Relating your PIRA and PUMA test marks to the national standard

Similar documents
Relating your PIRA and PUMA test marks to the national standard

Statistics and Quantitative Analysis U4320. Segment 8 Prof. Sharyn O Halloran

Passenger seat belt use in Durham Region

Investigation of Relationship between Fuel Economy and Owner Satisfaction

Topic 5 Lecture 3 Estimating Policy Effects via the Simple Linear. Regression Model (SLRM) and the Ordinary Least Squares (OLS) Method

Chapter 5 ESTIMATION OF MAINTENANCE COST PER HOUR USING AGE REPLACEMENT COST MODEL

PARTIAL LEAST SQUARES: WHEN ORDINARY LEAST SQUARES REGRESSION JUST WON T WORK

Preface... xi. A Word to the Practitioner... xi The Organization of the Book... xi Required Software... xii Accessing the Supplementary Content...

KEY STAGE. Level threshold tables and age standardised scores for key stage 2 tests in English, mathematics and science KEY STAGE KEY STAGE KEY STAGE

Integrating remote sensing and ground monitoring data to improve estimation of PM 2.5 concentrations for chronic health studies

When the points on the graph of a relation lie along a straight line, the relation is linear

Sharif University of Technology. Graduate School of Management and Economics. Econometrics I. Fall Seyed Mahdi Barakchian

Objectives. Materials TI-73 CBL 2

PHYSICS 111 LABORATORY Experiment #3 Current, Voltage and Resistance in Series and Parallel Circuits

Technical Papers supporting SAP 2009

Introduction. Materials and Methods. How to Estimate Injection Percentage

BAC and Fatal Crash Risk

FINAL REPORT AP STATISTICS CLASS DIESEL TRUCK COUNT PROJECT

Technical Manual for Gibson Test of Cognitive Skills- Revised

Getting Started with Correlated Component Regression (CCR) in XLSTAT-CCR

Lecture 2. Review of Linear Regression I Statistics Statistical Methods II. Presented January 9, 2018

The Session.. Rosaria Silipo Phil Winters KNIME KNIME.com AG. All Right Reserved.

INCREASING POWER DENSITY BY ADVANCED MANUFACTURING, MATERIALS, AND SURFACE TREATMENTS

Improving CERs building

QuaSAR Quantitative Statistics

Stat 301 Lecture 30. Model Selection. Explanatory Variables. A Good Model. Response: Highway MPG Explanatory: 13 explanatory variables

Correlation to the Common Core State Standards

Effect of Sample Size and Method of Sampling Pig Weights on the Accuracy of Estimating the Mean Weight of the Population 1

HISTOGRAMS, CUMULATIVE FREQUENCY AND BOX PLOTS

Installation design data for the 500SFK series fuse

Mandatory Experiment: Electric conduction

Rolling resistance as a part of total resistance plays a

Circumstances affecting the protection against electrode potential rise (EPR)

9.3 Tests About a Population Mean (Day 1)

The goal of the study is to investigate the effect of spring stiffness on ride height and aerodynamic balance.

Predicting Tractor Fuel Consumption

Passive Investors and Managed Money in Commodity Futures. Part 2: Liquidity. Prepared for: The CME Group. Prepared by:

Objective: Students will investigate rate of change (slope) using spring data from RC cars.

Motor Trend Yvette Winton September 1, 2016

Houghton Mifflin MATHEMATICS. Level 1 correlated to Chicago Academic Standards and Framework Grade 1

Scale Score to Percentile Rank Conversion Tables Spring 2018

Descriptive Statistics

d / cm t 2 / s 2 Fig. 3.1

Motor-CAD End Winding Spray Cooling Model

September 21, Introduction. Environmental Protection Agency ( EPA ), National Highway Traffic Safety

LET S ARGUE: STUDENT WORK PAMELA RAWSON. Baxter Academy for Technology & Science Portland, rawsonmath.

Level threshold tables and age standardised scores

Component Mass Study Statistical Benchmarking

Supplier Training: Fastener Torque

Tactical Wheeled Vehicle (TWV) Fuel Economy Improvement Breakeven Analysis. Presented at SCEA/IPSA

fruitfly fecundity example summary Tuesday, July 17, :13:19 PM 1

We trust that these data are helpful to you. If you have any questions, feel free to contact Dr. Joe Ludlum at or

Albert Sanzari IE-673 Assignment 5

Statistical Learning Examples

Inflation: the Value of the Pound

Fine particle pollution

Stat 401 B Lecture 27

Index. Calculated field creation, 176 dialog box, functions (see Functions) operators, 177 addition, 178 comparison operators, 178

TRUTH AND LIES: CONSUMER PERCEPTION VS. DATA

Fuel Strategy (Exponential Decay)

Studying the Factors Affecting Sales of New Energy Vehicles from Supply Side Shuang Zhang

Abstract. Background and Study Description

sponsoring agencies.)

SINGLE-PHASE CONVECTIVE HEAT TRANSFER AND PRESSURE DROP COEFFICIENTS IN CONCENTRIC ANNULI

REGULATION No. 117 (Tyres rolling noise and wet grip adhesion) Proposal for amendment to the document ECE/TRANS/WP.29/2010/63. Annex 8.

Header. Mrs Barlow 7EX. Mr Lines 7MX. Miss Gaston 7CX. Miss Stanwix 7PX. Miss Croft 7EY. Mr Cannessa 7MY. Mr Boreham 7PY.

Performance of VAV Parallel Fan-Powered Terminal Units: Experimental Results and Models

Important Formulas. Discrete Probability Distributions. Probability and Counting Rules. The Normal Distribution. Confidence Intervals and Sample Size

PVP Field Calibration and Accuracy of Torque Wrenches. Proceedings of ASME PVP ASME Pressure Vessel and Piping Conference PVP2011-

Design and Performance Study of Shell and Tube Heat Exchanger with Single Segmental Baffle Having Perpendicular & Parallel-Cut Orientation.

INVESTIGATION ONE: WHAT DOES A VOLTMETER DO? How Are Values of Circuit Variables Measured?

Reliability and Validity of Seat Interface Pressure to Quantify Seating Comfort in Motorcycles

Algebra 2 Plus, Unit 10: Making Conclusions from Data Objectives: S- CP.A.1,2,3,4,5,B.6,7,8,9; S- MD.B.6,7

REMOTE SENSING MEASUREMENTS OF ON-ROAD HEAVY-DUTY DIESEL NO X AND PM EMISSIONS E-56

TABLE 4.1 POPULATION OF 100 VALUES 2

SECOND ROW SEAT BELT SECOND ROW SEAT BELT. Second Row Seat Belt SB-13 EXPLODED VIEW SEAT BELT RETRACTOR < REMOVAL AND INSTALLATION >

College Board Research

Faraday's Law of Induction

Predicting Drivers Crash Risk Based-on Previous Crash History

Test-Retest Analyses of ACT Engage Assessments for Grades 6 9, Grades 10 12, and College

Electro-Proportional Terms and Definitions

Factors Affecting Vehicle Use in Multiple-Vehicle Households

2018 Linking Study: Predicting Performance on the NSCAS Summative ELA and Mathematics Assessments based on MAP Growth Scores

Co-Curricular Activities (CCA) Initial CCA Setup

Prepared: November 2011 (revised)

WHITE PAPER. Preventing Collisions and Reducing Fleet Costs While Using the Zendrive Dashboard

Integrated. Safety Handbook. Automotive. Ulrich Seiffert and Mark Gonter. Warrendale, Pennsylvania, USA INTERNATIONAL.

Parking Studies. Lecture Notes in Transportation Systems Engineering. Prof. Tom V. Mathew. 1 Overview 1

Voting Draft Standard

Folksam Mazda 6 Post-Impact Inspection 22/02/18

Quality of Life in Neurological Disorders. Scoring Manual

Child safety CHILD SEATS

Abstract. 1. Introduction. 1.1 object. Road safety data: collection and analysis for target setting and monitoring performances and progress

Follow this and additional works at:

Busy Ant Maths and the Scottish Curriculum for Excellence Foundation Level - Primary 1

At the end of this lesson, the students should be able to understand:

Flexible and intelligent Micro System solutions. for rural Africa!

Forecast the charging power demand for an electric vehicle. Dr. Wilson Maluenda, FH Vorarlberg; Philipp Österle, Illwerke VKW;

6th generation (2015+) Mustang Harness Mount Bar Installation Instructions

MJPower engineering. Variomatic Tuning guide.

Transcription:

Relating your PIRA and PUMA test marks to the national standard We have carried out a detailed statistical analysis between the results from the PIRA and PUMA tests for Year 2 and Year 6 and the scaled scores of the 2016 national tests. In doing this, the PIRA and PUMA test raw scores of 1,150 children for PiRA and over 1,400 children for PUMA from 56 schools across the country have been linked to the scaled scores achieved by the same children in their actual national tests*. In order to describe the relationship between the Rising Stars PIRA and PUMA tests and the national test scores, a statistical technique known as linear regression was used to model the relationship between the two variables. Outputs of this analysis are provided below as tables and graphs to assist teachers in using the findings to relate their own children s PiRA and PUMA marks to the national test scaled scores. *Please note that this will be indicative only. Other factors can influence a child s performance on any given day. How this analysis will help you The results of this analysis provide teachers with a clear indication of the marks children are likely to need in their PIRA and PUMA tests in order to achieve a scaled score of 100 or above when they sit their national tests. The Relationship Tables show what PIRA and PUMA raw marks were achieved by children who went on to achieve 100 or more in the national tests. Children achieving similar scores in the PIRA and PUMA tests in 2016-2017 would therefore be operating at the national standard. The analysis correlates all PIRA and PUMA marks to the full range of scaled scores from 85 to 115 at KS1 and 80 to 120 at KS2. This analysis should concentrate efforts to help identify those currently on track to perform slightly below the 100 scaled score, so you can use your PIRA and PUMA gap analysis to highlight the areas for development/intervention.

Using the Relationship Tables Relating your PIRA and PUMA test marks to the national standard The Relationship Tables have been created to enable you to monitor the marks children are likely to need in their PIRA and PUMA tests in order to achieve a scaled score of 100 or above when they sit their national tests The middle column shows you the child s Estimated Scaled Score. The numbers on either side of this are prediction and confidence intervals this simply means they show the lowest score the child is likely to achieve, and the highest. Because all children and all test experiences vary, it is impossible to say that they will definitely achieve the number in the middle column but you can be comfortable that there is a 95% chance they will achieve a score between the lowest and highest listed in that row. Using the Relationship Graphs For each table there is a corresponding graph which has the PIRA or PUMA test marks plotted against the national test scores, for easy reference. Either side of the central line are two lines directly above and below which show the upper and lower 95% s for the estimated scaled score of all pupils in the population. The outer lines show a prediction with 95% confidence for individual pupil s scaled score from their PIRA or PUMA test mark.

PUMA 6 Autumn Test Relationship Table PUMA 6 Autumn Test mark prediction limit National Test scores Estimated Scaled Score prediction limit 1 83 91 91 92 99 2 84 91 92 92 100 3 84 92 92 93 100 4 85 92 93 93 101 5 85 93 93 94 101 6 86 93 94 94 102 7 86 94 94 95 102 8 87 94 95 95 103 9 87 95 95 96 103 10 88 95 96 96 104 11 88 96 96 97 104 12 89 96 97 97 105 13 89 97 97 98 105 14 90 97 98 98 106 15 90 98 98 99 106 16 91 98 99 99 107 17 91 99 99 100 107 18 92 99 100 100 108 19 92 100 100 100 108 20 93 100 101 101 109 21 93 101 101 101 109 22 94 101 102 102 110 23 94 102 102 102 110 24 95 102 103 103 111 25 95 103 103 103 111 26 96 103 104 104 112 27 96 104 104 104 112 28 97 104 105 105 112 29 97 105 105 105 113 30 98 105 106 106 113 31 98 106 106 106 114 32 99 106 107 107 114 33 99 107 107 107 115 34 100 107 108 108 115 35 100 108 108 108 116 36 101 108 109 109 116 37 101 109 109 109 117 38 102 109 110 110 117 39 102 110 110 110 118 40 103 110 110 111 118 41 103 111 111 111 119 42 104 111 111 112 119 43 104 112 112 112 120 44 105 112 112 113 120 45 105 112 113 113 >120 46 106 113 113 114 >120 47 106 113 114 114 >120 48 107 114 114 115 >120 49 107 114 115 115 >120 50 108 115 115 116 >120

PUMA 6 Autumn Test Relationship Graph

PUMA 6 Spring Test Relationship Table PUMA 6 Spring Test mark prediction limit National Test scores Estimated Scaled Score prediction limit 1 83 90 91 91 99 2 83 91 91 92 99 3 84 91 92 92 100 4 84 92 92 93 100 5 85 92 93 93 101 6 85 92 93 94 101 7 85 93 94 94 102 8 86 93 94 95 102 9 86 94 94 95 103 10 87 94 95 95 103 11 87 95 95 96 103 12 88 95 96 96 104 13 88 96 96 97 104 14 89 96 97 97 105 15 89 97 97 98 105 16 90 97 98 98 106 17 90 98 98 99 106 18 91 98 99 99 107 19 91 99 99 99 107 20 91 99 100 100 108 21 92 100 100 100 108 22 92 100 100 101 109 23 93 101 101 101 109 24 93 101 101 102 109 25 94 102 102 102 110 26 94 102 102 103 110 27 95 103 103 103 111 28 95 103 103 104 111 29 96 103 104 104 112 30 96 104 104 104 112 31 97 104 105 105 113 32 97 105 105 105 113 33 98 105 106 106 114 34 98 106 106 106 114 35 98 106 106 107 115 36 99 107 107 107 115 37 99 107 107 108 115 38 100 108 108 108 116 39 100 108 108 109 116 40 101 108 109 109 117 41 101 109 109 110 117 42 102 109 110 110 118 43 102 110 110 111 118 44 103 110 111 111 119 45 103 111 111 112 119 46 104 111 112 112 120 47 104 112 112 113 120 48 104 112 113 113 >120 49 105 112 113 114 >120 50 105 113 113 114 >120

PUMA 6 Spring Test Relationship Graph

PUMA 6 Summer Test Relationship Table PUMA 6 Summer Test mark prediction limit National Test scores Estimated Scaled Score prediction limit 1 83 90 91 91 98 2 83 91 91 92 99 3 84 91 92 92 99 4 84 92 92 93 100 5 85 92 93 93 100 6 85 93 93 93 101 7 86 93 93 94 101 8 86 94 94 94 102 9 87 94 94 95 102 10 87 94 95 95 103 11 88 95 95 96 103 12 88 95 96 96 104 13 89 96 96 97 104 14 89 96 97 97 105 15 90 97 97 98 105 16 90 97 98 98 106 17 91 98 98 99 106 18 91 98 99 99 106 19 91 99 99 100 107 20 92 99 100 100 107 21 92 100 100 100 108 22 93 100 101 101 108 23 93 101 101 101 109 24 94 101 102 102 109 25 94 102 102 102 110 26 95 102 103 103 110 27 95 103 103 103 111 28 96 103 104 104 111 29 96 104 104 104 112 30 97 104 105 105 112 31 97 105 105 105 113 32 98 105 105 106 113 33 98 106 106 106 114 34 99 106 106 107 114 35 99 107 107 107 115 36 100 107 107 108 115 37 100 108 108 108 116 38 101 108 108 109 116 39 101 109 109 109 117 40 102 109 109 110 117 41 102 109 110 110 118 42 103 110 110 111 118 43 103 110 111 111 118 44 103 111 111 112 119 45 104 111 112 112 119 46 104 112 112 113 120 47 105 112 113 113 120 48 105 113 113 114 >120 49 106 113 114 114 >120 50 106 114 114 115 >120

PUMA 6 Summer Test Relationship Graph