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Patrick, Megan E.; Terry-McElrath, Yvonne M.; Berglund, Patricia; Pang, Yuk C.; Heeringa, Steven G.; Si, Yajuan – Institute for Social Research, 2023
The Monitoring the Future (MTF) study monitors historical and developmental changes in substance use prevalence among key subgroups of the general U.S. adolescent and adult population. The current study first devised and evaluated a cohort-specific pooled analysis weighing procedure for the MTF panel study that weighted back to the initial 12th…
Descriptors: Substance Abuse, Incidence, Adolescents, Adults
Schneider, Daniel; Harknett, Kristen – Sociological Methods & Research, 2022
In this article, we explore the use of Facebook targeted advertisements for the collection of survey data. We illustrate the potential of survey sampling and recruitment on Facebook through the example of building a large employee--employer linked data set as part of The Shift Project. We describe the workflow process of targeting, creating, and…
Descriptors: Social Media, Data Collection, Advertising, National Surveys
Shen, Ting; Konstantopoulos, Spyros – Practical Assessment, Research & Evaluation, 2022
Large-scale assessment survey (LSAS) data are collected via complex sampling designs with special features (e.g., clustering and unequal probability of selection). Multilevel models have been utilized to account for clustering effects whereas the probability weighting approach (PWA) has been used to deal with design informativeness derived from…
Descriptors: Sampling, Weighted Scores, Hierarchical Linear Modeling, Educational Research
Shen, Ting; Konstantopoulos, Spyros – Journal of Experimental Education, 2022
Large-scale education data are collected via complex sampling designs that incorporate clustering and unequal probability of selection. Multilevel models are often utilized to account for clustering effects. The probability weighted approach (PWA) has been frequently used to deal with the unequal probability of selection. In this study, we examine…
Descriptors: Data Collection, Educational Research, Hierarchical Linear Modeling, Bayesian Statistics
Cominole, Melissa; Ritchie, Nichole Smith; Cooney, Jennifer – National Center for Education Statistics, 2021
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…
Descriptors: Bachelors Degrees, College Graduates, Longitudinal Studies, Data Collection
Jennifer Wine; Beth Hustedt; Jennifer Cooney; Erin Thomsen – National Center for Education Statistics, 2023
This report describes the design, methods, and results of the 2016/20 Baccalaureate and Beyond Longitudinal Study (B&B:16/20) conducted by the U.S. Department of Education's National Center for Education Statistics (NCES). It is the second follow-up with a cohort of bachelor's degree recipients originally identified during the 2015-16 National…
Descriptors: Longitudinal Studies, College Graduates, Bachelors Degrees, College Students
Hustedt, Beth; Franklin, Jeff; Tate, Nicole – New Directions for Institutional Research, 2019
We discuss methods that can improve response rates in large-scale cross-sectional and longitudinal studies. Regardless of the specific study topic, sample member population, or method of contact, data collections should be designed with certain core principles in mind: legitimizing the study to prospective participants; presenting study…
Descriptors: Data Collection, Longitudinal Studies, Surveys, Response Rates (Questionnaires)
Duprey, Michael A.; Pratt, Daniel J.; Wilson, David H.; Jewell, Donna M.; Brown, Derick S.; Caves, Lesa R.; Kinney, Satkartar K.; Mattox, Tiffany L.; Ritchie, Nichole Smith; Rogers, James E.; Spagnardi, Colleen M.; Wescott, Jamie D. – National Center for Education Statistics, 2020
This data file documentation accompanies new data files for the High School Longitudinal Study of 2009 (HSLS:09) Postsecondary Education Transcript Study and Student Financial Aid Records Collection (PETS-SR). HSLS:09 follows a nationally representative sample of students who were ninth-graders in fall 2009 from high school into postsecondary…
Descriptors: Longitudinal Studies, High School Students, Sampling, Data Collection
Cominole, Melissa; Shepherd, Bryan; Siegel, Peter – National Center for Education Statistics, 2015
This publication describes the methods and procedures used in the 2008/12 Baccalaureate and Beyond Longitudinal Study (B&B:08/12). These graduates, who completed the requirements for a bachelor's degree during the 2007-08 academic year, were first interviewed as part of the 2008 National Postsecondary Student Aid Study (NPSAS:08), and then…
Descriptors: Longitudinal Studies, Bachelors Degrees, Higher Education, College Graduates
Tourangeau, Karen; Nord, Christine; Lê, Thanh; Sorongon, Alberto G.; Hagedorn, Mary C.; Daly, Peggy; Najarian, Michelle – National Center for Education Statistics, 2015
This manual provides guidance and documentation for users of the kindergarten (or base year) data of the Early Childhood Longitudinal Study, Kindergarten Class of 2010-11 (ECLS-K:2011). It begins with an overview of the ECLS-K:2011. Subsequent chapters provide details on the study data collection instruments and methods; the direct and indirect…
Descriptors: Children, Surveys, Longitudinal Studies, Kindergarten
Tourangeau, Karen; Nord, Christine; Lê, Thanh; Wallner-Allen, Kathleen; Hagedorn, Mary C.; Leggitt, John; Najarian, Michelle – National Center for Education Statistics, 2015
This manual provides guidance and documentation for users of the longitudinal kindergarten-first grade (K-1) data file of the Early Childhood Longitudinal Study, Kindergarten Class of 2010-11 (ECLS-K:2011). It mainly provides information specific to the first-grade rounds of data collection. Data for the ECLS-K:2011 are released in both a…
Descriptors: Children, Surveys, Longitudinal Studies, Kindergarten
Schneider, Barbara; Saw, Guan; Broda, Michael – AERA Open, 2016
Forty years ago, the National Center for Education Statistics initiated the national longitudinal studies program in response to congressional concern for policy-relevant information on school-to-work transitions. This program has grown substantially, and not unexpectedly, questions have arisen about its usefulness and present operation. This…
Descriptors: Longitudinal Studies, Educational Research, Program Descriptions, Educational Policy
Bachman, Jerald G.; Johnston, Lloyd D.; O'Malley, Patrick M.; Schulenberg, John E.; Miech, Richard A. – Institute for Social Research, 2015
The purpose of this paper is to provide a detailed description of the Monitoring the Future research design, including sampling design, data collection procedures, measurement content, and questionnaire format. This study assesses the changing lifestyles, values, and preferences of American youth on a continuing basis. Each year since 1975, at…
Descriptors: Research Projects, Youth, Life Style, Values
Wine, Jennifer; Janson, Natasha; Wheeless, Sara – National Center for Education Statistics, 2011
This report describes and evaluates the methods and procedures used in the 2004/09 Beginning Postsecondary Students Longitudinal Study (BPS:04/09). BPS:04/09 is the second and final follow-up interview for the cohort of first-time beginning postsecondary students identified in the 2004 National Postsecondary Student Aid Study. For the first time…
Descriptors: Longitudinal Studies, Postsecondary Education, College Students, Research Methodology
Peugh, James L. – Journal of School Psychology, 2010
Collecting data from students within classrooms or schools, and collecting data from students on multiple occasions over time, are two common sampling methods used in educational research that often require multilevel modeling (MLM) data analysis techniques to avoid Type-1 errors. The purpose of this article is to clarify the seven major steps…
Descriptors: Educational Research, Research Methodology, Data Analysis, Academic Achievement