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This publication describes the methods and procedures used for the 2017-18 National Postsecondary Student Aid Study, Administrative Collection (NPSAS:18-AC). It also provides information that will be helpful to analysts in accessing and understanding the restricted-use files containing the NPSAS:18-AC data. NPSAS:18-AC includes cross-sectional, nationally representative samples of undergraduate and graduate students enrolled in postsecondary education in the United States. NPSAS:18 AC also includes state-representative samples of undergraduate students in some states, as well as in public 2 year and in public 4 year institution sectors within some states. The study covers topics pertaining to student enrollment with a focus on how individuals and families finance postsecondary education. NPSAS:18-AC is based solely on data collected from administrative data sources and, unlike other NPSAS studies, does not include student survey data.

This publication describes the methods and procedures used for the 2008/18 Baccalaureate and Beyond Longitudinal Study (B&B:08/18). The B&B graduates, who completed the requirements for a bachelor's degree during the 2007-08 academic year, were first surveyed as part of the 2008 National Postsecondary Student Aid Study (NPSAS:08), and then followed up with in 2009 and 2012. The 2018 follow-up is the third and final survey for the B&B:08 cohort, conducted 10 years after completion of the bachelor's degree. This report details the methodology and outcomes of the B&B:08/18 student survey data collection and administrative records matching. [For "Baccalaureate and Beyond (B&B:08/18): First Look at the 2018 Employment and Educational Experiences of 2007-08 College Graduates. First Look. NCES 2021-241," see ED609932.]

IEA's International Computer and Information Literacy Study (ICILS) 2018 investigated how well students are prepared for study, work, and life in a digital world. ICILS 2018 measured international differences in students' computer and information literacy (CIL): their ability to use computers to investigate, create, participate, and communicate at home, at school, in the workplace, and in the community. Participating countries had an additional option for their students to complete an assessment of computational thinking (CT): their ability to recognize aspects of real-world problems appropriate for computational formulation, and to evaluate and develop algorithmic solutions to those problems, so that the solutions could be operationalized with a computer. This technical report follows the publication of several international and regional reports that presented the results of ICILS 2018. It provides a comprehensive account of the conceptual, methodological, and analytical implementation of the study. It includes detailed information on the development of the data-collection instruments used, including their translation and translation verification, on sampling design and implementation, sampling weights and participation rates, survey operation procedures, quality control of data collection, data management and creation of the international database, scaling procedures, and analysis of ICILS 2018 data. The technical report enables researchers to evaluate published reports and articles based on data from this study and, used in conjunction with the ICILS 2018 User Guide for the International Database, will provide guidance for their own analyses. [Contributors to this report include: John Ainley, Sebastian Meyer, Ralph Carstens, Ekaterina Mikheeva, Alex Daraganov, Lauren Musu, Lauren Musu, Louise Ockwell, David Ebbs, Wolfram Schulz, Julian Fraillon, Sabine Tieck, and Tim Friedman. For "IEA International Computer and Information Literacy Study 2018. User Guide for the International Database," see ED610700.]

IEA's International Computer and Information Literacy Study (ICILS) 2018 is designed to assess how well students are prepared for study, work, and life in a digital world. The study measures international differences in students' computer and information literacy (CIL): their ability to use computers to investigate, create, participate, and communicate at home, at school, in the workplace, and in the community. Participating countries also have an option for their students to complete an assessment of computational thinking (CT). The ICILS 2018 user guide describes the content and format of the data in the ICILS 2018 international database. It introduces the use of weighting and variance estimation variables for analyzing the ICILS 2018 data, and provides a comprehensive overview of how to work with IEA's International Database (IDB) Analyzer software. The ICILS 2018 user guide is accompanied by three appendices: the international versions of all questionnaires; an overview of national adaptations to the national versions of the ICILS 2018 international questionnaires; and a comprehensive catalogue of the derived variables used in the ICILS 2018 international report. [This guide was written with contributions from: Sabine Tieck, Sabine Meinck, Oriana Mora, and Wolfram Schulz.]

The Trends in International Mathematics and Science Study (TIMSS) is an international comparative study designed to measure trends in mathematics and science achievement at the fourth and eighth grades, as well as to collect information about educational contexts (such as students' schools, teachers, and homes) that may be related to student achievement. TIMSS has been administered every 4 years since 1995, with the sixth and most recent administration, in 2015, providing a 20-year trend line. The United States has participated in every administration of TIMSS, which includes 1995, 1999, 2003, 2007, 2011, and 2015 for the eighth grade and all but 1999 for the fourth grade (when it was not administered internationally). TIMSS is designed to align broadly with mathematics and science curricula in the participating education systems and, therefore, to reflect students' school-based learning. Because it is an international study, TIMSS provides valuable benchmarking information on how U.S. students compare to students around the world. The U.S. TIMSS and TIMSS Advanced 1995 & 2015 Technical Report and User's Guide provides an overview of the design and implementation of the Trends in International Mathematics and Science Study (TIMSS) 2015 and TIMSS Advanced 2015 in the United States and one participating benchmarking state: Florida. Part 1 of the report contains the full report; Appendix A: TIMSS 2015 and TIMSS Advanced 2015 Reruitment Materials; Appendix B: Informational Materials; and Appendix C: Field Staff Training Materials. Appendix D: TIMSS 2015 and TIMSS Advanced 2015 Questionnaires begins in Part 2, continues in Part 3, and ends in Part 4. Part 4 also contains Appendix E: TIMSS 2015 and TIMSS Advanced 2015 Questionnaire Adaptations; Appendix F: TIMSS 2015 Nonresponse Bias Analysis: Grades 4 and 8; and Appendix G: TIMSS 2015 Nonresponse Bias Analysis: Advanced. ["Trends in International Mathematics and Science Study (TIMSS). U.S. TIMSS 2015 and TIMSS Advanced 1995 & 2015 Technical Report and User's Guide. Part 2. NCES 2018-020," see ED589558; "Trends in International Mathematics and Science Study (TIMSS). U.S. TIMSS 2015 and TIMSS Advanced 1995 & 2015 Technical Report and User's Guide. Part 3. NCES 2018-020," see ED589559; and "Trends in International Mathematics and Science Study (TIMSS). U.S. TIMSS 2015 and TIMSS Advanced 1995 & 2015 Technical Report and User's Guide. Part 4. NCES 2018-020," see ED589560.]

The Progress in International Reading Literacy Study (PIRLS) is an international assessment of student performance in reading literacy at the fourth grade. PIRLS measures students in the fourth year of formal schooling because this is typically when students' learning transitions from a focus on "learning to read" to a focus on "reading to learn." PIRLS is a collaborative effort between participating countries and the International Association for the Evaluation of Educational Achievement (IEA). Administered every 5 years since 2001, PIRLS 2016 marks the survey's fourth administration. The current administration also provides the first results for ePIRLS: a computer-based extension of PIRLS designed to assess students' comprehension of online information. In 2016, some 58 education systems3 participated in the PIRLS assessment at the fourth year of formal schooling and 16 of these systems also participated in ePIRLS. The PIRLS framework focuses on two key content domains: purposes for reading and processes of comprehension. There are two key purposes for reading: reading for literary experience and reading to acquire and use information. The new ePIRLS is an innovative, computer-based assessment of online reading. It is designed to measure students' approaches to informational reading in an online environment. This report summarizes performance on PIRLS and ePIRLS 2016 from a U.S. perspective. PIRLS results are based on nationally representative samples of fourth-graders. The international data reported for PIRLS 2016 in this report cover 58 countries or other education systems, including the United States.

There has been little data collected on how much testing actually goes on in America's schools and how the results are used. So in the Spring of 2014, the Council staff developed and launched a survey of assessment practices. This report presents the findings from that survey and subsequent Council analysis and review of the data. It also offers an initial set of observations about testing in our school systems and how it might be improved. The report does not answer all questions on this complex issue, but it should give a more complete and well-rounded picture of the amount and range of tests administered in the nation's urban school systems. The following are appended: (1) The Federal Role in Assessment Policy; (2) Mandated Tests by District; (3) Georgia CTE [Career and Technical Education] Tests (4) Assessment Survey (5) Examples of Other Mandated Assessments; and (6) Council of the Great City Schools.

A defining characteristic of current U.S. educational policy is the use of data to inform decisions about resource allocation, teacher hiring, and curriculum and instruction. Perhaps the biggest challenge to data-driven decision making (DDDM) is that data use alone does not automatically result in improved teaching and learning. Research indicates that translating raw data into useable information and actionable knowledge for teachers requires not only adequate technical and social supports, but also an awareness of how educators in real-world settings actually use information to make decisions. Yet, little is known about DDDM in higher education, in general, and how postsecondary faculty make sense of and use data in their instructional decision-making processes, in particular. In this paper, we use naturalistic decision-making theory to generate practice-based descriptions of how 59 STEM faculty at three large public research universities used data as part of their course planning. Interview transcripts and notes taken while observing planning meetings were analyzed using an inductive approach to content analysis. In practice, respondents used different types of data and other information obtained from, for example, student assessments, end-of-semester evaluations, and conversations with colleagues. Results indicate that faculty generally collect and analyze data in informal, ad hoc scenarios ungoverned by institutional policy. Exceptions include disciplines with accreditation pressures and team-taught courses where structured (and supported) opportunities exist for faculty to collect, analyze, and reflect upon data about student learning. Thus, while numeric data are clearly viewed by this population of faculty as the most rigorous, in practice, even those that use quantitative data also use other sources of information. These results suggest an opportunity for educational leaders to design policies and professional development initiatives that facilitate a more formal collection of and reflection on data by faculty. In pursuing such technical solutions, however, policymakers and educational leaders must carefully negotiate the tension between rigor and relevance, and learn from the challenges experienced in the K-12 sector regarding DDDM.

The Teacher Education and Development Study in Mathematics (TEDS-M), conducted under the aegis of the International Association for the Evaluation of Educational Achievement (IEA), was designed to inform policy and practice in mathematics teacher education. For educational policymakers, TEDS-M contributes data on institutional arrangements that are effective in helping future teachers become sufficiently knowledgeable in mathematics and related teaching knowledge. For the teacher educators who design, implement, and evaluate teacher education curricula, TEDS-M contributes a shared terminology, a shared database, and benchmarks for examining their teacher education provision against what has proved possible and desirable to do in other settings. For mathematics teachers in schools, TEDS-M provides a better understanding of what qualified teachers of mathematics learn about the content and pedagogy of mathematics during their preservice education, as well as about the arrangements and conditions conducive to acquisition of this knowledge. For educators in general and for informed laypersons, TEDS-M provides a better understanding about what and how teachers learn as they prepare to teach. The TEDS-M joint-international research centers at Michigan State University (MSU) and the Australian Council for Educational Research (ACER) worked from 2006 to 2011 with the study's national research coordinators (NRCs) in the 17 participating countries to develop, implement, and report on the findings of this study. TEDS-M asked three key questions: (1) What are the policies that support primary and secondary teachers' achieved level and depth of mathematics and related teaching knowledge? (2) What learning opportunities available to prospective primary and lower- secondary mathematics teachers allow them to attain such knowledge? and (3) What level and depth of mathematics and related teaching knowledge have prospective primary and lower-secondary teachers attained by the end of their preservice teacher education? The first research question was addressed through individual case study country reports, questionnaires, and interviews issued by the TEDS-M international study centers. The second and third research questions were answered via surveys of nationally representative samples of: (1) Teacher education institutions and programs; (2) Teacher educators; (3) Future primary school teachers preparing to teach mathematics; and (4) Future lower-secondary school teachers also preparing to teach mathematics. The rest of this report presents the technical details associated with TEDS-M. A brief description of the content of each chapter is provided in Chapter 1. Individual chapters are authored by different groups of contributors and present references, exhibits, and notes as appropriate. Appended are: (1) TEDS-M 2008 Route Questionnaire; (2) TEDS-M Guidelines on Writing Country Reports; (3) Characteristics of National Samples: Implementations of the International Sampling Design in Participating Countries; (4) TEDS-M Sampling Frame Questionnaire and Forms; (5) Mobilizing National Support and Recruiting Institutions; (6) Future Primary Teachers' OTL and Belief Indices: International Reliability and Descriptive Statistics; (7) Future Secondary Teachers' OTL and Belief Indices: International Reliability and Descriptive Statistics; (8) Future Primary and Secondary Teachers' and Educators' OTL and Belief Scales: International Reliabilities; (9) Field Trial of the Institutional, Future Teacher, and Educator Questionnaires; (10) Model Fit Statistics for the Opportunity to Learn Scales; (11) Model Fit Statistics for the Opportunity to Learn Scales for Future Teachers and Educators by Country; (12) Changes for Calibration of MCK/MPCK Primary Items; (13) Changes for Calibration of MCK/MPCK Secondary Items; (14) Control Parameters for Model and Case Estimation: Mathematical Content Knowledge and Mathematics Pedagogical Content Knowledge; (15) Mathematics Content Knowledge and Mathematics Pedagogical Content Knowledge Scales; (16) TEDS-M Test Reliabilities; (17) Anchor Point Descriptions; (18) Program-Groupings: Future Teachers (Primary); (19) Program-Groupings: Future Teachers (Secondary); and (20) Listings of Organizations and Individuals Responsible for TEDS-M. [Contributors include Kiril Bankov; Alena Becker; Falk Brese; Soo-Yong Byun; Ralph Carstens; Jean Dumais; Lawrence Ingvarson; Yang Lu; Yukiko Maeda; Barbara Malak-Minkiewicz; Sabine Meinck; Ray Peck; Inese Berzina-Pitcher; Marc Reckase; Michael Rodríguez; Glenn Rowley; John Schwille; Sharon L. Senk; Maria Teresa Tatto; Alana Yu]

The "TIMSS 2011 International Results in Mathematics" summarizes fourth and eighth grade student achievement in each of the 63 countries and 14 benchmarking entities which participated in TIMSS 2011. The report includes trends in mathematics achievement over time for participants in the previous TIMSS 1995, 1999, 2003, and 2007 assessments, as well as student performance at the TIMSS International Benchmarks. Achievement results also are presented for mathematics content and cognitive domains. The "Mathematics Report" provides a rich array of information which describes the educational contexts for mathematics, including home environment support, students' backgrounds and attitudes toward mathematics, the mathematics curriculum, teachers' education and training, classroom characteristics and activities, and school contexts for mathematics learning and instruction. The following appendices are included: (1) Countries Participating in TIMSS 2011 and in Earlier TIMSS Assessments; (2) Characteristics of the Items in the TIMSS 2011 Mathematics Assessment; (3) Population Coverage and Sample Participation Rates; (4) Percentage of Students with Achievement Too Low for Estimation; (5) Average Percent Correct in the Mathematics Content and Cognitive Domains; (6) The Test-Curriculum Matching Analysis--Mathematics; (7) Percentiles and Standard Deviations of Mathematics Achievement; and (8) Organizations and Individuals Responsible for TIMSS 2011. (Contains 145 exhibits and 5 footnotes.)

One of the major objectives of large-scale educational surveys is reporting trends in academic achievement. For this purpose, a substantial number of items are carried from one assessment cycle to the next. The linking process that places academic abilities measured in different assessments on a common scale is usually based on a concurrent calibration of adjacent assessments using item response theory (IRT) models. It can be conjectured that the selection of common items has a direct effect on the estimation error of academic abilities due to item misfit, small changes in the common items, position effect, and other sources of construct-irrelevant changes between measurement occasions. Hence, the error due to the common-item sampling could be a major source of error for the ability estimates. In operational analyses, generally two sources of error are accounted for in variance estimation: student sampling error and measurement error. A double jackknifing procedure is proposed to include a third source of the estimation error, the error due to common-item sampling. Three different versions of the double jackknifing were implemented and compared. The data used in this study were item responses from Grade 4 students who took the NAEP 2004 and 2008 math long-term trend (LTT) assessments. These student samples used in this study are representative samples of Grade 4 student population in 2004 and in 2008 across the US. The results showed that these three double jackknifing approaches resulted in similar standard error estimates that were slightly higher than the estimates from the traditional approach, regardless of whether an item sampling scheme was used or items were dropped at random. (Contains 4 tables.)

This report presents selected findings from the school principal data files of the 2007-08 Schools and Staffing Survey (SASS). SASS is a nationally representative sample survey of public, private, and Bureau of Indian Education-funded (BIE) K-12 schools, principals, and teachers in the 50 states and the District of Columbia. The public school sample was designed so that national-, regional-, and state-level elementary, secondary, and combined public school estimates can be made. Public schools include both traditional public and public charter schools. The private school sample was designed so that national-, regional-, and affiliation-level estimates can be produced. BIE schools on the 2005-06 Common Core of Data (CCD) were sampled with certainty, and thus national estimates for BIE schools can be produced. The School District data file includes responses from school districts to the School District Questionnaire along with the "district items" taken from the Public School Questionnaire (With District Items) completed by the subset of public schools that were not associated with "traditional" school districts. These schools include state-run schools, traditional public schools in single-school districts, and independent charter schools. Selected findings reported in tabular form include: (1) In 2007-08, there were an estimated 118,610 school principals in the United States; 90,470 were public school principals, 27,960 were private school principals, and 170 were BIE-funded school principals (table 1); (2) Among public school principals, 81 percent were non-Hispanic White, 11 percent were non-Hispanic Black, and 7 percent were Hispanic. Among private school principals, 87 percent were non-Hispanic White, 7 percent were non-Hispanic Black, and 4 percent were Hispanic (table 2); (3) About 50 percent of public school principals and 53 percent of private school principals were female (table 3); (4) Among public schools, more principals held a master's degree (61 pee) as their highest degree than a bachelor's degree or less (1 percent) or an education specialist/professional diploma (29 percent) or a doctorate/first professional degree (8 percent). Among private schools, more principals held a master's degree (50 percent) as their highest degree earned, compared to those with a bachelor's degree or less (33 percent) or an education specialist/professional diploma (10 percent) or a doctorate/first professional degree (7 percent) (table 4); (5) The average annual salary of public school principals was $85,700. Principals in public secondary schools earned more ($90,300) than principals in public elementary schools ($85,200) and in public combined schools ($75,800). Among private school principals, the average annual salary was $57,500. Principals in private secondary schools earned more ($75,100) than principals in private elementary schools ($55,500) and in private combined schools ($55,100) (table 5); (6) On average, public school principals spent 58.4 hours per week on all school-related activities, including 20.8 hours per week interacting with students. Private school principals, on average, spent 53.6 hours per week on all school-related activities, including 19.2 hours per week interacting with students (table 6); (7) Public school principals had, on average, 7.5 years of experience as a principal, of which 4.2 years were spent in their current school. Private school principals had, on average, 10.0 years of experience as a principal, of which 6.8 were spent in their current school (table 7); and (8) About 90 percent of public school principals reported having a major influence on hiring new full-time teachers at their school, while 94 percent of private school principals reported the same (table 8). Four appendices are included: (1) Standard Error Tables; (2) Methodology and Technical Notes; (3) Description of Data Files; and (4) Description of Variables. (Contains 22 tables and 12 footnotes.)

The paper initially describes the sources of uncertainty in National Assessment of Educational Progress (NAEP) data and standard errors. As NAEP sample sizes have increased, greater precision has been attained by the program. For this reason, exclusion effects are increasingly important. Two scenarios of revised NAEP results are presented (for New York City and for the nation) that reflect the possible results if all excluded students had been included in the data analysis: the overall NAEP results from the two recalculation scenarios vary considerably. Even where exclusion rate is constant, exclusion may affect score comparisons. When exclusion rates are not constant over time, the effects of exclusions on data comparisons can be significant. NAEP results can be affected by the percentage of students identified as Students with Disabilities (SD) or limited English proficient (LEP) in states or districts, as well as exclusion rates. The paper presents estimated recalculated results for the Trial Urban District Assessments in the two scenarios above to show how the rank orders of the districts' performance might have changed substantially. Student subgroup results may also change with increased inclusion. The effects of exclusions on NAEP data reliability can be minimized: (1) by minimizing exclusions; (2) by establishing exclusion criteria that are as clear and objective as possible and working to assure that those criteria are adhered to; and (3) making practices and criteria across states as uniform as possible. Remedies for the general effect of exclusions include: (1) efforts to minimize exclusions should continue; (2) NAEP users should be reminded more often that the students tested do not represent the entire population; and (3) research should continue on the utility of imputation models that might be used to adjust for effects of exclusions. Consequences of differential exclusion policies may be serious. If such policies vary for the two time points, groups, or jurisdictions compared, then that first, fundamental inference is compromised. The observed change, contrast, or performance gap in fact represents some mixture of differences in actual student achievement distributions and differences in decision rules determining whom to test. The increasing accuracy of NAEP statistics has made even small distortions more important than they once were. For the most part, effects of exclusions on reliability can be offset by increasing sample sizes. Effects of exclusions on validity are more problematical. It is important that NAEP continues to keep exclusions to a minimum, and that efforts be made to work toward more uniform policies and practices for determining which students should be excluded from NAEP and which should be tested. (Contains 12 footnotes and 3 tables.) [This paper was commissioned by NAGB to serve as background information for conference attendees at the NAGB Conference on Increasing the Participation of SD and LEP Students in NAEP.]

The Programme for the International Assessment of Adult Competencies (PIAAC) has been planned as an ongoing program of assessment. The first cycle of the assessment has involved two "rounds." The first round, which is covered by this report, took place over the period of January 2008-October 2013. The main features of the first cycle of PIAAC are: (1) Skills assessed; (2) Other information on skills; (3) Test delivery; and (4) Adaptive testing. The following contents are included in this report: Section 1: Assessment and Instrument Design: (1) Assessment Design (Irwin Kirsch and Kentaro Yamamoto); (2) The Development of the PIAAC Cognitive Instruments (Mary Louise Lennon and Claudia Tamassia); The Development of the PIAAC Background Questionnaires (Jim Allen, Rolf van der Velden, Susanne Helmschrott, Silke Martin, Natascha Massing, Beatrice Rammstedt, Anouk Zabal, and Matthias von Davier); and (4) Translation, Adaptation, and Verification of Test and Survey Materials (Andrea Ferrari, Laura Wayrynen, Dorothée Behr and Anouk Zabal). Section 2: Platform Development: (5) Development of the Cognitive Items (Britta Upsing, Frank Goldhammer, Maya Schnitzler, Robert Baumann, Roland Johannes, Ingo Barkow and Heiko Rölke, Isabelle Jars, Thibaud Latour, Patrick Plichart, Raynald Jadoul, Christopher Henry, and Mike Wagner; (6) Development of Technical Support Tools (Britta Upsing, Frank Goldhammer, Maya Schnitzler, Robert Baumann, Roland Johannes, Ingo Barkow, Heiko Rölke, Isabelle Jars, Thibaud Latour, Patrick Plichart, Raynald Jadoul, Christopher Henry, and Mike Wagner; (7) Development of the CAPI Questionnaire Software (Thibaud Latour, Raynald Jadoul, and Mike Wagner); (8) Development of the Integrated Computer Platform (Britta Upsing, Frank Goldhammer, Maya Schnitzler, Robert Baumann, Roland Johannes, Ingo Barkow, Heiko Rölke, Isabelle Jars, Thibaud Latour, Patrick Plichart, Raynald Jadoul, Christopher Henry, and Mike Wagner); (9) The TAO platform (Raynald Jadoul, Patrick Plichart, Jérôme Bogaerts, Christophe Henry, Thibaud Latour, and Henri Tudor). Section 3: Field Operations and Quality Control: (10) Field operations (Pat Montalvan and Michael Lemay); (11) Quality Control Monitoring Activities (Pat Montalvan and Michael Lemay); (12) Scoring Reliability Studies Claudia Tamassia, Mary Louise Lennon and Kentaro Yamamoto); (13) Data Management Procedures (Ralph Carstens and Tim Daniel). Section 4: Sampling and Weighting: (14) Sampling design (Leyla Mohadjer, Tom Krenzke and Wendy Van de Kerchove); (15) Survey Weighting and Variance Estimation Leyla Mohadjer, Tom Krenzke and Wendy Van de Kerchove); (16) Indicators of the Quality of the Sample Data (Leyla Mohadjer, Tom Krenzke and Wendy Van de Kerchove). Section 5: Data Analysis and Data Products: (17) Scaling PIAAC Cognitive Data (Kentaro Yamamoto, Lale Khorramdel and Matthias von Davier); (18) Scaling Outcomes (Kentaro Yamamoto, Lale Khorramdel and Matthias von Davier); (19) Proficiency Scale Construction (Kentaro Yamamoto, Lale Khorramdel and Matthias von Davier); (20) Creating Simple and Complex Derived Variables and Validation of Background Questionnaire Data (Matthias von Davier, Jonathan Weeks, Henry Chen,Jim Allen and Rolf van der Velden); (21) PIAAC Proficiency Scales Claudia Tamassia and Mary Louise Lennon); (22) Generating Results for PIAAC (Alfred Rogers and John Barone); (23) International Database and Data Analysis Tools (Ralph Carstens, Tim Daniel, and Eugenio Gonzalez). The following are appended: (1) PIAAC main study item pool characteristics; (2) Contrast coding used in conditioning; (3) Design effect tables; (4) Changes to questionnaire items from IALS to PIAAC; (5) Mapping of ISCED levels to years of schooling; (6) PIAAC Consortium, staff, Expert Groups, National Project Managers and consultants; and (7) Data Adjudication in PIAAC (PIAAC Consortium and William Thorn, OECD). (Individual chapters contain references.)