Health and STEM Career Expectations 

and Science Literacy Achievement of 

U.S. 15-Year-Old Students

A Publication of the National Center for Education Statistics at IES

STATS IN BRIEF

June 2020

NCES 2020-034

U.S. DEPARTMENT OF EDUCATION

AUTHORS

Marissa Hall

Amy Rathbun

American Institutes for Research

PROJECT OFFICER 

Patrick Gonzales 

National Center for Education Statistics

Statistics in Brief publications describe key findings 

from statistical tables to provide useful information to 

a broad audience, including members of the general 

public. They address simple and topical issues and 

questions. They do not investigate more complex 

hypotheses, account for interrelationships among 

variables, or support causal inferences. We encourage 

readers who are interested in more complex questions 

and in-depth analysis to explore other NCES resources, 

including publications, online data tools, and public- and 

restricted-use datasets. See nces.ed.gov and references 

noted in the body of this document for more information.

This report was prepared in part under 

Contract No. ED-IES-12-D-0002 with the 

American Institutes for Research. Mention 

of trade names, commercial products, or 

organizations does not imply endorsement 

by the U.S. Government.

Demand for jobs in many 

science, technology, engineering, 

and mathematics (STEM) fields 

and in the health care1

1 In this brief, health care occupations are not considered STEM occupations; thus, health care and STEM careers are discussed and analyzed separately. For more information on this 

distinction, please see Appendix A: Classification of Occupations. 

 field is 

projected to grow faster than the 

average for all occupations in the 

United States (Bureau of Labor 

Statistics 2019). However, the 

current STEM and health care 

workforces are characterized by 

demographic disparities and a lack 

of diversity in many positions (e.g., 

see Funk and Parker 2018). For the 

United States to equitably meet 

national demands for STEM and 

health care jobs, it is important 

to understand key factors that 

may be associated with students’ 

decisions to pursue STEM and 

health-related careers. Some of 

these key factors may include 

student career expectations, 

academic achievement, and 

demographic characteristics. 

Indeed, a student’s career 

expectations and academic 

achievement can be key predictors 

in the decision to pursue science 

as a young adult. An analysis of 

data from 1988 through 2000 

from the National Education 

Longitudinal Study found that 

eighth-grade students who 

expected to work in science at 

age 30 were almost twice as likely 

to graduate from college with a 

major in life science and almost 

three-and-a-half times as likely to 

graduate with a major in physical 

science and engineering than their 

eighth-grade peers who expected 

to have a nonscience career (Tai et 

al. 2006). The same study found 

that eighth-grade mathematics 

achievement was not a significant 

predictor of graduating college 

with a degree in life science, but 

it was a predictor of graduating 

college with a degree in physical 

science and engineering, with 

high-performing students (i.e., 

those whose mathematics scores 

were more than one standard 

deviation above average) more 

likely to obtain this degree. The 

probability of earning a physical 

science or engineering degree 

was 34 percent for an averageperforming student with science 

career expectations, and it was 

51 percent for a high-performing 

student with science career 

expectations. 

Research also shows demographic 

disparities in students’ postsecondary pursuits within sciencerelated fields. In 2015–16, a greater 

percentage of female than male 

students earned bachelor’s degrees 

in health professions and in 

biological and biomedical sciences, 

whereas a greater percentage of 

male students earned degrees in 

engineering, computer sciences, 

and mathematics and statistics 

(National Center for Education 

Statistics [NCES] 2018). Also in 

2015–16, the percentage of Asian 
Page 2 of 23

students who completed bachelor’s 

degrees in STEM fields was higher 

than the overall percentage, while 

the percentages of Hispanic and 

Black students who received 

these degrees was lower (NCES 

2019). Degree completion by 

immigration status also varied: 

compared to their native-born U.S. 

peers, Jia (2019) found that the 

percentage of students completing 

a STEM major was not measurably 

different for Hispanic immigrant 

students but was significantly 

greater for Asian immigrant 

students. Socioeconomic status 

was also associated with the 

rate at which students pursue 

science-related majors: a greater 

percentage of students from 

families with income in the top 

25 percent or whose parents 

had at least a college education, 

for instance, declared a college 

major in natural sciences than did 

students whose family income 

fell in the bottom 25 percent or 

whose parents had a high school 

education or less (Chen 2009). 

This difference is compounded by 

STEM major students from lowincome backgrounds dropping 

out of college at a higher rate than 

their peers from high-income 

backgrounds (Chen 2013).

This Statistics in Brief contributes 

to the understanding of U.S. high 

school students’ expectations of 

health or STEM careers and their 

science achievement. Analyses 

between and within demographic 

groups highlight whether any 

disparities are evident years before 

young people begin their careers. 

Data Sources and 

Methodology

This Statistics in Brief uses data on 

U.S. 15-year-old students from the 

Program for International Student 

Assessment (PISA) to examine 

whether students’ health or STEM 

career expectations varied by 

demographic characteristics in 

2015. The brief also uses 2015 PISA 

data to examine students’ science 

achievement in relation to their 

career interests and background. 

The selected student demographic 

characteristics analyzed in this 

brief are gender; race/ethnicity; 

immigration status;2

2

 The PISA database contains country-specific variables relating to the country of birth of the student and their mother and father and an indicator of whether the country of birth was the 

same as the country of assessment. The index of immigrant background was calculated from these variables into the following categories: (1) native students (those students who had at 

least one parent born in the country), (2) second-generation students (those students born in the country of assessment but whose parent(s) were born in another country) and (3) firstgeneration students (those students born outside the country of assessment and whose parents were also born in another country). Students with missing responses for either the student or 

for both parents were assigned missing values for this variable. The terms “native-born,” “first generation,” and “second generation” may be used differently in other NCES briefs or reports.

 and 

economic, social, and cultural 

status3

3

 The PISA index of economic, social and cultural status (ESCS) was created using student reports on parental occupation, the highest level of parental education, and an index of home 

possessions related to family wealth, home educational resources, and possessions related to “classical” culture in the family home. The home possessions relating to “classical” culture in 

the family home include possessions such as works of classical literature, books of poetry, and works of art (e.g., paintings).

 (ESCS). A student’s ESCS 

falls into one of four national ESCS 

quarters: the top, middle-high, 

middle-low, or low quarters.

Students who participate in PISA 

are selected via a nationally 

representative sample. PISA results 

are not used to report individual 

scores, but rather to provide valid 

estimates of 15-year-old student 

achievement and characteristics at 

the country and subgroup levels. 

The assessment is administered 

every 3 years and places an 

emphasis on the application 

of knowledge and skills to realworld contexts. The core subjects 

assessed are science literacy, 

mathematics literacy, and reading 

literacy. At each administration, 

one of these subjects is the major 

domain, with a greater number 

of assessment and questionnaire 

items dedicated to that subject. 

The major domain rotates such 

that each core subject is the major 

domain every 9 years. After taking 

the PISA assessment, students 

also complete a questionnaire 

that includes items related to their 

background and interests. 

In 2015, science literacy was the 

major PISA assessment domain, 

and the student questionnaire 

included the following question 

related to career expectations: 

What kind of job do you expect 

to have when you are about 

30 years old? The PISA career 

question was both open-ended 

and administered in two cycles, 

two aspects that distinguished 

it from similar items in the 

questionnaires administered 

alongside the National Assessment 

of Educational Progress (NAEP) 

and the Trends in International 

Mathematics and Science Study 

(TIMSS). For PISA, students 

wrote their responses to the 

career question, which were then 

assigned International Standard 

Classification of Occupations 

(ISCO) codes during data 

processing. 

In PISA publications, the 

Organization for Economic 

Cooperation and Development 

(OECD) calls both health and 

STEM careers “science-related” 

because working in these fields 

requires further engagement 

with the study of science beyond 

compulsory education, typically in 

formal postsecondary education 

settings (OECD 2016, 2017). Given 

the differences between health 

and STEM careers, the fields are 

often studied separately; however, 

research using non-PISA data has 

also studied health and STEM 

career trajectories together (e.g., 

see Kimmel, Miller, and Eccles 

2012). 

In this brief, the first section of 

each study question looks at the 

student career expectations and 

science achievement results of 

students expecting either a career 

in health or STEM, grouping these 

fields together to present broader 

findings (and including student 

demographic subgroups with 

sample sizes that are too small to 

analyze when health careers are 

analyzed separately from STEM 
Page 3 of 23

careers). The next two sections 

of each study question offer a 

more specific look at health- and 

STEM-specific career expectation 

findings. The OECD career 

classification criteria4

4

 The OECD career classification used in this brief counts careers in the behavioral or social sciences as nonscience fields.

 defines 

these careers as follows:

• Health fields: For students 

expecting to be health 

professionals or health 

technicians. Careers in health 

fields include doctors, nurses, 

veterinarians, and medical and 

pharmaceutical technicians.

• STEM fields: For students 

expecting to be science, 

engineering, mathematics, or 

information and communication 

technologies (ICT) professionals 

or science-related technicians 

or associate professionals. 

Examples of student-reported 

STEM careers include systems 

analysts, biologists, and civil 

engineering technicians.

The science literacy scores of the 

following demographic groups 

were suppressed because reporting 

standards were not met for the 

indicated career expectations: 

Other-race/ethnicity students5

5

 Although data for “Other race” students are not shown in the figures because reporting standards were not met, they are included in the “All students” estimate. “Other race” includes 

both American Indian/Alaska Native and Native Hawaiian/Other Pacific Islander students.

with health or STEM career 

expectations; Asian students 

with health and STEM career 

expectations; and students of Two 

or more races with STEM career 

expectations. This brief does 

not study specific student career 

expectations (e.g., mechanical 

engineer, medical doctor) due to 

sample size restrictions in some 

demographic groups. Due to 

this brief’s focus on descriptive 

statistics, in addition to sample size 

limitations, the study questions do 

not examine interactions between 

demographic groups. 

All differences cited in this report 

are statistically significant at 

the p < .05 level. Although one 

estimate in this brief may appear to 

be larger than another, a statistical 

test may find that the apparent 

difference between them is not 

measurable due to the uncertainty 

around the sample estimates. 

In this case, the estimates are 

described as having no measurable 

difference, meaning the difference 

between them is not statistically 

significant. No adjustments were 

made for multiple comparisons. 

For more information on data 

and methods, please see the 

Methodology and Technical Notes 

at the end of the report.

Interpreting Score 

Gaps

PISA scores are reported on a 

scale from 0 to 1,000. The OECD 

transforms student scores to the 

PISA scale such that the mean 

science literacy score for OECD 

countries is set at 500 score points, 

with a standard deviation of 100. 

The OECD (2016) writes that 

“PISA scores are represented on 

a scale whose units do not have 

substantive meaning (unlike 

physical units, such as meters or 

grams) but are set in relation to 

the variation in results observed 

across all test participants.” One 

way to contextualize PISA results is 

through analysis of student scores 

at various percentiles, which can 

be used to make comparisons 

between or within countries. 

For instance, the 50th percentile 

score is the score below which 

50 percent of students scored; 

in the United States, the 2015 

science literacy score at the 

50th percentile was 495 points 

(figure 1, table A-1). 

FIGURE 1. Distribution of U.S. students’ scores on the PISA 

science literacy assessment: 2015

 

NOTE: This table shows the threshold (or cut) scores for selected percentiles. The percentile range is specific to the 

distribution of science literacy scores of U.S. students. 

SOURCE: Organization for Economic Cooperation and Development (OECD), Program for International Student 

Assessment (PISA), 2015.
Page 4 of 23

The 45th percentile is associated 

with a score of 482 points, 

which is 13 points lower than 

the 50th percentile score. U.S. 

students at the 40th, 30th, 20th, 

and 10th percentiles scored 

lower than students at the 50th 

percentile by 27, 55, 87, and 

128 points, respectively. 

Associating these score gaps with 

percentile gaps helps illustrate the 

varying magnitude of achievement 

differences.

Study Questions

1. In 2015, did the percentage of 15-year-olds who expected to have a health or STEM career vary by 

student demographic characteristics?

2. In 2015, were there science achievement differences between students expecting health or STEM 

careers and students expecting nonscience careers?

3. In 2015, among only those students expecting health or STEM careers, were there science 

achievement differences by student demographic characteristics?

Key Findings

• In 2015, some 40 percent of all U.S. 15-year-old students expected to have either a health or STEM career 

at age 30 (figure 2, table A-2). Specifically, 23 percent expected to have a health career and 16 percent 

expected to have a STEM career. Greater percentages of female, second-generation, and high-ESCS 

students expected to work in health or STEM fields than did male, native-born, and lower-ESCS6

6 “Lower-ESCS students” refers to all those students who do not fall within the high-ESCS quarter (i.e., the low, middle-low, and middle-high quarters).

students, respectively. Additionally, a greater percentage of Asian students than Black, Hispanic, and 

students of Two or more races expected to have health or STEM careers. While a greater percentage 

of female students expected to have a health career than did male students (37 percent vs. 9 percent), 

a greater percentage of male students expected to have a STEM career than did female students 

(26 percent vs. 7 percent). 

• On the PISA 2015 science literacy assessment, students who expected to work in health or STEM fields 

scored 516 points on average, outperforming by 28 points their peers with expectations of nonscience 

careers (figure 3, table A-3). The average score of students expecting health or STEM careers (516 points) 

corresponds to the 57th percentile7

7

 In this brief, scores by demographics are associated with a single percentile for ease of interpretation, although t tests, which account for estimates’ standard errors, reveal a range 

of percentiles from which a demographic score is not measurably different. For more information, see the Methodology and Technical Notes.

 of the U.S. distribution of scores, and the average score of nonscience 

career students (488 points) corresponds to the 47th percentile. Apart from female students, the health 

or STEM career vs. nonscience career score gap was significant within every key displayed demographic 

group examined in this brief. Students with health career expectations and students with STEM career 

expectations outperformed their peers with nonscience career expectations by 9 and 56 score points on 

average, respectively (figures 4 and 5).

• Among students expecting health or STEM careers, science score gaps were seen by gender, race/

ethnicity, immigration status, and ESCS (figure 6, table A-3). While males expecting careers in health 

outperformed females expecting careers in health by 41 points on average (figure 7), there was no 

measurable score gap between males and females expecting STEM careers (figure 8). The score gap 

between White and Black students with STEM career expectations was 113 points: the average score of 

White students (575 points) corresponds to the 77th percentile of the U.S. distribution of scores, and the 

average score of Black students (462 points) corresponds to the 37th percentile (figure 8). The score gap 

between low-ESCS students (449 points) and high-ESCS students (544 points) expecting health careers 

was 95 points. These scores correspond to the 33rd percentile and 70th percentile, respectively, of the 

U.S. distribution of scores.
Page 5 of 23

STUDY QUESTION 1: In 2015, did the percentage of 15-year-olds 

who expected to have a health or STEM career vary by student 

demographic characteristics?

Health or STEM Career 

Expectations

In 2015, some 40 percent of all 

U.S. 15-year-old students expected 

to have a either a health or STEM 

career at about age 30 (figure 

2, table A-2). Differences in the 

percentages of students expecting 

to have these careers were 

seen by gender, race/ethnicity, 

immigration status, and ESCS. 

A greater percentage of females 

than males foresaw themselves 

working in health or STEM fields 

(45 percent of female students 

vs. 35 percent of male students, 

table A-2). Although the percentage 

of White students with health 

or STEM career expectations 

(41 percent) was not measurably 

different from that of students of 

any other race/ethnicity, a greater 

percentage of Asian students 

reported health or STEM career 

expectations (50 percent) than 

did Black students (38 percent), 

Hispanic students (39 percent), 

and students of Two or more races 

(39 percent). In addition, a greater 

percentage of second-generation 

students expected to have health 

or STEM careers than did nativeborn students (44 percent vs. 

39  percent), as did students in the 

high ESCS quarter (46 percent) 

compared to students in all lower 

quarters, where the percentages 

ranged from 36 to 39 percent.

Health-Specific Career 

Expectations

Twenty-three percent of all 

students expected to have a 

career in health (figure 2, table 

A-2). For the most part, the 

percentage of students expecting 

to have a health career did not 

differ by displayed demographic 

characteristics. 

FIGURE 2. Percentage of U.S. 15-year-old students expecting 

careers in STEM, health, and nonscience fields, by 

demographic characteristics: 2015

 

NOTE: Detail may not sum to totals due to rounding. STEM and health careers were classified according to the 

OECD (2016) definition and refer to those jobs in science that require further science-related studies beyond 

compulsory education. PISA defines “second-generation” as students who were born in the United States but 

whose parent(s) were born in another country, and “first-generation” as those students born outside the United 

States and whose parents were also born in another country. “Other race” includes both American Indian/Alaska 

Native and Native Hawaiian/Other Pacific Islander students. Black includes African American, and Hispanic 

includes Latino. Race categories exclude persons of Hispanic ethnicity. The PISA index of economic, social and 

cultural status (ESCS) was created using student reports on parental occupation, the highest level of parental 

education, and an index of home possessions related to family wealth, home educational resources, and 

possessions related to “classical” culture in the family home. The home possessions relating to “classical” culture 

in the family home included possessions such as works of classical literature, books of poetry, and works of art 

(e.g., paintings). Although rounded numbers are displayed, the figures are based on unrounded data.

SOURCE: Organization for Economic Cooperation and Development (OECD), Program for International Student 

Assessment (PISA), 2015.
Page 6 of 23

However, the percentage of female 

students reporting health career 

expectations was approximately 

four times that of male students 

(37 percent vs. 9 percent). 

STEM-Specific Career 

Expectations

Sixteen percent of U.S. students 

expected to have a STEM career, 

and differences in STEM career 

expectations were seen by gender, 

race/ethnicity, immigration status, 

and ESCS (figure 2, table A-2). For 

example, almost four times as 

many males (26 percent) as females 

(7 percent) expected to have a 

STEM career, which contrasts 

the pattern of gender differences 

in health career expectations. 

In contrast to no differences in 

health career expectations by 

race/ethnicity, the percentage of 

White students expecting to have 

a STEM career (18 percent) was 

greater than that of Black students 

(12 percent), and the percentage of 

Asian students expecting to have 

a STEM career (23 percent) was 

greater than that of Black students 

(12 percent), Hispanic students 

(16 percent), and students of other 

races (12 percent). 
Page 7 of 23

STUDY QUESTION 2: In 2015, were there science achievement 

differences between students expecting health or STEM 

careers and students 

expecting 

nonscience careers? 

Health or STEM Career 

Expectations

On the PISA science literacy 

assessment, students expecting 

a career in health or STEM fields 

outperformed their 15-year-old 

peers expecting a nonscience 

career by an average of 28 score 

points; the average score of 

students intending to hold a 

health or STEM job was 516 points, 

compared to a score of 488 points 

for students intending to hold a 

nonscience job (figure 3, table A-3).

The achievement gap between 

students expecting a health 

or STEM job and students 

expecting a nonscience job was 

significant within every displayed 

demographic group except for 

female students. For example, 

male students with health or STEM 

career expectations scored, on 

average, 53 points higher than 

males expecting nonscience 

careers. Within racial/ethnic 

groups, score gaps between 

students expecting health or 

STEM careers and those expecting 

nonscience careers ranged from 

19 points for Black students to 

58 points for Asian students. 

FIGURE 3. Average science literacy scores and score gaps 

between U.S. 15-year-old students with expectations of health 

or STEM careers and those with expectations of nonscience 

careers, by demographic characteristics: 2015

 

* p < .05 Average science score of students expecting health or STEM careers is significantly different from that of 

students expecting nonscience careers.

NOTE: Scores are reported on a scale from 0 to 1,000. Health and STEM careers were classified according to the 

OECD (2016) definition and refer to those jobs that require further science-related studies beyond compulsory 

education. PISA defines “second-generation” as students who were born in the United States but whose parent(s) 

were born in another country, and “first-generation” as those students born outside the United States and whose 

parents were also born in another country. Although data for “Other race” students are not shown because 

reporting standards were not met, they are included in “All students.” “Other race” includes both American 

Indian/Alaska Native and Native Hawaiian/Other Pacific Islander students. Black includes African American, and 

Hispanic includes Latino. Race categories exclude persons of Hispanic ethnicity. The PISA index of economic, 

social and cultural status (ESCS) was created using student reports on parental occupation, the highest level of 

parental education, and an index of home possessions related to family wealth, home educational resources, and 

possessions related to “classical” culture in the family home. The home possessions relating to “classical” culture 

in the family home included possessions such as works of classical literature, books of poetry, and works of art 

(e.g., paintings). Although rounded numbers are displayed, the figures are based on unrounded data.

SOURCE: Organization for Economic Cooperation and Development (OECD), Program for International Student 

Assessment (PISA), 2015.
Page 8 of 23

Health-Specific Career 

Expectations

Fifteen-year-olds expecting to 

work in a health field scored 

497 points on the science literacy 

assessment, outperforming their 

peers expecting to work in a 

nonscience field by an average of 

9 score points (figure 4, table A-3). 

The average score of nonscience 

career students corresponds to 

the 47th percentile of the U.S. 

distribution of scores, and the 

average score of health career 

students corresponds to the 

50th percentile. 

There were few measurable 

differences between the science 

scores of health and nonscience 

career students, with measurable 

gaps observed for three groups: 

male students, native-born 

students, and students at the midhigh ESCS quarter. For example, 

there was a 44-point score gap 

between the average score of a 

male student intending to work 

in a health field (529 points) and 

a male student intending to work 

in a nonscience field (485 points).

FIGURE 4. Average science literacy scores and score gaps 

between U.S. 15-year-old students with expectations of health 

careers and those with expectations of nonscience careers, by 

demographic characteristics: 2015

 

* p < .05 Average science score of health career students is significantly different from that of nonscience career 

students.

NOTE: Scores are reported on a scale from 0 to 1,000. Health careers were classified according to the OECD 

(2016) definition and refer to those jobs in health fields that require further science-related studies beyond 

compulsory education. PISA defines “second-generation” as students who were born in the United States 

but whose parent(s) were born in another country, and “first-generation” as those students born outside the 

United States and whose parents were also born in another country. Although data for “Asian” and “Other race” 

students are not shown because reporting standards were not met, they are included in “All students.” “Other 

race” includes both American Indian/Alaska Native and Native Hawaiian/Other Pacific Islander students. Black 

includes African American, and Hispanic includes Latino. Race categories exclude persons of Hispanic ethnicity. 

The PISA index of economic, social and cultural status (ESCS) was created using student reports on parental 

occupation, the highest level of parental education, and an index of home possessions related to family wealth, 

home educational resources, and possessions related to “classical” culture in the family home. The home 

possessions relating to “classical” culture in the family home included possessions such as works of classical 

literature, books of poetry, and works of art (e.g., paintings). Although rounded numbers are displayed, the 

figures are based on unrounded data.

SOURCE: Organization for Economic Cooperation and Development (OECD), Program for International Student 

Assessment (PISA), 2015.
Page 9 of 23

STEM-Specific Career 

Expectations

Students anticipating STEM 

careers scored 544 points on 

the science literacy assessment 

on average, outperforming their 

peers anticipating nonscience 

careers by an average of 56 score 

points (figure 5, table A-3). The 

average score of nonscience 

career students corresponds to 

the 47th percentile of the U.S. 

distribution of scores; the average 

score of STEM career students 

corresponds to the 67th percentile. 

The science score gap between 

nonscience and STEM career 

students was significant within every displayed demographic 

group examined in this report. The 

score gap ranged from 33 points 

for Black students to 73 points for 

first-generation students.

Although there was no measurable 

science achievement gap between 

female students expecting either a 

health or STEM career and female 

students expecting a nonscience 

career (figure 3), different results 

were obtained when the gap was 

analyzed separately by health and 

STEM career expectations. While 

the average science achievement 

scores of females expecting a health 

career (489) and a nonscience 

career (491) were not measurably 

different (figure 4), the average 

science score of females expecting 

a STEM career (549) was higher 

than the score of females expecting 

a nonscience career (figure 5). 

However, since the percentage of 

female students expecting a health 

career (37 percent) was higher 

than the percentage expecting 

a STEM career (7 percent), the 

overall comparison of achievement 

scores—that is, the score gap 

between those female students 

expecting either a health or a 

STEM career and those expecting 

a nonscience career—follows the 

same pattern as that between 

female students expecting a 

health career and female students 

expecting a nonscience career.

FIGURE 5. Average science literacy scores and score gaps 

between U.S. 15-year-old students with expectations of STEM 

careers and those with expectations of nonscience careers, by 

demographic characteristics: 2015

 

* p < .05 Average science score of STEM career students is significantly different from that of nonscience career 

students.

NOTE: Scores are reported on a scale from 0 to 1,000. STEM careers were classified according to the OECD 

(2016) definition and refer to those jobs in STEM fields that require further science-related studies beyond 

compulsory education. PISA defines “second-generation” as students born in the United States but whose 

parent(s) were born in another country, and “first-generation” as those students born outside the United States 

and whose parents were also born in another country. Although data for “Asian” students, students of “Two 

or more races,” and “Other race” students are not shown because reporting standards were not met, they are 

included in “All students.” “Other race” includes both American Indian/Alaska Native and Native Hawaiian/

Other Pacific Islander students. Black includes African American, and Hispanic includes Latino. Race categories 

exclude persons of Hispanic ethnicity. The PISA index of economic, social and cultural status (ESCS) was created 

using student reports on parental occupation, the highest level of parental education, and an index of home 

possessions related to family wealth, home educational resources and possessions related to “classical” culture 

in the family home. The home possessions relating to “classical” culture in the family home included possessions 

such as works of classical literature, books of poetry, and works of art (e.g., paintings). Although rounded 

numbers are displayed, the figures are based on unrounded data.

SOURCE: Organization for Economic Cooperation and Development (OECD), Program for International Student 

Assessment (PISA), 2015. 
Page 10 of 23

STUDY QUESTION 3: In 2015, among only those students expecting 

health or STEM careers, were there science achievement 

differences by student demographic characteristics?

Health or STEM Career 

Expectations

Among 15-year-old students 

anticipating careers in health or 

STEM fields, achievement gaps 

were evident by gender, race/

ethnicity, immigration status, 

and ESCS (figure 6, table A-2). 

Although there was no measurable 

difference between the science 

scores of White and Asian 

students expecting health or STEM 

careers, achievement gaps were 

evident by all other displayed 

demographic categories. For 

instance, native-born students, 

who scored 526 points on average, 

outperformed their secondgeneration and first-generation 

counterparts by an average of 

28 and 50 points, respectively. 

These scores correspond to the 61st 

percentile (native-born students), 

51st percentile (second-generation 

students), and 42nd percentile 

(first-generation students) of the 

U.S. distribution of scores. By 

ESCS, average scores of students 

expecting health or STEM careers 

ranged from 469 points (low-ESCS 

students) to 561 points (high-ESCS 

students), a gap of 92 points.

FIGURE 6. Average science literacy scores and score gaps of 

U.S. 15-year-old students with expectations of health or STEM 

careers, by demographic characteristics: 2015

 

* p < .05 Science score gap is statistically significant.

NOTE: Scores are reported on a scale from 0 to 1,000. Health and STEM careers were classified according to the 

OECD (2016) definition and refer to those jobs that require further science-related studies beyond compulsory 

education. PISA defines “second-generation” as students born in the United States but whose parent(s) were 

born in another country, and “first-generation” as those students born outside the United States and whose 

parents were also born in another country. Data for “Other race” students are not shown because reporting 

standards were not met. “Other race” includes both American Indian/Alaska Native and Native Hawaiian/

Other Pacific Islander students. Black includes African American, and Hispanic includes Latino. Race categories 

exclude persons of Hispanic ethnicity. The PISA index of economic, social and cultural status (ESCS) was created 

using student reports on parental occupation, the highest level of parental education, and an index of home 

possessions related to family wealth, home educational resources and possessions related to “classical” culture 

in the family home. The home possessions relating to “classical” culture in the family home included possessions 

such as works of classical literature, books of poetry, and works of art (e.g., paintings). Although rounded 

numbers are displayed, the figures are based on unrounded data.

SOURCE: Organization for Economic Cooperation and Development (OECD), Program for International Student 

Assessment (PISA), 2015.
Page 11 of 23

Health-Specific Career 

Expectations

Analyzing only students 

expecting health careers, science 

achievement gaps were seen by all 

displayed demographic categories, 

although there was no measurable 

difference between the average 

scores of two sets of students 

(native-born versus secondgeneration students and White 

students versus students of Two or 

more races) (figure 7, table A-2). 

For example, male students 

outperformed female students 

by an average of 41 points, 

with the average score of  male 

students (529 points) and the 

average score of female students 

(489 points) corresponding to 

the 62nd and 47th percentiles of 

the U.S. distribution of scores, 

respectively. Native-born students 

expecting a health career scored 

58 points higher than their firstgeneration peers. The average 

achievement difference between 

low-ESCS students (449 points) 

and high-ESCS students 544 points)

expecting health careers was

95 points. These scores correspond

to the 33th percentile and

70th percentile, respectively, of 

the U.S. distribution of scores. 

FIGURE 7. Average science literacy scores and score gaps of 

U.S. 15-year-old students with expectations of health careers, 

by demographic characteristics: 2015

 

* p < .05 Science score gap is statistically significant.

NOTE: Scores are reported on a scale from 0 to 1,000. Health careers were classified according to the OECD 

(2016) definition and refer to those jobs in health fields that require further science-related studies beyond 

compulsory education. PISA defines “second-generation” as students born in the United States but whose 

parent(s) were born in another country, and “first-generation” as those students born outside the United States 

and whose parents were also born in another country. Data for “Asian” and “Other race” students are not shown 

because reporting standards were not met. “Other race” includes both American Indian/Alaska Native and Native 

Hawaiian/Other Pacific Islander students. Black includes African American, and Hispanic includes Latino. Race 

categories exclude persons of Hispanic ethnicity. The PISA index of economic, social and cultural status (ESCS) 

was created using student reports on parental occupation, the highest level of parental education, and an index 

of home possessions related to family wealth, home educational resources, and possessions related to “classical” 

culture in the family home. The home possessions relating to “classical” culture in the family home included 

possessions such as works of classical literature, books of poetry, and works of art (e.g., paintings). Although 

rounded numbers are displayed, the figures are based on unrounded data.

SOURCE: Organization for Economic Cooperation and Development (OECD), Program for International Student 

Assessment (PISA), 2015.
Page 12 of 23

STEM-Specific Career 

Expectations

Among students expecting STEM 

careers, there were achievement 

gaps by almost every displayed 

demographic category, although 

no measurable difference was 

observed in the average science 

scores of male and female students 

expecting STEM careers (figure 

8, table A-3). On average, White 

students scored 575 points, 

which was 113 points higher than 

Black students’ average score of 

462 points and 57 points higher 

than Hispanic students’ average 

score of 518 points. These average 

scores correspond to the 77th 

percentile (White students), 58th 

percentile (Hispanic students), and 

37th percentile (Black students) 

of the U.S. distribution of scores.

While lower percentages 

of females expected STEM 

careers than health careers 

(7 vs. 37 percent), those 

expecting STEM careers scored 

higher, on average, in science 

achievement. Specifically, the 

average score for a female 

student expecting a STEM career 

was 549 points, corresponding 

to the 69th percentile, while 

the average score for a female 

expecting a health career was 

489 points, corresponding to 

the 46th percentile.

FIGURE 8. Average science literacy scores and score gaps of 

U.S. 15-year-old students with expectations of STEM careers, 

by demographic characteristics: 2015

 

* p < .05 Science score gap is statistically significant.

NOTE: Scores are reported on a scale from 0 to 1,000. STEM careers were classified according to the OECD 

(2016) definition and refer to those jobs in STEM fields that require further science-related studies beyond 

compulsory education. PISA defines “second-generation” as students born in the United States but whose 

parent(s) were born in another country, and “first-generation” as those students born outside the United States 

and whose parents were also born in another country. Data for “Asian” students, students of “Two or more 

races,” and “Other race” students are not shown because reporting standards were not met. “Other race” 

includes both American Indian/Alaska Native and Native Hawaiian/Other Pacific Islander students. Black includes 

African American, and Hispanic includes Latino. Race categories exclude persons of Hispanic ethnicity. The PISA 

index of economic, social and cultural status (ESCS) was created using student reports on parental occupation, 

the highest level of parental education, and an index of home possessions related to family wealth, home 

educational resources and possessions related to “classical” culture in the family home. The home possessions 

relating to “classical” culture in the family home included possessions such as works of classical literature, books 

of poetry, and works of art (e.g., paintings). Although rounded numbers are displayed, the figures are based on 

unrounded data.

SOURCE: Organization for Economic Cooperation and Development (OECD), Program for International Student 

Assessment (PISA), 2015.

FIND OUT MORE

For questions about content, to download this Statistics in Brief, or to view this report online, go to 

http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2020034.

To access and explore PISA data, visit the PISA International Data Explorer at http://nces.ed.gov/

surveys/international/ide/.
Page 13 of 23

Methodology and 

Technical Notes

The estimates provided in this 

Statistics in Brief come from 

the 2015 administration of 

the Program for International 

Student Assessment (PISA). PISA 

is a cross-national comparative 

study that measures 15-year-old 

students’ reading, mathematics, 

and science literacy, and, in 2015, 

collaborative problem solving and 

financial literacy. It is coordinated 

by the Organization for Economic 

Cooperation and Development 

(OECD), with governmental 

sponsors in each participating 

education system. In the United 

States, PISA is conducted by the 

National Center for Education 

Statistics (NCES) within the U.S. 

Department of Education. 

To ensure comparability of the 

data across participating countries 

and education systems, the OECD 

provided detailed international 

requirements on the various 

aspects of data collection and 

implemented quality control 

procedures. Participating 

education systems were obliged to 

follow these requirements. These 

requirements—regarding the target 

population, sampling design, 

sample size, exclusions, and 

defining participation rates—are 

described briefly below. For more 

detailed information, see OECD 

(2017) and Kastberg, Chan, and 

Murray (2016).

Target Population and 

Exclusions

This Statistics in Brief only used 

data from the science literacy 

assessment. PISA’s international 

desired population is 15-yearolds attending both publicly 

and privately controlled schools 

in grade 7 and higher. More 

specifically, the technical 

standards required that students 

in the sample be 15 years and 

3 months old to 16 years and 

2 months old at the beginning of 

the testing period.

In order to keep PISA as inclusive 

as possible and to keep the 

exclusion rate down, the United 

States used the UH (Une Heure) 

instrument, designed for students 

with special education needs. Still, 

the guidelines allowed schools to 

be excluded for several approved 

reasons (for example, if they were 

in remote regions, very small, or 

provided only special education) 

and allowed students to be 

excluded in certain circumstances, 

including:

• Students with functional 

disabilities: Students with a 

moderate to severe permanent 

physical disability such that 

they cannot perform in the 

PISA testing environment.

• Students with intellectual 

disabilities: Students who 

have a mental or emotional 

disability and who have been 

found through testing to be 

cognitively delayed or who are 

considered in the professional 

opinion of qualified staff to be 

cognitively delayed such that 

they cannot perform in the 

PISA testing environment.

• Students with insufficient 

language experience:

Students who meet the three 

criteria of not being native 

speakers in the assessment 

language, having limited 

proficiency in the assessment 

language, and having less than 

1 year of instruction in the 

assessment language.

Overall estimated exclusions 

(including both school and 

student exclusions) were to be 

under 5 percent of the PISA target 

population. 

Percentiles

This brief contextualizes 

achievement differences by 

associating a specific score 

value with a percentile cut point 

within the overall distribution 

of U.S. scores. First, the analysis 

calculates the science literacy 

cut point scores associated with 

every whole-number percentile—

from the 1st percentile through 

the 99th—for all U.S. 15-year-olds. 

Then, a score estimate highlighted 

in this brief is associated with 

the percentile that most closely 

corresponds to this score; for 

instance, the average score of 

female students expecting health 

careers (488.7) falls at the 47th 

percentile (487.5) along the 

distribution. It should be noted 

that all score estimates and cut 

points are associated with an 

error margin, or standard error, 

since PISA is a sample-based 

study. Hence, t tests show that the 

average score of female health 

career students is not measurably 

different from the cut point scores 

associated with the 44th, 45th, 

46th, 47th, 48th, 49th, 50th, and 

51st percentiles. Naming a single 

percentile instead of a range of 

percentiles was deemed preferable 

because sample-based studies, in 

which population estimates are 

accompanied by standard errors, 

consistently defer to referencing 

single estimates, not a range of 

scores. 

Sampling Design and 

Sample Sizes

Although it is not possible to 

assess every single 15-year-old 

student, samples can provide 

representative values for 

education systems. As such, a 

representative sample of students 

was selected from a representative 

sample of schools (i.e., a two-stage 

stratified systematic sample). The 

sampling probabilities for cases 

in the sample are proportional to 

the estimated number of 15-yearold students in the school based 

on grade enrollments. The first 

stage refers to a sample of schools, 

while the second stage refers to a 

sample of students within schools. 

The PISA international contractors 
Page 14 of 23

(hereafter referred to as “the PISA 

consortium”), who are responsible 

for the design and implementation 

of PISA internationally, drew 

the sample of schools for each 

participating country and 

education system.

A minimum of 4,500 students 

from a minimum of 150 schools 

was required in each participating 

country and education system. 

Following the PISA consortium 

guidelines, replacement schools 

were identified at the same time 

the PISA sample was selected 

by assigning the two schools 

neighboring the sampled school 

in the frame as replacements. The 

international guidelines specified 

that within schools, a sample of up 

to 35 students was to be selected 

in an equal probability sample 

unless fewer than 35 students 

age 15 were available (in which 

case all 15-year-old students were 

selected). The PISA 2015 U.S. 

school sample consisted of 240 

schools. This number represents 

an increase from the international 

minimum requirement of 150 

and was implemented to offset 

anticipated school nonresponse 

and reduce design effects. The U.S. 

student sample allowed up to 52 

students per school to increase the 

accuracy and validity of the data.

Participation Rates

In order to minimize the potential 

for response biases, the OECD 

developed participation or 

response rate standards that 

apply to all participating countries 

and education systems and 

govern both whether or not a 

participating education system’s 

data are included in the PISA 

international dataset as well as 

the way in which national statistics 

are presented in the international 

reports. 

One hundred forty-two 

participating original schools 

and 35 replacement schools 

participated in the U.S. 

administration of PISA. This 

resulted in 177 participating 

schools and an overall weighted 

school response rate of 83 percent. 

The overall weighted student 

response rate was 90 percent and 

the U.S. overall student exclusion 

rate was 3 percent.

Schools were selected with 

probability proportionate to the 

school’s estimated enrollment of 

15-year-olds. Any school containing 

at least one of grades 7 through 

12 was included in the school 

sampling frame. Participating 

schools provided a list of 15-yearold students (typically in August 

or September 2015) from which 

the sample was drawn using 

sampling software provided by 

the international contractor.

Nonresponse Bias Analysis

In addition to the international 

response rate standards described 

above, the U.S. sample had to 

meet the statistical standards 

of the National Center for 

Education Statistics (NCES) of the 

U.S. Department of Education. 

For an assessment like PISA, 

NCES requires that a nonresponse 

bias analysis be conducted when 

the response rate for schools or 

students falls below 85 percent.

Of the 240 original sampled 

schools in the U.S. national 

sample, 213 were eligible 

(18 schools did not have any 

15-year-olds enrolled, 6 had closed, 

and 3 were otherwise ineligible), 

and 142 agreed to participate. 

The weighted school response 

rate before replacement was 

67 percent, requiring the United 

States to conduct a nonresponse 

bias analysis, which was used by 

the PISA consortium and the OECD 

to evaluate the quality of the final 

U.S. sample.

A nonresponse bias analysis 

was conducted in the United 

States at the school level in 

PISA 2015 to address potential 

problems in the data owing 

to school nonresponse. The 

investigation provides evidence 

that there is little potential for 

nonresponse bias in the PISAparticipating sample based on 

the characteristics studied. It also 

suggests that the use of substitute 

schools substantially reduced 

the potential for bias. Moreover, 

after the application of school 

nonresponse adjustments, there is 

no evidence of resulting potential 

bias in the final sample. For more 

details about the nonresponse bias 

analysis, please see https://nces.

ed.gov/surveys/pisa/pisa2015/

pisa2015highlights_8k.asp.

In this brief’s analysis, 96 to 100 

percent of the data were valid 

(i.e., not missing) for the student 

demographic variables of gender, 

race/ethnicity, immigration status, 

and ESCS in 2015. For the student 

health, STEM, and nonscience 

career variables, 95 percent of the 

data were valid. 

Science Literacy 

Assessment Development

The 2015 assessment science 

instruments were developed 

by international experts and 

PISA international consortium 

test developers and included 

items submitted by participating 

education systems. The science 

items, which included both trend 

items and new items developed 

for 2015, were reviewed by 

representatives of each country 

for possible bias and relevance 

to PISA’s goals and by PISA 

subject-matter expert groups. 

To further examine potential 

biases and design issues in the 

PISA assessment, all participating 

education systems field-tested 

the assessment items in spring 

2014. After the field trial, items 

that did not meet the established 

measurement criteria or were 

otherwise found to include 

intrinsic biases were dropped 

from the main assessment. 
Page 15 of 23

Most education systems 

participating in PISA 2015, 

including the United States, used 

the computer-based version of 

the assessment, although a paperbased version was also available. 

Approximately 65 percent of 

the science items were multiple 

choice and 35 percent were open 

response. The open-response 

items were graded by trained 

scorers following international 

coding guidelines. After the 

cognitive assessment, students 

also completed a questionnaire 

designed to provide information 

about their backgrounds, attitudes, 

and experiences in school. For 

more information about the 

PISA 2015 assessment design and 

questionnaires, see https://nces.

ed.gov/surveys/pisa/pisa2015/

index.asp.

Weighting

The use of sampling weights 

is necessary for computing 

statistically sound, nationally 

representative estimates. Survey 

weights adjust for the probabilities 

of selection for individual schools 

and students, for school or student 

nonresponse, and for errors in 

estimating the size of the school or 

the number of 15-year-olds in the 

school at the time of sampling. 

The internationally defined 

weighting specifications for PISA 

2015 included base weights and 

adjustments for nonresponse 

(Kastberg et al. 2017). The school 

base weight was defined as 

the reciprocal of the school’s 

probability of selection. (For 

substitute schools, the school base 

weight was set equal to the base 

weight of the original school it 

replaced.) The student base weight 

was given as the reciprocal of the 

probability of selection for each 

selected student from within a 

school.

The product of these base weights 

was then adjusted for school and 

student nonresponse. The school 

nonresponse adjustment was done 

individually for each education 

system by cross-classifying the 

explicit and implicit stratification 

variables defined as part of the 

sample design. In the case of the 

United States, two variables were 

used for stratification: school 

control (public/private) and census 

region. The student nonresponse 

adjustment was done within 

cells based first on their school 

nonresponse cell and their explicit 

stratum; within that, grade and 

gender were used when possible. 

Trimming factors at the school and 

student levels were used to reduce 

the size of large weights, since 

large weights can substantially 

increase sampling variance. All 

PISA analyses were conducted 

using these adjusted sampling 

weights. For more information on 

the nonresponse adjustments, see 

the OECD’s PISA 2015 Technical 

Report, chapter 8 (OECD 2017).

Scaling

For PISA 2015, each test form 

had a different subset of items. 

Because each student completed 

only a subset of all possible 

items, classical test scores, such 

as the percentage correct, are 

not accurate measures of student 

performance. Instead, scaling 

techniques were used to establish 

a common scale for all students. 

Item response theory (IRT) was 

used to estimate average scores for 

science, reading, and mathematics 

literacy for each education 

system, as well as for three science 

process and three science content 

subscales (Kastberg et al. 2017). 

IRT identifies patterns of response 

and uses statistical models 

to predict the probability of 

answering an item correctly 

as a function of the student’s 

proficiency in answering other 

questions. With this method, 

the performance of a sample 

of students in a subject area or 

subarea can be summarized 

on a simple scale or series of 

scales, even when students are 

administered different items. 

To keep student burden to 

a minimum, PISA administered 

a limited number of assessment 

items to each student—too few 

to produce accurate contentrelated scale scores for each 

student. To accommodate this 

situation, during the scaling 

process, plausible values were 

estimated to characterize students 

participating in the assessment, 

given their background 

characteristics. Plausible values 

are imputed values and not test 

scores for individuals in the usual 

sense. They represent what the 

performance of an individual on 

the entire assessment might have 

been, had it been observed. 

Ten plausible values were 

estimated for each student 

for each scale. These values 

represented the distribution of 

potential scores for all students 

in the population with similar 

characteristics and identical 

patterns of item response. 

Statistics describing performance 

on the PISA science, reading, and 

mathematics scales are based 

on plausible values. In PISA, the 

science, mathematics and reading 

literacy scales are from 0 to 1,000. 

For more information on PISA 

scaling and plausible values, see 

the OECD’s PISA 2015 Technical 

Report, chapter 9 (OECD 2017).

Sampling and 

Nonsampling Error and 

Variance Estimation

Two broad categories of error 

occur in estimates generated 

from surveys: nonsampling errors 

and sampling errors. In addition, 

variance estimation of PISA 

assessment scores needs to take 

into account the measurement 

error associated with the use of 

plausible values (Kastberg et al. 

2017).

Nonsampling error is a term 

used to describe variations 

in the estimates that may be 

caused by population coverage 
Page 16 of 23

limitations, nonresponse bias, 

and measurement error, as well as 

data collection, processing, and 

reporting procedures. The sources 

of nonsampling errors are typically 

problems such as unit and item 

nonresponse, the differences in 

respondents’ interpretations of the 

meaning of survey questions, and 

mistakes in data preparation. 

Sampling errors arise when a 

sample of the population, rather 

than the whole population, is 

used to estimate some statistic. 

Different samples from the same 

population would likely produce 

somewhat different estimates 

of the statistic in question. This 

means that there is a degree 

of uncertainty associated with 

statistics estimated from a sample. 

This uncertainty is referred to as 

sampling variance and is usually 

expressed as the standard error of 

a statistic estimated from sample 

data. Standard errors can be used 

as a measure of the precision 

expected from a particular sample. 

The approach used for calculating 

standard errors in PISA is the Fay 

method of balanced repeated 

replication (BRR) (Judkins 1990). 

This method of producing standard 

errors uses information about the 

sample design to produce more 

accurate standard errors than 

would be produced using simple 

random sample assumptions.

For analysis purposes, PISA 

datasets include sets of 10 plausible 

values for each of the PISA 2015 

scales. Thus, analysis of the PISA 

scales should be undertaken 

10 times, once for each plausible 

value. Results are then averaged, 

and significance tests must be 

adjusted for variation between 

the 10 sets of results. A special 

provision also needs to be made 

in the estimation of the standard 

errors and is best done using 

the International Database (IDB) 

Analyzer, which was developed for 

this purpose. The IDB Analyzer, 

available at www.iea.nl/data, 

can be used to combine and 

analyze data from PISA. It is a 

downloadable tool that creates 

SPSS or SAS syntax for combining 

files from across different countries 

and levels (student, teacher, 

school, etc.) and performing 

analysis that takes into account 

information from the sampling 

design in the computation of 

sampling variance and handles 

the plausible values. For more 

information on variance estimation 

of PISA data, see the OECD’s PISA 

2015 Technical Report, chapters 

8 and 9 (OECD 2017).

Interpreting Statistical 

Significance

Comparisons of average scores and 

percentile cut-point scores were 

tested with Student’s t statistic 

using the IDB Analyzer, which 

takes into account information 

from the sampling design in the 

computation of sampling variance 

and handles the plausible values 

to enable the user to compute 

descriptive statistics and conduct 

statistical hypothesis testing 

among groups in the population. 

Differences between estimates 

were tested against the probability 

of a Type I error8

8

 A Type I error occurs when one concludes that a difference observed in a sample reflects a true difference in the population from which the sample was drawn, when no such difference is 

present.

 or significance 

level. The statistical significance of 

each comparison was determined 

by calculating the Student’s t value 

for the difference between each 

pair of scores and comparing the 

t value with published tables of 

significance levels for two-tailed 

hypothesis testing. 

To test differences between 

independent estimates, Student’s 

t values were computed using the 

following formula:

t =

(E1

 – E2 )

√se1

 + se2

2 2

where E1 and E2 are the estimates 

being compared (e.g., the means 

of sample members of two 

groups) and se1 and se2 are their 

corresponding standard errors.

Because of the sampling design (in 

which schools and students within 

schools are randomly sampled), 

data within the education system 

from mutually exclusive sets of 

students (for example, males and 

females) are not independent. For 

example, to determine whether 

the performance of females differs 

from that of males would require 

estimating the correlation between 

females’ and males’ scores. A BRR 

procedure, mentioned above, 

was used to estimate the standard 

errors of differences between 

nonindependent samples within 

the United States. Use of the BRR 

procedure implicitly accounts 

for the correlation between 

groups when calculating 

the standard errors. When 

comparing differences between 

nonindependent groups within the 

education system (e.g., average 

science literacy scales between 

males and females), the following 

formula was used:

t =

(Egrp1

 – E

grp2 )

se(grp1 – grp2)

where E

grp1

 and E

grp2

 are the 

nonindependent group estimates 

being compared and se(grp1-grp2)

is the standard error of the 

difference calculated using BRR 

to account for the correlation 

between the estimates for the 

two nonindependent groups. For 

more details on the computation 

of standard errors of the difference 

between nonindependent groups, 

BRR, and international data 

analysis products, please see the 

PISA 2015 Technical Report

(OECD 2017). 
Page 17 of 23

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1143–44. 
Page 18 of 23

Appendix A: Classification of Occupations

The following open-ended question appeared in the PISA 2015 student questionnaire: “What kind of job do 

you expect to have when you are about 30 years old?” Students’ responses were coded to 4-digit International 

Standard Classification of Occupations (ISCO) codes. The list of the four job groups below appears in the PISA 

2015 Results (Volume I): Excellence and Equity in Education (OECD 2016).

The classification used in this report includes four groups of jobs:

1. Science and engineering professionals: All science and engineering professionals 

(submajor group 21), except product and garment designers (2163) and graphic and 

multimedia designers (2166).

2. Health professionals: All health professionals in submajor group 22 (e.g., doctors, 

nurses, veterinarians), with the exception of traditional and complementary medicine 

professionals (minor group 223).

3. ICT professionals: All information and communications technology professionals 

(submajor group 25). 

4. Science technicians and associate professionals, including:

• physical and engineering science technicians (minor group 311)

• life science technicians and related associate professionals (minor group 314)

• air traffic safety electronic technicians (3155)

• medical and pharmaceutical technicians (minor group 321), except medical and 

dental prosthetic technicians (3214)

• telecommunications engineering technicians (3522).

SOURCE: OECD. (2016). PISA 2015 Results (Volume I): Excellence and Equity in Education. OECD Publishing: Paris. 

Thus, in this brief, students expecting STEM careers were those who reported expecting to be a science and 

engineering professional, an ICT professional, or a nonmedical science technician or associate professional (i.e., 

all science technician/associate professionals listed above except for medical and pharmaceutical technicians); 

students expecting health careers were those who reported expecting to be a health professional or medical or 

pharmaceutical technician. 

As the Pew Research Center (2018) discusses, “there is no standard definition of STEM workers.” While some 

research categorizes health professionals and technicians as STEM workers, this brief did not. Since about half

of the science, technology, engineering, mathematics, and health workforce is composed of only health care 

practitioners and technicians (Funk and Parker 2018), this analysis was designed to distinguish between health 

careers and nonhealth STEM careers. 

The complete list of 2015 health and STEM careers used in this brief, and classified according to the 

aforementioned criteria, appears on the following pages.
Page 19 of 23

STEM, 2015:

ISCO-08 

Code ISCO-08 Career Label

2100 Science and engineering professionals

2110 Physical and earth science professionals

2111 Physicists and astronomers

2112 Meteorologists

2113 Chemists

2114 Geologists and geophysicists

2120 Mathematicians, actuaries and 

statisticians

2130 Life science professionals

2131 Biologists, botanists, zoologists and 

related professionals

2132 Farming, forestry and fisheries advisers

2133 Environmental protection professionals

2140 Engineering professionals (excluding 

electrotechnology)

2141 Industrial and production engineers

2142 Civil engineers

2143 Environmental engineers

2144 Mechanical engineers

2145 Chemical engineers

2146 Mining engineers, metallurgists and 

related professionals

2149 Engineering professionals not 

elsewhere classified

2150 Electrotechnology engineers

2151 Electrical engineers

2152 Electronics engineers

2153 Telecommunications engineers

2160 Architects, planners, surveyors and 

designers

2161 Building architects

2164 Town and traffic planners

2165 Cartographers and surveyors

2162 Landscape architects

2500 Information and communications 

technology professionals

2510 Software and applications developers 

and analysts

2511 Systems analysts

2512 Software developers

2513 Web and multimedia developers

ISCO-08 

Code ISCO-08 Career Label

2514 Applications programmers

2519 Software and applications developers 

and analysts not elsewhere classified

2520 Database and network professionals

2521 Database designers and administrators

2522 Systems administrators

2523 Computer network professionals

2529 Database and network professionals not 

elsewhere classified

3110 Physical and engineering science 

technicians

3111 Chemical and physical science 

technicians

3112 Civil engineering technicians

3113 Electrical engineering technicians

3114 Electronics engineering technicians

3115 Mechanical engineering technicians

3116 Chemical engineering technicians

3117 Mining and metallurgical technicians

3118 Draughtspersons

3119 Physical and engineering science 

technicians not elsewhere classified

3140 Life science technicians and related 

associate professionals

3141 Life science technicians (excluding 

medical)

3142 Agricultural technicians

3143 Forestry technicians

3150 Ship and aircraft controllers and 

technicians

3151 Ships engineers

3152 Ships deck officers and pilots

3153 Aircraft pilots and related associate 

professionals

3154 Air traffic controllers

3155 Air traffic safety electronics technicians

3522 Telecommunications engineering 

technicians

NOTE: The table above was generated from the PISA 2015 international codebook. A 

career appearing in this table means that there was at least one student (out of all PISAparticipating education systems) who had that career response; it does not mean that U.S.

students expect to hold all of the careers above.

SOURCE: OECD. (2016). PISA 2015 Results (Volume I): Excellence and Equity in Education. 

OECD Publishing: Paris.
Page 20 of 23

Health, 2015:

ISCO-08 

Code ISCO-08 Career Label

2200 Health professionals

2210 Medical doctors

2211 Generalist medical practitioners

2212 Specialist medical practitioners

2220 Nursing and midwifery professionals

2221 Nursing professionals

2222 Midwifery professionals

2240 Paramedical practitioners

2250 Veterinarians

2260 Other health professionals

2261 Dentists

2262 Pharmacists

2263 Environmental and occupational health 

and hygiene professionals

2264 Physiotherapists

ISCO-08 

Code ISCO-08 Career Label

2265 Dieticians and nutritionists

2266 Audiologists and speech therapists

2267 Optometrists and ophthalmic opticians

2269 Health professionals not elsewhere 

classified

3210 Medical and pharmaceutical 

technicians

3211 Medical imaging and therapeutic 

equipment technicians

3212 Medical and pathology laboratory 

technicians

3213 Pharmaceutical technicians and 

assistants

NOTE: The table above was generated from the PISA 2015 international codebook. A 

career appearing in this table means that there was at least one student (out of all PISAparticipating education systems) who had that career response; it does not mean that U.S.

students expect to hold all of the careers above. 

SOURCE: OECD. (2016). PISA 2015 Results (Volume I): Excellence and Equity in Education. 

OECD Publishing: Paris.
Page 21 of 23

Appendix B: Tables

Table A-1. Distribution of U.S. students’ scores on the PISA science literacy assessment: 2015

Percentile Cut score s.e.

1 281.7 6.62

2 303.0 5.45

3 317.6 4.81

4 328.0 4.22

5 336.4 4.13

6 343.5 3.85

7 350.6 4.22

8 356.7 4.22

9 362.3 4.02

10 367.6 3.89

11 372.4 3.81

12 377.2 3.89

13 381.7 3.90

14 386.0 3.81

15 390.1 3.6

16 393.8 3.67

17 397.8 3.74

18 401.7 3.67

19 405.1 3.55

20 408.5 3.62

21 411.9 3.69

22 415.3 3.89

23 418.7 3.60

24 421.9 3.48

25 424.9 3.68

26 428.2 3.75

27 431.4 3.70

28 434.4 3.61

29 437.3 3.61

30 440.1 3.69

31 443.2 3.92

32 446.2 3.82

33 448.9 3.78

34 451.8 3.93

35 454.7 4.05

36 457.6 3.98

37 460.5 3.92

38 463.2 3.79

39 465.8 3.74

40 468.6 3.78

Percentile Cut score s.e.

41 471.2 3.81

42 474.0 3.81

43 476.7 3.74

44 479.3 3.69

45 481.9 3.74

46 484.7 3.87

47 487.5 3.86

48 490.1 3.63

49 492.6 3.72

50 495.2 3.82

51 498.1 3.94

52 500.8 3.85

53 503.4 3.85

54 506.0 3.87

55 508.7 3.80

56 511.3 3.92

57 514.3 4.01

58 517.0 3.85

59 519.7 3.76

60 522.5 3.72

61 525.1 3.81

62 528.0 3.85

63 530.9 3.83

64 533.8 3.95

65 536.9 3.92

66 539.8 3.78

67 542.6 3.71

68 545.5 3.82

69 548.6 3.80

70 551.5 3.87

71 554.8 3.91

72 557.7 3.80

73 560.8 3.97

74 564.2 3.92

75 567.5 3.89

76 570.7 3.75

77 573.9 3.74

78 577.4 3.85

79 580.8 3.75

80 584.1 3.76

Percentile Cut score s.e.

81 588.0 3.93

82 591.7 3.76

83 595.4 3.67

84 599.3 3.70

85 603.4 3.49

86 607.2 3.35

87 611.3 3.54

88 615.8 3.66

89 620.4 3.63

90 625.5 3.89

91 630.6 3.60

92 636.3 3.94

93 642.7 4.37

94 649.9 4.27

95 658.3 4.86

96 667.4 4.62

97 678.4 4.44

98 692.5 4.75

99 714.6 6.35

NOTE: This table shows the threshold (or cut) scores for 

the 1st through 99th percentiles. The percentile range is 

specific to the distribution of science literacy scores of 

U.S. students. Standard error is noted by s.e.

SOURCE: Organization for Economic Cooperation and 

Development (OECD), Program for International Student 

Assessment (PISA), 2015.
Page 22 of 23

Table A-2. Percentage of U.S. 15-year-olds expecting careers in health, STEM, and nonscience fields, 

by type of field and demographic characteristics: 2015

[Standard errors appear in parentheses]

Demographic 

characteristics

Nonscience 

careers

Health or STEM careers

Total Health careers STEM careers

All students 60.0 (0.83) 40.0 (0.83) 23.5 (0.70) 16.5 (0.61)

Gender

Male 64.9 (1.12) 35.1 (1.12) 9.5 (0.66) 25.7 (0.99)

Female 55.4 (1.11) 44.6 (1.11) 37.2 (1.15) 7.5 (0.63)

Race/ethnicity1

White, non-Hispanic 59.1 (1.20) 40.9 (0.79) 23.3 (1.14) 17.6 (0.79)

Black, non-Hispanic 62.2 (2.17) 37.8 (1.17) 25.8 (1.67) 12.0 (1.17)

Hispanic 61.5 (1.31) 38.5 (1.03) 22.6 (1.11) 15.9 (1.03)

Asian, non-Hispanic 49.8 (4.86) 50.2 (3.24) 26.9 (3.28) 23.3 (3.24)

Two or more races 61.0 (2.89) 39.0 (2.27) 21.9 (2.55) 17.1 (2.27)

Other race 64.1 (6.98) 35.9 (3.87) 23.5 (5.47) 12.4 (3.87)

Immigration status2

Native-born 61.0 (0.93) 39.0 (0.93) 22.9 (0.81) 16.1 (0.67)

Second-generation 55.8 (1.71) 44.2 (1.71) 25.5 (1.62) 18.7 (1.22)

First-generation 57.2 (2.53) 42.8 (2.53) 25.0 (2.00) 17.8 (1.91)

ESCS status3

Low ESCS 63.5 (1.22) 36.5 (1.22) 22.9 (1.22) 13.5 (1.04)

Mid-low ESCS 62.6 (1.40) 37.4 (1.40) 21.5 (1.04) 15.9 (1.18)

Mid-high ESCS 60.9 (1.54) 39.1 (1.54) 23.8 (1.20) 15.3 (1.00)

High ESCS 53.6 (1.39) 46.4 (1.39) 25.4 (1.24) 21.0 (1.07)

1

 “Other race” includes both American Indian/Alaska Native and Native Hawaiian/Other Pacific Islander students. Black includes African American, and Hispanic includes Latino. Race 

categories exclude persons of Hispanic ethnicity. 

2

 PISA defines “second-generation” as students born in the United States but whose parent(s) were born in another country, and “first-generation” as those students born outside the United 

States and whose parents were also born in another country.

3

 The PISA index of economic, social and cultural status (ESCS) was created using student reports on parental occupation, the highest level of parental education, and an index of home 

possessions related to family wealth, home educational resources and possessions related to “classical” culture in the family home. The home possessions relating to “classical” culture in 

the family home included possessions such as works of classical literature, books of poetry, and works of art (e.g., paintings).

NOTE: Detail may not sum to totals due to rounding. STEM and health careers were classified according to the OECD (2016) definition and refer to those jobs that require further sciencerelated studies beyond compulsory education. 

SOURCE: Organization for Economic Cooperation and Development (OECD), Program for International Student Assessment (PISA), 2015.
Page 23 of 23

Table A-3. Average science scores of U.S. 15-year-olds expecting careers in health, STEM, and 

nonscience fields, by type of field and demographic characteristics: 2015

[Standard errors appear in parentheses]

Demographic 

characteristics

Nonscience 

careers

Health or STEM careers

Total Health careers STEM careers

All students 487.9 (2.97) 516.2 (3.74) 496.8 (3.73) 543.8 (4.83)

Gender

Male 485.3 (3.27) 538.6 (4.62) 529.4 (5.69) 542.1 (5.68)

Female 490.9 (3.57) 498.9 (4.41) 488.7 (4.36) 549.5 (7.29)

Race/ethnicity1

White, non-Hispanic 522.8 (2.97) 547.7 (3.72) 527.3 (4.08) 574.7 (5.54)

Black, non-Hispanic 428.2 (4.71) 447.5 (7.33) 441.0 (6.61) 461.6 (12.56)

Hispanic 462.3 (4.47) 487.9 (7.10) 466.8 (6.64) 518.0 (9.15)

Asian, non-Hispanic 495.5 (15.66) 553.7 (11.94) ‡ † ‡ †

Two or more races 495.4 (9.01) 521.8 (7.55) 506.0 (10.51) ‡ †

Other race ‡ † ‡ † ‡ † ‡ †

Immigration status2

Native-born 497.1 (3.26) 526.3 (3.84) 505.5 (3.90) 555.9 (5.36)

Second-generation 471.2 (6.09) 498.5 (8.59) 490.4 (9.99) 509.5 (10.09)

First-generation 444.6 (6.90) 476.6 (10.87) 447.2 (13.19) 518.0 (14.95)

ESCS status3

Low ESCS 452.0 (4.13) 469.2 (5.53) 449.1 (6.83) 503.3 (7.51)

Mid-low ESCS 471.9 (4.07) 497.1 (5.34) 475.3 (6.27) 526.6 (7.32)

Mid-high ESCS 498.9 (3.77) 524.8 (5.72) 512.5 (5.87) 544.1 (8.24)

High ESCS 535.6 (4.56) 561.3 (4.23) 544.0 (4.91) 582.2 (6.27)

† Not applicable.

‡ Reporting standards not met due to sample size of less than 63 students.

1

 “Other race” includes both American Indian/Alaska Native and Native Hawaiian/Other Pacific Islander students. Black includes African American, and Hispanic includes Latino. Race 

categories exclude persons of Hispanic ethnicity. 

2

 PISA defines “second-generation” as students born in the United States but whose parent(s) were born in another country, and “first-generation” as those students born outside the United 

States and whose parents were also born in another country.

3

 The PISA index of economic, social and cultural status (ESCS) was created using student reports on parental occupation, the highest level of parental education, and an index of home 

possessions related to family wealth, home educational resources and possessions related to “classical” culture in the family home. The home possessions relating to “classical” culture in 

the family home included possessions such as works of classical literature, books of poetry, and works of art (e.g., paintings).

NOTE: STEM and health careers were classified according to the OECD (2016) definition and refer to those jobs that require further science-related studies beyond compulsory education. 

SOURCE: Organization for Economic Cooperation and Development (OECD), Program for International Student Assessment (PISA), 2015.