Standardized Assessment for Clinical Practitioners: a Primer Lawrence G
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Multiple Linear Regression
Multiple Linear Regression The MULTIPLE LINEAR REGRESSION command performs simple multiple regression using least squares. Linear regression attempts to model the linear relationship between variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable (RESPONSE), and the others are considered to be dependent variables (PREDICTORS). How To Run: STATISTICS->REGRESSION->MULTIPLE LINEAR REGRESSION... Select DEPENDENT (RESPONSE) variable and INDEPENDENT variables (PREDICTORS). To force the regression line to pass through the origin use the CONSTANT (INTERCEPT) IS ZERO option from the ADVANCED OPTIONS. Optionally, you can add following charts to the report: o Residuals versus predicted values plot (use the PLOT RESIDUALS VS. FITTED option); o Residuals versus order of observation plot (use the PLOT RESIDUALS VS. ORDER option); o Independent variables versus the residuals (use the PLOT RESIDUALS VS. PREDICTORS option). For the univariate model, the chart for the predicted values versus the observed values (LINE FIT PLOT) can be added to the report. Use THE EMULATE EXCEL ATP FOR STANDARD RESIDUALS option to get the same standard residuals as produced by Excel Analysis Toolpak. Results Regression statistics, analysis of variance table, coefficients table and residuals report are produced. Regression Statistics 2 R (COEFFICIENT OF DETERMINATION, R-SQUARED) is the square of the sample correlation coefficient between 2 the PREDICTORS (independent variables) and RESPONSE (dependent variable). In general, R is a percentage of response variable variation that is explained by its relationship with one or more predictor variables. In simple words, the R2 indicates the accuracy of the prediction. The larger the R2 is, the more the total 2 variation of RESPONSE is explained by predictors or factors in the model. -
The Statistical Analysis of Distributions, Percentile Rank Classes and Top-Cited
How to analyse percentile impact data meaningfully in bibliometrics: The statistical analysis of distributions, percentile rank classes and top-cited papers Lutz Bornmann Division for Science and Innovation Studies, Administrative Headquarters of the Max Planck Society, Hofgartenstraße 8, 80539 Munich, Germany; [email protected]. 1 Abstract According to current research in bibliometrics, percentiles (or percentile rank classes) are the most suitable method for normalising the citation counts of individual publications in terms of the subject area, the document type and the publication year. Up to now, bibliometric research has concerned itself primarily with the calculation of percentiles. This study suggests how percentiles can be analysed meaningfully for an evaluation study. Publication sets from four universities are compared with each other to provide sample data. These suggestions take into account on the one hand the distribution of percentiles over the publications in the sets (here: universities) and on the other hand concentrate on the range of publications with the highest citation impact – that is, the range which is usually of most interest in the evaluation of scientific performance. Key words percentiles; research evaluation; institutional comparisons; percentile rank classes; top-cited papers 2 1 Introduction According to current research in bibliometrics, percentiles (or percentile rank classes) are the most suitable method for normalising the citation counts of individual publications in terms of the subject area, the document type and the publication year (Bornmann, de Moya Anegón, & Leydesdorff, 2012; Bornmann, Mutz, Marx, Schier, & Daniel, 2011; Leydesdorff, Bornmann, Mutz, & Opthof, 2011). Until today, it has been customary in evaluative bibliometrics to use the arithmetic mean value to normalize citation data (Waltman, van Eck, van Leeuwen, Visser, & van Raan, 2011). -
A Review of Standardized Testing Practices and Perceptions in Maine
A Review of Standardized Testing Practices and Perceptions in Maine Janet Fairman, Ph.D. Amy Johnson, Ph.D. Ian Mette, Ph.D Garry Wickerd, Ph.D. Sharon LaBrie, M.S. April 2018 Maine Education Policy Research Institute Maine Education Policy Research Institute McLellan House, 140 School Street 5766 Shibles Hall, Room 314 Gorham, ME 04038 Orono, ME 04469-5766 207.780.5044 or 1.888.800.5044 207.581.2475 Published by the Maine Education Policy Research Institute in the Center for Education Policy, Applied Research, and Evaluation (CEPARE) in the School of Education and Human Development, University of Southern Maine, and the Maine Education Policy Research Institute in the School of Education and Human Development at the University of Maine at Orono. The Maine Education Policy Research Institute (MEPRI), is jointly funded by the Maine State Legislature and the University of Maine System. This institute was established to conduct studies on Maine education policy and the Maine public education system for the Maine Legislature. Statements and opinions by the authors do not necessarily reflect a position or policy of the Maine Education Policy Research Institute, nor any of its members, and no official endorsement by them should be inferred. The University of Maine System does not discriminate on the basis of race, color, religion, sex, sexual orientation, national origin or citizenship status, age, disability, or veteran's status and shall comply with Section 504, Title IX, and the A.D.A in employment, education, and in all other areas of the University. The University provides reasonable accommodations to qualified individuals with disabilities upon request. -
(QQ-Plot) and the Normal Probability Plot Section
MAT 2377 (Winter 2012) Quantile-Quantile Plot (QQ-plot) and the Normal Probability Plot Section 6-6 : Normal Probability Plot Goal : To verify the underlying assumption of normality, we want to compare the distribution of the sample to a normal distribution. Normal Population : Suppose that the population is normal, i.e. X ∼ N(µ, σ2). Thus, X − µ 1 µ Z = = X − ; σ σ σ where Z ∼ N(0; 1). Hence, there is a linear association between a normal variable and a standard normal random variable. If our sample is randomly selected from a normal population, then we should be able to observer this linear association. Consider a random sample of size n : x1; x2; : : : ; xn. We will obtain the order statistics (i.e. order the values in an ascending order) : y1 ≤ y2 ≤ ::: ≤ yn: We will compare the order statistics (called sample quantiles) to quantiles from a standard normal distribution N(0; 1). We rst need to compute the percentile rank of the ith order statistic. In practice (within the context of QQ-plots), it is computed as follows i − 3=8 i − 1=2 p = or (alternatively) p = : i n + 1=4 i n Consider yi, we will compare it to a lower quantile zi of order pi from N(0; 1). We get −1 zi = Φ (pi) : 1 The plot of zi against yi (or alternatively of yi against zi) is called a quantile- quantile plot or QQ-plot If the data are normal, then it should exhibit a linear tendency. To help visualize the linear tendency we can overlay the following line 1 x z = x + ; s s where x is the sample mean and s is the sample standard deviation. -
Test Scores Explanation for Parents and Teachers: Youtube Video Script by Lara Langelett July 2019 1
Test Scores Explanation for Parents and Teachers: YouTube Video Script By Lara Langelett July 2019 1 Introduction: Hello Families and Teachers. My name is Lara Langelett and I would like to teach you about how to read your child’s or student’s test scores. The purpose of this video message is to give you a better understanding of standardized test scores and to be able to apply it to all normed assessment tools. First I will give a definition of what the bell curve is by using a familiar example. Then I will explain the difference between a percentile rank and percentage. Next, I will explain Normed-Referenced Standard test scores. In addition, the MAPs percentile rank and the RIT scores will be explained. Finally, I will explain IQ scores on the bell curve. 2 Slide 1: Let’s get started: here is a bell curve; it is shaped like a bell. 3 Slide 2: To understand the Bell Curve, we will look at a familiar example of basketball players in the U.S.A. Everyone, on this day, who plays basketball fits into this bell curve around the United States. I’m going to emphasis “on this day” as this is important piece information of for explaining standardized scores. 4 Slide 3: On the right side of the bell curve we can section off a part of the bell curve (2.1 % blue area). Inside this section are people who play basketball at the highest skill level, like the U.S. Olympic basketball team. These athletes are skilled athletes who have played basketball all their life, practice on a daily basis, have extensive knowledge of the game, and are at a caliber that the rest of the basketball population have not achieved. -
HMH ASSESSMENTS Glossary of Testing, Measurement, and Statistical Terms
hmhco.com HMH ASSESSMENTS Glossary of Testing, Measurement, and Statistical Terms Resource: Joint Committee on the Standards for Educational and Psychological Testing of the AERA, APA, and NCME. (2014). Standards for educational and psychological testing. Washington, DC: American Educational Research Association. Glossary of Testing, Measurement, and Statistical Terms Adequate yearly progress (AYP) – A requirement of the No Child Left Behind Act (NCLB, 2001). This requirement states that all students in each state must meet or exceed the state-defined proficiency level by 2014 on state Ability – A characteristic indicating the level of an individual on a particular trait or competence in a particular area. assessments. Each year, the minimum level of improvement that states, school districts, and schools must achieve Often this term is used interchangeably with aptitude, although aptitude actually refers to one’s potential to learn or is defined. to develop a proficiency in a particular area. For comparison see Aptitude. Age-Based Norms – Developed for the purpose of comparing a student’s score with the scores obtained by other Ability/Achievement Discrepancy – Ability/Achievement discrepancy models are procedures for comparing an students at the same age on the same test. How much a student knows is determined by the student’s standing individual’s current academic performance to others of the same age or grade with the same ability score. The ability or rank within the age reference group. For example, a norms table for 12 year-olds would provide information score could be based on predicted achievement, the general intellectual ability score, IQ score, or other ability score. -
Nine Facts About the SAT That Might Surprise You
Nine Facts About the SAT That Might Surprise You By Lynn Letukas COLLEGE BOARD RESEARCH Statistical Report RESEARCH Executive Summary The purpose of this document is to identify and dispel rumors that are frequently cited about the SAT. The following is a compilation of nine popular rumors organized into three areas: Student Demographics, Test Preparation/Test Prediction, and Test Utilization. Student Demographics Student demographics claims are those that primarily center around a specific demographic group that is said to over-/underperform on the SAT. Presumably, it is reasoned, if the SAT were a fair test, no student demographic characteristics would matter, as average scores would be similar across groups. Therefore, some people assume that any difference in SAT performance by demographics must indicate test bias for/against a demographic group. Rumor 1: The SAT Is a Wealth Test. According to Peter Sacks, author of Standardized Minds, “one can make a good guess about a child’s standardized test scores simply by looking at how many degrees her parents have and what kind of car they drive.”1 This comment is illustrative of frequent criticism that the SAT is merely a “wealth test” because there is a correlation between student scores and socioeconomic status (i.e., parental income and educational attainment). Proponents of this claim often support their arguments by stating that the SAT only covers content that is learned by wealthy students, rather than material covered in high school.2 Fact: Rigorous Course Taking in High School Better Explains Why Students Do Well (or Not) on the SAT. While SAT scores may be correlated with socioeconomic status (parental income and education), correlation does not mean two phenomena are causally related (e.g., parental income causes students to do well on the SAT). -
Measures of Center
Statistics is the science of conducting studies to collect, organize, summarize, analyze, and draw conclusions from data. Descriptive statistics consists of the collection, organization, summarization, and presentation of data. Inferential statistics consist of generalizing form samples to populations, performing hypothesis tests, determining relationships among variables, and making predictions. Measures of Center: A measure of center is a value at the center or middle of a data set. The arithmetic mean of a set of values is the number obtained by adding the values and dividing the total by the number of values. (Commonly referred to as the mean) x x x = ∑ (sample mean) µ = ∑ (population mean) n N The median of a data set is the middle value when the original data values are arranged in order of increasing magnitude. Find the center of the list. If there are an odd number of data values, the median will fall at the center of the list. If there is an even number of data values, find the mean of the middle two values in the list. This will be the median of this data set. The symbol for the median is ~x . The mode of a data set is the value that occurs with the most frequency. When two values occur with the same greatest frequency, each one is a mode and the data set is bimodal. Use M to represent mode. Measures of Variation: Variation refers to the amount that values vary among themselves or the spread of the data. The range of a data set is the difference between the highest value and the lowest value. -
Public School Admissions and the Myth of Meritocracy: How and Why Screened Public School Admissions Promote Segregation
BUERY_FIN.DOCX(DO NOT DELETE) 4/14/20 8:46 PM PUBLIC SCHOOL ADMISSIONS AND THE MYTH OF MERITOCRACY: HOW AND WHY SCREENED PUBLIC SCHOOL ADMISSIONS PROMOTE SEGREGATION RICHARD R. BUERY, JR.* Public schools in America remain deeply segregated by race, with devastating effects for Black and Latinx students. While residential segregation is a critical driver of school segregation, the prevalence of screened admissions practices can also play a devastating role in driving racial segregation in public schools. New York City, one of the most segregated school systems in America, is unique in its extensive reliance on screened admissions practices, including the use of standardized tests, to assign students to sought-after public schools. These screens persist despite their segregative impact in part because they appeal to America’s embrace of the idea of meritocracy. This Article argues that Americans embrace three conceptions of merit which shield these screens from proper scrutiny. The first is individual merit—the idea that students with greater ability or achievement deserve access to better schools. The second is systems merit—the idea that poor student performance on an assessment is a failure of the system that prepared the student for the assessment. The third is group merit—the idea that members of some groups simply possess less ability. Each of these ideas has a pernicious impact on perpetuating racial inequality in public education. INTRODUCTION ................................................................................. 102 I. THE APPEAL OF THREE DIMENSIONS OF MERIT .................... 104 A. Merit of the Individual .................................................... 104 B. Merit of the System ......................................................... 107 C. Merit of the Group .......................................................... 109 II. THE MYTH OF MERITOCRACY IN K12 ADMISSIONS ............. -
Rethinking School Accountability: Opportunities for Massachusetts
RETHINKING SCHOOL ACCOUNTABILITY OPPORTUNITIES FOR MASSACHUSETTS UNDER THE EVERY STUDENT SUCCEEDS ACT Senator Patricia D. Jehlen, Chair Senate Sub-committee to the Joint Committee on Education May 2018 The special Senate Subcommittee to the Joint Committee on Education was established per order of the Massachusetts State Senate in February, 2017: Ms. Chang-Díaz presented the following order, to wit: Ordered, That there shall be a special Senate sub-committee to the Joint Committee on Education, to consist of five members from the current Senate membership on the Joint Committee on Education, chaired by the Senate vice chair of the Joint Committee on Education, for the purpose of making an investigation and study of the Commonwealth’s alignment with and opportunities presented by the Every Student Succeeds (ESSA) Act, Public Law 114-95. The subcommittee shall submit its report and related legislation, if any, to the joint committee on Education once its report is completed. Senate Sub-committee chair and report author Senator Patricia D. Jehlen, with gratitude to those who contributed ideas, data, and comments Senate Sub-committee Members: Senator Michael J. Barrett Senator Jason M. Lewis Senator Barbara A. L'Italien Senator Patrick M. O'Connor Staff: Victoria Halal, Matthew Hartman, Emily Wilson, Kat Cline, Dennis Burke, Erin Riley, Sam Anderson, Daria Afshar Sponsored Events: (6/13/17) Panel Discussion: Life & Learning in MA Turnaround Schools https://www.youtube.com/watch?v=KbErK6rLQAY&t=2s (12/12/17) Mildred Avenue School Site Visit -
How and Why Standardized Tests Systematically Underestimate
St. John's Law Review Volume 80 Number 1 Volume 80, Winter 2006, Number 1 Article 7 How and Why Standardized Tests Systematically Underestimate African-Americans' True Verbal Ability and What to Do About It: Towards the Promotion of Two New Theories with Practical Applications Dr. Roy Freedle Follow this and additional works at: https://scholarship.law.stjohns.edu/lawreview This Symposium is brought to you for free and open access by the Journals at St. John's Law Scholarship Repository. It has been accepted for inclusion in St. John's Law Review by an authorized editor of St. John's Law Scholarship Repository. For more information, please contact [email protected]. THE RONALD H. BROWN CENTER FOR CIVIL RIGHTS AND ECONOMIC DEVELOPMENT SYMPOSIUM HOW AND WHY STANDARDIZED TESTS SYSTEMATICALLY UNDERESTIMATE AFRICAN-AMERICANS' TRUE VERBAL ABILITY AND WHAT TO DO ABOUT IT: TOWARDS THE PROMOTION OF TWO NEW THEORIES WITH PRACTICAL APPLICATIONS DR. ROY FREEDLEt INTRODUCTION In this Article, I want to raise a number of issues, both theoretical and practical, concerning the need for a total reassessment of especially the verbal intelligence of minority individuals. The issues to be raised amount to a critical reappraisal of standardized multiple-choice tests of verbal intelligence, such as the Law School Admissions Test ("LSAT"). I want to probe very deeply into why such standardized tests systematically underestimate verbal intelligence. This leads me first to review the prospects for a new standardized test of verbal intelligence associated with the studies of Joseph Fagan and Cynthia Holland.1 These studies show us that the races are equal; this result leads us to question the construct validity of many current standardized tests of verbal aptitude. -
How to Use MGMA Compensation Data: an MGMA Research & Analysis Report | JUNE 2016
How to Use MGMA Compensation Data: An MGMA Research & Analysis Report | JUNE 2016 1 ©MGMA. All rights reserved. Compensation is about alignment with our philosophy and strategy. When someone complains that they aren’t earning enough, we use the surveys to highlight factors that influence compensation. Greg Pawson, CPA, CMA, CMPE, chief financial officer, Women’s Healthcare Associates, LLC, Portland, Ore. 2 ©MGMA. All rights reserved. Understanding how to utilize benchmarking data can help improve operational efficiency and profits for medical practices. As we approach our 90th anniversary, it only seems fitting to celebrate MGMA survey data, the gold standard of the industry. For decades, MGMA has produced robust reports using the largest data sets in the industry to help practice leaders make informed business decisions. The MGMA DataDive® Provider Compensation 2016 remains the gold standard for compensation data. The purpose of this research and analysis report is to educate the reader on how to best use MGMA compensation data and includes: • Basic statistical terms and definitions • Best practices • A practical guide to MGMA DataDive® • Other factors to consider • Compensation trends • Real-life examples When you know how to use MGMA’s provider compensation and production data, you will be able to: • Evaluate factors that affect compensation andset realistic goals • Determine alignment between medical provider performance and compensation • Determine the right mix of compensation, benefits, incentives and opportunities to offer new physicians and nonphysician providers • Ensure that your recruitment packages keep pace with the market • Understand the effects thatteaching and research have on academic faculty compensation and productivity • Estimate the potential effects of adding physicians and nonphysician providers • Support the determination of fair market value for professional services and assess compensation methods for compliance and regulatory purposes 3 ©MGMA.