[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ disabled_on=”on|on|off” _builder_version=”4.27.4″ background_color=”#30a1da” background_enable_image=”off” background_size=”contain” background_position=”top_left” background_blend=”multiply” vertical_offset_tablet=”0″ horizontal_offset_tablet=”0″ custom_margin=”||0px|” custom_padding=”0px||0px||false|false” bottom_divider_height=”50px” bottom_divider_flip=”vertical” z_index_tablet=”0″ border_radii=”off||175px||” box_shadow_horizontal_tablet=”0px” box_shadow_vertical_tablet=”0px” box_shadow_blur_tablet=”40px” box_shadow_spread_tablet=”0px” locked=”off” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.27.4″ background_enable_color=”off” background_image=”https:\/\/nscresearchcenter.org\/wp-content\/uploads\/ResearchWorkingwData-HalfCircle.webp” background_size=”custom” background_image_width=”64%” background_position=”bottom_right” background_horizontal_offset=”-6%” background_vertical_offset=”-8%” vertical_offset_tablet=”0″ horizontal_offset_tablet=”0″ width=”100%” max_width=”1176px” custom_margin=”0px|auto|0px|auto|false|false” custom_padding=”0px||0px||false|false” z_index_tablet=”0″ box_shadow_horizontal_tablet=”0px” box_shadow_vertical_tablet=”0px” box_shadow_blur_tablet=”40px” box_shadow_spread_tablet=”0px” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”1_2″ _builder_version=”4.27.4″ background_color=”rgba(255,255,255,0.86)” custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||” theme_builder_area=”post_content”][et_pb_text admin_label=”Intro” _builder_version=”4.27.4″ text_font=”|600|||||||” text_font_size=”28px” text_line_height=”1.4em” header_2_font=”–et_global_heading_font|500|||||||” header_2_font_size=”30px” header_2_line_height=”1.2em” background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_margin=”73px||||false|false” custom_padding=”0px|40px||40px|false|true” use_border_color=”off” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n
Helping you make the most of Clearinghouse data<\/h2>\n
[\/et_pb_text][et_pb_text admin_label=”Intro” _builder_version=”4.27.4″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_margin=”||99px|||” custom_padding=”0px|50px||40px|false|false” use_border_color=”off” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n
The National Student Clearinghouse\u00ae<\/sup> Research Center™ strives to comprehensively document the nature and scope of Clearinghouse education data, while identifying processes that can enhance the ability of researchers, educators, and policymakers to leverage the data for informed decision-making.<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.27.4″ custom_padding=”0px||||false|false” global_colors_info=”{}” custom_padding__hover=”|||” theme_builder_area=”post_content”][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ custom_padding_last_edited=”off|desktop” _builder_version=”4.16″ custom_padding=”20px|||” custom_padding_tablet=”50px|0|50px|0″ width_unit=”off” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_row column_structure=”3_5,2_5″ make_equal=”on” _builder_version=”4.27.4″ _module_preset=”default” custom_margin=”20px|auto||auto||” custom_padding=”28px|||||” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”3_5″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text admin_label=”Intro” _builder_version=”4.27.4″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_margin=”||1px|||” custom_padding=”0px|||” use_border_color=”off” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n The documents and tables that appear on this site represent the findings of these efforts. We hope they address many of the questions that researchers have regarding Clearinghouse data.<\/p>\n If you would like to suggest additional analyses, or have suggestions for other content that would be helpful for the research community, please submit your ideas via our contact form<\/a>.<\/p>\n Much of the documentation on this site was originally made possible by a grant from the Lumina Foundation<\/a>. Lumina Foundation, an Indianapolis-based private foundation, is committed to enrolling and graduating more students from college \u2014 especially 21st century students: low-income students, students of color, first-generation students, and adult learners. Lumina’s goal is to increase the percentage of Americans who hold high-quality degrees and credentials to 60 percent by 2025. Lumina pursues this goal in three ways: by identifying and supporting effective practice, through public policy advocacy, and by using communications and convening power to build public will for change.<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”2_5″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text ul_type=”square” _builder_version=”4.27.4″ _module_preset=”default” ul_text_color=”#a3a3a3″ custom_margin_tablet=”” custom_margin_phone=”” custom_margin_last_edited=”on|desktop” custom_padding=”16px|21px|12px|0px|false|false” border_radii=”off||40px||” border_width_all=”1px” border_color_all=”#cccccc” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row padding_mobile=”off” column_padding_mobile=”on” _builder_version=”4.16″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_padding=”32px|||||” make_fullwidth=”off” use_custom_width=”off” width_unit=”off” custom_width_px=”1080px” custom_width_percent=”80%” locked=”off” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||” theme_builder_area=”post_content”][et_pb_image src=”https:\/\/nscresearchcenter.org\/wp-content\/uploads\/dotline-orange-1.png” title_text=”dotline-orange” _builder_version=”4.24.2″ _module_preset=”default” custom_margin=”||51px|||” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_text admin_label=”IHEP Paper” module_id=”workingdata-1″ _builder_version=”4.27.4″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” use_border_color=”off” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n [\/et_pb_text][et_pb_text admin_label=”IHEP Paper” _builder_version=”4.16″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_margin=”||50px|||” use_border_color=”off” global_colors_info=”{}” theme_builder_area=”post_content”]In this paper we describe in detail the existing content, value, uses, and costs of the Clearinghouse system as a component of the national postsecondary data infrastructure. We also describe some limitations to its use related to the data\u2019s ownership and the agreements that govern its use. In this paper, we recommend an incremental approach rather than a new data collection system or a change in policy, regulation, or legislation. The data and the infrastructure we recommend already exist. We also suggest four paths to making better use of this comprehensive national resource to meet the needs of policy, accountability, research, and consumer information audiences.<\/p>\n NOTE: \u201cThe National Student Clearinghouse as an Integral Part of the National Postsecondary Data Infrastructure\u201d<\/em> paper was released on May 18, 2016, as part of the \u201cEnvisioning the National Postsecondary Data Infrastructure in the 21st Century\u201d<\/em> series. [\/et_pb_text][et_pb_text admin_label=”Coverage” _builder_version=”4.27.4″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_margin=”||50px|||” use_border_color=”off” global_colors_info=”{}” theme_builder_area=”post_content”]The Research Center presents the Clearinghouse\u2019s coverage of enrollments at all Title IV eligible degree-granting institutions. The resulting workbook contains detailed coverage calculations by state, sector, and level of institution. It is based on historical IPEDS institutional characteristics data and the Carnegie Classification of Institutions of Higher Education and therefore captures any changes in institutional sectors and levels that occurred during the years included (2017 to present). Coverage workbook for prior years (2003-2016) does not include the new information as described below.<\/p>\n The table with coverage rates from 2017 onward includes a refined definition of four-year institutions that breaks out Primarily Associate Degree Granting Baccalaureate Institutions (PABs) into a separate category. The data are being provided in an Excel format for the convenience of researchers who would like to make coverage-based adjustments in studies that use longitudinal Clearinghouse data.<\/p>\n [\/et_pb_text][et_pb_text admin_label=”Data Elements” _builder_version=”4.27.4″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” vertical_offset_tablet=”0″ horizontal_offset_tablet=”0″ custom_margin=”||50px|||” z_index_tablet=”500″ text_text_shadow_horizontal_length_tablet=”0px” text_text_shadow_vertical_length_tablet=”0px” text_text_shadow_blur_strength_tablet=”1px” link_text_shadow_horizontal_length_tablet=”0px” link_text_shadow_vertical_length_tablet=”0px” link_text_shadow_blur_strength_tablet=”1px” ul_text_shadow_horizontal_length_tablet=”0px” ul_text_shadow_vertical_length_tablet=”0px” ul_text_shadow_blur_strength_tablet=”1px” ol_text_shadow_horizontal_length_tablet=”0px” ol_text_shadow_vertical_length_tablet=”0px” ol_text_shadow_blur_strength_tablet=”1px” quote_text_shadow_horizontal_length_tablet=”0px” quote_text_shadow_vertical_length_tablet=”0px” quote_text_shadow_blur_strength_tablet=”1px” header_text_shadow_horizontal_length_tablet=”0px” header_text_shadow_vertical_length_tablet=”0px” header_text_shadow_blur_strength_tablet=”1px” header_2_text_shadow_horizontal_length_tablet=”0px” header_2_text_shadow_vertical_length_tablet=”0px” header_2_text_shadow_blur_strength_tablet=”1px” header_3_text_shadow_horizontal_length_tablet=”0px” header_3_text_shadow_vertical_length_tablet=”0px” header_3_text_shadow_blur_strength_tablet=”1px” header_4_text_shadow_horizontal_length_tablet=”0px” header_4_text_shadow_vertical_length_tablet=”0px” header_4_text_shadow_blur_strength_tablet=”1px” header_5_text_shadow_horizontal_length_tablet=”0px” header_5_text_shadow_vertical_length_tablet=”0px” header_5_text_shadow_blur_strength_tablet=”1px” header_6_text_shadow_horizontal_length_tablet=”0px” header_6_text_shadow_vertical_length_tablet=”0px” header_6_text_shadow_blur_strength_tablet=”1px” box_shadow_horizontal_tablet=”0px” box_shadow_vertical_tablet=”0px” box_shadow_blur_tablet=”40px” box_shadow_spread_tablet=”0px” use_border_color=”off” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n Since the 2008-09 academic year, the Clearinghouse has provided its participating institutions with the option to include thirteen additional data elements in their enrollment submissions. These additional data elements help make Clearinghouse data more comprehensive and enable StudentTracker<\/a> participants to utilize a more robust data set. Since it is optional for institutions to report these elements, institutions and researchers may find it helpful to know how frequently these elements have been reported. This document provides the most up-to-date data coverage of the additional data elements, disaggregated by institutional sector.<\/p>\n Student gender is currently an optional data element in Clearinghouse Enrollment Reporting<\/a>, but it is important for a variety of research projects. In order to make use of gender for research projects, the Research Center has developed the gender imputation process described in the following PDF. Using this process, the Research Center is able to obtain gender for over 91 percent of enrollment records.<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/nscresearchcenter.org\/wp-content\/uploads\/dotline-orange-1.png” title_text=”dotline-orange” _builder_version=”4.24.2″ _module_preset=”default” custom_margin=”||51px|||” locked=”off” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_divider show_divider=”off” _builder_version=”4.16″ height=”10px” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_divider][et_pb_text admin_label=”FERPA Implications” module_id=”workingdata-4″ _builder_version=”4.27.4″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_margin=”-9px||24px|||” use_border_color=”off” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n [\/et_pb_text][et_pb_text admin_label=”Credential Level Lookup Table” _builder_version=”4.27.4″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_margin=”||36px|||” custom_padding=”6px|||||” use_border_color=”off” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n This crosswalk table provides a general guideline for mapping between the 8-digit postsecondary institution codes in the Clearinghouse\u2019s database and the 6-digit IPEDS unit IDs. Every postsecondary institution and branch code combination ever used in the Clearinghouse to provide a variety of NSC data and research services (e.g., enrollment reporting, transcript services) are contained once (and only once) in this table. Each of the colleges\u2019 main and branch campus code combinations are mapped to IPEDS unit IDs wherever possible. Take note that this general guideline does not always produce a one-on-one matching: Multiple Clearinghouse institution codes may map to a single IPEDS unit ID, while some Clearinghouse institution codes do not map to any IPEDS unit ID as those were created for compliance reporting or administrative purposes.<\/p>\n [\/et_pb_text][et_pb_text admin_label=”Credential Level Lookup Table” _builder_version=”4.27.4″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_margin=”||7px|||” custom_padding=”6px|||||” use_border_color=”off” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n Our publications often include analyses by institutional characteristics such as whether an institution is primarily online and\/or multi-state, primarily associate degree-granting baccalaureate institution, or serving a minority student population. The following lookup tables provide lists of institutions identified in the Clearinghouse data as of July 2025 that meet the definition of each institution category. To illustrate how each institutional category could be applied to research studies, we list a recent publication that utilizes each characteristic as well as the original data source(s) from which the categories were derived.<\/p>\n [\/et_pb_text][et_pb_text disabled_on=”off|on|off” admin_label=”CIP Code Lookup Table” _builder_version=”4.27.4″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_margin=”||0px|||” custom_padding=”36px|||||” use_border_color=”off” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n This lookup table allows StudentTracker<\/a> users to easily assign a standardized Classification of Instructional Program (CIP) code to the free-text course of study descriptions reported to the Clearinghouse through its DegreeVerify<\/a> service. This mapping of free-text course of study descriptions to CIP codes is based on the same logic used by the Research Center in its own research projects. In the lookup table, each unique course of study description is associated with the single CIP code under which it most frequently appears in postsecondary institutions\u2019 data submissions to the Clearinghouse. The lookup table is available in both Excel and tab-delimited text formats.<\/p>\n This lookup table allows StudentTracker<\/a> users to easily assign a standardized credential level (certificate, associate, bachelor\u2019s, etc.) to the free-text degree titles reported to the Clearinghouse through its DegreeVerify<\/a> service. This mapping of free-text degree titles to standardized credential levels is based on the degree levels reported by institutions when available, and invokes text-parsing logic used by the Research Center in its own research projects for cases where institutions did not explicitly report a degree level. The lookup table is available in both Excel and tab-delimited text formats.<\/p>\n [\/et_pb_text][et_pb_text admin_label=”Considerations in Using ST” _builder_version=”4.21.0″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_margin=”||50px|||” use_border_color=”off” global_colors_info=”{}” theme_builder_area=”post_content”]Thousands of high schools currently use StudentTracker\u00ae<\/sup> for High Schools<\/a> reports from the Research Center to measure how many of their graduates go on to college, where they attend, and how many persist to graduation. The reports were designed to help schools to measure their success in preparing students for college, and to evaluate the effectiveness of college access programs and curricula.<\/p>\n Increasingly, the results are also being used to inform accountability metrics for schools, districts, and states concerned about the performance of secondary schools. In many cases, these metrics are published without fully explaining the source and nature of the data and its limitations.<\/p>\n As the most comprehensive source of national student-level college enrollment data, the Clearinghouse is committed to providing transparency regarding the quality, reliability, and accuracy of its data, and of the reports that can be derived from it. Our belief is that the more our data users understand about the data, the better equipped they will be to use it wisely. This document is intended to inform high schools and districts about what to expect from Clearinghouse data accessed through StudentTracker reports.<\/p>\n [\/et_pb_text][et_pb_text admin_label=”FERPA Implications” _builder_version=”4.25.0″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_margin=”||50px|||” use_border_color=”off” global_colors_info=”{}” theme_builder_area=”post_content”]The Family Educational Rights and Privacy Act (FERPA) is a federal law that protects the privacy of student education records. As a result of FERPA, parents and eligible students may request that an institution not disclose directory information about them. It is important for users of the StudentTracker<\/a> detail report to understand how frequently directory information is blocked, and how block rates vary by geographical regions, institutional sectors, and student demographics. The brief PDF below includes a high-level overview of national results and is accompanied by a workbook containing more detailed state-level results tables.<\/p>\n [\/et_pb_text][et_pb_text admin_label=”Notes from the Field” _builder_version=”4.27.4″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” use_border_color=”off” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n Our goal is to provide a venue to encourage the sharing of lessons learned about using our data in research, and also to offer greater transparency about the strengths and weaknesses of the data. We hope this helps researchers to become better informed when using our data for policy analysis and academic research.<\/p>\n We are particularly grateful to the researchers below for their valuable contributions, which help us to better understand how our data are being used in the field and help other researchers to use it more effectively. We are always open to feedback<\/a> and will continue to post reports by others discussing Clearinghouse data.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”3_4,1_4″ _builder_version=”4.27.4″ _module_preset=”default” custom_padding=”25px||30px|||” border_color_all=”#E3E3E3″ border_width_top=”1px” border_color_top=”#DB602D” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”3_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”16px” custom_margin=”||9px|||” global_colors_info=”{}” theme_builder_area=”post_content”]2020<\/strong> Report<\/a><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”3_4,1_4″ _builder_version=”4.27.4″ _module_preset=”default” custom_padding=”25px||30px|||” border_color_all=”#E3E3E3″ border_width_top=”1px” border_color_top=”#DB602D” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”3_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”16px” custom_margin=”||9px|||” global_colors_info=”{}” theme_builder_area=”post_content”]2018<\/strong> Tech Tip<\/a><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”3_4,1_4″ _builder_version=”4.27.4″ _module_preset=”default” custom_padding=”25px||30px|||” border_color_all=”#E3E3E3″ border_width_top=”1px” border_color_top=”#DB602D” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”3_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”16px” custom_margin=”||9px|||” global_colors_info=”{}” theme_builder_area=”post_content”]2017<\/strong> Report<\/a> | Notes<\/a><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”3_4,1_4″ _builder_version=”4.27.4″ _module_preset=”default” custom_padding=”25px||30px|||” border_color_all=”#E3E3E3″ border_width_top=”1px” border_color_top=”#DB602D” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”3_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”16px” custom_margin=”||9px|||” global_colors_info=”{}” theme_builder_area=”post_content”]2015<\/strong> Note: NSC data coverage rates in \u201cThe Missing Manual\u201d are from 2011. For current and historical coverage rates (2003-present) by state and sector, see Data Coverage<\/a>.<\/em> Reports<\/a> | Notes<\/a><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”3_4,1_4″ _builder_version=”4.27.4″ _module_preset=”default” custom_padding=”25px||30px|||” border_color_all=”#E3E3E3″ border_width_top=”1px” border_color_top=”#DB602D” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”3_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”16px” custom_margin=”||9px|||” global_colors_info=”{}” theme_builder_area=”post_content”]2013<\/strong> Reports<\/a>\u00a0| Notes<\/a><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”3_4,1_4″ _builder_version=”4.27.4″ _module_preset=”default” custom_padding=”25px||30px|||” border_color_all=”#E3E3E3″ border_width_top=”1px” border_color_top=”#DB602D” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”3_4″ _builder_version=”4.27.4″ _module_preset=”default” custom_padding=”|0px|||false|false” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”16px” custom_margin=”||9px|||” global_colors_info=”{}” theme_builder_area=”post_content”]2010<\/strong> Notes<\/a><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”3_4,1_4″ _builder_version=”4.27.4″ _module_preset=”default” custom_padding=”25px||30px|||” border_color_all=”#E3E3E3″ border_width_top=”1px” border_color_top=”#DB602D” border_width_bottom=”1px” border_color_bottom=”#DB602D” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”3_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”16px” custom_margin=”||9px|||” global_colors_info=”{}” theme_builder_area=”post_content”]2010<\/strong> Reports<\/a> | Notes<\/a><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" The National Student Clearinghouse\u00ae Research Center™ strives to comprehensively document the nature and scope of Clearinghouse education data, while identifying processes that can enhance the ability of researchers, educators, and policymakers to leverage the data for informed decision-making.<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":5,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"The National Student Clearinghouse Research Center strives to comprehensively document the nature and scope of Clearinghouse education data, while identifying processes that can enhance the ability of researchers, educators, and policymakers to leverage the data for informed decision-making. The documents and tables that appear on this site represent the findings of these efforts. We hope they address many of the questions that researchers have regarding Clearinghouse data. If you would like to suggest additional analyses, or have suggestions for other content that would be helpful for the research community, please submit your ideas via our contact form<\/a>.\r\n\r\nMuch of the documentation on this site was originally made possible by a grant from the Lumina Foundation. Lumina Foundation, an Indianapolis-based private foundation, is committed to enrolling and graduating more students from college \u2014 especially 21st century students: low-income students, students of color, first-generation students, and adult learners. Lumina's goal is to increase the percentage of Americans who hold high-quality degrees and credentials to 60 percent by 2025. Lumina pursues this goal in three ways: by identifying and supporting effective practice, through public policy advocacy, and by using our communications and convening power to build public will for change. For more information, log on to www.luminafoundation.org<\/a>.\r\n\r\n\n
The National Student Clearinghouse as an Integral Part of the National Postsecondary Data Infrastructure<\/h2>\n
![\"PDF\"](\"\/wp-content\/uploads\/icon-pdf.png\"){kind=icon}
\u00a0 NSC as an Integral Part of the National Postsecondary Data Infrastructure<\/a><\/p>\n
\n[\/et_pb_text][et_pb_image src=”https:\/\/nscresearchcenter.org\/wp-content\/uploads\/dotline-orange-1.png” title_text=”dotline-orange” _builder_version=”4.24.2″ _module_preset=”default” custom_margin=”||51px|||” locked=”off” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_text admin_label=”Coverage” module_id=”datacov” _builder_version=”4.27.4″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” use_border_color=”off” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\nData Coverage<\/h2>\n
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\u00a0 Enrollment Coverage 2003-2016 (March 2022)<\/a><\/p>\n\u00a0 Enrollment Coverage 2017-2024 (September 2025)<\/a>[\/et_pb_text][et_pb_image src=”https:\/\/nscresearchcenter.org\/wp-content\/uploads\/dotline-orange-1.png” title_text=”dotline-orange” _builder_version=”4.24.2″ _module_preset=”default” custom_margin=”||51px|||” locked=”off” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_text admin_label=”Data Elements” module_id=”workingdata-3″ _builder_version=”4.27.4″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” use_border_color=”off” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\nData Elements<\/h2>\n
\u00a0 Reporting of NSC Additional Data Elements (September 2025)<\/a><\/p>\n\u00a0 Gender Imputation (March 2013)<\/a><\/p>\nLookup Tables<\/h2>\n
Clearinghouse School Code to IPEDS Unit-ID Crosswalk Table<\/h3>\n
![\"TAB\"](\"\/wp-content\/uploads\/icon-tab.png\"){kind=icon}
\u00a0 Clearinghouse Institution Code to IPEDS Unit-ID Crosswalk Table (April 2023)<\/a><\/p>\nInstitution Lookup Tables<\/h3>\n
\u00a0 Primarily Online and Multi-State Institutions (July 2025)<\/a>\u00a0 Historically Black Colleges and Universities (HBCUs) (July 2025)<\/a>\u00a0 Hispanic Serving Institutions (HSIs) (July 2025)<\/a>\u00a0 Primarily Associate Degree Granting Baccalaureate Institutions (PABs) (July 2025)<\/a><\/p>\nCIP Code Lookup Table<\/h3>\n
\u00a0 CIP Code Lookup Table<\/a>
\n\u00a0 CIP Code Lookup Table (June 2025)<\/a>
\n[\/et_pb_text][et_pb_text admin_label=”Credential Level Lookup Table” _builder_version=”4.27.4″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_margin=”||50px|||” custom_padding=”36px|||||” use_border_color=”off” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\nCredential Level Lookup Table<\/h3>\n
\u00a0 Credential Level Lookup Table<\/a>
\n\u00a0 Credential Level Lookup Table (June 2025)<\/a>
\n[\/et_pb_text][et_pb_image src=”https:\/\/nscresearchcenter.org\/wp-content\/uploads\/dotline-orange-1.png” title_text=”dotline-orange” _builder_version=”4.24.2″ _module_preset=”default” custom_margin=”||51px|||” locked=”off” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_text admin_label=”Considerations in Using ST” module_id=”workingdata-5″ _builder_version=”4.27.4″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_margin=”34px|||||” use_border_color=”off” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\nUsing StudentTracker for High Schools Reports: Considerations for Measuring the College Enrollment Rates of High School Graduates<\/em><\/h2>\n
\u00a0 Considerations in Using StudentTracker for High Schools Reports (December 2014)<\/a>
\n[\/et_pb_text][et_pb_image src=”https:\/\/nscresearchcenter.org\/wp-content\/uploads\/dotline-orange-1.png” title_text=”dotline-orange” _builder_version=”4.24.2″ _module_preset=”default” custom_margin=”||51px|||” locked=”off” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_text admin_label=”FERPA Implications” module_id=”workingdata-6″ _builder_version=”4.27.4″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” use_border_color=”off” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\nFERPA Implications for Researchers<\/h2>\n
\u00a0 Impact of Directory Information Blocks on StudentTracker Results (November 2017)<\/a>
\n\u00a0 FERPA Block Rate Details (July 2024)<\/a>
\n[\/et_pb_text][et_pb_image src=”https:\/\/nscresearchcenter.org\/wp-content\/uploads\/dotline-orange-1.png” title_text=”dotline-orange” _builder_version=”4.24.2″ _module_preset=”default” custom_margin=”||51px|||” locked=”off” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_text admin_label=”Notes from the Field” module_id=”notesfromfield” _builder_version=”4.27.4″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” use_border_color=”off” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\nNotes from the Field<\/h2>\n
Researchers Talk About Using Clearinghouse Data<\/h3>\n
\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]“Tracking Two-Year College Outcomes: Comparing National Student Clearinghouse and Illinois Community College Board as Sources of Two-Year College Data”<\/strong> by Jenny Nagaoka and Shelby Mahaffie, examines two-year college outcomes of Chicago Public School (CPS) graduates entering two-year postsecondary institutions, and evaluates the strengths and limitations of the data obtained through both StudentTracker data from the National Student Clearinghouse, and from the Illinois Longitudinal Data System (ILDS).
\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”16px” custom_margin=”36px|||||” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n
\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]Tech Tip posted on the Association for Institutional Research site<\/strong>: Pensacola State has developed a process using the SAS Enterprise Guide to guarantee exact formatting from a CSV file of the applicants to the institution that populates new data, daily. By Eugenio Hernandez, Research Analyst, Institutional Research, Pensacola State College & Michael A. Johnston, Director, Institutional Research, Pensacola State College.
\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”16px” custom_margin=”36px|||||” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n
\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]“How to Measure Community College Effectiveness in Serving Transfer Students”<\/strong> by John Fink and Davis Jenkins, provides guidance on how community colleges can use degree and enrollment data from the National Student Clearinghouse to monitor and evaluate transfer student bachelor degree outcomes.
\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”16px” custom_margin=”36px|||||” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n
\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]The Missing Manual – Using National Student Clearinghouse Data to Track Postsecondary Outcomes <\/strong>by Dynarski, Hemelt, and Hyman discussing potential impact of coverage and FERPA blocks on results<\/p>\n
\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”16px” custom_margin=”36px|||||” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n
\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]The Missing Manual – Using National Student Clearinghouse Data to Track Postsecondary Outcomes<\/strong> by Dynarski, Hemelt, and Hyman discussing potential impact of coverage and FERPA blocks on results
\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”16px” custom_margin=”36px|||||” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n
\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]Letter to Colleagues:<\/strong> Observations on the Use of Clearinghouse Data for Research Purposes<\/strong> by Sara Goldrick-Rab and Douglas N. Harris, University of Wisconsin-Madison
\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”16px” custom_margin=”36px|||||” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n
\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]College Graduation Rates:<\/strong> Behind the Numbers<\/strong> by ACE
\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”16px” custom_margin=”36px|||||” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n
\r\n\r\nUsing StudentTracker for High Schools Reports: Considerations for Measuring the College Enrollment Rates of High School Graduates<\/em><\/h4>\r\nThousands of high schools currently use StudentTracker reports from the National Student Clearinghouse Research Center to measure how many of their graduates go on to college, where, and how many persist to graduation. The reports were designed to help schools to measure their success in preparing students for college, and to evaluate the effectiveness of college access programs and curricula. Increasingly, the results are also being used to inform accountability metrics for schools, districts and states concerned about the performance of secondary schools. In many cases, these metrics are published without fully explaining the source and nature of the data and its limitations.\r\n\r\nAs the most comprehensive source of national student-level college enrollment data, the Clearinghouse is committed to providing transparency regarding the quality, reliability and accuracy of its data, and of the reports that can be derived from it. Our belief is that the more our data users understand about the data, the better equipped they will be to use it wisely. This document is intended to inform high schools and districts about what to expect from National Student Clearinghouse data accessed through StudentTracker reports.\r\n\r\n
![\"PDF\"](\"https:\/\/nscresearchcenter.org\/pubproof\/wp-content\/uploads\/icon_pdf.jpg\" "\\"PDF"){kind=icon}
Considerations in Using StudentTracker for High Schools Reports<\/a>\r\n\r\n
\r\n\r\nCoverage<\/h4>\r\nThe National Student Clearinghouse Research Center has analyzed the Clearinghouse's coverage of enrollments at all postsecondary, Title IV, degree-granting institutions. The resulting workbook contains detailed coverage calculations by state, sector and level of institution, resulting in 5,670 data points. It is based on historical IPEDS institutional characteristics, and therefore captures any changes in institutional sectors and levels that occurred during the years included (2003-04 to present). The data are being provided in an Excel format for the convenience of researchers who would like to make coverage-based adjustments in studies that use longitudinal Clearinghouse data.\r\n\r\n
![\"XLS\"](\"https:\/\/nscresearchcenter.org\/pubproof\/wp-content\/uploads\/icon_xls.jpg\" "\\"XLS"){kind=icon}
Enrollment Coverage Workbook<\/a>\r\n\r\n
\r\n\r\nData Elements<\/h4>\r\nSince the 2008-09 academic year, the Clearinghouse has provided its participating institutions with the option to include thirteen additional data elements in their enrollment submissions. These additional data elements help make Clearinghouse data more comprehensive and enable StudentTracker participants to utilize a more robust data set. Since it is optional for institutions to report these elements, institutions and researchers may find it helpful to know how frequently these elements have been reported. This document provides this information for the last five academic years, disaggregated by institutional sector.\r\n\r\n
Reporting_of_NSC_Additional_Data_Elements<\/a>\r\nStudent gender is currently an optional data element in Clearinghouse Enrollment Reporting, but it is important for a variety of research projects. In order to make use of gender for research projects, the Research Center has developed the gender imputation process described in the following PDF. Using this process, the Research Center is able to obtain gender for over 91 percent of enrollment records.\r\n\r\n Gender Imputation<\/a>\r\n\r\n
\r\n\r\nFERPA Implications for Researchers<\/h4>\r\nThe Family Educational Rights and Privacy Act (FERPA) is a federal law that protects the privacy of student education records. As a result of FERPA, parents and eligible students may request that an institution not disclose directory information about them. It is important for users of the StudentTracker detail report to understand how frequently directory information is blocked, and whether it is blocked consistently across geographical regions, institutional sectors, and student demographics. The National Student Clearinghouse Research Center found that the national directory information block rate was 4.29 percent (average for three selected years), with a great deal of variation among states and across institutional sectors. The report below includes a high-level overview followed by an appendix containing detailed state-level results tables.\r\n\r\n
<\/a> Impact of Directory Information Blocks on StudentTracker\u2120<\/sup> Results<\/a>\r\n\r\n
\r\n\r\nNotes from the Field<\/h4>\r\n
Researchers Talk About Using Clearinghouse Data<\/h5>\r\nOur goal is to provide a venue to encourage the sharing of lessons learned about using our data in research, and also to offer greater transparency about the strengths and weaknesses of the data. We hope this helps researchers to become better informed when using our data for policy analysis and academic research.\r\n\r\nWe are particularly grateful to the researchers below for their valuable contributions, which help us to better understand how our data are being used in the field and help other researchers to use it more effectively. We are always open to feedback<\/a> and will continue to post reports by others discussing Clearinghouse data.\r\n
![\"SAS\"](\"https:\/\/nscresearchcenter.org\/pubproof\/wp-content\/uploads\/icon_sas.jpg\" "\\"SAS"){kind=icon}
![\"SPSS\"](\"https:\/\/nscresearchcenter.org\/pubproof\/wp-content\/uploads\/icon_spss.jpg\" "\\"SPSS"){kind=icon}
![\"icon_csv\"](\"https:\/\/nscresearchcenter.org\/pubproof\/wp-content\/uploads\/icon_tab1.jpg\" "\\"TAB"){kind=icon}
![\"icon_csv\"](\"https:\/\/nscresearchcenter.org\/pubproof\/wp-content\/uploads\/icon_csv.jpg\" "\\"CSV"){kind=icon}
2014 -