The National Study of Learning Mindsets, [United States], 2015-2016 (ICPSR 37353)
Version Date: Jul 27, 2020 View help for published
Principal Investigator(s): View help for Principal Investigator(s)
David S. Yeager, University of Texas at Austin
https://doi.org/10.3886/ICPSR37353.v3
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Additional information about this collection can be found in Version History.
2020-07-27 A total of 17 non-consenting students' responses were removed from the Grades Wide and Test Scores datasets and their corresponding codebooks. In the Student Disposition dataset and codebook all 383 non-consenting students were removed. Because of this, the study consent variable (s1_nonconsent) was removed because survey participation implies consent.
2020-04-29 Study was originally released only to create DOI; this version includes new data and documentation.
2019-06-24 ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection:
- Created variable labels and/or value labels.
- Checked for undocumented or out-of-range codes.
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Summary View help for Summary
The National Study of Learning Mindsets (NSLM), also known as Students' Perspectives about the Transition to High School (PATHS), is a longitudinal research study and program evaluation administered by the University of Texas. The purpose of NSLM is to determine if introducing students to a positive growth mindset as they transition to high school will help improve students' academic success during high school and beyond. A growth mindset was defined as a belief amongst students that intelligence can grow and develop, learning is purposeful, hard work can lead to gained skills and accomplished goals, and that these beliefs can maintain motivation in the face of obstacles.
The sample consisted of more than 16,000 9th grade students across 76 high schools in 27 states. Number of courses taken, course level, and GPA before and after high school were measured within english, math, social studies, and science. Numerous demographic variables were collected for students, teachers, and schools. Student demographics included gender, race, and age. Teacher demographics consisted of race, teaching methods, and educational background. School characteristics such as location, course offerings, and students' average test scores were assessed.
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Country
Restrictions View help for Restrictions
This data collection may not be used for any purpose other than statistical reporting and analysis. Use of these data to learn the identity of any person or establishment is prohibited. To protect respondent privacy, some of the data files in this collection are restricted from general dissemination. To obtain these restricted files researchers must agree to the terms and conditions of a Restricted Data Use Agreement.
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Data Collection Notes View help for Data Collection Notes
- For additional information on The National Study of Learning Mindsets study, please visit the Mindset Scholars Network website.
- See the National Study of Learning Mindsets for current information about NSLM activities, including information to access intervention materials.
Study Purpose View help for Study Purpose
The National Study of Learning Mindsets addresses the following primary research questions:
- Can the growth mindset intervention improve the grades of lower-performing students in U.S. public schools?
- Can the growth mindset intervention motivate students to enroll in challenging math and science courses?
- Can the growth mindset intervention reduce group-based inequalities in academic performance in U.S. public schools?
- Do the effects of the growth mindset intervention depend on schools' formal resources (e.g., the curriculum and instruction)?
- Are the effects of the growth mindset intervention larger in schools or classrooms that are supportive of growth mindset beliefs?
Study Design View help for Study Design
Students were asked to complete two randomly assigned, 25-minute online sessions using their school's computer resources. These sessions included a the growth mindset exercise and a control exercise.
In the treatment condition, students read and listened to materials describing scientific evidence about how the brain works and about people's ability to grow intellectual abilities over time. The treatment condition also encouraged students to think about why they might want to grow their brain in order to make a difference on something they personally care about. The students also reflected on how to put these beliefs into practice, for instance, by completing a brief writing assignment providing advice for future ninth graders that might ease their transition to high school based on what the participants had just learned from the intervention.
Students in the control group did activities that were closely matched to each of the treatment conditions, but they lacked the growth mindset message.
Sample View help for Sample
A total of 139 schools, selected from a sampling frame of over 12,000 regular U.S. public high schools, were invited to administer the intervention and provide student records. 76 schools agreed to participate in the study. 65 of the 76 schools provided all requested records, including both survey data and administrative records for students. The remaining 11 schools provided only the student and teacher survey data.
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Universe View help for Universe
Ninth grade students and their math teachers in 2015 in US public schools
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Description of Variables View help for Description of Variables
The Grades Long dataset contains information on one record per course grades and participating students' GPA, course level, and classification.
The Grades Wide dataset contains information on one record per student grades and how many courses participating students were taking, as well as their GPAs in each course.
The Math Teacher Disposition dataset contains administrative variables on what surveys teachers participated in.
The School Disposition dataset contains administrative variables on participating schools.
The Student Dispostion dataset contains administrative information on students' survey participation as well as weight variables.
The Student-Math Teacher Crosswalk dataset contains information on participating students' math classes, whether or not their teachers participated in the survey, and their grades.
The Student Survey dataset contains demographic information such as gender, race, and age, as well as variables on participating students' experiences in school from an online survey.
The Teacher Survey dataset contains participating teachers' demographic information such as race, their teaching methods, and their education.
The Test Scores dataset contains information on number of tests taken and whether or not students met state standards for their given subject.
The Grade 10 Math Course Selection dataset contains information on participating students' course selections.
The School dataset contains information on participating schools' demographics, such as location, the courses they offer, and average test scores.
HideOriginal Release Date View help for Original Release Date
2019-06-24
Version History View help for Version History
2020-07-27 A total of 17 non-consenting students' responses were removed from the Grades Wide and Test Scores datasets and their corresponding codebooks. In the Student Disposition dataset and codebook all 383 non-consenting students were removed. Because of this, the study consent variable (s1_nonconsent) was removed because survey participation implies consent.
2020-04-29 Study was originally released only to create DOI; this version includes new data and documentation.
2019-06-24 ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection:
- Created variable labels and/or value labels.
- Checked for undocumented or out-of-range codes.