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8 MAP Mathematics RIT Score as Predictor Mathematics Benchmark MAP Mathematics EXPLORE PLAN ACT RIT Range 8th Grade 10th Grade 11th Grade 130 0% 0% 0% 135 0% 0% 0% 140 0% 0% 0% 145 0% 0% 0% 150 0% 0% 0% 155 0% 0% 0% 160 0% 0% 0% 165 0% 0% 0% 170 0% 0% 0% 175 0% 0% 0% 180 0% 0% 0% 185 0% 0% 0% 190 0% 0% 0% 195 0% 0% 0% 200 0% 0% 0% 205 0% 0% 0% 210 0% 0% 0% 215 1% 0% 0% 220 3% 1% 0% 225 7% 1% 0% % 3% 0% % 6% 1% % 18% 2% % 35% 17% % 55% 37% % 75% 62% % 92% 84% % 95% 99% % 100% 100% % 100% 100% % 100% 100% % 100% 100% % 100% 100% % 100% 100% % 100% 100% *Note: This table shows the proportion of students in the study sample who, based on a MAP mathematics score taken during the same (season), met the associated college readiness benchmark. Example: an eighth grade student scoring 240 on a MAP mathematics test taken during the same season would have a 53% chance of meeting the EXPLORE college readiness benchmark in mathematics.

11 REFERENCES ACT Issues in College Readiness: What are ACT's College Readiness Benchmarks (IC ). Retrieved from ACT website: Northwest Evaluation Association RIT Scale Norms. Portland, OR: Northwest Evaluation Association.

12 APPENDIX 1: METHODOLOGY This linking study examines the concurrent relationship between EXPLORE/PLAN/ACT and MAP assessments with the goal of publishing benchmarks on the RIT scale that are predictive of the ACT s college readiness benchmarks (ACT, 2010). Appendix Table 1 describes the three ACT assessments with their respective college readiness targets by content area. Appendix Table 1 ACT College Readiness Cut Points EXPLORE PLAN ACT Grade 8 Grade 9 Grade 10 Grade Math Study Sample NWEA solicited all known partner districts that administer both the EXPLORE/PLAN/ACT and MAP assessments to participate in this study. While not every eligible partner participated, the final study sample was large enough (unique total student n=29,417) to proceed with the analysis. Appendix Table 2 contains the distinct sample count. Appendix Table 2 Sample Counts Test Grade Unique Unique District Unique School Unique Student State Count Count Count Count ACT Explore Plan

13 Each district s EXPLORE/PLAN/ACT test records were matched to their corresponding MAP data via a robust matching algorithm housed in NWEA s Growth Research Database. The bulk of the study used matched students who took both assessments in same term. In the cases where students took the NWEA MAP test in different terms, we employed the following prioritized matching process. Priority 1 MAP given in SAME term as EXPLORE/PLAN/ACT Priority 2 - MAP test given one term BEFORE EXPLORE/PLAN/ACT Priority 3 - MAP test given two terms BEFORE EXPLORE/PLAN/ACT Priority 4 - MAP test given one term AFTER EXPLORE/PLAN/ACT Priority 5 - Map test given two terms AFTER EXPLORE/PLAN/ACT In order to ensure comparable RIT scores, we took the MAP percentile associated with the RIT score and substituted the RIT score associated with that percentile in the term the EXPLORE/PLAN/ACT was administered. For example, a winter test score under Priority 2 with a percentile of 75 would be substituted for the corresponding spring RIT score associated with the 75 th percentile. Appendix Table 3 contains the distribution of unique students by the EXPLORE/PLAN/ACT test, MAP Grade and prioritized matching scheme. Appendix Table 3 Prioritized Matching Count (Unique Students) by ACT Test and MAP Grade EXPLORE/PLAN/ACT Test and MAP Grade ACT P1 - Exact P2-1 Term Prior Priority Matching P3-2 Terms Prior P4-1 Term After Total PLAN ,822 EXPLORE 8 13, ,915 Total 20, ,417 Analysis The goal of the analysis is to find the statistical model that best describes the scale relationship between EXPLORE/PLAN/ACT and the NWEA MAP assessments. We tested multiple models including Ordinary Least Squares Regression (Linear and Quadratic) and Hierarchical Linear Model (HLM). The best model was determined by correlation (r) and overall model fit. Akaike Information Criterion (AIC) is the measure of model fit we used. Generally speaking, the AIC examines the tradeoffs between model accuracy and complexity whereby the model with the lowest AIC value is said to be the most parsimonious

14 Separate linear (1a) and quadratic (1b) regression routines were run for each relationship model. (1a) where X= RIT (1b) where X= RIT Based on the correlations and AIC fit statistics, the quadratic regression best described the shape of the scale relationship (see Appendix Table 4).

15 Appendix Table 4 Initial Model Correlations Test Model Linear Regression Quadratic Regression r AIC r AIC ACT Language Usage to ACT Language Usage to ACT Math to Math ACT to ACT to PLAN Language Usage to PLAN Language Usage to PLAN Math to Math PLAN to PLAN to EXPLORE Language Usage to EXPLORE Language Usage to EXPLORE Math to Math EXPLORE to EXPLORE to The next step in the analytic process was to determine whether any between-school variation existed in our scale relationships by running a simple unconstrained HLM model (2): (2) is the ACT/PLAN/EXPLORE score for student i in school j; is the grand mean (students within schools) variance in intercept between schools within school variance

16 Appendix Table 4 contains the Intraclass Correlation Coefficient (ICC) for each test and model. The ICC measures the proportion of variance in the dependent variable that is accounted for by our grouping structure.

17 Appendix Table 5 Intraclass Correlation Coefficients (ICC) by Test and Model Test Model t 00 σ 2 ICC ACT Language Usage to ACT Language Usage to ACT Math to Math ACT to ACT to Plan Language Usage to Plan Language Usage to Plan Math to Math Plan to Plan to Explore Language Usage to Explore Language Usage to Explore Math to Math Explore to Explore to The ICCs offer a somewhat conflicting picture on the appropriateness of using a multilevel model in the case of this study. For instance, the EXPLORE assessments have the least amount of between-group variance (less than six percent) and the nearly the most number grouping levels (between 29 and 51 individual schools depending on the model). While no well-established ICC thresholds exist per se, it would appear the EXPLORE would not be a good candidate when compared to the ACT and PLAN ICCs. One explanation for the observed differences could be related to the specific analytic sample used. While we could have employed two separate methods (Quadratic for EXPLORE, HLM for ACT and PLAN),

18 we felt the quadratic model offered transparency and consistency while maintaining good model fit characteristics. Figures 1 through 3 illustrate the final fitted model for EXPLORE/PLAN/ACT Math to NWEA MAP Math. Each figure contains notes referencing specific sample or estimation characteristics. Please note standardized residuals greater than 2 or less than -2 were removed from the final model to eliminate potential sources of statistical noise. We should also note the MAP assessment measures student performance relative to state content standards rather than discrete college readiness standards. This difference in content alignment could possibility degrade the published regression coefficients between MAP and EXPLORE/PLAN/ACT. Figure 1 - ACT Math to NWEA Math The ACT Math scatter plot and residuals exhibit pronounced curvilinear shapes. The sample also becomes sparse toward the top end of the distribution, making those estimates less reliable.

19 Figure 2 - PLAN Math to NWEA Math The PLAN Math scatter plot and residuals display the same curvilinear pattern as the ACT but appears to have more variance along the fit line. Like the ACT, the sample also becomes sparse toward the top end of the distribution, making those estimates less reliable.

20 Figure 3 - EXPLORE Math to NWEA Math The EXPLORE Math scatter plot and residuals have a slight curvilinear profile especially when compared to the ACT and PLAN assessments. The EXPLORE assessment has a pronounced ceiling effect in reference to the NWEA MAP assessment, meaning the NWEA assessment has more stretch than EXPLORE at the eighth grade level.

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