This document summarizes a study linking the NWEA RIT scale to South Carolina's PASS and HSAP assessments in mathematics and reading for grades 2-8 and high school. Researchers analyzed test results from over 37,000 students who took both the NWEA MAP test and the PASS in spring 2009. They used an equipercentile method to estimate RIT cut scores corresponding to each PASS performance level. Similar methodology was used to link MAP scores to the HSAP. The report provides tables showing the estimated RIT cut scores and probabilities of achieving performance levels on PASS/HSAP based on MAP scores.
Case study analysis using Regression to determine which variables effect University Ranking based on ANOVA output values. Comparison of the predicted rank versus the actual ranking received.
This article is used to give a basic information regarding the change points that occur in excel and in other files. The detection methods are proposed and they are analyzed with a real time example. The features and application of the change point is also discussed in the later. Copy the link given below and paste it in new browser window to get more information on Q-Q Plot:- http://www.transtutors.com/homework-help/statistics/q-q-plot.aspx
Aligning Benchmarks With High Stakes Assessments 2009dvodicka
Overview of descriptive and inferential options for evaluating alignment of internal and external assessments to help improve student achievement.
Presented at Data Director User Conference in November 2009.
Predicting Proficiency… How MAP Predicts State Test PerformanceNWEA
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Predicting Proficiency… How MAP predicts State Test Performance
Paul Stern, District Enterprise Analyst, Vancouver Public Schools, Sarah Johnson, Accountability Project manager, Highline Public Schools, Burien, WA
Fusion 2012, the NWEA summer conference in Portland, Oregon
NWEA routinely produces “Linking Studies” that explore the alignment between the RIT Scale and state student proficiency exams. This presentation will share the results of an alignment study that applied a methodology developed by the Highline School District. The presentation will focus on how the results of the two methods differ and how Vancouver Public Schools will use this information to inform instruction and guide student interventions.
Learning outcome:
- Learn how to define proficiency using MAP cut scores.
- Understand the alignment of MAP to Washington’s State Assessments.
- Learn how alignment studies can be conducted and used to inform instruction
Audience:
- Experienced data user
- Advanced data user
- District leadership
- Curriculum and Instruction
Vancouver Public Schools serves approximately 22,000 students in Vancouver, WA, an urban/suburban district across the river from Portland. The presenter is the enterprise analyst within the Information Technology Services department focused on predictive analytics and performance measurement.
Measures and Strengths of AssociationRemember that while w.docxARIV4
Measures and Strengths of Association
Remember that while we may find two variables to be involved in a relationship, we also want to know the
strength of the association. Each type of variable has its own measure to determine this though. Three
measures will be discussed in this paper, Lambda, Gamma, and Pearson’s r.
Lambda
Lambda is a measure of association which should be used when both variables are nominal. Essentially
this means that knowing a person’s attribute on one variable will help you guess their attribute on the
other (Babbie et al., 2014).
Gamma
Gamma is used to explore the relationship between two ordinal variables. It can also be used to measure
association between one dichotomous nominal and one ordinal variable (Babbie et al., 2014, p. 227).
Unlike lambda, gamma indicates a strength of an association and a direction. The closer to -1.00 or +1.00,
the stronger the relationship, whereas the closer to 0 the weaker the relationship. You can determine the
direction of a relationship the following way:
A negative association is indicated by a negative sign. This means that as one variable increases the other
decreases- the variables are moving away from each other. For example, as social class increases, prejudice
decreases. On the other hand, a positive association, indicated by a plus or positive sign, means that both
variables change in the same direction, either increase or decrease. For example, as social class increases,
so too does prejudice or as social class decreases, so too does prejudice.
Correlation Coefficient- Pearson’s r
Pearson’s r, also known as the correlation coefficient, is the test measure used to determine the
association between interval/ratio variables. This measure is similar to Gamma in how it can be understood
and establish direction of association.
Value of Measures of Association
0.00
+ or - .01 to .09
+ or - .10 to .29
+ or - .30 to .99
Strength of Association
None- no assocation at all
Weak- uninteresting association
Moderate- worth making note of
Evidence of a strong association- extremely
interesting
Perfect- strongest association possible 1.00
Measures of Association in SPSS
Analyze – Descriptive Statistics – Crosstabs
Place your dependent variable in the Row and your independent variable in the Column. Click "Statistics"
to choose which test you will run for the measure/strength of association. You will select Lambda for
nominal variables, Gamma for ordinal variables (or one ordinal and one dichotomous nominal), or
Pearson’s r for interval/ratio variables.
Measures of Association in SPSS- Understanding Output
Lambda
The test above is looking at the relationship between one’s political affiliation and their race. We look at
the value .036, which is the measure when political party is the DV (see in table). This means that we can
improve our guessing of political affiliation by 4% if we know that person’s race. Based on our notes abo ...
Case study analysis using Regression to determine which variables effect University Ranking based on ANOVA output values. Comparison of the predicted rank versus the actual ranking received.
This article is used to give a basic information regarding the change points that occur in excel and in other files. The detection methods are proposed and they are analyzed with a real time example. The features and application of the change point is also discussed in the later. Copy the link given below and paste it in new browser window to get more information on Q-Q Plot:- http://www.transtutors.com/homework-help/statistics/q-q-plot.aspx
Aligning Benchmarks With High Stakes Assessments 2009dvodicka
Overview of descriptive and inferential options for evaluating alignment of internal and external assessments to help improve student achievement.
Presented at Data Director User Conference in November 2009.
Predicting Proficiency… How MAP Predicts State Test PerformanceNWEA
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Predicting Proficiency… How MAP predicts State Test Performance
Paul Stern, District Enterprise Analyst, Vancouver Public Schools, Sarah Johnson, Accountability Project manager, Highline Public Schools, Burien, WA
Fusion 2012, the NWEA summer conference in Portland, Oregon
NWEA routinely produces “Linking Studies” that explore the alignment between the RIT Scale and state student proficiency exams. This presentation will share the results of an alignment study that applied a methodology developed by the Highline School District. The presentation will focus on how the results of the two methods differ and how Vancouver Public Schools will use this information to inform instruction and guide student interventions.
Learning outcome:
- Learn how to define proficiency using MAP cut scores.
- Understand the alignment of MAP to Washington’s State Assessments.
- Learn how alignment studies can be conducted and used to inform instruction
Audience:
- Experienced data user
- Advanced data user
- District leadership
- Curriculum and Instruction
Vancouver Public Schools serves approximately 22,000 students in Vancouver, WA, an urban/suburban district across the river from Portland. The presenter is the enterprise analyst within the Information Technology Services department focused on predictive analytics and performance measurement.
Measures and Strengths of AssociationRemember that while w.docxARIV4
Measures and Strengths of Association
Remember that while we may find two variables to be involved in a relationship, we also want to know the
strength of the association. Each type of variable has its own measure to determine this though. Three
measures will be discussed in this paper, Lambda, Gamma, and Pearson’s r.
Lambda
Lambda is a measure of association which should be used when both variables are nominal. Essentially
this means that knowing a person’s attribute on one variable will help you guess their attribute on the
other (Babbie et al., 2014).
Gamma
Gamma is used to explore the relationship between two ordinal variables. It can also be used to measure
association between one dichotomous nominal and one ordinal variable (Babbie et al., 2014, p. 227).
Unlike lambda, gamma indicates a strength of an association and a direction. The closer to -1.00 or +1.00,
the stronger the relationship, whereas the closer to 0 the weaker the relationship. You can determine the
direction of a relationship the following way:
A negative association is indicated by a negative sign. This means that as one variable increases the other
decreases- the variables are moving away from each other. For example, as social class increases, prejudice
decreases. On the other hand, a positive association, indicated by a plus or positive sign, means that both
variables change in the same direction, either increase or decrease. For example, as social class increases,
so too does prejudice or as social class decreases, so too does prejudice.
Correlation Coefficient- Pearson’s r
Pearson’s r, also known as the correlation coefficient, is the test measure used to determine the
association between interval/ratio variables. This measure is similar to Gamma in how it can be understood
and establish direction of association.
Value of Measures of Association
0.00
+ or - .01 to .09
+ or - .10 to .29
+ or - .30 to .99
Strength of Association
None- no assocation at all
Weak- uninteresting association
Moderate- worth making note of
Evidence of a strong association- extremely
interesting
Perfect- strongest association possible 1.00
Measures of Association in SPSS
Analyze – Descriptive Statistics – Crosstabs
Place your dependent variable in the Row and your independent variable in the Column. Click "Statistics"
to choose which test you will run for the measure/strength of association. You will select Lambda for
nominal variables, Gamma for ordinal variables (or one ordinal and one dichotomous nominal), or
Pearson’s r for interval/ratio variables.
Measures of Association in SPSS- Understanding Output
Lambda
The test above is looking at the relationship between one’s political affiliation and their race. We look at
the value .036, which is the measure when political party is the DV (see in table). This means that we can
improve our guessing of political affiliation by 4% if we know that person’s race. Based on our notes abo ...
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
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The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
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• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
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How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
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Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
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Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
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Sc linking study august2010 revised final
1. SOUTH CAROLINA
LINKING STUDY
A Study of the Alignment of the NWEA RIT Scale with South
Carolina’s Palmetto Assessment of State Standards (PASS)
and with the High School Assessment Program (HSAP)
August 2010
Revised April 2011
The Kingsbury Center at Northwest Evaluation Association
3. S C ( P A S S & H S A P ) N W E A A l i g n m e n t 2 0 1 0 3 | P a g e
A STUDY OF THE ALIGNMENT OF THE NWEA RIT SCALE WITH SOUTH
CAROLINA’S PALMETTO ASSESSMENT OF STATE STANDARDS (PASS)
AND HIGH SCHOOL ASSESSMENT PROGRAM (HSAP)
KINGSBURY CENTER AT NWEA
AUGUST 2010
In March 2010, NWEA completed a project to connect the scale of Palmetto Assessment of State
Standards (PASS) used for South Carolina mathematics and reading assessments with NWEA’s RIT scale.
Information from the PASS assessments was used in a study to establish performance-level scores on
the RIT scale that would indicate a good chance of success on these tests. In August 2010, this study was
updated to include the relationship between the NWEA RIT scale and South Carolina’s High School
Assessment Program (HSAP) scale. This report is the combined results for both South Carolina
assessments.
To perform the PASS analysis for grades 3-8, we linked together state test and NWEA test results for a
sample of 37,000 South Carolina students from 126 schools who completed both exams in the spring of
2009. An equipercentile method was used to estimate the RIT score equivalent to each state
performance level by determining the percentage of the population within the selected study group that
performed at each level on the state test, and finding the equivalent percentile ranges within the NWEA
dataset to estimate the cut scores. For example, if 40% of the study group population in grade 3
mathematics performed below the proficient level on the state test, we would find the RIT score that
would be equivalent to the 40th
percentile for the study population (this would not be the same as the
40th
percentile in the NWEA norms). This RIT score would be the estimated point on the NWEA RIT scale
that would be equivalent to the minimum score for proficiency on the state test.
2nd
grade results were extrapolated using the distribution of scores from the 3rd
grade students. For
instance, if 40% of the study group population in grade 3 mathematics performed below the proficient
level on the state test, we would find the RIT score that would be equivalent to the 40th
percentile for
the 2nd
grade study population. The original PASS Linking Study published in March 2010 also
extrapolated 9th
and 10th
grade scores based on 8th
grade distributions. However, now that HSAP data is
available, those extrapolations are not included.
The methodology for the HSAP analysis for grades 9-12 was the approximately same as the
methodology for PASS. However, because South Carolina students can take the HSAP test during any
high school year, all 3266 student records from 34 schools in either the spring of 2009 or the spring of
2008 were aligned to a single grade level which was repeated for 9th
through 12th
grades. The majority of
students took the test in the 10th
grade.
More complete documentation about our linking study methodology can be found on our website.
4. S C ( P A S S & H S A P ) N W E A A l i g n m e n t 2 0 1 0 4 | P a g e
In the following pages, Tables 1 through 4 show the best estimate of the minimum RIT equivalent to
each PASS or HSAP performance level for same-season (spring) and prior-season (fall) RIT scores. These
tables can be used to identify students who may need additional help to perform well on these tests.
Tables 5 through 8 show the estimated probability of achieving “Meets Standard” or better on
PASS/HSAP, based on that student’s RIT score on MAP. These tables can be used to assist in identifying
students who are not likely to pass these assessments, thereby increasing the probability that
intervention strategies will be planned and implemented. These tables can also be useful for identifying
target RIT-score objectives likely to correspond to successful performance on PASS.
Table 9 shows the correlation coefficients between MAP and PASS, and for MAP and HSAP, for reading
and mathematics at each of the grades 3 through 8 and high school. These statistics show the degree to
which MAP and PASS, and MAP and HSAP, are linearly related. Values at or near 1.0 suggest a perfect
linear relationship, and values near 0 indicate no linear relationship.
Table 10 shows the percentages of students at each grade and within each subject whose status on
PASS/HSAP (i.e., whether or not the student “met standards”) was accurately predicted by their MAP
performance and using the estimated cut scores within the current study. This table can be used to
understand the predictive validity of MAP with respect to PASS and HSAP.
NOTE:
This study was revised in April 2011 to correct an error on page 10—the table showing the probability of
a student passing the state Reading test based on Spring MAP scores was offset by one row. The other
tables, as well as the information for all tables in the NWEA reporting system, are correct and have not
been changed.
In addition to this correction, all of the probability tables (pages 9-12) have been revised to conform to
current practices of showing the minimum probability as 1% (instead of 0%) and the maximum
probability as 99% (instead of 100%).
5. S C ( P A S S & H S A P ) N W E A A l i g n m e n t 2 0 1 0 5 | P a g e
TABLE 1 – MINIMUM ESTIMATED SAME-SEASON (SPRING) RIT CUT SCORES
CORRESPONDING TO SOUTH CAROLINA PERFORMANCE LEVELS – MATHEMATICS
MATH-Current Season
Cut Scores and Percentiles for each State Performance Level
Grade Level 1 Level 2 Level 3 Level 4
Cut
Score
Cut
Score
Percen-
tile
Cut
Score
Percen-
tile
Cut
Score
Percen-
tile
2 <186 186 35 197 69
3 <198 198 35 208 69
4 <203 203 27 218 69
5 <212 212 32 231 78
6 <218 218 34 235 75
7 <223 223 36 241 77
8 <231 231 43 247 79
High <223 223 21 238 47 250 74
*
Note: the cut scores shown in this table are the minimum estimated scores. Meeting the minimum MAP cut
score corresponds to a 50% probability of achieving that performance level. Use the probabilities in Tables 5-8 to
determine the appropriate “target” scores for a desired level of certainty.
Note: bolded, italicized text denotes extrapolated cut score
6. S C ( P A S S & H S A P ) N W E A A l i g n m e n t 2 0 1 0 6 | P a g e
TABLE 2 – MINIMUM ESTIMATED SAME-SEASON (SPRING) RIT CUT SCORES
CORRESPONDING TO SOUTH CAROLINA PERFORMANCE LEVELS – READING
READING-Current Season
Cut Scores and Percentiles for each State Performance Level
Grade Level 1 Level 2 Level 3 Level 4
Cut
Score
Cut
Score
Percen-
tile
Cut
Score
Percen-
tile
Cut
Score
Percen-
tile
2 <180 180 23 192 54
3 <189 189 23 201 54
4 <198 198 26 212 67
5 <200 200 19 217 66
6 <209 209 30 222 69
7 <212 212 30 226 70
8 <216 216 32 230 72
High <208 208 12 225 44 236 74
*
Note: the cut scores shown in this table are the minimum estimated scores. Meeting the minimum MAP cut
score corresponds to a 50% probability of achieving that performance level. Use the probabilities in Tables 5-8 to
determine the appropriate “target” scores for a desired level of certainty.
Note: bolded, italicized text denotes extrapolated cut score
7. S C ( P A S S & H S A P ) N W E A A l i g n m e n t 2 0 1 0 7 | P a g e
TABLE 3 – MINIMUM ESTIMATED PRIOR-SEASON (FALL) RIT CUT SCORES
CORRESPONDING TO SOUTH CAROLINA PERFORMANCE LEVELS – MATHEMATICS
MATH-Prior Season
Cut Scores and Percentiles for each State Performance Level
Grade Level 1 Level 2 Level 3 Level 4
Cut
Score
Cut
Score
Percen-
tile
Cut
Score
Percen-
tile
Cut
Score
Percen-
tile
2 <175 175 35 184 69
3 <188 188 35 198 69
4 <196 196 27 209 69
5 <206 206 32 222 78
6 <213 213 34 228 75
7 <219 219 36 236 77
8 <227 227 43 243 79
High <221 221 20 235 46 247 73
*
Note: the cut scores shown in this table are the minimum estimated scores. Meeting the minimum MAP cut
score corresponds to a 50% probability of achieving that performance level. Use the probabilities in Tables 5-8 to
determine the appropriate “target” scores for a desired level of certainty.
Note: bolded, italicized text denotes extrapolated cut score
8. S C ( P A S S & H S A P ) N W E A A l i g n m e n t 2 0 1 0 8 | P a g e
TABLE 4 – MINIMUM ESTIMATED PRIOR-SEASON (FALL) RIT CUT SCORES
CORRESPONDING TO SOUTH CAROLINA PERFORMANCE LEVELS – READING
READING-Prior Season
Cut Scores and Percentiles for each State Performance Level
Grade Level 1 Level 2 Level 3 Level 4
Cut
Score
Cut
Score
Percen-
tile
Cut
Score
Percen-
tile
Cut
Score
Percen-
tile
2 <169 169 23 181 54
3 <181 181 23 193 54
4 <192 192 26 207 67
5 <196 196 19 213 66
6 <206 206 30 219 69
7 <210 210 30 223 70
8 <214 214 32 228 72
High <207 207 12 224 44 234 74
*
Note: the cut scores shown in this table are the minimum estimated scores. Meeting the minimum MAP cut
score corresponds to a 50% probability of achieving that performance level. Use the probabilities in Tables 5-8 to
determine the appropriate “target” scores for a desired level of certainty.
Note: bolded, italicized text denotes extrapolated cut score
9. S C ( P A S S & H S A P ) N W E A A l i g n m e n t 2 0 1 0 9 | P a g e
TABLE 5 –ESTIMATED PROBABILITY OF “MEETING STANDARDS” OR BETTER ON THE
MATHEMATICS TEST IN SAME SEASON (SPRING), BY STUDENT GRADE AND RIT RANGE
*
Note: This table provides the estimated probability of passing the state test based on a MAP test score taken
during that same (spring) season. Example: if a third grade student scored 170 on a MAP test taken during the
spring season, her/his estimated probability of passing the state test is 7%.
RIT Range 2 3 4 5 6 7 8 High
130 1% 1% 1% 1% 1% 1% 1% 1%
135 1% 1% 1% 1% 1% 1% 1% 1%
140 1% 1% 1% 1% 1% 1% 1% 1%
145 2% 1% 1% 1% 1% 1% 1% 1%
150 3% 1% 1% 1% 1% 1% 1% 1%
155 5% 2% 1% 1% 1% 1% 1% 1%
160 8% 3% 2% 1% 1% 1% 1% 1%
165 13% 4% 3% 1% 1% 1% 1% 1%
170 20% 7% 4% 2% 1% 1% 1% 1%
175 29% 11% 7% 3% 2% 1% 1% 1%
180 40% 17% 11% 5% 3% 2% 1% 1%
185 52% 25% 17% 8% 4% 3% 1% 2%
190 64% 36% 25% 12% 7% 4% 2% 4%
195 75% 48% 36% 18% 11% 7% 3% 6%
200 83% 60% 48% 27% 17% 11% 5% 9%
205 89% 71% 60% 38% 25% 17% 8% 14%
210 93% 80% 71% 50% 36% 25% 13% 21%
215 96% 87% 80% 62% 48% 36% 20% 31%
220 97% 92% 87% 73% 60% 48% 29% 43%
225 98% 95% 92% 82% 71% 60% 40% 55%
230 99% 97% 95% 88% 80% 71% 52% 67%
235 99% 98% 97% 92% 87% 80% 64% 77%
240 99% 99% 98% 95% 92% 87% 75% 85%
245 99% 99% 99% 97% 95% 92% 83% 90%
250 99% 99% 99% 98% 97% 95% 89% 94%
255 99% 99% 99% 99% 98% 97% 93% 96%
260 99% 99% 99% 99% 99% 98% 96% 98%
265 99% 99% 99% 99% 99% 99% 97% 99%
270 99% 99% 99% 99% 99% 99% 98% 99%
275 99% 99% 99% 99% 99% 99% 99% 99%
280 99% 99% 99% 99% 99% 99% 99% 99%
285 99% 99% 99% 99% 99% 99% 99% 99%
290 99% 99% 99% 99% 99% 99% 99% 99%
295 99% 99% 99% 99% 99% 99% 99% 99%
300 99% 99% 99% 99% 99% 99% 99% 99%
MATH-Current Season
Estimated Probability of Passing State Test Based on Observed MAP Score
10. S C ( P A S S & H S A P ) N W E A A l i g n m e n t 2 0 1 0 10 | P a g e
TABLE 6 –ESTIMATED PROBABILITY OF “MEETING STANDARDS” OR BETTER ON THE
READING TEST IN SAME SEASON (SPRING), BY STUDENT GRADE AND RIT SCORE
*
Note: This table provides the estimated probability of passing the state test based on a MAP test score taken
during that same (spring) season. Example: if a third grade student scored 190 on a MAP test taken during the
spring season, her/his estimated probability of passing the state test is 57%.
RIT Range 2 3 4 5 6 7 8 High
130 1% 1% 1% 1% 1% 1% 1% 1%
135 1% 1% 1% 1% 1% 1% 1% 1%
140 2% 1% 1% 1% 1% 1% 1% 1%
145 4% 1% 1% 1% 1% 1% 1% 1%
150 6% 2% 1% 1% 1% 1% 1% 1%
155 9% 4% 2% 1% 1% 1% 1% 1%
160 14% 6% 3% 2% 1% 1% 1% 1%
165 22% 10% 4% 4% 1% 1% 1% 1%
170 31% 16% 7% 6% 2% 2% 1% 2%
175 43% 23% 11% 9% 4% 3% 2% 4%
180 55% 33% 17% 14% 6% 5% 3% 6%
185 67% 45% 25% 22% 10% 8% 5% 9%
190 77% 57% 36% 31% 16% 12% 8% 14%
195 84% 69% 48% 43% 23% 18% 13% 21%
200 90% 78% 60% 55% 33% 27% 20% 31%
205 94% 86% 71% 67% 45% 38% 29% 43%
210 96% 91% 80% 77% 57% 50% 40% 55%
215 98% 94% 87% 84% 69% 62% 52% 67%
220 99% 96% 92% 90% 78% 73% 64% 77%
225 99% 98% 95% 94% 86% 82% 75% 85%
230 99% 99% 97% 96% 91% 88% 83% 90%
235 99% 99% 98% 98% 94% 92% 89% 94%
240 99% 99% 99% 99% 96% 95% 93% 96%
245 99% 99% 99% 99% 98% 97% 96% 98%
250 99% 99% 99% 99% 99% 98% 97% 99%
255 99% 99% 99% 99% 99% 99% 98% 99%
260 99% 99% 99% 99% 99% 99% 99% 99%
265 99% 99% 99% 99% 99% 99% 99% 99%
270 99% 99% 99% 99% 99% 99% 99% 99%
275 99% 99% 99% 99% 99% 99% 99% 99%
280 99% 99% 99% 99% 99% 99% 99% 99%
285 99% 99% 99% 99% 99% 99% 99% 99%
290 99% 99% 99% 99% 99% 99% 99% 99%
295 99% 99% 99% 99% 99% 99% 99% 99%
300 99% 99% 99% 99% 99% 99% 99% 99%
READING-Current Season
Estimated Probability of Passing State Test Based on Observed MAP Score
11. S C ( P A S S & H S A P ) N W E A A l i g n m e n t 2 0 1 0 11 | P a g e
TABLE 7 – ESTIMATED PROBABILITY OF “MEETING STANDARDS” OR BETTER ON THE
MATHEMATICS TEST IN PRIOR SEASON (FALL), BY STUDENT GRADE AND RIT RANGE
*
Note: This table provides the estimated probability of passing the state test in spring, based on a MAP test score
taken during the previous (fall) season. Example: if a third grade student scored 170 on a MAP test taken during
the fall season, her/his estimated probability of passing the state test in spring is 17%.
RIT Range 2 3 4 5 6 7 8 High
130 1% 1% 1% 1% 1% 1% 1% 1%
135 2% 1% 1% 1% 1% 1% 1% 1%
140 4% 1% 1% 1% 1% 1% 1% 1%
145 6% 2% 1% 1% 1% 1% 1% 1%
150 9% 3% 1% 1% 1% 1% 1% 1%
155 14% 4% 2% 1% 1% 1% 1% 1%
160 22% 7% 3% 1% 1% 1% 1% 1%
165 31% 11% 5% 2% 1% 1% 1% 1%
170 43% 17% 8% 3% 2% 1% 1% 1%
175 55% 25% 13% 5% 3% 1% 1% 1%
180 67% 36% 20% 8% 4% 2% 1% 2%
185 77% 48% 29% 13% 7% 4% 2% 3%
190 84% 60% 40% 20% 11% 6% 3% 4%
195 90% 71% 52% 29% 17% 10% 5% 7%
200 94% 80% 64% 40% 25% 16% 8% 11%
205 96% 87% 75% 52% 36% 23% 12% 17%
210 98% 92% 83% 64% 48% 33% 18% 25%
215 99% 95% 89% 75% 60% 45% 27% 35%
220 99% 97% 93% 83% 71% 57% 38% 48%
225 99% 98% 96% 89% 80% 69% 50% 60%
230 99% 99% 97% 93% 87% 78% 62% 71%
235 99% 99% 98% 96% 92% 86% 73% 80%
240 99% 99% 99% 97% 95% 91% 82% 87%
245 99% 99% 99% 98% 97% 94% 88% 92%
250 99% 99% 99% 99% 98% 96% 92% 95%
255 99% 99% 99% 99% 99% 98% 95% 97%
260 99% 99% 99% 99% 99% 99% 97% 98%
265 99% 99% 99% 99% 99% 99% 98% 99%
270 99% 99% 99% 99% 99% 99% 99% 99%
275 99% 99% 99% 99% 99% 99% 99% 99%
280 99% 99% 99% 99% 99% 99% 99% 99%
285 99% 99% 99% 99% 99% 99% 99% 99%
290 99% 99% 99% 99% 99% 99% 99% 99%
295 99% 99% 99% 99% 99% 99% 99% 99%
300 99% 99% 99% 99% 99% 99% 99% 99%
MATH-Prior Season
Estimated Probability of Passing State Test Based on Observed MAP Score
12. S C ( P A S S & H S A P ) N W E A A l i g n m e n t 2 0 1 0 12 | P a g e
TABLE 8 – ESTIMATED PROBABILITY OF “MEETING STANDARDS” OR BETTER ON THE
READING TEST IN PRIOR SEASON (FALL), BY STUDENT GRADE AND RIT SCORE
*
Note: This table provides the estimated probability of passing the state test in spring, based on a MAP test score
taken during the previous (fall) season. Example: if a third grade student scored a 190 on a MAP test taken during
the fall season, her/his estimated probability of passing the state test in spring is 75%.
RIT Range 2 3 4 5 6 7 8 High
130 2% 1% 1% 1% 1% 1% 1% 1%
135 4% 1% 1% 1% 1% 1% 1% 1%
140 6% 2% 1% 1% 1% 1% 1% 1%
145 10% 3% 1% 1% 1% 1% 1% 1%
150 16% 5% 2% 1% 1% 1% 1% 1%
155 23% 8% 3% 2% 1% 1% 1% 1%
160 33% 13% 5% 3% 1% 1% 1% 1%
165 45% 20% 8% 5% 2% 1% 1% 1%
170 57% 29% 12% 8% 3% 2% 1% 2%
175 69% 40% 18% 13% 5% 4% 2% 4%
180 78% 52% 27% 20% 8% 6% 4% 6%
185 86% 64% 38% 29% 13% 9% 6% 10%
190 91% 75% 50% 40% 20% 14% 10% 15%
195 94% 83% 62% 52% 29% 22% 16% 23%
200 96% 89% 73% 64% 40% 31% 23% 33%
205 98% 93% 82% 75% 52% 43% 33% 45%
210 99% 96% 88% 83% 64% 55% 45% 57%
215 99% 97% 92% 89% 75% 67% 57% 69%
220 99% 98% 95% 93% 83% 77% 69% 79%
225 99% 99% 97% 96% 89% 84% 78% 86%
230 99% 99% 98% 97% 93% 90% 86% 91%
235 99% 99% 99% 98% 96% 94% 91% 94%
240 99% 99% 99% 99% 97% 96% 94% 96%
245 99% 99% 99% 99% 98% 98% 96% 98%
250 99% 99% 99% 99% 99% 99% 98% 99%
255 99% 99% 99% 99% 99% 99% 99% 99%
260 99% 99% 99% 99% 99% 99% 99% 99%
265 99% 99% 99% 99% 99% 99% 99% 99%
270 99% 99% 99% 99% 99% 99% 99% 99%
275 99% 99% 99% 99% 99% 99% 99% 99%
280 99% 99% 99% 99% 99% 99% 99% 99%
285 99% 99% 99% 99% 99% 99% 99% 99%
290 99% 99% 99% 99% 99% 99% 99% 99%
295 99% 99% 99% 99% 99% 99% 99% 99%
300 99% 99% 99% 99% 99% 99% 99% 99%
READING-Prior Season
Estimated Probability of Passing State Test Based on Observed MAP Score
13. S C ( P A S S & H S A P ) N W E A A l i g n m e n t 2 0 1 0 13 | P a g e
TABLE 9 – CORRELATION COEFFICIENTS BETWEEN MAP AND SOUTH CAROLINA TEST
FOR EACH GRADE AND TEST SUBJECT
Grade Math Correlation
Pearson's r
Reading Correlation
Pearson's r
3 0.807 0.787
4 0.837 0.778
5 0.858 0.778
6 0.850 0.786
7 0.844 0.780
8 0.845 0.781
High 0.866 0.815
TABLE 10 – PERCENTAGE OF STUDENTS WHOSE STATUS WAS ACCURATELY
PREDICTED BY THEIR MAP PERFORMANCE USING REPORTED CUT SCORES
Grade Sample
Size
MAP Accurately
Predicted
State Performance
MAP Underestimated
State Performance
MAP Overestimated
State Performance
Mathematics
3 6475 86.04% 6.39% 7.34%
4 6363 86.64% 5.72% 7.51%
5 6103 88.33% 4.54% 6.90%
6 6004 80.63% 6.26% 7.88%
7 5992 84.61% 5.81% 8.76%
8 5686 81.23% 7.49% 9.34%
High
3266 88.17% 5.46% 6.37%
Reading
3 6475 85.36% 6.33% 8.31%
4 6363 88.54% 5.03% 6.43%
5 6103 88.28% 5.54% 6.18%
6 6004 86.73% 6.26% 7.01%
7 5992 85.58% 6.93% 7.49%
8 5686 86.26% 6.44% 7.30%
High
3231 89.27% 4.90% 5.83%
14. S C ( P A S S & H S A P ) N W E A A l i g n m e n t 2 0 1 0 14 | P a g e
The Kingsbury Center at NWEA
5885 SW Meadows Road, Suite 200
Lake Oswego, OR 97035-3526
www.kingsburycenter.org
Tel 503-624-1951
Fax 503-639-7873