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An intelligence quotient (IQ) is a total score derived from a set of standardised tests or subtests designed to assess human intelligence. The abbreviation "IQ" was coined by the psychologist William Stern for the German term Intelligenzquotient, his term for a scoring method for intelligence tests at University of Breslau he advocated in a 1912 book.
Psychometrics is a field of study within psychology concerned with the theory and technique of measurement. Psychometrics generally covers specialized fields within psychology and education devoted to testing, measurement, assessment, and related activities. Psychometrics is concerned with the objective measurement of latent constructs that cannot be directly observed. Examples of latent constructs include intelligence, introversion, mental disorders, and educational achievement. The levels of individuals on nonobservable latent variables are inferred through mathematical modeling based on what is observed from individuals' responses to items on tests and scales.
The SAT is a standardized test widely used for college admissions in the United States. Since its debut in 1926, its name and scoring have changed several times. For much of its history, it was called the Scholastic Aptitude Test and had two components, Verbal and Mathematical, each of which was scored on a range from 200 to 800. Later it was called the Scholastic Assessment Test, then the SAT I: Reasoning Test, then the SAT Reasoning Test, then simply the SAT.
A natural experiment is a study in which individuals are exposed to the experimental and control conditions that are determined by nature or by other factors outside the control of the investigators. The process governing the exposures arguably resembles random assignment. Thus, natural experiments are observational studies and are not controlled in the traditional sense of a randomized experiment. Natural experiments are most useful when there has been a clearly defined exposure involving a well defined subpopulation such that changes in outcomes may be plausibly attributed to the exposure. In this sense, the difference between a natural experiment and a non-experimental observational study is that the former includes a comparison of conditions that pave the way for causal inference, but the latter does not.
A standardized test is a test that is administered and scored in a consistent, or "standard", manner. Standardized tests are designed in such a way that the questions and interpretations are consistent and are administered and scored in a predetermined, standard manner.
In statistics, an effect size is a value measuring the strength of the relationship between two variables in a population, or a sample-based estimate of that quantity. It can refer to the value of a statistic calculated from a sample of data, the value of a parameter for a hypothetical population, or to the equation that operationalizes how statistics or parameters lead to the effect size value. Examples of effect sizes include the correlation between two variables, the regression coefficient in a regression, the mean difference, or the risk of a particular event happening. Effect sizes complement statistical hypothesis testing, and play an important role in power analyses, sample size planning, and in meta-analyses. The cluster of data-analysis methods concerning effect sizes is referred to as estimation statistics.
Educational assessment or educational evaluation is the systematic process of documenting and using empirical data on the knowledge, skill, attitudes, aptitude and beliefs to refine programs and improve student learning. Assessment data can be obtained from directly examining student work to assess the achievement of learning outcomes or can be based on data from which one can make inferences about learning. Assessment is often used interchangeably with test, but not limited to tests. Assessment can focus on the individual learner, the learning community, a course, an academic program, the institution, or the educational system as a whole. The word "assessment" came into use in an educational context after the Second World War.
Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics and other fields, to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later classification.
In statistics, econometrics, epidemiology and related disciplines, the method of instrumental variables (IV) is used to estimate causal relationships when controlled experiments are not feasible or when a treatment is not successfully delivered to every unit in a randomized experiment. Intuitively, IVs are used when an explanatory variable of interest is correlated with the error term (endogenous), in which case ordinary least squares and ANOVA give biased results. A valid instrument induces changes in the explanatory variable but has no independent effect on the dependent variable and is not correlated with the error term, allowing a researcher to uncover the causal effect of the explanatory variable on the dependent variable.
Formative assessment, formative evaluation, formative feedback, or assessment for learning, including diagnostic testing, is a range of formal and informal assessment procedures conducted by teachers during the learning process in order to modify teaching and learning activities to improve student attainment. The goal of a formative assessment is to monitor student learning to provide ongoing feedback that can help students identify their strengths and weaknesses and target areas that need work. It also helps faculty recognize where students are struggling and address problems immediately. It typically involves qualitative feedback for both student and teacher that focuses on the details of content and performance. It is commonly contrasted with summative assessment, which seeks to monitor educational outcomes, often for purposes of external accountability.
Thomas Joseph Kane is an American education economist who currently holds the position of Walter H. Gale Professor of Education and Economics at the Harvard Graduate School of Education. He has performed research on education policy, labour economics and econometrics. During Bill Clinton's first term as U.S. President, Kane served on the Council of Economic Advisers.
Eric Alan Hanushek is an economist who has written prolifically on public policy with a special emphasis on the economics of education. Since 2000, he has been a Paul and Jean Hanna Senior Fellow at the Hoover Institution, an American public policy think tank located at Stanford University in California. He was awarded the Yidan Prize for Education Research in 2021.
Joshua David Angrist is an Israeli–American economist and Ford Professor of Economics at the Massachusetts Institute of Technology. Angrist, together with Guido Imbens, was awarded the Nobel Memorial Prize in Economics in 2021 "for their methodological contributions to the analysis of causal relationships".
Matching is a statistical technique that evaluates the effect of a treatment by comparing the treated and the non-treated units in an observational study or quasi-experiment. The goal of matching is to reduce bias for the estimated treatment effect in an observational-data study, by finding, for every treated unit, one non-treated unit(s) with similar observable characteristics against which the covariates are balanced out. By matching treated units to similar non-treated units, matching enables a comparison of outcomes among treated and non-treated units to estimate the effect of the treatment reducing bias due to confounding. Propensity score matching, an early matching technique, was developed as part of the Rubin causal model, but has been shown to increase model dependence, bias, inefficiency, and power and is no longer recommended compared to other matching methods. A simple, easy-to-understand, and statistically powerful method of matching known as Coarsened Exact Matching or CEM.
Class size refers to the number of students a teacher faces during a given period of instruction.
As an educational reform goal, class size reduction (CSR) aims to increase the number of individualized student-teacher interactions intended to improve student learning. A reform long holding theoretical attraction to many constituencies, some have claimed CSR as the most studied educational reform of the last century. Until recently, interpretations of these studies have often been contentious. Some educational groups like the American Federation of Teachers and National Education Association are in favor of reducing class sizes. Others argue that class size reduction has little effect on student achievement. Many are concerned about the costs of reducing class sizes.
Value-added modeling is a method of teacher evaluation that measures the teacher's contribution in a given year by comparing the current test scores of their students to the scores of those same students in previous school years, as well as to the scores of other students in the same grade. In this manner, value-added modeling seeks to isolate the contribution, or value added, that each teacher provides in a given year, which can be compared to the performance measures of other teachers. VAMs are considered to be fairer than simply comparing student achievement scores or gain scores without considering potentially confounding context variables like past performance or income. It is also possible to use this approach to estimate the value added by the school principal or the school as a whole.
Educator effectiveness is a United States K-12 school system education policy initiative that measures the quality of an educator performance in terms of improving student learning. It describes a variety of methods, such as observations, student assessments, student work samples and examples of teacher work, that education leaders use to determine the effectiveness of a K-12 educator.
Victor Chaim Lavy is an Israeli economist and professor at the University of Warwick and the Hebrew University of Jerusalem. His research interests include labour economics, the economics of education, and development economics. Lavy belongs to the most prominent education economists in the world.
Susanna Loeb is an American education economist and director of the Annenberg Institute at Brown University. She was previously the Barnett Family Professor of Education at the Stanford Graduate School of Education, where she also served as founding director of the Center for Education Policy Analysis (CEPA). Moreover, she directs Policy Analysis for California Education (PACE). Her research interests include the economics of education and the relationship between schools and educational policies, in particular school finance and teacher labor markets.
References
- ↑ Angrist, J. D.; Lavy, V. (1999). "Using Maimonides' Rule to Estimate the Effect of Class Size on Scholastic Achievement". Quarterly Journal of Economics . 114 (2): 533–575. CiteSeerX 10.1.1.363.2101 . doi:10.1162/003355399556061. S2CID 149871459.
- ↑ Case, Anne (July 2004). "The Primacy of Education" (PDF). Working Papers. Research Program in Development Studies. Archived from the original (PDF) on 19 March 2013. Retrieved 5 March 2013.
- ↑ Understanding poverty p.278
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