Reply to Kid didn’t finish MAP-R?

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[quote=Anonymous][quote=Anonymous]Interesting… here is NWEA’s analysis of the new algorithm — https://www.nwea.org/uploads/Research-MAP-Growth-with-enhanced-item-selection-algorithm-updates-on-score-compatibility_NWEA_Research_Guide.pdf

They find minor differences in the RIT and not the dramatic shifts PPs are reporting [/quote]

Applying the concordance table
Changes in the rate of growth in math relative to the 2020 norms mean that these norms are less
applicable to math scores derived from MAP with EISA and will provide less meaningful context for student
achievement and growth. Therefore, we strongly recommend that districts leverage the concordance
table to support decision-making that is consistent with historical decisions based on the 2020 norms. The
concordance tables should be used until new norms are released in 2025. There are two categories of use
cases for applying the concordance table: adjusting practice and adjusting data.
Adjusting practice. This category of use cases involves adjusting cut scores, such as those used for program-
placement decisions. Given the increases in winter and spring scores, using unadjusted cut scores results
in underidentifying students for intervention services and overidentifying students for talented and gifted
programs.6 By using the concordance study to adjust these cut scores in math, partners can make decisions
about student placement that are more consistent with past decisions, ensuring that students receive the
appropriate level of support. Similarly, the cut scores used for determining which students are on track for
proficiency, based on NWEA linking studies, should also be adjusted based on the concordance table.
Adjusting data. The second use case category involves using the concordance study to adjust individual
scores. It is important to note that the concordance table cannot be applied to aggregate data or
directly to growth metrics. Instead, it must first be applied to individual scores, which can then be used
to recalculate growth metrics at the student, grade, school, or other grouping levels. This ensures those
metrics are not upwardly biased and is strongly recommended when normative growth metrics are used
for evaluation or decision-making. Additionally, adjusting data is necessary when scores will be used in
longitudinal research that spans the transition period

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