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This study tests whether such variables sufficiently separate confounding effects of family background from the primary outcomes of education research. Using ECLS-K data from the 1998-99 kindergarten cohort and a split-sample design with stepwise regression and multilevel modeling, it evaluates over 200 background variables to predict reading and mathematics achievement.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/beyond-free-lunch-which-family-background-measures-matter-602386/602386/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/beyond-free-lunch-which-family-background-measures-matter-602386/602386.png","ImageObject",300,407,{"name":92,"@type":93},"dawugda","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address about school achievement research?","Question",{"text":112,"@type":113},"It examines whether commonly used adjustment variables such as free-lunch eligibility and other basic socioeconomic indicators are enough to disentangle family-background confounding from education study variables.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What data and methods are used to evaluate family background measures?",{"text":117,"@type":113},"The study uses the Early Childhood Longitudinal Study (ECLS-K) from the 1998-99 kindergarten cohort and applies a split-sample design, stepwise regression, and multilevel modeling to analyze more than 200 background variables.",{"name":119,"@type":110,"acceptedAnswer":120},"Which additional variables are found to complement traditional socioeconomic status measures?",{"text":121,"@type":113},"The findings identify variables such as the number of children in the household, the mother’s age at first birth, and children’s books at home as important supplements.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},602386,1792192622,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":24,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},3985747870947,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","education policy analysis archives  \nA peer-reviewed, independent, open access, multilingual journal  \nArizona State University  \n\n| Volume 18 Number 11 | May 25th, 2010 | ISSN 1068–2341 |\n| --- | --- | --- |\n\nBeyond Free Lunch:  \nWhich Family Background Measures Matter? 1  \nSarah Theule Lubienski  \nCorinna Crawford Crane  \nUniversity of Illinois at Urbana-Champaign  \nCitation: Lubienski, S. T., & Crane, C. C. (2010) Beyond free lunch: Which family background measures matter? Education Policy Analysis Archives, 18(11) . Retrieved [date], from [http://epaa.asu.edu/ojs/article/view/756](http://epaa.asu.edu/ojs/article/view/756)  \nAbstract: Most studies of school achievement use free-lunch eligibility or other basic indicators to adjust for differences in students’ socioeconomic backgrounds. This study determines whether these variables are enough to separate the confounding effects of students’backgrounds from the main variables of interest in education studies. The Early Childhood Longitudinal Study dataset from the kindergarten class cohort of 1998-99 (ECLS-K) provides an unusually vast array of information regarding children’s home resources and experiences. This plethora of parent-reported data raises questions about which variables researchers should include in their analyses, and it provides an extraordinary opportunity to examine this question. Using a split-sample design, stepwise regression, and multi-level modeling, this study systematically examines over 200 ECLS-K student background variables to determine which factors predict reading and mathematics achievement after typical SES controls are employed. The study identifies several variables that are important supplements to traditional SES measures, including the  \n1 Accepted under the editorship of Sherman Dorn. The authors are grateful to Joseph P. Robinson, Christopher Lubienski and Eric Camburn for their advice at various stages of this research. However, all errors and omissions are the authors’alone. This research was generously supported by a Faculty Fellows Award from the College of Education, University of Illinois at Urbana-Champaign.  \nEducation Policy Analysis Archives Vol. 18 No. 11 2  \nnumber of children in the household, mother’s age at first birth, and children’s books at home. Results indicate the extent to which “value added” studies can be flawed when using only basic demographic variables. The findings hold implications for data collection and accountability efforts, including NCLB, teacher evaluation plans, and the design of state longitudinal data systems.  \nKeywords: social class; socioeconomic status; Early Childhood Longitudinal Study (ECLS-K); largescale data; multi-level modeling; mathematics; reading; elementary school.  \nMás allá del almuerzo escolar gratis: ¿Cuáles son las indicadores del contexto familiar que importan?  \nResumen: La mayoría de estudios sobre el desempeño de los estudiantes que usan calificación delas comidas escolares u otros indicadores de diferencias regulares socio-económico en el nivel de los estudiantes. El Este estudio determinó si estas variables son suficientes para distinguir los efectos de confusión de la situación socioeconómica otras variables de interés en los estudios en la Educación. El conjunto de datos del Estudio Longitudinal de la primera infancia con una clase de kindergarten de 1998-1999 (ECLS-K), proporciona un diferenciada gama de información sobre la vivienda de los niños y sus experiencias. Esta multiplicidad de datos informados por los padres, que plantea cuestiones acerca de las variables los investigadores deben incluir en sus análisis, y proporcionan una oportunidad extraordinaria para examinar este que se trate. Utilizando el diseñode la muestra dividida, la regresión \"Por etapas\" y los modelos multinivel, este estudio examina variar sistemáticamente el contexto familiar de más de 200 ECLS-K estudiantes para determinar qué factores predicen el rendimiento en lectura y matemáticas después de","cbCaiqce4YKSJfRd","https://ap.wps.com/l/cbCaiqce4YKSJfRd","pdf",503677,43,"English","# Article information\n## Beyond Free Lunch: Which Family Background Measures Matter?\n## Abstract\n## Keywords\n## Acknowledgements","[{\"question\":\"What problem does the study address about school achievement research?\",\"answer\":\"It examines whether commonly used adjustment variables such as free-lunch eligibility and other basic socioeconomic indicators are enough to disentangle family-background confounding from education study variables.\"},{\"question\":\"What data and methods are used to evaluate family background measures?\",\"answer\":\"The study uses the Early Childhood Longitudinal Study (ECLS-K) from the 1998-99 kindergarten cohort and applies a split-sample design, stepwise regression, and multilevel modeling to analyze more than 200 background variables.\"},{\"question\":\"Which additional variables are found to complement traditional socioeconomic status measures?\",\"answer\":\"The findings identify variables such as the number of children in the household, the mother’s age at first birth, and children’s books at home as important supplements.\"}]","Beyond Free Lunch - Which Family Background Measures Matter? | PDF",1792094583,108]