.89

Test-Retest (Fall/Spring)

Gender: Female

7

7,665

0.87

0.86, 0.87

Test-Retest (Winter/Spring)

Gender: Female

7

5,476

0.87

0.87, 0.88

Test-Retest (Fall/Winter)

Gender: Female

8

5,127

0.87

0.87, 0.88

Test-Retest (Fall/Spring)

Gender: Female

8

6,792

0.87

0.86, 0.87

Test-Retest (Winter/Spring)

Gender: Female

8

5,235

0.87

0.86, 0.88

Test-Retest (Fall/Winter)

Gender: Male

3

5,417

0.89

0.88, 0.89

Test-Retest (Fall/Spring)

Gender: Male

3

7,508

0.86

0.86, 0.87

Test-Retest (Winter/Spring)

Gender: Male

3

5,716

0.89

0.89, 0.90

Test-Retest (Fall/Winter)

Gender: Male

4

5,935

0.87

0.86, 0.88

Test-Retest (Fall/Spring)

Gender: Male

4

7,763

0.86

0.86, 0.87

Test-Retest (Winter/Spring)

Gender: Male

4

6,219

0.88

0.87, 0.89

Test-Retest (Fall/Winter)

Gender: Male

5

6,153

0.89

0.88, 0.89

Test-Retest (Fall/Spring)

Gender: Male

5

7,669

0.87

0.87, 0.88

Test-Retest (Winter/Spring)

Gender: Male

5

6,392

0.88

0.88, 0.89

Test-Retest (Fall/Winter)

Gender: Male

6

6,481

0.88

0.87, 0.88

Test-Retest (Fall/Spring)

Gender: Male

6

8,415

0.86

0.86, 0.87

Test-Retest (Winter/Spring)

Gender: Male

6

6,590

0.87

0.87, 0.88

Test-Retest (Fall/Winter)

Gender: Male

7

5,922

0.87

0.86, 0.87

Test-Retest (Fall/Spring)

Gender: Male

7

8,182

0.86

0.85, 0.86

Test-Retest (Winter/Spring)

Gender: Male

7

6,070

0.86

0.86, 0.87

Test-Retest (Fall/Winter)

Gender: Male

8

5,377

0.86

0.85, 0.87

Test-Retest (Fall/Spring)

Gender: Male

8

7,093

0.85

0.84, 0.85

Test-Retest (Winter/Spring)

Gender: Male

8

5,493

0.85

0.85, 0.86



[1] Samejima, F. (1977.) A Use of the Information Function in Tailored Testing. Applied Psychological Measurement, 1(3), 233-247.

[2] Samejima, F. (1994.) Estimation of Reliability Coefficients Using the Test Information Function and its Modifications. Applied Psychological Measurement, 18(3), 229-244.

[3] Green, B. F., Bock, R. D., Humphreys, L. G., Linn, R. L., and Reckase, M. D. (1984.) Technical Guidelines for Assessing Computerized Adaptive Tests. Journal of Educational Measurement, 21(4), 347-360.

[4] [4] 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
RatingFull bubbledFull bubbledFull bubbledFull bubbledFull bubbledFull bubbledFull bubbled
  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 ELA 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

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 PARCC data were from the Spring 2016 administration of the PARCC assessment, spanning approximately from March 2016 through June 2016.

Size

Table 10, Table 11, and Table 12 summarize the total number of students, as functions of grade, state, region, and division.

Male

51.09%

Female

48.89%

Unknown

0.02%

Other SES Indicators

Not Provided

Free or reduced-price lunch

Not Provided

White, Non-Hispanic

45.11%

Black, Non-Hispanic

6.42%

Hispanic

23.70%

American Indian/Alaska Native:

1.84%

Asian/Pacific Islander:

8.62%

Multi-Ethnic

2.88%

Not Specified or Other

11.43%

Disability classification

Not Provided

First language

Not Provided

Language proficiency status

Not Provided

 

Table 10: Number of Students Per State by Grade

 

2

3

4

5

6

7

8

Total

CO

2,967

2,967

3,185

3,097

3,846

3,659

3,192

19,946

DC

167

167

160

140

221

165

191

1,044

IL

12,140

12,140

12,064

12,129

12,766

12,525

10,970

72,594

NJ

643

643

665

610

649

622

548

3,737

NM

208

208

205

201

197

208

186

1,205

RI

209

209

199

202

217

190

200

1,217

Total

16,334

16,334

16,478

16,379

17,896

17,369

15,287

99,743

 

Table 11: Number of Students Per Region by Grade

 

2

3

4

5

6

7

8

Total

Midwest

12,140

12,140

12,064

12,129

12,766

12,525

10,970

72,594

Northeast

852

852

864

812

866

812

748

4,954

South

167

167

160

140

221

165

191

1,044

West

3,175

3,175

3,390

3,298

4,043

3,867

3,378

21,151

Total

16,334

16,334

16,478

16,379

17,896

17,369

15,287

99,743

 

Table 12: Number of Students Per Division by Grade

 

2

3

4

5

6

7

8

Total

East North Central

12,140

12,140

12,064

12,129

12,766

12,525

10,970

72,594

Middle Atlantic

643

643

665

610

649

622

548

3,737

Mountain

3,175

3,175

3,390

3,298

4,043

3,867

3,378

21,151

New England

209

209

199

202

217

190

200

1,217

South Atlantic

167

167

160

140

221

165

191

1,044

Total

16,334

16,334

16,478

16,379

17,896

17,369

15,287

99,743

 

 

  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 scores on the PARCC test, also administered in Spring 2016 for Grades 3–8. 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 Grades 3–8. 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 2016.

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.

As may be seen from correlation measures internal to our MAP Growth system and external to our system (PARCC), the correlation coefficients are highly similar (generally at or above 0.75) across measures supporting the claim that our measures are useful for predicting to other important measures in the educational system and can serve well for the screening purpose.

It is also worth noting, correlations internal to the system for MAP Growth Reading are more proximal than correlations for PARCC. It is of no surprise that the correlations of MAP Growth Reading with PARCC ELA are somewhat tempered, especially when considering PARCC ELA includes the measurement of writing and MAP Growth Reading does not.

 

Type of Validity

Grade

Test or

Criterion

N

Coefficient

Confidence

Interval

Construct (Fall)

2

MAP Growth: Reading (Fall 2015)

11,655

0.84

0.83, 0.84

Construct (Fall)

2

MAP Growth: Reading (Winter 2016)

9,030

0.81

0.81, 0.82

Construct (Fall)

2

MAP Growth: Reading (Spring 2015)

11,768

0.84

0.83, 0.84

Construct (Fall)

2

MAP Growth: Reading (Spring 2016)

12,410

0.80

0.79, 0.80

Predictive (Fall)

2

PARCC: ELA

12,410

0.77

0.76, 0.77

Construct (Winter)

2

MAP Growth: Reading (Fall 2015)

9,861

0.87

0.87, 0.88

Construct (Winter)

2

MAP Growth: Reading (Winter 2016)

8,912

0.86

0.85, 0.86

Construct (Winter)

2

MAP Growth: Reading (Spring 2015)

10,192

0.89

0.88, 0.89

Construct (Winter)

2

MAP Growth: Reading (Spring 2016)

10,341

0.84

0.83, 0.84

Predictive (Winter)

2

PARCC: ELA

10,341

0.80

0.79, 0.80

Construct (Spring)

2

MAP Growth: Reading (Fall 2015)

11,491

0.88

0.87, 0.88

Construct (Spring)

2

MAP Growth: Reading (Winter 2016)

9,249

0.87

0.87, 0.88

Construct (Spring)

2

MAP Growth: Reading (Spring 2016)

12,425

0.85

0.84, 0.85

Predictive (Spring)

2

PARCC: ELA

12,425

0.79

0.78, 0.80

Concurrent (Spring/Spring)

3

PARCC ELA

16,334

0.83

0.83, 0.84

Concurrent (Spring/Spring)

4

PARCC ELA

16,478

0.84

0.83, 0.84

Concurrent (Spring/Spring)

5

PARCC ELA

16,379

0.82

0.82, 0.83

Concurrent (Spring/Spring)

6

PARCC ELA

17,896

0.81

0.81, 0.82

Concurrent (Spring/Spring)

7

PARCC ELA

17,369

0.80

0.79, 0.80

Concurrent (Spring/Spring)

8

PARCC ELA

15,287

0.78

0.78, 0.79

Predictive (Fall/Spring)

3

PARCC ELA

14,812

0.81

0.80, 0.81

Predictive (Fall/Spring)

4

PARCC ELA

15,182

0.81

0.81, 0.82

Predictive (Fall/Spring)

5

PARCC ELA

15,222

0.81

0.80, 0.82

Predictive (Fall/Spring)

6

PARCC ELA

16,305

0.80

0.79, 0.80

Predictive (Fall/Spring)

7

PARCC ELA

15,852

0.79

0.78, 0.79

Predictive (Fall/Spring)

8

PARCC ELA

13,887

0.78

0.77, 0.78

Predictive (Winter/Spring)

3

PARCC ELA

11,158

0.82

0.82, 0.83

Predictive (Winter/Spring)

4

PARCC ELA

12,130

0.82

0.82, 0.83

Predictive (Winter/Spring)

5

PARCC ELA

12,668

0.81

0.81, 0.82

Predictive (Winter/Spring)

6

PARCC ELA

12,595

0.80

0.80, 0.81

Predictive (Winter/Spring)

7

PARCC ELA

11,550

0.79

0.78, 0.80

Predictive (Winter/Spring)

8

PARCC ELA

10,730

0.77

0.77, 0.78

 

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

Not 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 0.70s and 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: Reading (Fall 2015)

1,117

0.83

0.81, 0.85

Construct (Fall)

Ethnicity: Asian or Pacific Islander

2

MAP Growth: Reading (Winter 2016)

910

0.82

0.80, 0.84

Construct (Fall)

Ethnicity: Asian or Pacific Islander

2

MAP Growth: Reading (Spring 2015)

1,136

0.83

0.82, 0.85

Construct (Fall)

Ethnicity: Asian or Pacific Islander

2

MAP Growth: Reading (Spring 2016)

1,145

0.80

0.78, 0.82

Predictive (Fall)

Ethnicity: Asian or Pacific Islander

2

PARCC: ELA

1,145

0.73

0.71, 0.76

Construct (Winter)

Ethnicity: Asian or Pacific Islander

2

MAP Growth: Reading (Fall 2015)

1,026

0.85

0.83, 0.86

Construct (Winter)

Ethnicity: Asian or Pacific Islander

2

MAP Growth: Reading (Winter 2016)

906

0.85

0.83, 0.86

Construct (Winter)

Ethnicity: Asian or Pacific Islander

2

MAP Growth: Reading (Spring 2015)

1,043

0.87

0.85, 0.88

Construct (Winter)

Ethnicity: Asian or Pacific Islander

2

MAP Growth: Reading (Spring 2016)

1,047

0.81

0.79, 0.83

Predictive (Winter)

Ethnicity: Asian or Pacific Islander

2

PARCC: ELA

1,047

0.75

0.72, 0.78

Construct (Spring)

Ethnicity: Asian or Pacific Islander

2

MAP Growth: Reading (Fall 2015)

1,152

0.87

0.86, 0.88

Construct (Spring)

Ethnicity: Asian or Pacific Islander

2

MAP Growth: Reading (Winter 2016)

939

0.86

0.84, 0.87

Construct (Spring)

Ethnicity: Asian or Pacific Islander

2

MAP Growth: Reading (Spring 2016)

1,180

0.83

0.81, 0.84

Predictive (Spring)

Ethnicity: Asian or Pacific Islander

2

PARCC: ELA

1,180

0.77

0.74, 0.79

Construct (Fall)

Ethnicity: Black

2

MAP Growth: Reading (Fall 2015)

524

0.81

0.78, 0.84

Construct (Fall)

Ethnicity: Black

2

MAP Growth: Reading (Winter 2016)

335

0.79

0.74, 0.82

Construct (Fall)

Ethnicity: Black

2

MAP Growth: Reading (Spring 2015)

549

0.81

0.78, 0.84

Construct (Fall)

Ethnicity: Black

2

MAP Growth: Reading (Spring 2016)

566

0.77

0.74, 0.81

Predictive (Fall)

Ethnicity: Black

2

PARCC: ELA

566

0.76

0.72, 0.79

Construct (Winter)

Ethnicity: Black

2

MAP Growth: Reading (Fall 2015)

382

0.84

0.81, 0.87

Construct (Winter)

Ethnicity: Black

2

MAP Growth: Reading (Winter 2016)

339

0.82

0.78, 0.85

Construct (Winter)

Ethnicity: Black

2

MAP Growth: Reading (Spring 2015)

413

0.86

0.84, 0.89

Construct (Winter)

Ethnicity: Black

2

MAP Growth: Reading (Spring 2016)

428

0.81

0.77, 0.84

Predictive (Winter)

Ethnicity: Black

2

PARCC: ELA

428

0.79

0.76, 0.83

Construct (Spring)

Ethnicity: Black

2

MAP Growth: Reading (Fall 2015)

539

0.85

0.83, 0.87

Construct (Spring)

Ethnicity: Black

2

MAP Growth: Reading (Winter 2016)

349

0.85

0.81, 0.87

Construct (Spring)

Ethnicity: Black

2

MAP Growth: Reading (Spring 2016)

597

0.82

0.79, 0.84

Predictive (Spring)

Ethnicity: Black

2

PARCC: ELA

597

0.76

0.73, 0.80

Construct (Fall)

Ethnicity: Hispanic

2

MAP Growth: Reading (Fall 2015)

2,670

0.81

0.79, 0.82

Construct (Fall)

Ethnicity: Hispanic

2

MAP Growth: Reading (Winter 2016)

2,330

0.78

0.76, 0.79

Construct (Fall)

Ethnicity: Hispanic

2

MAP Growth: Reading (Spring 2015)

2,767

0.82

0.81, 0.83

Construct (Fall)

Ethnicity: Hispanic

2

MAP Growth: Reading (Spring 2016)

2,844

0.76

0.75, 0.78

Predictive (Fall)

Ethnicity: Hispanic

2

PARCC: ELA

2,844

0.73

0.72, 0.75

Construct (Winter)

Ethnicity: Hispanic

2

MAP Growth: Reading (Fall 2015)

2,422

0.85

0.84, 0.86

Construct (Winter)

Ethnicity: Hispanic

2

MAP Growth: Reading (Winter 2016)

2,367

0.83

0.82, 0.84

Construct (Winter)

Ethnicity: Hispanic

2

MAP Growth: Reading (Spring 2015)

2,507

0.88

0.87, 0.89

Construct (Winter)

Ethnicity: Hispanic

2

MAP Growth: Reading (Spring 2016)

2,577

0.80

0.79, 0.82

Predictive (Winter)

Ethnicity: Hispanic

2

PARCC: ELA

2,577

0.78

0.76, 0.79

Construct (Spring)

Ethnicity: Hispanic

2

MAP Growth: Reading (Fall 2015)

2,810

0.87

0.86, 0.88

Construct (Spring)

Ethnicity: Hispanic

2

MAP Growth: Reading (Winter 2016)

2,433

0.84

0.83, 0.85

Construct (Spring)

Ethnicity: Hispanic

2

MAP Growth: Reading (Spring 2016)

3,009

0.82

0.81, 0.84

Predictive (Spring)

Ethnicity: Hispanic

2

PARCC: ELA

3,009

0.76

0.75, 0.78

Construct (Fall)

Ethnicity: Multi-Ethnic

2

MAP Growth: Reading (Fall 2015)

343

0.88

0.85, 0.90

Construct (Fall)

Ethnicity: Multi-Ethnic

2

MAP Growth: Reading (Winter 2016)

277

0.84

0.80, 0.87

Construct (Fall)

Ethnicity: Multi-Ethnic

2

MAP Growth: Reading (Spring 2015)

362

0.87

0.84, 0.89

Construct (Fall)

Ethnicity: Multi-Ethnic

2

MAP Growth: Reading (Spring 2016)

373

0.81

0.77, 0.84

Predictive (Fall)

Ethnicity: Multi-Ethnic

2

PARCC: ELA

373

0.78

0.74, 0.82

Construct (Winter)

Ethnicity: Multi-Ethnic

2

MAP Growth: Reading (Fall 2015)

293

0.89

0.86, 0.91

Construct (Winter)

Ethnicity: Multi-Ethnic

2

MAP Growth: Reading (Winter 2016)

279

0.87

0.83, 0.89

Construct (Winter)

Ethnicity: Multi-Ethnic

2

MAP Growth: Reading (Spring 2015)

305

0.87

0.84, 0.89

Construct (Winter)

Ethnicity: Multi-Ethnic

2

MAP Growth: Reading (Spring 2016)

315

0.81

0.76, 0.84

Predictive (Winter)

Ethnicity: Multi-Ethnic

2

PARCC: ELA

315

0.79

0.74, 0.83

Construct (Spring)

Ethnicity: Multi-Ethnic

2

MAP Growth: Reading (Fall 2015)

346

0.88

0.85, 0.90

Construct (Spring)

Ethnicity: Multi-Ethnic

2

MAP Growth: Reading (Winter 2016)

279

0.87

0.83, 0.89

Construct (Spring)

Ethnicity: Multi-Ethnic

2

MAP Growth: Reading (Spring 2016)

377

0.82

0.79, 0.85

Predictive (Spring)

Ethnicity: Multi-Ethnic

2

PARCC: ELA

377

0.78

0.73, 0.82

Construct (Fall)

Ethnicity: Not Specified or Other

2

MAP Growth: Reading (Fall 2015)

2,261

0.83

0.81, 0.84

Construct (Fall)

Ethnicity: Not Specified or Other

2

MAP Growth: Reading (Winter 2016)

1,195

0.78

0.76, 0.80

Construct (Fall)

Ethnicity: Not Specified or Other

2

MAP Growth: Reading (Spring 2015)

1,816

0.83

0.81, 0.84

Construct (Fall)

Ethnicity: Not Specified or Other

2

MAP Growth: Reading (Spring 2016)

2,279

0.78

0.76, 0.79

Predictive (Fall)

Ethnicity: Not Specified or Other

2

PARCC: ELA

2,279

0.76

0.75, 0.78

Construct (Winter)

Ethnicity: Not Specified or Other

2

MAP Growth: Reading (Fall 2015)

1,380

0.86

0.84, 0.87

Construct (Winter)

Ethnicity: Not Specified or Other

2

MAP Growth: Reading (Winter 2016)

1,082

0.84

0.82, 0.85

Construct (Winter)

Ethnicity: Not Specified or Other

2

MAP Growth: Reading (Spring 2015)

1,382

0.87

0.86, 0.88

Construct (Winter)

Ethnicity: Not Specified or Other

2

MAP Growth: Reading (Spring 2016)

1,391

0.82

0.80, 0.83

Predictive (Winter)

Ethnicity: Not Specified or Other

2

PARCC: ELA

1,391

0.78

0.76, 0.80

Construct (Spring)

Ethnicity: Not Specified or Other

2

MAP Growth: Reading (Fall 2015)

1,834

0.87

0.86, 0.88

Construct (Spring)

Ethnicity: Not Specified or Other

2

MAP Growth: Reading (Winter 2016)

1,206

0.86

0.85, 0.88

Construct (Spring)

Ethnicity: Not Specified or Other

2

MAP Growth: Reading (Spring 2016)

1,858

0.85

0.83, 0.86

Predictive (Spring)

Ethnicity: Not Specified or Other

2

PARCC: ELA

1,858

0.78

0.77, 0.80

Construct (Fall)

Ethnicity: White

2

MAP Growth: Reading (Fall 2015)

4,602

0.80

0.79, 0.81

Construct (Fall)

Ethnicity: White

2

MAP Growth: Reading (Winter 2016)

3,836

0.77

0.76, 0.78

Construct (Fall)

Ethnicity: White

2

MAP Growth: Reading (Spring 2015)

4,987

0.80

0.79, 0.81

Construct (Fall)

Ethnicity: White

2

MAP Growth: Reading (Spring 2016)

5,046

0.75

0.74, 0.76

Predictive (Fall)

Ethnicity: White

2

PARCC: ELA

5,046

0.71

0.69, 0.72

Construct (Winter)

Ethnicity: White

2

MAP Growth: Reading (Fall 2015)

4,225

0.83

0.82, 0.84

Construct (Winter)

Ethnicity: White

2

MAP Growth: Reading (Winter 2016)

3,795

0.82

0.80, 0.83

Construct (Winter)

Ethnicity: White

2

MAP Growth: Reading (Spring 2015)

4,395

0.85

0.84, 0.86

Construct (Winter)

Ethnicity: White

2

MAP Growth: Reading (Spring 2016)

4,431

0.79

0.78, 0.80

Predictive (Winter)

Ethnicity: White

2

PARCC: ELA

4,431

0.74

0.72, 0.75

Construct (Spring)

Ethnicity: White

2

MAP Growth: Reading (Fall 2015)

4,674

0.84

0.83, 0.85

Construct (Spring)

Ethnicity: White

2

MAP Growth: Reading (Winter 2016)

3,897

0.83

0.82, 0.84

Construct (Spring)

Ethnicity: White

2

MAP Growth: Reading (Spring 2016)

5,247

0.80

0.79, 0.81

Predictive (Spring)

Ethnicity: White

2

PARCC: ELA

5,247

0.74

0.73, 0.75

Construct (Fall)

Gender: Female

2

MAP Growth: Reading (Fall 2015)

5,737

0.84

0.83, 0.85

Construct (Fall)

Gender: Female

2

MAP Growth: Reading (Winter 2016)

4,391

0.82

0.81, 0.83

Construct (Fall)

Gender: Female

2

MAP Growth: Reading (Spring 2015)

5,793

0.84

0.83, 0.85

Construct (Fall)

Gender: Female

2

MAP Growth: Reading (Spring 2016)

6,098

0.80

0.79, 0.81

Predictive (Fall)

Gender: Female

2

PARCC: ELA

6,098

0.78

0.77, 0.79

Construct (Winter)

Gender: Female

2

MAP Growth: Reading (Fall 2015)

4,842

0.87

0.86, 0.88

Construct (Winter)

Gender: Female

2

MAP Growth: Reading (Winter 2016)

4,329

0.86

0.85, 0.87

Construct (Winter)

Gender: Female

2

MAP Growth: Reading (Spring 2015)

5,015

0.89

0.88, 0.89

Construct (Winter)

Gender: Female

2

MAP Growth: Reading (Spring 2016)

5,068

0.84

0.83, 0.85

Predictive (Winter)

Gender: Female

2

PARCC: ELA

5,068

0.80

0.79, 0.81

Construct (Spring)

Gender: Female

2

MAP Growth: Reading (Fall 2015)

5,641

0.87

0.87, 0.88

Construct (Spring)

Gender: Female

2

MAP Growth: Reading (Winter 2016)

4,495

0.87

0.87, 0.88

Construct (Spring)

Gender: Female

2

MAP Growth: Reading (Spring 2016)

6,085

0.85

0.84, 0.86

Predictive (Spring)

Gender: Female

2

PARCC: ELA

6,085

0.80

0.79, 0.81

Construct (Fall)

Gender: Male

2

MAP Growth: Reading (Fall 2015)

5,917

0.84

0.83, 0.84

Construct (Fall)

Gender: Male

2

MAP Growth: Reading (Winter 2016)

4,639

0.80

0.79, 0.81

Construct (Fall)

Gender: Male

2

MAP Growth: Reading (Spring 2015)

5,973

0.84

0.83, 0.85

Construct (Fall)

Gender: Male

2

MAP Growth: Reading (Spring 2016)

6,310

0.79

0.78, 0.80

Predictive (Fall)

Gender: Male

2

PARCC: ELA

6,310

0.75

0.74, 0.76

Construct (Winter)

Gender: Male

2

MAP Growth: Reading (Fall 2015)

5,017

0.87

0.86, 0.88

Construct (Winter)

Gender: Male

2

MAP Growth: Reading (Winter 2016)

4,582

0.85

0.84, 0.86

Construct (Winter)

Gender: Male

2

MAP Growth: Reading (Spring 2015)

5,175

0.89

0.88, 0.89

Construct (Winter)

Gender: Male

2

MAP Growth: Reading (Spring 2016)

5,271

0.83

0.82, 0.84

Predictive (Winter)

Gender: Male

2

PARCC: ELA

5,271

0.79

0.78, 0.80

Construct (Spring)

Gender: Male

2

MAP Growth: Reading (Fall 2015)

5,848

0.88

0.87, 0.88

Construct (Spring)

Gender: Male

2

MAP Growth: Reading (Winter 2016)

4,753

0.87

0.86, 0.87

Construct (Spring)

Gender: Male

2

MAP Growth: Reading (Spring 2016)

6,337

0.85

0.84, 0.85

Predictive (Spring)

Gender: Male

2

PARCC: ELA

6,337

0.78

0.77, 0.79

Concurrent (Spring/Spring)

Ethnicity: American Indian or Alaskan

3

PARCC Reading

380

0.81

0.77, 0.84

Concurrent (Spring/Spring)

Ethnicity: American Indian or Alaskan

4

PARCC Reading

340

0.81

0.77, 0.84

Concurrent (Spring/Spring)

Ethnicity: American Indian or Alaskan

5

PARCC Reading

303

0.80

0.75, 0.83

Concurrent (Spring/Spring)

Ethnicity: American Indian or Alaskan

6

PARCC Reading

333

0.78

0.73, 0.82

Concurrent (Spring/Spring)

Ethnicity: American Indian or Alaskan

7

PARCC Reading

341

0.79

0.75, 0.83

Predictive (Fall/Spring)

Ethnicity: American Indian or Alaskan

3

PARCC Reading

294

0.76

0.71, 0.81

Predictive (Fall/Spring)

Ethnicity: American Indian or Alaskan

4

PARCC Reading

312

0.80

0.76, 0.84

Predictive (Fall/Spring)

Ethnicity: American Indian or Alaskan

5

PARCC Reading

277

0.80

0.75, 0.83

Predictive (Fall/Spring)

Ethnicity: American Indian or Alaskan

6

PARCC Reading

314

0.74

0.68, 0.79

Predictive (Fall/Spring)

Ethnicity: American Indian or Alaskan

7

PARCC Reading

330

0.78

0.73, 0.81

Predictive (Winter/Spring)

Ethnicity: American Indian or Alaskan

3

PARCC Reading

367

0.77

0.73, 0.81

Predictive (Winter/Spring)

Ethnicity: American Indian or Alaskan

4

PARCC Reading

328

0.76

0.71, 0.80

Predictive (Winter/Spring)

Ethnicity: American Indian or Alaskan

5

PARCC Reading

291

0.76

0.71, 0.81

Predictive (Winter/Spring)

Ethnicity: American Indian or Alaskan

6

PARCC Reading

318

0.76

0.71, 0.81

Predictive (Winter/Spring)

Ethnicity: American Indian or Alaskan

7

PARCC Reading

327

0.78

0.73, 0.82

Concurrent (Spring/Spring)

Ethnicity: Asian or Pacific Islander

3

PARCC Reading

1,456

0.81

0.79, 0.82

Concurrent (Spring/Spring)

Ethnicity: Asian or Pacific Islander

4

PARCC Reading

1,421

0.82

0.80, 0.84

Concurrent (Spring/Spring)

Ethnicity: Asian or Pacific Islander

5

PARCC Reading

1,406

0.81

0.79, 0.83

Concurrent (Spring/Spring)

Ethnicity: Asian or Pacific Islander

6

PARCC Reading

1,490

0.82

0.80, 0.84

Concurrent (Spring/Spring)

Ethnicity: Asian or Pacific Islander

7

PARCC Reading

1,402

0.81

0.79, 0.83

Concurrent (Spring/Spring)

Ethnicity: Asian or Pacific Islander

8

PARCC Reading

1,306

0.80

0.78, 0.82

Predictive (Fall/Spring)

Ethnicity: Asian or Pacific Islander

3

PARCC Reading

1,392

0.79

0.77, 0.81

Predictive (Fall/Spring)

Ethnicity: Asian or Pacific Islander

4

PARCC Reading

1,340

0.80

0.78, 0.81

Predictive (Fall/Spring)

Ethnicity: Asian or Pacific Islander

5

PARCC Reading

1,340

0.80

0.77, 0.81

Predictive (Fall/Spring)

Ethnicity: Asian or Pacific Islander

6

PARCC Reading

1,445

0.79

0.77, 0.81

Predictive (Fall/Spring)

Ethnicity: Asian or Pacific Islander

7

PARCC Reading

1,378

0.79

0.77, 0.81

Predictive (Fall/Spring)

Ethnicity: Asian or Pacific Islander

8

PARCC Reading

1,281

0.78

0.75, 0.80

Predictive (Winter/Spring)

Ethnicity: Asian or Pacific Islander

3

PARCC Reading

1,031

0.81

0.78, 0.83

Predictive (Winter/Spring)

Ethnicity: Asian or Pacific Islander

4

PARCC Reading

1,154

0.80

0.77, 0.82

Predictive (Winter/Spring)

Ethnicity: Asian or Pacific Islander

5

PARCC Reading

1,111

0.80

0.77, 0.82

Predictive (Winter/Spring)

Ethnicity: Asian or Pacific Islander

6

PARCC Reading

1,159

0.82

0.80, 0.84

Predictive (Winter/Spring)

Ethnicity: Asian or Pacific Islander

7

PARCC Reading

1,026

0.80

0.78, 0.82

Predictive (Winter/Spring)

Ethnicity: Asian or Pacific Islander

8

PARCC Reading

927

0.77

0.74, 0.80

Concurrent (Spring/Spring)

Ethnicity: Black

3

PARCC Reading

1,015

0.80

0.78, 0.82

Concurrent (Spring/Spring)

Ethnicity: Black

4

PARCC Reading

1,001

0.79

0.77, 0.81

Concurrent (Spring/Spring)

Ethnicity: Black

5

PARCC Reading

985

0.76

0.73, 0.78

Concurrent (Spring/Spring)

Ethnicity: Black

6

PARCC Reading

1,211

0.77

0.74, 0.79

Concurrent (Spring/Spring)

Ethnicity: Black

7

PARCC Reading

1,229

0.76

0.73, 0.78

Concurrent (Spring/Spring)

Ethnicity: Black

8

PARCC Reading

964

0.67

0.64, 0.71

Predictive (Fall/Spring)

Ethnicity: Black

3

PARCC Reading

912

0.78

0.76, 0.81

Predictive (Fall/Spring)

Ethnicity: Black

4

PARCC Reading

894

0.76

0.74, 0.79

Predictive (Fall/Spring)

Ethnicity: Black

5

PARCC Reading

903

0.75

0.72, 0.78

Predictive (Fall/Spring)

Ethnicity: Black

6

PARCC Reading

1,120

0.75

0.72, 0.77

Predictive (Fall/Spring)

Ethnicity: Black

7

PARCC Reading

1,153

0.74

0.72, 0.77

Predictive (Fall/Spring)

Ethnicity: Black

8

PARCC Reading

880

0.69

0.65, 0.72

Predictive (Winter/Spring)

Ethnicity: Black

3

PARCC Reading

607

0.78

0.75, 0.81

Predictive (Winter/Spring)

Ethnicity: Black

4

PARCC Reading

601

0.77

0.74, 0.80

Predictive (Winter/Spring)

Ethnicity: Black

5

PARCC Reading

788

0.76

0.73, 0.79

Predictive (Winter/Spring)

Ethnicity: Black

6

PARCC Reading

842

0.74

0.70, 0.76

Predictive (Winter/Spring)

Ethnicity: Black

7

PARCC Reading

830

0.75

0.71, 0.77

Predictive (Winter/Spring)

Ethnicity: Black

8

PARCC Reading

754

0.69

0.65, 0.73

Concurrent (Spring/Spring)

Ethnicity: Hispanic

3

PARCC Reading

3,743

0.81

0.80, 0.82

Concurrent (Spring/Spring)

Ethnicity: Hispanic

4

PARCC Reading

3,835

0.80

0.79, 0.81

Concurrent (Spring/Spring)

Ethnicity: Hispanic

5

PARCC Reading

3,782

0.79

0.78, 0.80

Concurrent (Spring/Spring)

Ethnicity: Hispanic

6

PARCC Reading

4,379

0.79

0.78, 0.80

Concurrent (Spring/Spring)

Ethnicity: Hispanic

7

PARCC Reading

4,265

0.77

0.76, 0.79

Concurrent (Spring/Spring)

Ethnicity: Hispanic

8

PARCC Reading

3,632

0.77

0.76, 0.78

Predictive (Fall/Spring)

Ethnicity: Hispanic

3

PARCC Reading

3,389

0.80

0.79, 0.81

Predictive (Fall/Spring)

Ethnicity: Hispanic

4

PARCC Reading

3,574

0.78

0.76, 0.79

Predictive (Fall/Spring)

Ethnicity: Hispanic

5

PARCC Reading

3,506

0.77

0.76, 0.79

Predictive (Fall/Spring)

Ethnicity: Hispanic

6

PARCC Reading

3,999

0.78

0.77, 0.79

Predictive (Fall/Spring)

Ethnicity: Hispanic

7

PARCC Reading

3,839

0.76

0.74, 0.77

Predictive (Fall/Spring)

Ethnicity: Hispanic

8

PARCC Reading

3,352

0.77

0.75, 0.78

Predictive (Winter/Spring)

Ethnicity: Hispanic

3

PARCC Reading

2,784

0.81

0.80, 0.82

Predictive (Winter/Spring)

Ethnicity: Hispanic

4

PARCC Reading

3,083

0.78

0.77, 0.80

Predictive (Winter/Spring)

Ethnicity: Hispanic

5

PARCC Reading

3,336

0.77

0.76, 0.78

Predictive (Winter/Spring)

Ethnicity: Hispanic

6

PARCC Reading

3,480

0.77

0.76, 0.79

Predictive (Winter/Spring)

Ethnicity: Hispanic

7

PARCC Reading

3,228

0.75

0.73, 0.76

Predictive (Winter/Spring)

Ethnicity: Hispanic

8

PARCC Reading

2,932

0.75

0.74, 0.77

Concurrent (Spring/Spring)

Ethnicity: Multi-Ethnic

3

PARCC Reading

553

0.81

0.78, 0.84

Concurrent (Spring/Spring)

Ethnicity: Multi-Ethnic

4

PARCC Reading

487

0.81

0.78, 0.84

Concurrent (Spring/Spring)

Ethnicity: Multi-Ethnic

5

PARCC Reading

445

0.85

0.82, 0.87

Concurrent (Spring/Spring)

Ethnicity: Multi-Ethnic

6

PARCC Reading

510

0.81

0.77, 0.83

Concurrent (Spring/Spring)

Ethnicity: Multi-Ethnic

7

PARCC Reading

474

0.79

0.75, 0.82

Concurrent (Spring/Spring)

Ethnicity: Multi-Ethnic

8

PARCC Reading

401

0.77

0.73, 0.81

Predictive (Fall/Spring)

Ethnicity: Multi-Ethnic

3

PARCC Reading

504

0.78

0.75, 0.81

Predictive (Fall/Spring)

Ethnicity: Multi-Ethnic

4

PARCC Reading

446

0.81

0.77, 0.84

Predictive (Fall/Spring)

Ethnicity: Multi-Ethnic

5

PARCC Reading

421

0.83

0.80, 0.86

Predictive (Fall/Spring)

Ethnicity: Multi-Ethnic

6

PARCC Reading

489

0.79

0.75, 0.82

Predictive (Fall/Spring)

Ethnicity: Multi-Ethnic

7

PARCC Reading

453

0.77

0.72, 0.80

Predictive (Fall/Spring)

Ethnicity: Multi-Ethnic

8

PARCC Reading

381

0.77

0.72, 0.80

Predictive (Winter/Spring)

Ethnicity: Multi-Ethnic

3

PARCC Reading

384

0.81

0.77, 0.84

Predictive (Winter/Spring)

Ethnicity: Multi-Ethnic

4

PARCC Reading

375

0.79

0.75, 0.83

Predictive (Winter/Spring)

Ethnicity: Multi-Ethnic

5

PARCC Reading

387

0.84

0.81, 0.87

Predictive (Winter/Spring)

Ethnicity: Multi-Ethnic

6

PARCC Reading

363

0.80

0.76, 0.84

Predictive (Winter/Spring)

Ethnicity: Multi-Ethnic

7

PARCC Reading

348

0.75

0.70, 0.79

Predictive (Winter/Spring)

Ethnicity: Multi-Ethnic

8

PARCC Reading

303

0.76

0.71, 0.81

Concurrent (Spring/Spring)

Ethnicity: Not Specified or Other

3

PARCC Reading

2,490

0.83

0.81, 0.84

Concurrent (Spring/Spring)

Ethnicity: Not Specified or Other

4

PARCC Reading

2,533

0.83

0.81, 0.84

Concurrent (Spring/Spring)

Ethnicity: Not Specified or Other

5

PARCC Reading

2,409

0.80

0.79, 0.82

Concurrent (Spring/Spring)

Ethnicity: Not Specified or Other

6

PARCC Reading

2,327

0.81

0.80, 0.83

Concurrent (Spring/Spring)

Ethnicity: Not Specified or Other

7

PARCC Reading

1,093

0.79

0.77, 0.81

Concurrent (Spring/Spring)

Ethnicity: Not Specified or Other

8

PARCC Reading

550

0.79

0.75, 0.82

Predictive (Fall/Spring)

Ethnicity: Not Specified or Other

3

PARCC Reading

2,428

0.79

0.78, 0.81

Predictive (Fall/Spring)

Ethnicity: Not Specified or Other

4

PARCC Reading

2,477

0.80

0.78, 0.81

Predictive (Fall/Spring)

Ethnicity: Not Specified or Other

5

PARCC Reading

2,349

0.79

0.77, 0.80

Predictive (Fall/Spring)

Ethnicity: Not Specified or Other

6

PARCC Reading

2,059

0.81

0.79, 0.82

Predictive (Fall/Spring)

Ethnicity: Not Specified or Other

7

PARCC Reading

1,001

0.78

0.76, 0.81

Predictive (Fall/Spring)

Ethnicity: Not Specified or Other

8

PARCC Reading

445

0.79

0.76, 0.83

Predictive (Winter/Spring)

Ethnicity: Not Specified or Other

3

PARCC Reading

1,314

0.81

0.79, 0.83

Predictive (Winter/Spring)

Ethnicity: Not Specified or Other

4

PARCC Reading

1,322

0.82

0.80, 0.83

Predictive (Winter/Spring)

Ethnicity: Not Specified or Other

5

PARCC Reading

1,257

0.81

0.79, 0.83

Predictive (Winter/Spring)

Ethnicity: Not Specified or Other

6

PARCC Reading

977

0.82

0.79, 0.84

Predictive (Winter/Spring)

Ethnicity: Not Specified or Other

7

PARCC Reading

308

0.75

0.69, 0.79

Predictive (Winter/Spring)

Ethnicity: Not Specified or Other

8

PARCC Reading

361

0.78

0.73, 0.81

Concurrent (Spring/Spring)

Ethnicity: White

3

PARCC Reading

6,675

0.79

0.78, 0.80

Concurrent (Spring/Spring)

Ethnicity: White

4

PARCC Reading

6,843

0.80

0.79, 0.81

Concurrent (Spring/Spring)

Ethnicity: White

5

PARCC Reading

7,026

0.78

0.78, 0.79

Concurrent (Spring/Spring)

Ethnicity: White

6

PARCC Reading

7,629

0.77

0.76, 0.78

Concurrent (Spring/Spring)

Ethnicity: White

7

PARCC Reading

8,543

0.76

0.75, 0.77

Concurrent (Spring/Spring)

Ethnicity: White

8

PARCC Reading

8,282

0.75

0.74, 0.75

Predictive (Fall/Spring)

Ethnicity: White

3

PARCC Reading

5,873

0.76

0.75, 0.77

Predictive (Fall/Spring)

Ethnicity: White

4

PARCC Reading

6,122

0.77

0.76, 0.78

Predictive (Fall/Spring)

Ethnicity: White

5

PARCC Reading

6,407

0.77

0.76, 0.78

Predictive (Fall/Spring)

Ethnicity: White

6

PARCC Reading

6,865

0.76

0.74, 0.77

Predictive (Fall/Spring)

Ethnicity: White

7

PARCC Reading

7,678

0.76

0.75, 0.76

Predictive (Fall/Spring)

Ethnicity: White

8

PARCC Reading

7,407

0.73

0.72, 0.74

Predictive (Winter/Spring)

Ethnicity: White

3

PARCC Reading

4,656

0.79

0.77, 0.80

Predictive (Winter/Spring)

Ethnicity: White

4

PARCC Reading

5,258

0.79

0.78, 0.80

Predictive (Winter/Spring)

Ethnicity: White

5

PARCC Reading

5,480

0.77

0.76, 0.78

Predictive (Winter/Spring)

Ethnicity: White

6

PARCC Reading

5,444

0.77

0.76, 0.78

Predictive (Winter/Spring)

Ethnicity: White

7

PARCC Reading

5,470

0.76

0.75, 0.77

Predictive (Winter/Spring)

Ethnicity: White

8

PARCC Reading

5,320

0.74

0.73, 0.75

Concurrent (Spring/Spring)

Gender: Female

3

PARCC Reading

8,033

0.83

0.83, 0.84

Concurrent (Spring/Spring)

Gender: Female

4

PARCC Reading

8,054

0.84

0.83, 0.85

Concurrent (Spring/Spring)

Gender: Female

5

PARCC Reading

8,107

0.82

0.82, 0.83

Concurrent (Spring/Spring)

Gender: Female

6

PARCC Reading

8,667

0.82

0.81, 0.83

Concurrent (Spring/Spring)

Gender: Female

7

PARCC Reading

8,394

0.81

0.80, 0.82

Concurrent (Spring/Spring)

Gender: Female

8

PARCC Reading

7,509

0.80

0.79, 0.81

Predictive (Fall/Spring)

Gender: Female

3

PARCC Reading

7,302

0.81

0.80, 0.82

Predictive (Fall/Spring)

Gender: Female

4

PARCC Reading

7,418

0.82

0.81, 0.83

Predictive (Fall/Spring)

Gender: Female

5

PARCC Reading

7,552

0.81

0.80, 0.82

Predictive (Fall/Spring)

Gender: Female

6

PARCC Reading

7,888

0.80

0.80, 0.81

Predictive (Fall/Spring)

Gender: Female

7

PARCC Reading

7,665

0.80

0.79, 0.81

Predictive (Fall/Spring)

Gender: Female

8

PARCC Reading

6,792

0.79

0.78, 0.80

Predictive (Winter/Spring)

Gender: Female

3

PARCC Reading

5,440

0.83

0.82, 0.84

Predictive (Winter/Spring)

Gender: Female

4

PARCC Reading

5,911

0.83

0.82, 0.84

Predictive (Winter/Spring)

Gender: Female

5

PARCC Reading

6,276

0.81

0.80, 0.82

Predictive (Winter/Spring)

Gender: Female

6

PARCC Reading

6,004

0.81

0.80, 0.82

Predictive (Winter/Spring)

Gender: Female

7

PARCC Reading

5,476

0.81

0.80, 0.82

Predictive (Winter/Spring)

Gender: Female

8

PARCC Reading

5,235

0.79

0.78, 0.80

Concurrent (Spring/Spring)

Gender: Male

3

PARCC Reading

8,297

0.83

0.83, 0.84

Concurrent (Spring/Spring)

Gender: Male

4

PARCC Reading

8,423

0.83

0.83, 0.84

Concurrent (Spring/Spring)

Gender: Male

5

PARCC Reading

8,270

0.82

0.82, 0.83

Concurrent (Spring/Spring)

Gender: Male

6

PARCC Reading

9,227

0.81

0.80, 0.82

Concurrent (Spring/Spring)

Gender: Male

7

PARCC Reading

8,969

0.79

0.79, 0.80

Concurrent (Spring/Spring)

Gender: Male

8

PARCC Reading

7,776

0.78

0.77, 0.79

Predictive (Fall/Spring)

Gender: Male

3

PARCC Reading

7,508

0.81

0.80, 0.81

Predictive (Fall/Spring)

Gender: Male

4

PARCC Reading

7,763

0.81

0.80, 0.82

Predictive (Fall/Spring)

Gender: Male

5

PARCC Reading

7,669

0.81

0.80, 0.82

Predictive (Fall/Spring)

Gender: Male

6

PARCC Reading

8,415

0.80

0.79, 0.80

Predictive (Fall/Spring)

Gender: Male

7

PARCC Reading

8,182

0.78

0.78, 0.79

Predictive (Fall/Spring)

Gender: Male

8

PARCC Reading

7,093

0.77

0.76, 0.78

Predictive (Winter/Spring)

Gender: Male

3

PARCC Reading

5,716

0.82

0.81, 0.83

Predictive (Winter/Spring)

Gender: Male

4

PARCC Reading

6,219

0.82

0.81, 0.83

Predictive (Winter/Spring)

Gender: Male

5

PARCC Reading

6,392

0.82

0.81, 0.82

Predictive (Winter/Spring)

Gender: Male

6

PARCC Reading

6,590

0.80

0.79, 0.81

Predictive (Winter/Spring)

Gender: Male

7

PARCC Reading

6,070

0.78

0.77, 0.79

Predictive (Winter/Spring)

Gender: Male

8

PARCC Reading

5,493

0.76

0.75, 0.78

 

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

Not Provided

Sample Representativeness

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

Primary Classification Accuracy Sample

 

Representation: New England, Middle Atlantic, East North Central, West 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 were from the Spring 2016 administration of the PARCC assessment, spanning approximately from March 2016 through June 2016.

Size: Table 3, Table 4, and Table 5 summarize the total number of students, as functions of grade, state, region, and division.

 

Table 3: Number of Students Per State by Grade

 

2

3

4

5

6

7

8

Total

CO

2,967

2,967

3,185

3,097

3,846

3,659

3,192

19,946

DC

167

167

160

140

221

165

191

1,044

IL

12,140

12,140

12,064

12,129

12,766

12,525

10,970

72,594

NJ

643

643

665

610

649

622

548

3,737

NM

208

208

205

201

197

208

186

1,205

RI

209

209

199

202

217

190

200

1,217

Total

16,334

16,334

16,478

16,379

17,896

17,369

15,287

99,743

 

Table 4: Number of Students Per Region by Grade

 

2

3

4

5

6

7

8

Total

Midwest

12,140

12,140

12,064

12,129

12,766

12,525

10,970

72,594

Northeast

852

852

864

812

866

812

748

4,954

South

167

167

160

140

221

165

191

1,044

West

3,175

3,175

3,390

3,298

4,043

3,867

3,378

21,151

Total

16,334

16,334

16,478

16,379

17,896

17,369

15,287

99,743

 

Table 5: Number of Students Per Division by Grade

 

2

3

4

5

6

7

8

Total

East North Central

12,140

12,140

12,064

12,129

12,766

12,525

10,970

72,594

Middle Atlantic

643

643

665

610

649

622

548

3,737

Mountain

3,175

3,175

3,390

3,298

4,043

3,867

3,378

21,151

New England

209

209

199

202

217

190

200

1,217

South Atlantic

167

167

160

140

221

165

191

1,044

Total

16,334

16,334

16,478

16,379

17,896

17,369

15,287

99,743

 

Male

51.09%

Female

48.89%

Unknown

0.02%

Other SES Indicators

Not Provided

Free or reduced-price lunch

Not Provided

White, Non-Hispanic

45.11%

Black, Non-Hispanic

6.42%

Hispanic

23.70%

American Indian/Alaska Native:

1.84%

Asian/Pacific Islander:

8.62%

Multi-Ethnic

2.88%

Not Specified or Other

11.43%

Disability classification

Not Provided

First language

Not Provided

Language proficiency status

Not Provided

 

Bias Analysis Conducted

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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:

Each test record included the student’s recorded ethnic group membership (Native American, Asian, African American, Hispanic, and European/Anglo American).  

 

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

The DIF analysis of the items initially proposed for the Common Core-aligned reading test events that were administered during the Spring testing term of the 2011-2012 academic year and the Fall, Winter, and Spring testing terms of the 2012-2013 academic year from the tests administered in Arizona, California, Colorado, Georgia, Minnesota, Mississippi, New Mexico, South Carolina, and Texas. It is worth noting that many items in the item pools align to both Common Core State Standards and state content standards. Each test record included the student’s recorded ethnic group membership (Native American, Asian, African American, Hispanic, and European/Anglo American), the student’s gender, and their item responses. The number of students and the number of test items are provided in Table 13.

Table 13Number of Students and Common Core-Aligned Test Items Included in the DIF Analyses

Content area

Items

Students

Ethnic Group

% of Students

Reading

1,550

250,734

Native American

2.5

Asian/Pacific Islander

4.1

African American

22.0

Hispanic

15.3

European/Anglo American

53.6

Multi-Racial

2.4

 

Data from all states and all grades were combined for each content area. This aggregation was made because DIF was focused narrowly on how students of the same ability but of different gender or from different ethnic groups respond to test items. The intent was to neutralize, as much as possible, the effects of differential curricular and instructional emphasis that could potentially influence the DIF analysis. Retaining states and grades as part of the analysis could have led to conclusions that were tangential to the primary focus.

Winsteps (version 3.75.0) was invoked to carry out the analysis as outlined above.  Calibrations were retained in their original logit metric. The numbers of items exhibiting DIF for each ethnic focal group are reported in Table 14. Similarly, the numbers of items exhibiting gender-specific DIF are reported in Table 15. The numbers of items reported in these tables are based on a minimum of 800 student responses for each group involved in the comparison, ensuring that each comparison had adequate power to detect DIF. To help in summarizing results, the Educational Testing Service (ETS) delta method of categorizing DIF[4] is incorporated into the tables as ETS Class. The delta method allows items exhibiting negligible DIF (difference < .43 logits) to be differentiated from those exhibiting moderate DIF (difference ≥ .43 and < .64 logits) from those exhibiting severe DIF (difference ≥ .63 logits).

Table 14demonstrates that: a) the overwhelming number of items exhibit negligible, if any, DIF; b) the numbers of items falling into the moderate and severe DIF categories are far fewer than would be expected by chance. From Table 15we can see that DIF related to gender is very rare.

Potential impact. It is important to note that the numbers of items reported in Table 14as evidencing moderate or severe DIF (ETS Classes B and C), represent duplicated counts. That is, a single item may be represented as exhibiting DIF in more than one ethnic group. If the item pools used in this study remained as they are for operational tests, assuming current test lengths and the observation that a typical test presents items within a fairly narrow range (≈ 20 RITs), then these percentages translate into a potential impact of one item (2.5% of the test) exhibiting DIF to be presented in tests. Even these minimal levels of potential impact are unlikely to be an actual threat to test validity. NWEA employed follow-up procedures to shrink potential impact even further.

Actions taken. All items revealed as exhibiting moderate DIF are subjected to an extra review by NWEA Content Specialists to identify the source(s) for differential functioning. For each item, these specialists make a judgment to: 1) remove the item from the item bank, 2) revise the item and re-submit it for field-testing, or 3) to retain the item as is. Items exhibiting severe DIF are removed from the bank. These procedures are consistent with and act to extended periodic Item Quality Reviews, which remove or flag items for revision and re-field-testing problem items.

Table 14: Differential Item Functioning for Common Core-Aligned Reading Items
(N = 4,660)

 

Reference is Anglo Students

 

Reference is Base Calibration (All Students)

Focal Group

ETS Class*

N Items**

% of Items

 

Focal Group

ETS Class*

N Items**

% of Items

African American

A

1759

96.2

African American

A

4617

99.2

 

B

59

3.2

B

38

0.8

 

C

10

0.5

C

0

0.0

 

Asian

A

1512

90.9

Asian

A

4606

99.6

 

B

116

7.0

B

15

0.3

 

C

35

2.1

C

2

0.0

 

Hispanic

A

1742

95.6

Hispanic

A

4631

99.4

 

B

66

3.6

B

28

0.6

 

C

14

0.8

C

1

0.0

 

Multi-Ethnic

A

1457

95.2

Multi-Ethnic

A

4481

100.0

 

B

63

4.1

B

0

0.0

 

C

11

0.7

C

2

0.0

 

Native American/ Alaskan Native

A

1520

93.4

Native American/ Alaskan Native

A

4572

99.6

 

B

87

5.3

B

18

0.4

 

C

20

1.2

C

0

0.0

 

Pacific Islander/ Hawaiian

A

106

89.1

Pacific Islander/ Hawaiian

A

1770

99.6

 

B

11

9.2

B

8

0.4

 

C

2

1.7

C

0

0.0

 

 

Anglo

A

2340

97.5

 

B

51

2.1

 

C

8

0.3

 

* A = |DIF| < .43 logits; B = .43 ≤|DIF| < .64 logits; C = |DIF| ≥ .64 logits

 

** The number of items with 400 of more contrasts with the reference group or reference calibration.

 

                         

 

Table 15: Differential Item Functioning Related to Gender for Common-Core-Aligned Items by Content Area

ETS Class*

N Items**

% of Items

Reading (N = 4660)

A

4547

97.6

B

91

2.0

C

22

0.5

A = |DIF| < .43 logits; B = .43 ≤|DIF| < .64 logits; C = |DIF| ≥ .64 logits

All items were responded to by over 800 students.

 

 



[1] Linacre, J. M. and 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.

[4] Holland, P. W. and Thayer, D. T. (1985.) An Alternative Definition of the ETS Delta Scale of Item Difficulty. ETS Research Report No. 85-43. Princeton, NJ: Educational Testing Service.

 

Administration Format

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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