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1. |
Record Nr. |
UNINA9910494590703321 |
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Autore |
Shiel Gerry |
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Titolo |
National assessments of educational achievement . Volume 4 Analyzing data from a national assessment of educational achievement / / Gerry Shiel, Fernando Cartwright ; Vincent Greaney and Thomas Kellaghan, series editors |
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Pubbl/distr/stampa |
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Washington, District of Columbia : , : World Bank Group, , 2015 |
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©2015 |
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ISBN |
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Descrizione fisica |
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1 online resource (297 p.) |
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Collana |
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National assessments of educational achievement ; ; v. 4 |
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Disciplina |
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Soggetti |
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Educational tests and measurements |
Educational evaluation |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters. |
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Nota di contenuto |
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Cover; CONTENTS; PREFACE; ABOUT THE AUTHORS AND EDITORS; ACKNOWLEDGMENTS; ABBREVIATIONS; INTRODUCTION; Note; Part I: An Introduction to the Statistical Analysis of National Assessment Data; 1. THE DATABASE FOR ANALYSES; Saving the CD Files to Your Hard Drive or Server; Survey Instruments; TABLES; 1.1 Mathematics Test: Distribution of Items by Content Area and Process; Sampling Weights; 1.2 Abbreviated Questionnaire Descriptions; SPSS; EXERCISES; 1.1 Running Descriptive Statistics in SPSS and Saving the Files; EXERCISE FIGURES; 1.1.A: Weight Cases Dialog Box |
1.1.B: SPSS Descriptives Dialog BoxWesVar; Notes; 2. EXPLORING NATIONAL ASSESSMENT DATA USING SPSS; Measures of Central Tendency; Measures of Spread; Measures of Position; Measures of Shape; FIGURES; 2.1 Normal Distribution Showing Standard Deviation Units; 2.2 Examples of Distributions with Positive, Negative, and No Skews; Exploring a Data Set Using SPSS; 2.1 Running Explore in SPSS, Single Dependent Variable (One Level); EXERCISE TABLES; 2.1.A: Case-Processing Summary; 2.1.B: Descriptive Statistics; 2.1.A: Stem-and-Leaf Plot for Mathematics Scale Scores |
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2.1.B: Box Plot for Mathematics Scale Scores2.2 Running Explore in SPSS, Single Dependent Variable (More Than One Level); 2.2.A: Box Plots for Mathematics Scale Scores by Region; Notes; 3. AN INTRODUCTION TO WESVAR; Setting Up a Data File in WesVar; Adding Variable Labels; Computing Descriptive Statistics in WesVar; 3.1 Adding Variable Labels in WesVar; 3.1 Generating Descriptive Statistics in WesVar; 3.1.A: New WesVar Workbook; 3.1.B: Specifying Variables for Analysis in WesVar Descriptives; 3.1.C: Output from WesVar Descriptives; 3.1.D: Exporting a WesVar File |
Calculating a Mean Score and Its Standard Error3.2 Computing a Mean Score and Its Standard Error in WesVar; 3.2.A: Specifying a Computed Statistic in a WesVar Table; 3.2.B: Output for WesVar Tables: Computing Mean Score; Computing Mean Scores and Standard Errors for Subgroups in the Population; 3.3 Computing Mean Scores and Standard Errors in WesVar, Four Regions; 3.3.A: WesVar Workbook before Computing Mean Scores by Region; Notes; 3.3.B: WesVar Output for Computing Mean Scores by Region; 4. COMPARING THE ACHIEVEMENTS OF TWO OR MORE GROUPS; Examining the Difference Between Two Mean Scores |
4.1 Evaluating the Difference between Two Mean Scores4.1.A: WesVar Workbook before Assessing the Difference between Two Mean Scores; 4.1.B: WesVar Output: Mean Mathematics Scores of Students with and without Electricity at Home; 4.1.C: WesVar Output: Mean Score Difference in Mathematics between Students with and without Electricity at Home; 4.1.A: Comparison of Mean Mathematics Scores of Students with and without Electricity at Home; Examining Differences Between Three or More Mean Scores; 4.2 Evaluating Differences among Three or More Mean Scores |
4.2.A: WesVar Workbook Showing Adjustment to Alpha Level |
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Sommario/riassunto |
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This is the fourth and last volume in the set 'National Assessments of Educational Achievement.' Effective assessment of the performance of educational systems is a key component in developing policies to optimize the development of human capital around the world. The five books in the National Assessments of Educational Achievement series introduce key concepts in national assessments of student achievement levels, from policy issues to address when designing and carrying out assessments through test development, questionnaire design, sampling, organizing and carrying out data collection, dat |
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2. |
Record Nr. |
UNINA9910404080803321 |
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Autore |
Etcheverry Venturini Lorena |
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Titolo |
Overcoming Data Scarcity in Earth Science |
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Pubbl/distr/stampa |
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MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
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ISBN |
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Descrizione fisica |
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1 electronic resource (94 p.) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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heavily Environmental mathematical models represent one of the key aids for scientists to forecast, create, and evaluate complex scenarios. These models rely on the data collected by direct field observations. However, assembly of a functional and comprehensive dataset for any environmental variable is difficult, mainly because of i) the high cost of the monitoring campaigns and ii) the low reliability of measurements (e.g., due to occurrences of equipment malfunctions and/or issues related to equipment location). The lack of a sufficient amount of Earth science data may induce an inadequate representation of the response’s complexity in any environmental system to any type of input/change, both natural and human-induced. In such a case, before undertaking expensive studies to gather and analyze additional data, it is reasonable to first understand what enhancement in estimates of system performance would result if all the available data could be well exploited. Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. Different approaches are available to deal with missing data. Traditional statistical data completion methods are used in different domains to deal with single and multiple imputation problems. More recently, machine learning techniques, such as clustering and classification, have been proposed to complete missing data. This book showcases the body of knowledge that is aimed at improving the capacity to exploit the available data to better represent, |
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understand, predict, and manage the behavior of environmental systems at all practical scales. |
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