Computer and Internet 

Use by Students in 2003

Statistical Analysis Report 

September 2006

U.S. Department of Education 

Institute of Education Sciences 

NCES 2006–065 

Matthew DeBell 

Education Statistics Services Institute 

American Institutes for Research

Chris Chapman 

National Center for Education Statistics 
 

U.S. Department of Education

Margaret Spellings 

Secretary

Institute of Education Sciences 

Grover J. Whitehurst 

Director

National Center for Education Statistics

Mark S. Schneider 

Commissioner

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Suggested Citation 

DeBell, M., and Chapman, C. (2006). Computer and Internet Use by Students in 2003 (NCES 2006–

065). U.S. Department of Education. Washington, DC: National Center for Education Statistics. 

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iii

Highlights 

This report uses data from the Current Population Survey, October 2003 School 

Enrollment and Computer Use Supplement to examine the use of computers and the Internet by 

American children enrolled in nursery school and students in kindergarten through grade 12, age 

3 and older.1 The report examines the overall rate of use (that is, the percentage of individuals in 

the population who are users), the ways in which students2 use the technologies, where the use 

occurs (home, school, and other locations), and the relationships of these aspects of computer 

and Internet use to demographic and socioeconomic characteristics such as students’ age and 

race/ethnicity and their parents’ education and family income. All statistical comparisons in this 

report were tested for significance at the 95 percent confidence level (p<.05), and all reported 

differences are statistically significant and are five percentage points or larger, unless otherwise 

noted. 

This report confirms that patterns of computer and Internet use seen in previous research 

(DeBell and Chapman 2003) are observed in more recent data representing a different population 

than has been described previously. Key findings are as follows: 

• Most students use computers and a majority use the Internet (table 1). About 91 

percent (53 million persons) of children age 3 and over and in nursery school through 

grade 12 use computers,3

 and about 59 percent (35 million persons) use the Internet. 

 

1

 Current Population Survey interviews were conducted in about 56,000 households in October 2003 and collected 

information regarding 29,075 persons age 3 and older enrolled in nursery school or grades K through 12. One 

respondent per household was interviewed and that respondent provided information about the household and about 

individual household members, including information about computer and Internet use. Because a household’s 

respondent may not have full information regarding computer and Internet use by other members of the household, 

this method is a potential source of error in the data, though there is no way to quantify the possible error. The 

overall response rate on the survey was 86.9 percent. 

2

 Data on “nursery school” enrollment may not reflect enrollment in all kinds of early childhood programs and may 

include enrollment in programs that education experts would not call schools. For example, some day care 

programs that do not have a curriculum may be reported as nursery school, and some programs that have a 

curriculum may not be reported. Because the CPS questionnaire asks about enrollment in “school,” this report refers 

to the population enrolled in nursery school through the 12th grade as “students” for ease of presentation. It is 

correct to regard this population as children enrolled in programs reported as nursery school, kindergarten, or grades 

1 through 12. 

3

 Computer users are identified by three CPS questions that ask if the household member uses computers at home, at 

school, or at his or her main job. Because estimates of overall computer use do not include people who use 

computers only at locations other than home, work, and school (such as libraries or friends’ homes), the estimates 

may slightly understate the total number of people who use computers at any location. 
 

iv

• Use begins early (figure 1, table 1). About two-thirds of children in nursery school 

and 80 percent of kindergartners use computers, and 97 percent of students in grades 

9–12 do so. About 23 percent of children in nursery school use the Internet, and this 

number rises to 50 percent by grade 3 and to 79 percent in grades 9–12. 

• Public school students and private school students differ in their use of these 

technologies. Private school students are more likely than public school students to 

use computers at home (76 percent compared to 66 percent; table 3), but public 

school students are more likely to use computers and the Internet at school (table 6) 

and overall (table 1). 

• There is a “digital divide” (table 1). Computer and Internet use are divided along 

demographic and socioeconomic lines. Use of both technologies is higher among 

Whites than among Blacks and Hispanics.4 Students living with more highly 

educated parents are more likely to use these technologies than those living with less 

well educated parents, and those living in households with higher family incomes are 

more likely to use computers and the Internet than those living in lower income 

households. 

• Schools help bridge the digital divide. Many disadvantaged students use the 

Internet only at school (table 7). Among the group of students who access the 

Internet at only one location (20 percent of students), 60 percent of those from 

families in poverty and 63 percent of those whose parents have not earned at least a 

high school credential do so at school.5

 In comparison, 33 percent of those from 

families not in poverty and 24 percent of those with at least one parent who attended 

graduate school do so only at school. This illustrates the role of schools in bridging 

the digital divide (table 7). 

• Disability status, metropolitan status, and family/household type are associated 

with the digital divide. Consistent with the findings of previous research (U.S. 

 

4

 “White,” “Black,” “Asian,” “American Indian,” and “More than one race” refer to White non-Hispanic; Black nonHispanic; Asian or Pacific Islander, non-Hispanic; American Indian, Aleut, or Eskimo, non-Hispanic; and More than 

one race, non-Hispanic, respectively, and are used throughout this report for ease of presentation. Hispanics may be 

of any race. 

5

 Households with reported income in categories below the poverty threshold for their household size (as defined by 

the Census Bureau for 2003) were classified as poor, and households with income above the poverty threshold were 

classified as not poor. For details of the poverty definition, see Appendix A: Methodological and Technical Notes. 
 

v

Department of Commerce 2002; DeBell and Chapman 2003), students without a 

physical disability are more likely than their disabled peers to use computers and the 

Internet. When not controlling for other factors, students from two-parent households 

are more likely to use the Internet than those from single-parent households,6

 and 

students living outside of central cities are more likely to use the Internet than those 

living in central cities. However, when controlling for other factors such as family 

income and parental education, the association of household type with computer and 

Internet use is not statistically significant, and the association of Internet use with 

residence outside of metropolitan areas is not statistically significant (table 2). 

• There are no differences between the sexes in overall computer or Internet use 

rates. In contrast to the 1990s, when boys were more likely than girls to use 

computers and the Internet, overall computer and Internet use rates for boys and girls 

are now about the same (table 1). 

• There are large demographic and socioeconomic differences in the use of home 

computers (table 3). Whites and Asians are more likely to use computers at home 

than are Blacks, Hispanics, and American Indians. Thirty-seven percent of those 

from families with incomes below $20,000 use computers at home, compared to 88 

percent of those living in families with annual incomes over $75,000. The gap 

between students whose parents have the least and the most education is similar to the 

income gap; while 35 percent of those whose parents did not complete high school 

use computers at home, 88 percent of those living with at least one parent who has 

attended graduate school use a computer at home. At school, these gaps are 

narrower, as the use of computers at school is more equal overall. 

• Use of home computers is common for playing games, working on school 

assignments, and connecting to the Internet. A majority (56 percent) of students 

use home computers to play games (table 5). Forty-seven percent use computers to 

complete school assignments and 45 percent use computers to connect to the Internet. 

 

6

 The categories for family/household structure include “male-headed single-householder” and “female-headed 

single-householder.” “Single father” and “single mother” (or “single parent,” when referring to both) are used for 

ease of presentation. Some single-householders include nonrelatives or relatives other than the father or mother 

such as a grandfather or grandmother. 
 

vi

Sixty-two to 69 percent of students in grades 6 through 12 use home computers to 

complete school assignments, 54-64 percent to connect to the Internet, and 57-61 

percent to play games. 

• Differences in how computers are used often are larger than overall differences 

in rates of use. In the population of students enrolled in nursery school through 

grade 12, there is a 13-percentage-point gap in computer use between students from 

households where no parent has received a high school credential and those from 

households where at least one parent has some graduate school education (table 1). 

The difference between these groups’ use of computers for word processing is 34 

percentage points, and the difference for using computers to complete school 

assignments is 37 points (table 5). 

• Home and school are common locations for Internet access. About 45 percent of 

students access the Internet from home, and 43 percent access the Internet from 

school (table 6). 

• Considering all locations, use of the Internet is common for work on school 

assignments, e-mail, and games. About 46 percent of students use the Internet to 

complete school assignments, while 36 percent use the Internet for e-mail or instant 

messaging and 38 percent use it to play games (table 8A). 
 

vii

Acknowledgements 

 The authors wish to thank Susan Patrick, director of the U.S. Department of Education’s 

Office of Educational Technology, for helping to make this report possible. The authors would 

also like to thank the respondents for providing the data on which the report is based, the Census 

Bureau for collecting the data, and several reviewers for their helpful comments: Tim Magner of 

the Office of Educational Technology at the U.S. Department of Education; Bernie Greene, 

Valena Plisko, and Marilyn Seastrom of the U.S. Department of Education’s National Center for 

Education Statistics (NCES); Jerry West of Mathematica Policy Research, Inc. (formerly of 

NCES); and Rachel Dinkes, Deven Carlson, and Heather Freilich of the Education Statistics 

Services Institute of the American Institutes for Research (AIR). Sandy Eyster (AIR) provided 

essential feedback and programming assistance and Alexa Van Brunt (AIR) and Sarah Grady 

(MacroSys Research and Technology) provided research support. 
 

viii

Contents 

Highlights.......................................................................................................................................iii 

Acknowledgements....................................................................................................................... vii 

List of Tables ................................................................................................................................. ix 

List of Figures................................................................................................................................. x 

Introduction..................................................................................................................................... 1 

Use in 2003 and User Characteristics ............................................................................................. 4 

Home and School Computer Use.................................................................................................. 14 

Home Computer Activities ........................................................................................................... 20 

Locations of Internet Use.............................................................................................................. 24 

Internet Activities.......................................................................................................................... 30 

Conclusions................................................................................................................................... 37 

References..................................................................................................................................... 39 

Appendix A: Methodological and Technical Notes...................................................................... 45 

Appendix B: Supplemental Tables ............................................................................................... 58 
 

ix

List of Tables

Table Page 

 

1 Percentage of children in nursery school and students in grades K-12 who use 

computers and the Internet, by student and family/household characteristics: 

2003 

6 

2 Logistic regression analyses of student and family/household characteristics 

and computer and Internet use: 2003 

11 

3 Percentage of children in nursery school and students in grades K-12 using 

computers at home and at school, by student and family/household 

characteristics: 2003 

15 

4 Logistic regression analyses of student and family/household characteristics 

and computer use at home and at school: 2003 

19 

5 Percentage of children in nursery school and students in grades K-12 using 

home computers for specific activities, by student and family/household 

characteristics: 2003 

21 

6 Percentage of children in nursery school and students in grades K-12 who use 

the Internet at specific locations, by student and family/household 

characteristics: 2003 

25 

7 Percentage of single-location Internet-using children in nursery school and 

students in grades K-12 who use the Internet at specific locations: 2003 

27 

8A Percentage of children in nursery school and students in grades K-12 using the 

Internet for specific activities, by sex and grade level: 2003 

32 

8B Table 8B. Percentage of children in nursery school and students in grades K-12 

using the Internet for specific activities, by parent educational attainment and 

race/ethnicity: 2003 

33 

8C Percentage of children in nursery school and students in grades K-12 using the 

Internet for specific activities, by family income: 2003 

35 

B-1 Percentage of adults who use computers and the Internet, by adult and 

family/household characteristics: 2003 

58 

B-2 Percentage of children in nursery school and students in grades K-12 using 

computers and the Internet, by grade level: 2003 

59 

B-3 Percentage of home computer users among children in nursery school and 

students in grades K-12, using home computers for specific activities, by 

student and family/household characteristics: 2003 

60 

B-4 Percentage of children and adolescents age 5–17 who use computers 

and the Internet, by child and family/household characteristics: 2001, 2003 

62 
 

x

List of Figures 

 

Figure Page 

 

1 Percentage of children in nursery school and students in grades K-12 using 

computers or the Internet, by grade level: 2003 

7 

2 Percentage of children in nursery school and students in grades K-12 using 

computers at home and at school, by parent educational attainment: 2003 

16 

3 Percentage of children in nursery school and students in grades K-12 using 

computers at home and at school, by family income: 2003 

17 

4 Percentage of children in nursery school and students in grades K-12 using 

computers at home and at school, by race/ethnicity: 2003 

17 

5 Percentage of children in nursery school and students in grades K-12 who use 

the Internet at only one location, by location and poverty status: 2003 

30 
 

1

Introduction 

This report describes computer and Internet use by children enrolled in nursery school 

and students in kindergarten through grade 12 who are age 3 or older.1 The purpose of this 

report is to examine rates of use (that is, the percentage of individuals in the population who are 

users), how these technologies are used, where they are used, and how subgroups within the 

population may differ in their use of these technologies. The following research questions are 

answered: 

• What percentage of students uses computers and what percentage uses the Internet? 

• Does students’ use differ by grade level and by socioeconomic and demographic 

characteristics such as race/ethnicity, family income, and parents’ educational 

attainment? 

• What percentage of students use computers at home and at school? 

• For what activities do students use computers at home? 

• Where do students use the Internet? 

• How many students use the Internet in only one place, and how do single-location users 

differ from other users? 

• What do students do on the Internet? 

• How do students’ Internet activities vary by their demographic and socioeconomic 

characteristics? 

Data for this report come from the Current Population Survey (CPS), October 2003 

Computer and Internet Use Supplement. The CPS is a sample survey representative of the 

civilian noninstitutional population in the United States. The survey is conducted in 

approximately 56,000 households. In October 2003 information was collected regarding 29,075 

children enrolled in nursery school and students in kindergarten through 12th grade. This sample 

 

1

 Data on “nursery school” enrollment may not reflect enrollment in all kinds of early childhood programs and may 

include enrollment in programs that education experts would not call schools. For example, some day care 

programs that do not have a curriculum may be reported as nursery school, and some programs that have a 

curriculum may not be reported. Because the CPS questionnaire asks about enrollment in “school,” this report refers 

to the population enrolled in nursery school through the 12th grade as “students” for ease of presentation. It is 

correct to regard this population as children enrolled in programs reported as nursery school, kindergarten, or grades 

1 through 12. 
 

2

represents 58.3 million children. A member of each household who is at least 15 years old 

provides information about household members. As a result of this data collection method, data 

regarding computer and Internet use by students were not collected directly from students in 

most cases, but from another member of the student’s household. It is possible that this proxy 

reporting method introduces some error to the data, but it is not possible to quantify the potential 

error. The overall unweighted response rate on the survey was 86.9 percent. For further detail 

about CPS survey methods, see Appendix A: Methodological and Technical Notes. 

This report follows an earlier NCES report on a similar topic, Computer and Internet Use 

by Children and Adolescents in 2001 (DeBell and Chapman 2003).2

 The earlier report differs 

from the present report by examining children and adolescents age 5-17, whether or not they 

were enrolled in school, while the present report examines children who are age 3 or older and 

are reported as enrolled in nursery school through grade 12. Many of the same patterns and 

differences noted in the earlier report are found in the present report. However, it is important to 

note the difference in the populations examined. There is substantial overlap between the two 

populations, but they are not the same, so comparisons between the estimates in the two reports 

must be made with caution.3 To permit direct comparisons of computer and Internet use in 2003 

and 2001, appendix table B-4 provides estimates of computer and Internet use by children and 

adolescents age 5-17 in 2003 and in 2001, regardless of their enrollment status. 

The remainder of this introductory section presents background information on the 

significance of computer and Internet use. The next section provides an overview of current 

usage rates and user characteristics. Subsequent sections then examine rates of computer use at 

home and at school, home computer activities, Internet access locations, and Internet activities. 

A methodological appendix provides details about the data and statistical methods. All 

 

2

 Portions of this report are reprinted from the earlier version (DeBell and Chapman 2003), with updates, or from an 

Issue Brief presenting summary statistics for 2003 (DeBell 2005). 3

 The population examined here (age 3 and older, enrolled in nursery school through grade 12) includes 55 percent 

of children age 3 to 4, 97 percent of children age 5 through 17, 28 percent of 18-year-olds, 7 percent of 19-year-olds, 

2 percent of 20-year-olds, and very few older adults still enrolled in primary or secondary school (the percentage 

rounds to zero). The present report focuses on the student population because it was collected during the school 

year and provides reliable estimates of enrolled students. The previous report focused on the school-age population 

because its data were collected in September when some children may have been on summer vacation. 
 

3

differences discussed in this report are statistically significant at the p < .05 level, and all 

reported differences are five percentage points or larger, unless otherwise noted.4 

Background: Significance of Computer and Internet Use 

Students’ computer and Internet activities and rates of use are of interest for several 

reasons. Use of computers and the Internet has been associated with improvements in people’s 

education, labor market prospects, and everyday lives. Since these technologies have the 

potential to improve access to information, to help get tasks done better or more quickly, and to 

facilitate communication (see National Research Council 1999), computer and Internet use rates 

are indicators of the standard of living. Since the use of computers helps students gain computer 

literacy, use rates may indicate how well prepared the current generation of students will be to 

enter a workforce where computer literacy is in demand (U.S. Department of Education 1999b). 

Computer use may also promote cognitive development in both children and adults, specifically 

in the area of visual intelligence, where certain computer activities—particularly games—may 

enhance the ability to monitor several visual stimuli at once, to read diagrams, recognize icons, 

and visualize spatial relationships (Greenfield et al. 1994a, 1994b; Subrahmanyam and 

Greenfield 1994; also see Weikart 1995; Thelen 1996; Healy 1999). Other findings suggest that 

computer use may have a positive influence on student motivation at the elementary and 

secondary levels (Schofield 1997) and, depending on how computers are used, has the potential 

to improve academic performance (Niemiec and Walberg 1992; Bangert-Drowns 1993; 

Christmann, Badgett, and Lucking 1997; Holden 1998; Wegerif 2004). 

While high use rates are positive indicators in the domains mentioned above, they may 

also indicate risks for negative outcomes. To the extent that time spent using computers 

displaces time spent on athletics and other physical activity, extensive computer use may 

contribute to a sedentary lifestyle that puts children at risk for obesity and associated health 

problems (Hill and Peter 1998; Attewell, Suazo-Garcia, and Battle 2003; also see Ho and Lee 

 

4

 The five-percentage-point reporting criterion was based upon a judgment that smaller differences may not be 

meaningful even if they are statistically significant. Due to the large sample size of the CPS, many differences 

smaller than five percentage points are statistically significant. 
 

4

2001). Use of computer game controls or keyboards also entails a risk of injury from repetitive 

motion (Laester, Maxwell, and Hedge 1998; Oates, Evans, and Hedge 1998; Harris and Straker 

2000; Macgregor 2000). Other concerns include the potential for Internet use to increase social 

isolation among adolescents (Kraut et al. 1998; Sanders et al. 2000) and the potential for violent 

games to cause increases in hostility and aggression (Chambers and Ascione 1987; Schutte et al. 

1988; Irwin 1995; Kirsh 1998; van Schie 1998; also see Scott 1995). Such games are also 

believed by some to desensitize players to violence and to other people’s suffering (Grossman 

1995; Grossman and DeGaetano 1999); others view the evidence regarding harmful effects of 

violent games as inconclusive (Olson 2004; Bensley and VanEnwyk 2000). The Internet is also 

a medium that exposes or provides access for some young people to several kinds of 

inappropriate material. This may include pornography and inappropriate advertising (Mitchell, 

Finkelhor, and Wolak 2003; Thornburgh and Lin 2002; Shields and Behrman 2000), online 

sexual solicitation (Mitchell, Finkelhor, and Wolak 2003), and the means to acquire cigarettes 

(Ribisl, Williams, and Kim 2003) or materials to facilitate cheating on school assignments 

(Lathrop and Foss 2000). 

As computer and Internet use become increasingly widespread, larger percentages of the 

population will both enjoy the potential benefits and be exposed to the associated risks. The 

potential effects of these technologies on users make it important to describe the populations who 

are using them and to learn more about how they are being used. This report describes the 

population of students who may experience both the benefits and risks of computer and Internet 

use. 

Use in 2003 and User Characteristics 

Table 1 shows that in 2003, 91 percent of students in nursery school through grade 12 

and age 3 or older (53 million persons) used computers5

 and 59 percent (35 million persons) used 

 

5

 Computer users are identified by three CPS questions that ask if the subject uses computers at home, at school, or 

at his or her main job if the subject is employed and age 15 or older. Because estimates of overall computer use do 

not include people who use computers only at locations other than home, work, and school, such as libraries or 

community centers, the estimates may slightly understate the total number of people who use computers at any 
 

5

the Internet. Figure 1 graphs the relationship between grade level and the use of these 

technologies (data shown in appendix table B-2). Eighty percent of students already use 

computers by the time they are in kindergarten, and a majority (56 percent) use the Internet in 

grade 4 (table B-2). Among high school students (grades 9–12), 97 percent use computers and 

79 percent use the Internet (table 1). 

 location. In addition, the estimates reflect only a dichotomous measure of computer usage, as data on the intensity 

or frequency of use are not available from the CPS. 
 

6

Table 1. 

 

Percent s.e. Percent s.e.

58,273 91 0.3 59 0.4

4,928 66 1.5 23 1.3

3,719 80 1.4 32 1.7

20,043 91 0.4 50 0.8

12,522 95 0.4 70 0.9

17,062 97 0.3 79 0.7

28,269 91 0.4 61 0.6

30,005 91 0.4 58 0.6

35,145 93 0.3 67 0.5

10,215 85 1.2 44 1.7

8,875 86 0.9 47 1.4

2,293 91 1.6 58 2.7

346 86 4.8 47 7.0

1,400 92 1.9 65 3.3

646 82 3.3 49 4.3

47,949 91 0.3 61 0.5

50,653 91 0.3 60 0.5

7,620 86 0.8 54 1.2

5,691 82 1.1 37 1.4

13,804 89 0.6 54 0.9

16,548 93 0.4 63 0.8

8,590 92 0.6 67 1.1

10,713 95 0.5 73 0.9

40,987 92 0.3 62 0.5

3,129 90 1.2 55 1.9

13,463 89 0.6 52 0.9

694 89 2.6 55 4.1

2,840 80 1.6 28 1.8

55,434 91 0.3 61 0.4

10,173 84 1.1 40 1.5

39,016 93 0.4 66 0.7

8,815 85 0.8 41 1.1

9,273 87 0.7 50 1.1

7,499 93 0.7 62 1.2

9,834 93 0.5 66 1.0

13,769 95 0.4 74 0.8

13,229 88 0.6 50 0.9

26,670 92 0.4 63 0.6

10,370 91 0.7 59 1.3

$50,000–$74,999

School enrollment

Public

Private

Family income

Poverty status

In poverty

Not in poverty

Female householder

Other arrangement

Non-metropolitan

Percentage of children in nursery school and students in grades K-12 who use computers 

and the Internet, by student and family/household characteristics: 2003

$75,000 or more

Metropolitan status

Metropolitan, central city

Metropolitan, not central city

Under $20,000

$20,000–$34,999

$35,000–$49,999

Not Spanish-only 

Household language

Spanish-only

Graduate education

Family/household type

Two-parent married household

Male householder

Less than high school credential

High school credential

Some college

Bachelor's degree

Disabled 

Not disabled

Family & household characteristic

Parent educational attainment

Asian

American Indian

More than one race

Physical disability status

Race/ethnicity1

White

Hispanic

Black

9-12

Sex

Female 

Male

Nursery school

Kindergarten

1-5

6-8

1

 White, Black, Asian, More than one race, and American Indian respectively indicate White, nonHispanic; Black, non-Hispanic; Asian or Pacific Islander, non-Hispanic; More than one race, nonHispanic; and American Indian, Aleut, or Eskimo, non-Hispanic. Hispanics may be of any race.

NOTE: s.e. is standard error. Detail may not sum to total because of rounding or missing data.

SOURCE: U.S. Census Bureau, Current Population Survey, October 2003.

Percent using 

computers

Percent using the 

Internet

Number of

students 

(in thousands)

Characteristic

Total

Student characteristic

Grade level
 

7

Figure 1. Percentage of children in nursery school and students in grades K-12 using 

 computers or the Internet, by grade level: 2003 

 

NOTE: “N” is nursery school. “K” is kindergarten. Population is limited to age 3 and older. 

SOURCE: U.S. Census Bureau, Current Population Survey (CPS), October 2003. 

Digital Divide and Grade Level Differences 

Although the adoption of these technologies has been rapid, it has occurred at disparate 

rates for different segments of American society. How computers and the Internet are used, and 

whether they are used at all, often vary by socioeconomic status (assessed in this report with 

three measures: parent educational attainment, poverty status, and family income) and other 

characteristics such as race/ethnicity, household composition, and metropolitan status, such that 

the inequality of use has been termed a “digital divide” (U.S. Department of Commerce 1998; 

Norris 2001). This is particularly true of adults. While 86 percent of adults with an annual 

family income over $75,000 used the Internet in 2003, only 31 percent of adults with an annual 

family income below $20,000 did so (table B-1). Large racial/ethnic and educational differences 

exist as well. Sixty-five percent of White and Asian adults used the Internet, compared to 51 

percent of American Indians, 46 percent of Blacks, and 36 percent of Hispanics.6

 About 84 

 

6

 “White,” “Black,” “Asian,” “American Indian,” and “More than one race” refer to White non-Hispanic; Black nonHispanic; Asian or Pacific Islander, non-Hispanic; American Indian, Aleut, or Eskimo, non-Hispanic; and More than 

one race, non-Hispanic, respectively, and are used throughout this report for ease of presentation. Hispanics may be 

of any race. 

0 

25 

50 

75 

100 

N K 1 2 3 4 5 6 7 8 9 10 11 12

Grade level

Computer use

Internet use

Percent
 

8

percent of adults with a bachelor’s degree used the Internet, but 47 percent with only a high 

school education were Internet users, and the rate of Internet use was only 20 percent among 

adults who had not graduated from high school. As discussed below, this digital divide exists for 

elementary and secondary school students as well, though many differences are smaller than 

those found between various groups of adults. 

Table 1 shows computer and Internet use rates for students by individual, family, and 

household characteristics. Looking at individual characteristics, students in grades 9-12 are 

about 15 percentage points more likely than kindergartners to use computers and about 45 

percentage points more likely than kindergartners to use the Internet. Public school students are 

more likely than private school students to use both technologies. White students are more likely 

to use these technologies than their Black or Hispanic peers. In addition, those who are not 

physically disabled are more likely to use computers and the Internet than those with physical 

disabilities. 

The family and household settings children experience are also related to computer and 

Internet use. Students from two-parent households are more likely to use the Internet than those 

from single-parent households,7

 and those living with a parent who has attended graduate school 

are more likely to use both technologies than those living with no parent who has graduated from 

high school. In addition, those living in households where a language other than Spanish is 

spoken are more likely to use computers and the Internet than those living in Spanish 

monolingual homes. Students in families with the highest incomes are more likely to use 

computers and the Internet than those in families with the lowest incomes. Students who live 

outside of central cities are more likely to use the Internet than those living in central cities. 

A closer look at these differences provides a better picture of the magnitude of the 

differences in technology use between certain groups of students. For instance, the rate of 

computer use for Black students is 7 percentage points lower than that of their White peers, and 

 

7

 The categories for family structure in the CPS data are “two parent, male-headed single-householder, femaleheaded single-householder, and other arrangement.” When referring to the single householder categories, we use 

“single parent” or “single father” and “single mother” for ease of presentation. Some single-householders include 

non-relatives or relatives other than the father or mother such as a grandfather or grandmother. 
 

9

the difference in Internet use is even more pronounced at 21 percentage points. Differences 

between Whites and Hispanics are similar (8 percentage points and 23 percentage points, 

respectively). Approximately 82 percent of students from households where no parent has 

graduated from high school use computers and 37 percent of them use the Internet. In contrast, 

approximately 95 percent of students from households where at least one parent attended 

graduate school use computers and 73 percent of them use the Internet. Eighty-four percent of 

poor students use computers compared to 93 percent of students who are not poor, and the 

difference in Internet use is even larger.8

 

There is also a clear difference in students’ computer and Internet use in households 

where Spanish is the only language spoken compared to other households. For students living in 

households where only Spanish is spoken, the rate of computer use is 11 percentage points lower 

than those in households where other languages (usually English) are spoken. Internet use by 

students in Spanish-speaking monolingual households is less than half that of students in other 

households. 

Disabilities are also associated with differences. The differences between disabled and 

non-disabled children and adolescents are smaller than those found for adults,9

 but they still exist 

(DeBell and Chapman 2003). Comparing students with and without disabilities, the rates of 

computer and Internet use are both about 10 percentage points lower for those with disabilities. 

One important difference in use has all but disappeared, however. In the past, males have 

used computers and the Internet at significantly higher rates than females (Clemente 1998; 

Riccobono 1986) and have reported more experience and skill with these technologies 

(Schumacher and Morahan-Martin 2001). More recently, as the use of these technologies has 

become more widespread, this gender gap has closed for both adults and children. Among both 

adults and youths today there are now no important differences between the sexes in overall 

 

8

 Households with reported income in categories below the poverty threshold for their household size (as defined by 

the Census Bureau for 2003) were classified as poor, and households with income above the poverty threshold were 

classified as not poor. For details of the poverty definition, see Appendix A: Methodological and Technical Notes. 

9

 Adults with a disability are roughly half as likely to use the Internet as non-disabled adults and even less likely to 

have ever used a computer (U.S. Department of Commerce 2000). Adults with visual impairments use computers 

and the Internet at significantly lower rates than the rest of the adult population (Gerber and Kirchner 2001). 
 

10

computer or Internet use rates (DeBell and Chapman 2003; U.S. Department of Commerce 2002; 

Miller, Schweingruber, and Brandenburg 2001, reporting on computers only). Estimates in table 

1 and appendix table B-1 are consistent with this pattern. 

 

Many of the characteristics presented in table 1 are related to one another. For example, 

the average income in two-parent households is higher than the average income in other 

households (Ricciuti 2004). Since income is also associated with Internet use (table 1), it is 

reasonable to ask whether the difference in Internet use between single-mother and two-parent 

households could be explained by lower income in single-mother households. Multiple 

regression analysis answers questions like this by showing how an independent variable (such as 

family/household type) is related to the dependent variable (such as Internet use) when other 

independent variables (such as income) are held constant, or statistically controlled. (For details, 

see Appendix A: Methodological and Technical Notes.) In order to show the association of each 

of the characteristics in table 1 with computer and Internet use, two regression analyses were 

performed in which computer use and Internet use were the dependent variables and the 

characteristics in table 1 were the independent variables (grade level, sex, race/ethnicity, 

disability status, public/private enrollment, parent educational attainment, family/household type, 

household language, poverty status, household income,10 and metropolitan status). The results 

are presented in table 2.

The regression analyses (table 2) support several of the key findings about differences 

that were shown in table 1. Increases in grade level,11 parental education, and family income are 

all associated with an increase in a student’s odds of using computers and the Internet. That is, 

children in higher grades are more likely to use the technologies than children in lower grades, 

children in households with a parent who has completed high school or higher levels of 

education are more likely to use the technologies than children living with parents who 

 

10 Poverty status is a function of income and household size, so the income and poverty variables are related. 

Multicollinearity diagnostics were run on the regression models, and multicollinearity does not cause misleading 

results in the reported models. See appendix A for details. 

11 Note that in the regression analyses, grade level is measured as a continuous variable. Groupings were used in the 

bivariate tables. 
 

11

Parameter 

estimate

Standard 

error

Odds 

ratio

Parameter 

estimate

Standard 

error

Odds 

ratio

Student characteristic

Grade level 0.24 * 0.01 1.27 0.25 * 0.01 1.28

Sex 

Female

Male -0.01 0.07 0.99 -0.08 0.04 0.93

Race/ethnicity

1

White

Hispanic -0.45 * 0.13 0.64 -0.45 * 0.09 0.64

Black -0.61 * 0.11 0.54 -0.62 * 0.07 0.54

Asian -0.41 * 0.19 0.66 -0.43 * 0.12 0.65

American Indian -0.82 0.45 0.44 -0.66 * 0.33 0.52

More than one race -0.06 0.26 0.94 0.14 0.16 1.15

Physical disability status

Not disabled

Disabled -0.68 * 0.27 0.50 -0.38 0.21 0.68

Missing disability status 0.16 0.10 1.17 -0.40 * 0.06 0.67

School enrollment

Public

Private -0.48 * 0.10 0.62 -0.23 * 0.07 0.80

Family & household characteristic

Parent educational attainment

Less than high school credential

High school credential 0.32 * 0.13 1.37 0.35 * 0.10 1.42

Some college 0.66 * 0.14 1.94 0.66 * 0.10 1.94

Bachelor's degree 0.61 * 0.16 1.84 0.80 * 0.11 2.23

Graduate education 1.10 * 0.17 3.01 1.03 * 0.11 2.81

Missing parent education -0.22 0.18 0.80 -0.25 0.14 0.78

Family/household type

Two-parent married household

Male householder 0.02 0.16 1.02 -0.07 0.10 0.94

Female householder 0.14 0.10 1.15 0.08 0.06 1.09

Other arrangement 0.08 0.32 1.08 0.17 0.20 1.18

Household language

Not Spanish-only

Spanish-only -0.31 0.19 0.73 -0.71 * 0.16 0.49

Poverty status

Not in poverty

In poverty -0.07 0.17 0.94 -0.32 * 0.12 0.73

Family income

Under $20,000

$20,000–$34,999 0.05 0.16 1.05 0.05 0.12 1.05

$35,000–$49,999 0.46 * 0.21 1.58 0.29 * 0.14 1.34

$50,000–$74,999 0.43 * 0.21 1.54 0.38 * 0.14 1.46

$75,000 or more 0.52 * 0.21 1.68 0.64 * 0.14 1.89

Missing family income2 0.10 0.20 1.01 -0.06 0.14 0.94

Metropolitan status

Metropolitan, central city

Metropolitan, not central city 0.10 0.09 1.10 0.16 * 0.06 1.17

Non-metropolitan 0.15 0.13 1.16 0.07 0.08 1.07

Missing metropolitan status 0.07 0.12 1.07 0.17 * 0.08 1.18

Constant 0.35 0.24 1.41 -1.88 * 0.17 0.15

reference category reference category

Table 2. Logistic regression analyses of student and family/household characteristics and computer and Internet use: 2003

Computer use Internet use

reference category reference category

Independent variable

reference category reference category

reference category reference category

reference category reference category

reference category reference category

reference category reference category

reference category reference category

reference category reference category

NOTE: The number of cases analyzed is 29,075. Missing data are modeled to retain 11,745 cases for which data are missing 

for one or more independent variables. Estimates are for students in grade 12 or below and age 3 or older.

SOURCE: U.S. Census Bureau, Current Population Survey, October 2003.

reference category reference category

* p < .05

1

 White, Black, Asian, More than one race, and American Indian respectively indicate White, non-Hispanic; Black, nonHispanic; Asian or Pacific Islander, non-Hispanic; More than one race, non-Hispanic; and American Indian, Aleut, or 

Eskimo, non-Hispanic. Hispanics may be of any race.

2

 Missing family income also indicates missing poverty status. For convenience this variable is presented under family 

income. The reference category for missing family income and poverty status is known family income and known poverty 

status.
 

12

did not complete high school, and children living in families with incomes of $35,000 or more 

are more likely to use the technologies than children living in families with incomes below 

$20,000. Hispanic and Black students are less likely to use computers and the Internet than 

White children. In addition, the multivariate analyses suggest that White students are more 

likely to use both technologies than are Asian students and are more likely to use the Internet 

than are American Indian students. Private school students are less likely to use computers and 

the Internet than public school students. Students with disabilities are less likely to use 

computers than their non-disabled peers, and those from Spanish monolingual households are 

less likely to use the Internet than those from households where other languages are spoken. 

Also, no significant differences are observed between boys and girls. In these respects, the 

overall picture of technology use that is revealed by the regression analyses is similar to the 

picture suggested by the bivariate analyses in table 1. 

One contrast between the results of the regression analyses and the bivariate analyses is 

that the magnitude of the differences shown in the multivariate analyses of computer and Internet 

use is often smaller than the magnitude of the differences shown in the bivariate comparisons.12 

For example, the bivariate odds ratios for computer use comparisons of Hispanics to Whites and 

the highest-to-lowest parent education categories are .41 and 4.06 (not shown in tables13), 

respectively, while the odds ratios in the regression analysis are .54 and 1.68 (table 2). The 

bivariate odds ratios for Internet use comparisons of Hispanics to Whites and the highest-tolowest parent education categories are .38 and 4.42, while the odds ratios in the logistic 

regression analysis are .64 and 1.89. In each of these cases, the odds ratio is closer to 1.00 in the 

logistic regression analysis than in the bivariate comparison, indicating a weaker association in 

 

12 It is to be expected for odds ratios in multiple regression to be closer to 1.00 than bivariate odds ratios because 

regression controls for other factors. This usually attenuates associations, relative to a bivariate analysis, because 

variables added to a multiple regression analysis account for some of the association observed in the bivariate 

comparison. 

13 These bivariate odds ratios are not shown in tables, but they can be calculated from the information shown in 

tables, subject to rounding error. The odds ratio is the odds of one occurrence divided by the odds of another 

occurrence. Odds are the probability of an event occurring divided by the probability of non-occurrence. For 

example, table 1 shows that 93 percent of White students use computers and 85 percent of Hispanic students do so. 

The odds ratio for the comparison of Hispanic to White computer use equals (.85/(1-.85))/(.93/(1-.93)) = .43, which 

means that the odds of a Hispanic student using a computer are .43 times the odds of a White student doing so. The 

figure of .41 reported in the text is based on unrounded data and thus differs from this estimate of .43 due to 

rounding in the percentages reported in table 1. 
 

13

the regression analysis (because an odds ratio of 1.00 indicates no difference) when controlling 

for the independent variables. 

 Other differences from table 1 were not found to be statistically significant in the 

regression analysis. Table 1 shows computer use and Internet use differences between students 

in single-parent households and students who do not live in single-parent households, and it 

shows computer use differences between those living in poverty and those not in poverty, but 

these results are not observed in the regression analysis. At the bivariate level in table 1, these 

factors are related to computer and Internet use, but when other factors are considered these 

relationships are no longer significant. This suggests that other characteristics that were 

controlled for in the regression models, such as family income and parent educational attainment, 

may account for the differences in students’ odds of using computers and the Internet. 

Notwithstanding the lack of observed statistical significance of certain variables in the regression 

analysis, it is still important to examine the bivariate statistics for these variables because the 

bivariate statistics accurately describe the population.14

Having looked at the overall rates of computer and Internet use among students and their 

associations with user characteristics, the next sections describe where and how students use 

computers and the Internet. 

 

14 For example, after controlling for other factors such as family income, family structure is not significantly related 

to computer and Internet use. However, it is still the case that fewer students from single-parent families use 

computers and the Internet. 
 

14

Home and School Computer Use 

 

Home and school are the primary locations where students use computers. Table 3 

compares the rates of computer use at these two locations. Eighty-three percent of students use 

computers at school and 68 percent use computers at home. In general, more use computers at 

school than at home, but among those from households where the highest parental education was 

a graduate education, and those from families with incomes above $75,000, the rate of use at 

home (88 percent in both categories) was similar to the rate of use at school (84 and 86 percent, 

respectively). 

Table 3 reveals that patterns of demographic and socioeconomic differences in the use of 

home computers parallel those found in the overall use of computers. There are racial/ethnic 

gaps in home use: 46 percent of Blacks, 48 percent of Hispanics, and 43 percent of American 

Indians use computers at home, compared to 78 percent of Whites and 74 percent of Asians. 

There is also a difference in home use between students from the lowest and highest family 

incomes. Only 37 percent of those from families with incomes less than $20,000 use computers 

at home, compared to 88 percent of those living in families with annual incomes over $75,000. 

The gap between students whose parents have the least and the most education is similarly large: 

while 35 percent of those whose parents did not complete high school use computers at home, 88 

percent of those living with at least one parent who has attended graduate school use a computer 

at home. 

Table 3 also shows that differences in school computer use are not as pronounced as the 

differences in home use. While the gap in home computer use by parental education level noted 

above is 53 percentage points, at school it is 6 percentage points. While the differences in home 

computer use between students living in two-parent households compared to those living in 

single-father or single-mother households were 18 and 22 percentage points, respectively, there 

is little or no difference between these groups in terms of computer use at school. Although 

there are some differences in school computer use by race/ethnicity, home language, and 

socioeconomic status (parental education, family income, and poverty), these gaps are narrower 

than they are for home computer use. 
 

15

Table 3.

Percent s.e. Percent s.e.

58,273 68 0.4 83 0.3

4,928 51 1.5 43 1.5

3,719 60 1.7 67 1.7

20,043 64 0.7 85 0.5

12,522 72 0.9 91 0.6

17,062 75 0.7 91 0.5

28,269 68 0.6 84 0.5

30,005 67 0.6 83 0.5

35,135 78 0.5 85 0.4

10,215 48 1.7 80 1.3

8,875 46 1.4 82 1.0

2,293 74 2.4 79 2.2

346 43 6.9 83 5.2

1,400 74 3.1 86 2.4

646 55 4.2 76 3.7

47,949 68 0.5 84 0.4

50,653 66 0.5 85 0.3

7,620 76 1.1 71 1.1

5,691 35 1.4 78 1.2

13,804 55 0.9 84 0.7

16,548 72 0.8 86 0.6

8,590 82 0.9 84 0.9

10,713 88 0.7 84 0.8

40,987 74 0.5 83 0.4

3,129 56 1.9 83 1.4

13,463 52 0.9 84 0.7

694 49 4.1 84 3.0

2,840 32 1.9 76 1.7

55,434 69 0.4 84 0.3

10,173 39 1.5 80 1.2

39,016 76 0.7 85 0.6

8,815 37 1.1 80 0.9

9,273 55 1.1 81 0.9

7,499 71 1.1 86 0.9

9,834 80 0.9 84 0.8

13,769 88 0.6 86 0.6

13,229 57 0.9 81 0.7

26,670 73 0.6 84 0.5

Non-metropolitan 10,370 66 1.2 85 0.9 1

 White, Black, Asian, More than one race, and American Indian respectively indicate White, nonHispanic; Black, non-Hispanic; Asian or Pacific Islander, non-Hispanic; More than one race, nonHispanic; and American Indian, Aleut, or Eskimo, non-Hispanic. Hispanics may be of any race.

NOTE: s.e. is standard error. Detail may not sum to total because of rounding or missing data.

SOURCE: U.S. Census Bureau, Current Population Survey, October 2003.

$75,000 or more

Metropolitan status

Metropolitan, central city

Metropolitan, not central city

Under $20,000

$20,000–$34,999

$35,000–$49,999

$50,000–$74,999

Poverty status

In poverty

Not in poverty

Family income

Other arrangement

Household language

Spanish-only 

Not Spanish-only 

Family/household type

Two-parent married household

Male householder

Female householder

High school credential

Some college

Bachelor's degree

Graduate education

Not disabled

Family & household characteristic

Parent educational attainment

Less than high school credential

School enrollment

Public

Private

American Indian

More than one race

Physical disability status

Disabled

White

Hispanic

Black

Asian 

Sex

Female

Male

Race/ethnicity1

Kindergarten

1-5

6-8

9-12

Total

Student characteristic

Grade level

Nursery school

Percentage of children in nursery school and students in grades K-12 using computers at 

home and at school, by student and family/household characteristics: 2003

Percent using 

computers at 

home

Percent using 

computers at 

school

Number of

students 

(in thousands)

Characteristic
 

16

The narrowing of differences in usage rates at school compared to home is illustrated in 

figures 2, 3, and 4. Figure 2 shows that the percentage of students using computers at home 

increases as their parents’ educational attainment increases, but that the percentage using 

computers at school is more nearly equal across the levels of parental education. Figure 3 shows 

the same pattern for family income: home computer use rates increase as family income 

increases, but use of computers at school remains relatively high across all levels of income. 

Similarly, figure 4 shows that differences in computer use between racial/ethnic groups tend to 

be smaller at school than at home. 

Figure 2. Percentage of children in nursery school and students in grades K-12 using computers 

at home and at school, by parent educational attainment: 2003 

35

55

72

82

88

78 84 86 84 84

0

25

50

75

100

Less than

high school

credential

High

school

credential

Some

college

Bachelor's

degree

Graduate

education

Parent educational attainment

Percent

Home

School

SOURCE: U.S. Census Bureau, Current Population Survey (CPS), October 2003. 
 

17

Figure 3. Percentage of children in nursery school and students in grades K-12 using 

 computers at home and at school, by family income: 2003 

37

55

71

80

88

80 81 86 84 86

0

25

50

75

100

Under

$20,000

$20,000-

$34,999

$35,000-

$49,999

$50,000-

$74,999

$75,000

or more

Family income

Percent

Home

School

SOURCE: U.S. Census Bureau, Current Population Survey (CPS), October 2003. 

Figure 4. Percentage of children in nursery school and students in grades K-12 using computers 

at home and at school, by race/ethnicity: 2003 

78

48 46

74

43

74

85 80 82 79 83 86

0

25

50

75

100

White Hisp. Black Asian Amer.

Indian

More

than one

race

Race/ethnicity

Percent

Home

School

SOURCE: U.S. Census Bureau, Current Population Survey (CPS), October 2003. 
 

18

Although comparisons of many groups reveal smaller differences at school than at home, 

this general pattern does not hold for comparisons of students in public and private school. 

Private school students are more likely to use computers at home than public school students are 

(76 percent compared to 66 percent), but this difference is reversed and increased at school: 85 

percent of public school students use computers at school, compared to 71 percent of private 

school students. One illustration of the public-private difference is that public school students 

are about as likely to use computers at school as private school students are to use computers 

anywhere (tables 1 and 3). Private schools have been slower to provide computer access than 

public schools (see Parsad, Skinner, and Farris 2001; Kleiner and Lewis 2003), and the greater 

use of computers by public school students at school and overall may reflect greater availability 

of computers in public schools than in private schools. 

Table 4 shows the results of regression analyses of the characteristics associated with 

computer use at home and at school. As noted in the discussion of table 2, regression analyses 

can show how an independent variable is related to the dependent variable when other 

independent variables are held constant, or statistically controlled. The regression analyses show 

that, when controlling for the listed characteristics, computer use at home is less likely among 

Hispanic, Black, and American Indian students than it is among White students. Computer use 

at home is more likely among students whose parents are married and live with the child, whose 

parents are not in the lowest educational attainment category, and whose family incomes are not 

in the lowest category. Table 4 also shows that computer use at school is more likely among 

students attending public schools than among those attending private schools, and that computer 

use at school is more likely among students with a parent who has attended at least some college 

than among those without a high school credential. Note that the analysis in table 4 does not 

account for students’ access to computers at home or at school; it analyzes the use of computers 

in the entire student population, regardless of access. 
 

19

Table 4. Logistic regression analyses of student and family/household characteristics and computer use at home 

and at school: 2003

Parameter 

estimate

Standard 

error

Odds 

ratio

Parameter 

estimate

Standard 

error

Odds 

ratio

Student characteristic

Grade level 0.12 * 0.01 1.12 0.19 * 0.01 1.21

Sex 

Female

Male -0.07 0.05 0.93 0.01 0.05 1.01

Race/ethnicity

1

White

Hispanic -0.64 * 0.09 0.53 -0.16 0.11 0.85

Black -0.90 * 0.07 0.41 -0.14 0.09 0.87

Asian -0.13 0.13 0.88 -0.46 * 0.14 0.63

American Indian -1.05 * 0.32 0.35 -0.28 0.40 0.76

More than one race 0.01 0.17 1.01 0.19 0.20 1.21

Physical disability status

Not disabled

Disabled -0.20 0.21 0.82 -0.53 * 0.23 0.59

Missing disability status -0.26 * 0.06 0.77 -0.06 0.08 0.94

School enrollment

Public

Private 0.01 0.07 1.01 -0.69 * 0.07 0.50

Family & household characteristic

Parent educational attainment

Less than high school credential

High school credential 0.29 * 0.10 1.34 0.20 0.12 1.22

Some college 0.89 * 0.10 2.43 0.36 * 0.12 1.43

Bachelor's degree 1.09 * 0.11 2.98 0.32 * 0.13 1.38

Graduate education 1.43 * 0.12 4.16 0.45 * 0.13 1.57

Missing parent education -0.02 0.13 0.98 -0.25 0.15 0.78

Family/household type

Two-parent married household

Male householder -0.36 * 0.10 0.70 -0.03 0.13 0.97

Female householder -0.22 * 0.06 0.80 0.16 * 0.08 1.17

Other arrangement -0.43 * 0.19 0.65 0.15 0.26 1.16

Household language

Not Spanish-only

Spanish-only -0.46 * 0.15 0.63 -0.21 0.17 0.81

Poverty status

Not in poverty

In poverty -0.09 0.11 0.92 -0.11 0.14 0.90

Family income

Under $20,000

$20,000–$34,999 0.38 * 0.11 1.47 -0.03 0.14 0.97

$35,000–$49,999 0.79 * 0.13 2.21 0.15 0.17 1.16

$50,000–$74,999 1.03 * 0.14 2.81 0.03 0.17 1.03

$75,000 or more 1.28 * 0.14 3.61 0.21 0.17 1.23

Missing family income2 0.46 * 0.13 1.59 -0.06 0.17 0.94

Metropolitan status

Metropolitan, central city

Metropolitan, not central city 0.13 * 0.06 1.14 0.08 0.07 1.08

Non-metropolitan 0.04 0.08 1.04 0.17 0.10 1.19

Missing metropolitan status 0.14 0.08 1.15 0.10 0.09 1.11

Constant -1.02 * 0.17 0.36 0.18 0.20 1.20

Computer use at home Computer use at school

reference category reference category

reference category reference category

reference category reference category

reference category reference category

reference category reference category

reference category reference category

SOURCE: U.S. Census Bureau, Current Population Survey, October 2003.

Independent variable

* p < .05

1

 White, Black, Asian, More than one race, and American Indian respectively indicate White, non-Hispanic; Black, nonHispanic; Asian or Pacific Islander, non-Hispanic; More than one race, non-Hispanic; and American Indian, Aleut, or 

Eskimo, non-Hispanic. Hispanics may be of any race. 2

 Missing family income also indicates missing poverty status. For convenience this variable is presented under family 

income. The reference category for missing family income and poverty status is known family income and known poverty 

status.

NOTE: The number of cases analyzed is 29,075. Missing data are modeled to retain 11,745 cases for which data are 

missing for one or more independent variables. Estimates are for students in grade 12 or below and age 3 or older.

reference category reference category

reference category reference category

reference category reference category

reference category reference category
 

20

Home Computer Activities 

Children use computers for a wide variety of activities encompassing education, play, and 

communication, making computers a significant part of young people’s lives. Table 5 shows the 

percentage of all students (including those without home computers) who use home computers 

for selected activities. Because students without home computers are included in the analysis, 

the table does not support conclusions about how students with computers use them. By 

analyzing students with or without home computers, the table shows the percentage of the entire 

student population using computers at home for selected activities. (See appendix table B-3 for 

estimates of the percentage of computer users engaged in selected home computer activities.) 

A majority of students play computer games, and a majority of 6th-12th-grade students 

use home computers to complete school assignments and connect to the Internet. A majority of 

students in grades 9-12 also use home computers for e-mail, and about half use computers for 

word processing.15 

The percentage of students using computers for specific activities varies by individual, 

family, and household characteristics. As shown in table 5, many of the characteristics that are 

associated with the digital divide (e.g., race/ethnicity, family income, and parent educational 

attainment) are also associated with differences in the ways children and adolescents use 

computers at home. In many cases, differences in how computers are used are even more 

pronounced than the differences in the rates of use. Some differences are seen when considering 

parent educational attainment. There is a 13-percentage-point gap in computer use between 

students from households where no parent has received a high school credential and those from 

households where at least one parent has some graduate school education (table 1). The 

difference between these two groups’ use of computers for word processing is 34 percentage 

points, and is 37 percentage points for using computers to complete school assignments. The 

disparity is even larger for the use of home computers to connect to the Internet. Approximately 

17 percent of students with the least educated parents use computers to connect to the Internet 

compared to 63 percent of those living with the most educated parents (a 46 point difference). 

 

15 CPS data concerning computer activities were only collected for activities with home computers, not computers at 

any other location. Questions concerning some applications were only asked for users age 15 and older. These 

applications were spreadsheets or databases, graphics and design, and managing household records or finances. 
21 

Percent s.e. Percent s.e. Percent s.e. Percent s.e. Percent s.e. Percent s.e. P

Total 58,273 56 0.4 47 0.4 45 0.4 32 0.4 31 0.4 — — 

Student characteristic

Grade level

Nursery school 4,928 43 1.5 8 0.8 15 1.1 5 0.7 4 0.6 — — Kindergarten 3,719 52 1.8 12 1.1 21 1.5 8 0.9 7 0.9 — — 1-5 20,043 56 0.8 35 0.7 34 0.7 19 0.6 16 0.6 — — 6-8 12,522 61 0.9 62 0.9 54 1.0 42 1.0 40 0.9 — — 9-12 17,062 57 0.8 69 0.8 64 0.8 52 0.8 56 0.8 26 0.7 

Sex

Female 28,269 55 0.6 49 0.6 46 0.6 34 0.6 34 0.6 8 0.3 Male 30,005 57 0.6 46 0.6 44 0.6 30 0.6 29 0.6 8 0.3 

Race/ethnicity3

White 35,145 66 0.5 54 0.6 54 0.6 38 0.6 39 0.6 9 0.3 Hispanic 10,215 37 1.6 34 1.6 26 1.5 20 1.3 16 1.2 4 0.6 Black 8,875 38 1.3 35 1.3 27 1.2 20 1.1 18 1.1 5 0.6 Asian 2,293 54 2.7 52 2.7 46 2.7 36 2.6 33 2.6 9 1.5 American Indian 346 30 6.4 27 6.2 22 5.8 19 5.5 16 5.2 5 3.1 More than one race 1,400 61 3.4 51 3.5 48 3.5 36 3.3 32 3.2 7 1.8 

Physical disability status

Disabled 646 46 4.2 33 4.0 36 4.1 20 3.4 24 3.6 7 2.1 Not disabled 47,949 57 0.5 48 0.5 46 0.5 32 0.5 32 0.5 8 0.3 

School enrollment

Public 50,653 55 0.5 48 0.5 45 0.5 32 0.4 31 0.4 8 0.3 Private 7,620 63 1.2 43 1.2 45 1.2 31 1.1 29 1.1 6 0.6 

See notes at end of table.

Table 5. Percentage of children in nursery school and students in grades K-12 using home computers for specific activities, by student and family/household c

Student Characteristic

Total number 

of students 

(in thousands)

Home computer activity

Word 

processing

Connect to the 

Internet E-mail

Graphics, 

photos, images, 

audio, video2

Play games1

Complete 

school 

assignments

S

o
22 

Table 5. Percentage of children in nursery school and students in grades K-12 using home computers for specific activities, by student and family/household characteri

Percent s.e. Percent s.e. Percent s.e. Percent s.e. Percent s.e. Percent s.e. Pe

Family & household characteristic

Parent educational attainment

Less than high sch. credential 5,691 27 1.3 24 1.2 17 1.1 14 1.0 11 0.9 3 0.5 High school credential 13,804 45 0.9 39 0.9 34 0.9 23 0.8 24 0.8 6 0.4 Some college 16,548 61 0.8 51 0.8 48 0.8 33 0.8 33 0.8 8 0.5 Bachelor's degree 8,590 68 1.1 57 1.2 56 1.2 40 1.1 39 1.1 9 0.7 Graduate education 10,713 74 0.9 61 1.0 63 1.0 48 1.0 44 1.0 11 0.7 

Family/household type

Two-parent married household 40,987 62 0.5 51 0.5 49 0.5 35 0.5 34 0.5 8 0.3 Male householder 3,129 45 1.9 42 1.9 37 1.9 24 1.7 27 1.7 10 1.2 Female householder 13,463 42 0.9 37 0.9 32 0.9 23 0.8 24 0.8 6 0.4 Other arrangement 694 41 4.0 39 4.0 37 4.0 23 3.5 28 3.7 6 1.9 

Household language

Spanish-only 2,840 26 1.8 24 1.7 13 1.4 12 1.3 9 1.1 2 0.5 Not Spanish-only 55,434 57 0.5 48 0.5 46 0.5 33 0.4 32 0.4 8 0.2 

Poverty status

In poverty 10,173 32 1.5 26 1.4 19 1.2 15 1.1 12 1.0 3 0.5 Not in poverty 39,016 64 0.8 53 0.8 52 0.8 37 0.8 37 0.8 9 0.5 

Family income

Under $20,000 8,815 31 1.1 25 1.0 19 0.9 15 0.8 12 0.8 3 0.4 $20,000–$34,999 9,273 44 1.1 37 1.1 32 1.0 22 0.9 22 0.9 5 0.5 $35,000–$49,999 7,499 59 1.2 49 1.2 45 1.2 32 1.2 31 1.2 7 0.6 $50,000–$74,999 9,834 66 1.0 55 1.1 54 1.1 36 1.1 37 1.1 9 0.6 $75,000 or more 13,769 75 0.8 63 0.9 66 0.9 47 0.9 47 0.9 12 0.6 

Metropolitan status

Metropolitan, central city 13,229 46 0.9 40 0.9 36 0.9 26 0.8 24 0.8 6 0.4 Metropolitan, not central city 26,670 60 0.6 51 0.7 50 0.7 36 0.6 35 0.6 8 0.4 Non-metropolitan 10,370 55 1.3 44 1.3 40 1.3 29 1.2 29 1.2 7 0.7 

NOTE: s.e. is standard error. Detail may not sum to totals because of rounding, missing data, and multiple response. Estimates are for all students age 3 or older in nursery schoothose who do not have computers at home. For estimates of the percentage of computer users who use computers for specific activities, see appendix table B-3.

3 White, Black, Asian, More than one race, and American Indian respectively indicate White, non-Hispanic; Black, non-Hispanic; Asian or Pacific Islander, non-Hispanic; More 

and American Indian, Aleut, or Eskimo, non-Hispanic. Hispanics may be of any race.

Home computer activity

— Not available. Data were not collected.

1 Refers to playing games without using the Internet.2 Questions about noted computer activities were asked only about persons age 15 and older. Most of these students were in grades 9 and above.

S

o

Total number 

of students 

(in thousands)

SOURCE: U.S. Census Bureau, Current Population Survey, October 2003.# Rounds to zero.

Graphics, 

photos, images, 

audio, video2

Complete 

school 

assignments

Connect to the 

Play games Internet E-mail 1

Student Characteristic Word 

processing
23

 

There are also some relatively large differences in home computer use activities by 

race/ethnicity and family income. Whites, Asians, and students of more than one race are more 

likely than Hispanics, Blacks, and American Indians to use home computers for word processing 

(36–38 percent versus 19–20 percent), connecting to the Internet (46-54 percent versus 22–27 

percent), e-mail (32–39 percent versus 16–18 percent), and completing school assignments (51–

54 versus 27–35 percent). Compared to students from families with incomes under $20,000 

annually, students living in families with incomes of $75,000 or more per year are nearly four 

times more likely to use e-mail at home (47 versus 12 percent), about three times as likely to use 

home computers for word processing, and more than twice as likely to use home computers to 

complete school assignments. 

 Differences in home computer activities are, in part, a function of home access, and 

recent research has shown that income differences and race/ethnicity differences in home 

computer use activities are attenuated when home use is controlled (DeBell and Chapman 2003). 

For example, since relatively few students living in families with low incomes use computers at 

home at all, it is not surprising that few of these students use home computers for any given 

activity. 
24

 

Locations of Internet Use 

The high rate at which young people use the Internet is partly attributable to the 

availability of this technology in schools, where a major effort to move schools online 

contributed to nearly all public schools (98 percent) having some level of Internet access by 2000 

(Cattagni and Farris 2001; U.S. Department of Education 2000). Although a comparable 

estimate is not available for private schools, they have made major gains as well (Levin, Hurst, 

and Burns 2000). However, differences among schools persist in student access to computer 

resources, including student/computer and computer/classroom ratios, both of which affect the 

availability and convenience of Internet access at school (Parsad, Skinner, and Farris 2001; 

Rathbun and West 2003). 

Although Internet access is nearly universal in schools and is far from universal in 

homes, students are about equally likely to use home and school as Internet access locations. 

Among all students, 45 percent access the Internet from home and 43 percent access it from 

school. Ten percent of students access the Internet from a public library and 9 percent use it at 

someone else’s home (table 6). 

Public school students and private school students differ in the rates at which they access 

the Internet at school. Although public and private school students are equally likely to access 

the Internet at their home, public school students are more likely to access the Internet at school 

by 11 percentage points (45 percent compared to 34 percent). 

Differences associated with socioeconomic disadvantages in computer and Internet use 

are apparent in the locations where students log onto the Internet. Computers are used to access 

the Internet, so it is not surprising that Internet home-use rates are highest among those groups 

who are most likely to use computers at home. These groups include Asians, Whites, students 

living with more highly educated parents, those living with two parents, and those from families 

with higher incomes. 
25

 

Table 6.

Percent s.e. Percent s.e. Percent s.e. Percent s.e.

Total 58,273 45 0.4 43 0.4 10 0.3 9 0.3

Student characteristic

Grade level

Nursery school 4,928 15 1.1 7 0.8 1 0.3 1 0.4

Kindergarten 3,719 21 1.5 15 1.3 2 0.6 3 0.6

1-5 20,043 34 0.7 33 0.7 6 0.4 5 0.3

6-8 12,522 54 1.0 55 1.0 13 0.7 11 0.6

9-12 17,062 64 0.8 63 0.8 15 0.6 14 0.6

Sex

Female 28,269 46 0.6 44 0.6 10 0.4 9 0.4

Male 30,005 44 0.6 42 0.6 9 0.4 8 0.3

Race/ethnicity1

White 35,145 54 0.6 49 0.6 9 0.3 10 0.3

Hispanic 10,215 26 1.5 31 1.5 9 1.0 5 0.7

Black 8,875 27 1.2 36 1.3 12 0.9 8 0.7

Asian 2,293 46 2.7 40 2.7 12 1.8 4 1.1

American Indian 346 22 5.8 39 6.8 5 3.0 5 3.2

More than one race 1,400 48 3.5 47 3.5 13 2.3 13 2.3

Physical disability status

Disabled 646 36 4.1 35 4.1 10 2.6 16 3.1

Not disabled 47,949 46 0.5 44 0.5 11 0.3 10 0.3

50,653 45 0.5 45 0.5 10 0.3 9 0.3

7,620 45 1.2 34 1.2 7 0.6 7 0.6

See notes at end of table.

School

School enrollment

Percentage of children in nursery school and students in grades K-12 who use the Internet at specific locations, by student and

family/household characteristics: 2003

Public library

Someone else's 

home User characteristic

Internet use locations

Number of

students 

(in thousands)

Own home

Public

Private

 
26

 

Table 6.

Percent s.e. Percent s.e. Percent s.e. Percent s.e.

Family & household characteristic

Parent educational attainment

Less than high sch. credential 5,691 17 1.1 28 1.3 9 0.8 5 0.6

High school credential 13,804 34 0.9 41 0.9 9 0.5 8 0.5

Some college 16,548 48 0.8 46 0.8 10 0.5 10 0.5

Bachelor's degree 8,590 56 1.2 47 1.2 9 0.7 8 0.6

Graduate education 10,713 63 1.0 51 1.0 10 0.6 11 0.6

Family/household type

Two-parent married household 40,987 49 0.5 45 0.5 9 0.3 8 0.3

Male householder 3,129 37 1.9 41 1.9 12 1.3 10 1.2

Female householder 13,463 32 0.9 40 0.9 11 0.6 11 0.6

Other arrangement 694 37 4.0 43 4.1 11 2.5 7 2.1

Household language

Spanish-only 2,840 13 1.4 19 1.6 8 1.1 3 0.7

Not Spanish-only 55,434 46 0.5 45 0.5 10 0.3 9 0.3

Poverty status

In poverty 10,173 19 1.2 31 1.4 10 0.9 7 0.8

Not in poverty 39,016 52 0.8 47 0.8 10 0.5 10 0.5

Family income

Under $20,000 8,815 19 0.9 32 1.1 10 0.7 8 0.6

$20,000–$34,999 9,273 32 1.0 38 1.1 11 0.7 9 0.6

$35,000–$49,999 7,499 45 1.2 45 1.2 11 0.8 9 0.7

$50,000–$74,999 9,834 54 1.1 47 1.1 9 0.6 9 0.6

$75,000 or more 13,769 66 0.9 52 0.9 9 0.5 11 0.6

Metropolitan status

Metropolitan, central city 13,229 36 0.9 35 0.9 11 0.6 8 0.5

Metropolitan, not central city 26,670 50 0.7 46 0.7 10 0.4 9 0.4

Non-metropolitan 10,370 40 1.3 46 1.3 7 0.7 8 0.7

Internet use locations

User characteristic

Percentage of children in nursery school and students in grades K-12 who use the Internet at specific 

locations, by student and family/household characteristics: 2003—Continued

SOURCE: U.S. Census Bureau, Current Population Survey, October 2003.

Number of

children 

(in thousands)

Own home School Public library

Someone else's 

home

1

 White, Black, Asian, More than one race, and American Indian respectively indicate White, non-Hispanic; Black, non-Hispanic; Asian or 

Pacific Islander, non-Hispanic; More than one race, non-Hispanic; and American Indian, Aleut, or Eskimo, non-Hispanic. Hispanics may be of 

any race.

NOTE: s.e. is standard error. Detail may not sum to totals because of rounding, missing data, or multiple response. Percentages may exceed 100 

because students may use the Internet at more than one location.

Although many users access the Internet at home, several groups of users rely more 

heavily on access through schools or other locations. Students are more likely to use the Internet 

at school than at home when they have any of several characteristics: Hispanic or Black 

race/ethnicity, no parent who attended college, a single-mother head of household, a Spanishmonolingual household, or family income below $35,000 per year (table 7). Many of these users 

do not have computers at home (Newburger 2001), while nearly all schools have Internet access, 

which may explain the tendency of these populations to connect to the Internet from school. 
27

 

Table 7. 

Percent s.e. Percent s.e. Percent s.e. Percent s.e.

Total 11,651 56 1.0 40 1.0 2 0.3 2 0.3

Student characteristic

Grade level

Nursery school 699 71 3.7 23 3.4 2 1.1 4 1.6

Kindergarten 653 62 4.1 30 3.9 2 1.3 6 2.0

1-5 4,345 52 1.6 44 1.6 2 0.4 2 0.4

6-8 2,466 55 2.2 41 2.1 2 0.7 2 0.6

9-12 3,489 58 1.8 39 1.8 2 0.6 1 0.4

Sex

Female 5,673 56 1.4 40 1.4 2 0.4 2 0.4

Male 5,979 57 1.4 39 1.4 2 0.4 2 0.4

Race/ethnicity1

White 7,467 64 1.2 33 1.2 1 0.3 2 0.3

Hispanic 1,945 43 3.8 50 3.8 4 1.6 3 1.3

Black 1,512 35 3.2 59 3.3 3 1.2 3 1.1

Asian 395 63 6.3 33 6.1 5 2.8 # 0.5

American Indian 80 27 12.9 71 13.2 # # 2 4.5

More than one race 253 63 7.9 35 7.8 # # 3 2.6

Physical disability status

Disabled 92 63 10.9 32 10.6 5 4.7 # #

Not disabled 9,390 57 1.1 38 1.1 3 0.4 2 0.3

10,209 54 1.1 42 1.1 2 0.3 2 0.3

1,442 74 2.5 20 2.3 2 0.9 3 0.9

See notes at end of table.

School enrollment

Public

Private

Someone else's 

home

Internet use locations

User characteristic

Percentage of single-location Internet-using children in nursery school and students in grades K-12 who use the Internet at specific 

locations: 2003

Number of

students 

(in thousands)

Own home School Public library
28

 

Table 7. 

Percent s.e. Percent s.e. Percent s.e. Percent s.e.

Family & household characteristic

Parent educational attainment

Less than high sch. credential 961 29 3.2 63 3.4 6 1.6 2 1.0

High school credential 2,738 41 2.0 53 2.1 3 0.7 2 0.6

Some college 3,333 60 1.8 37 1.8 1 0.4 2 0.6

Bachelor's degree 1,837 68 2.3 30 2.3 1 0.6 1 0.4

Graduate education 2,324 73 2.0 24 1.9 1 0.5 2 0.6

Family/household type

Two-parent married household 8,607 61 1.1 36 1.1 2 0.3 2 0.3

Male householder 569 47 4.5 46 4.5 6 2.1 1 0.9

Female householder 2,346 41 2.2 52 2.2 3 0.8 4 0.9

Other arrangement 130 34 9.0 56 9.4 8 5.1 2 2.9

Household language

Spanish-only 406 36 5.2 53 5.4 9 3.0 2 1.4

Not Spanish-only 11,245 57 1.0 39 1.0 2 0.3 2 0.3

Poverty status

In poverty 1,717 30 3.5 60 3.7 6 1.7 4 1.5

Not in poverty 8,121 63 1.7 33 1.6 2 0.5 2 0.5

Family income

Under $20,000 1,511 30 2.5 60 2.7 5 1.3 5 1.2

$20,000–$34,999 1,702 40 2.6 53 2.6 5 1.1 3 0.9

$35,000–$49,999 1,616 57 2.7 40 2.6 1 0.7 1 0.6

$50,000–$74,999 2,041 67 2.3 31 2.2 2 0.7 1 0.4

$75,000 or more 2,968 76 1.7 22 1.7 # 0.2 2 0.6

Metropolitan status

Metropolitan, central city 2,391 55 2.2 39 2.2 4 0.8 2 0.6

Metropolitan, not central city 5,305 63 1.4 34 1.4 2 0.4 2 0.4

Non-metropolitan 2,230 44 2.8 52 2.8 2 0.8 2 0.8

Someone else's 

home

Internet use locations

User characteristic

Percentage of single-location Internet-using children in nursery school and students in grades K-12 who use 

the Internet at specific locations: 2003—Continued

Number of

children 

(in thousands)

Own home School Public library

# Rounds to zero.

1

 White, Black, Asian, More than one race, and American Indian respectively indicate White, non-Hispanic; Black, non-Hispanic; Asian or 

Pacific Islander, non-Hispanic; More than one race, non-Hispanic; and American Indian, Aleut, or Eskimo, non-Hispanic. Hispanics may be of 

any race.

NOTE: s.e. is standard error. Detail may not sum to totals because of rounding or missing data.

SOURCE: U.S. Census Bureau, Current Population Survey, October 2003.

Table 7 looks at students who access the Internet through one location only. Such 

students make up approximately 34 percent of all students who use the Internet and number 

approximately 11.7 million. Looking at the data in this way helps to more clearly illustrate the 

role schools play in providing Internet access for students by showing how heavily students from 

some backgrounds rely on schools for access. The table shows that the home is the most likely 

point of access among those who access the Internet from only one location. More than half of 

single-location Internet users rely solely on home access. However, several groups of singlelocation users—those from households where no parents have education beyond a high school 

credential, those from monolingual Spanish households, those in poverty, those with family 
29

 

incomes below $35,000 per year, those living outside metropolitan areas, and Black and 

American Indian students—rely more heavily on access to the Internet from school than on 

access from any other single location. Sixty-three percent of students who have no parent who 

has earned a high school credential and who have only one point of access for the Internet rely 

on school facilities for this access. Conversely, 24 percent of students who use the Internet in 

only one place and who live with parents who attended graduate school do so through school 

facilities. Among students with a single point of access, those from Spanish-monolingual 

households are more likely to rely solely on school for access, compared to other students (55 

percent compared to 39 percent). Likewise, poor students who use the Internet in only one place 

were more likely than other students to rely solely on school as an access point (60 percent 

compared to 33 percent; figure 5). 
30

 

Figure 5. Percentage of children in nursery school and students in grades K-12 who use the 

 Internet at only one location, by location and poverty status: 2003 

30

60

6 4

63

33

2 2

0

25

50

75

100

Own home School Other home Public library

Single location of use

Percent

In poverty

Not in poverty

SOURCE: U.S. Census Bureau, Current Population Survey, October 2003. 

Internet Activities16

The Internet supports a broad range of activities. Students use the Internet as a medium 

to communicate, to find information, to have fun, and to do homework. While electronic mail is 

the Internet application most widely used by adults (U.S. Department of Commerce 2000), 

among students, e-mail (or instant messaging) and playing games are approximately tied for the 

rank of the second most popular Internet activity, after completing school assignments (table 

8A). About 46 percent of students use the Internet for school work, while 36 percent use e-mail 

or instant messaging and 38 percent play games. 

 

16 In contrast to the discussion of computer activities, which was limited by the CPS design to activities at home, this 

discussion of Internet activities applies to activities at any location where the Internet is used. CPS data do not allow 

separate examination of Internet activities at home or at school. 
31

 

As shown in table 8A, from 8 to 21 percent of students use the Internet to find 

information about news, weather, sports, and products, to watch or listen to television, movies, or 

radio, and to make purchases.17 Smaller percentages use the Internet for other purposes. 

The activities for which students use the Internet vary by grade level. Among children in 

nursery school and kindergarten, the most common activity is playing games (15 percent in 

nursery school and 21 percent in kindergarten). Among students in grades 1 through 5, 

completing school assignments is about as common as playing games (31 and 32 percent, 

respectively). At higher grade levels, game playing levels off at or below 50 percent, while using 

the Internet for school assignments and e-mail or instant messaging increase. Completing school 

assignments is the most common activity for which students in grades 6–8 and 9–12 use the 

Internet (61 and 73 percent, respectively). E-mail or instant messaging (46 percent) is about as 

common as playing games (47 percent) in grades 6-8, and is more common than playing games 

in grades 9–12 (64 percent compared to 49 percent). 

Tables 8B and 8C show that patterns of difference in other aspects of the digital divide 

are reflected in the rates at which students use the Internet for specific applications. Students 

whose parents have any graduate education or whose family income is $75,000 or more are 

roughly twice as likely as students whose parents have not completed high school or whose 

family income is less than $20,000 to use the Internet to complete school assignments, and are 

more than twice as likely to use the Internet to find information. White students, Asian students, 

and students of more than one race are more likely to use the Internet to complete school 

assignments than are Hispanic and Black students. White students and students of more than one 

race are more likely than American Indian students to use the Internet to complete school 

assignments. 

 

17 The large number of Internet use options makes presenting extensive demographic details by type of activity 

unwieldy. In order to keep the tables to a practical size, characteristics associated with differences in computer use 

in earlier sections of the report are presented in tables 8A, 8B, and 8C. 
32 

Percent s.e. Percent s.e. Percent s.e. Percent s.e. Percent s.e. Percent s.e. Pe

Completing school assignments 46 0.4 48 0.6 45 0.6 7 0.8 10 1.1 31 0.7 E-mail or instant messaging 36 0.4 39 0.6 33 0.6 6 0.7 8 1.7 19 0.6 Playing games 38 0.4 37 0.6 39 0.6 15 1.1 21 2.5 32 0.7 News/weather/sports 21 0.4 20 0.5 23 0.5 2 0.4 4 1.2 10 0.5 Find information on products 21 0.4 20 0.5 22 0.5 3 0.5 4 1.2 9 0.4 Watch/listen to TV, movies, or radio 13 0.3 13 0.4 13 0.4 2 0.5 4 1.2 6 0.4 Make purchases 8 0.2 7 0.3 9 0.4 1 0.3 1 0.7 3 0.2 Phone calls 1 0.1 1 0.1 1 0.1 # 0.1 1 0.6 1 0.1 Taking a course online 1 0.1 1 0.1 1 0.1 # 0.1 # 0.3 1 0.1 Find government information1 — † — † — † — † — † — † Find health information2 — † — † — † — † — † — † Find a job1 — † — † — † — † — † — † Online banking1 — † — † — † — † — † — † Trading stocks1 — † — † — † — † — † — † 

† Not applicable.# Rounds to zero.

2 Questions about noted activities were asked only about persons age 12 and older. Estimates for these activities are for students in grades 6 through 12 who are age 12 or older.

— Not available. Data were not collected.

Grade level

All students Female Male Nursery school Kindergarten

NOTE: s.e. is standard error. Detail may not sum to totals because of rounding and multiple response. SOURCE: U.S. Census Bureau, Current Population Survey, October 2003.

Table 8A. Percentage of children in nursery school and students in grades K-12 using the Internet for specific activities, by sex and grade level: 2003 

Internet activity Grade 1-5 

Sex

1 Questions about noted activities were asked only about persons age 15 and older. Estimates for these activities are for students in grades 9 through 12 who are age 15 or older.
33 

Table 8B.

Percent s.e. Percent s.e. Percent s.e. Percent 

Completing school assignments 27 1.3 42 0.9 49 0.8 53 E-mail or instant messaging 15 1.0 30 0.8 38 0.8 44 Playing games 22 1.2 33 0.9 41 0.8 43 News/weather/sports 10 0.8 16 0.7 22 0.7 25 Find information on products 9 0.8 17 0.7 23 0.7 25 Watch/listen to TV, movies, or radio 6 0.7 10 0.6 14 0.6 13 Make purchases 2 0.4 5 0.4 8 0.5 10 Phone calls 1 0.2 1 0.2 1 0.2 1 Taking a course online 1 0.3 1 0.2 1 0.2 1 See footnotes at end of table.

Percentage of children in nursery school and students in grades K-12 using the Internet for speciparent educational attainment and race/ethnicity: 2003

Internet activity

Some college

Bachelor'

degree

Parent educational attainment

Less than high 

school 

credential

High school 

credential
34 

Table 8B.

Percent s.e. Percent s.e. Percent s.e. Percent s.e. Percen

52 0.6 33 1.6 37 1.3 48 2.3 344 0.6 22 1.4 22 1.1 36 2.2 244 0.6 25 1.4 30 1.3 37 2.2 226 0.5 13 1.1 15 1.0 22 1.9 126 0.5 12 1.1 14 1.0 20 1.8 114 0.4 9 1.0 12 0.9 12 1.5 10 0.4 4 0.6 3 0.5 10 1.3 

1 0.1 1 0.3 1 0.3 2 0.6 

1 0.1 1 0.4 1 0.3 2 0.6 

NOTE: s.e. is standard error. Detail may not sum to totals because of rounding and multiple response. SOURCE: U.S. Census Bureau, Current Population Survey, October 2003.

Race/ethnicity1

Percentage of children in nursery school and students in grades K-12 using the Internet for specific activities

Black Asian 

Ame

Ind

attainment and race/ethnicity: 2003—Continued

Internet activity

News/weather/sportsFind information on products

1 White, Black, Asian, More than one race, and American Indian respectively indicate White, non-Hispanic; Black, non-Hispanic; Asian or Pacifi

More than one race, non-Hispanic; and American Indian, Aleut, or Eskimo, non-Hispanic. Hispanics may be of any race.

Watch/listen to TV, movies, or radioMake purchasesPhone callsTaking a course online

# Rounds to zero.‡ Reporting standards not met.

Playing gamesE-mail or instant messagingCompleting school assignments

White Hispanic
35 

Percent s.e. Percent s.e. Percent s.e. Percent s

Completing school assignments 31 1.1 37 1.1 47 1.2 51 1E-mail or instant messaging 18 0.9 27 1.0 36 1.2 41 1Playing games 24 1.0 31 1.0 41 1.2 43 1News/weather/sports 11 0.7 16 0.8 20 1.0 24 0Find information on products 11 0.7 16 0.8 22 1.0 24 0Watch/listen to TV, movies, or radio 7 0.6 10 0.7 12 0.8 14 0Make purchases 3 0.4 5 0.5 7 0.6 8 0Phone calls 1 0.2 1 0.2 1 0.3 1 0Taking a course online 1 0.2 1 0.3 1 0.3 1 0

 $50,000– 

$74,999 

SOURCE: U.S. Census Bureau, Current Population Survey, October 2003.NOTE: s.e. is standard error. Detail may not sum to totals because of rounding and multiple response. Table 8C. Percentage of children in nursery school and students in grades K-12 using the Internet for specific activities, by family incoInternet activity

Family income

Less than 

$20,000

 $20,000– 

$34,999

 $35,000– 

$49,999
 

36

Although the difference between boys and girls in Internet use rates has closed, there are 

differences by sex in the types of Internet activities. As shown in table 8A, girls are more likely 

to use the Internet for e-mail. Girls are slightly more likely to use the Internet for school 

assignments, and boys are slightly more likely to use it for games, shopping, and finding 

information about news, weather, or sports. Although these differences of 2 or 3 percentage 

points are small, they are consistent with research on adults’ uses of the Internet, which has 

shown that although gender differences in overall use rates have vanished, gender differences 

persist in preferences for Internet activities, with men favoring entertainment and women 

favoring communication and educational assistance (Weiser 2000; also see Odell et al. 2000; 

Singh 2002). Some recent research on college students has revealed similar differences in 

Internet activities—women used more e-mail and men used the Web more (Jackson et al. 2001). 
 

37

Conclusions 

 

This report uses data from the October 2003 Current Population Survey (CPS) to 

examine the use of computers and the Internet by children in nursery school and students in 

kindergarten through grade 12. The report examines overall technology use, where these 

technologies are used, how they are used, and the relationships of patterns of use to 

socioeconomic and demographic characteristics such as age, race/ethnicity, parental education, 

and family income. 

Computer and Internet use by students is widespread and begins at an early age. About 9 

in 10 students use computers, and more than half use the Internet. Computer use is more 

common at lower grade levels than Internet use, but by grades 9-12 more than three quarters of 

youths are online. 

 Children and adolescents commonly use computers for playing games, completing school 

assignments, word processing, e-mail, and connecting to the Internet. Online, the most frequent 

activities are school work, e-mail, games, and finding news and product information. 

There are demographic and socioeconomic differences in the use of these information 

technologies. Family income and parents’ education are both positively associated with 

computer and Internet use, while living in a Spanish-monolingual household is negatively 

associated with Internet use. Use of both technologies is higher among Whites than among 

Blacks and Hispanics, and computer use is higher among the non-disabled than among the 

disabled. The findings for each of these variables remain statistically significant when 

controlling for other variables.18 Consistent with findings from recent studies (e.g., DeBell and 

Chapman 2003; U.S. Department of Commerce 2002), however, sex differences in rates of 

computer and Internet use no longer exist. 

 

18 Note that the analysis of computer use rates at home is for the entire student population, including those who do 

not have computers at home. 
 

38

Looking at where students use computers, more use computers at school than at home. 

For some groups of students, use at school exceeds use at home by 30 percentage points or more. 

These groups include Blacks and Hispanics, those whose parents did not complete high school, 

those living with a single mother, those living in households where Spanish is the only language 

spoken, and those living in families with incomes below $20,000 per year. Home use is about as 

prevalent as school use among students who are members of families with incomes of $75,000 or 

more per year, and whose parents earned at least a bachelor’s degree. 

Schools do appear to help narrow the digital divide in terms of computer use. 

Differences in the rates of computer use are smaller at school than they are at home when 

considering such characteristics as race/ethnicity, family income, and parental education. 

Although nearly all schools have Internet access and more students use computers at 

school than at home, the home is as widely used an Internet access location as school. Forty-five 

percent of students use the Internet at home, compared to 43 percent at school. Highlighting the 

digital divide, those who rely more heavily on access at school come from households with 

annual family incomes below $35,000 and whose parents did not attend college. 

While CPS data do support analyses of the use of technology, they do not support 

analysis of the quality of user experiences, the convenience of access, or the time spent using the 

technologies overall or for selected activities. Future research could address these issues of 

quality, convenience, and time, and also continue to study the digital divide to track trends in 

usage. 
 

39

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45

Appendix A: Methodological and Technical Notes 

Current Population Survey (CPS) Design 

 The CPS is a representative sample survey of all households in the United States. The 

survey is conducted in approximately 56,000 dwelling units in 754 primary sampling units. 

Dwelling units are in-sample for 4 successive monthly interviews, out-of-sample for the next 8 

months, and then returned to the sample for the following 4 months. The sample frame is a 

complete list of dwelling-unit addresses at the time of the Census updated by demolitions and 

new construction and field listings. The population, referred to as the civilian, noninstitutionalized population, excludes members of the armed forces, inmates of correctional 

institutions, and patients in long-term medical or custodial facilities. For details on the CPS 

methodology, see U.S. Census Bureau, Technical Paper 63 Revised: Current Population Survey 

– Design and Methodology, TP63RV. Washington, DC, 2002. 

 A member of each household who is at least 15 years old serves as the informant for that 

household, supplying data for each member of the household. As a result of this data collection 

method, data regarding computer and Internet use by children and adolescents were not collected 

directly from the users in most cases, but from another member of the user’s household. Because 

a household’s informant may not have full information regarding computer and Internet use by 

other members of the household (especially when that use occurs at school), this method is a 

potential source of error in the data. 

The CPS includes questions about computer and Internet use only in periodic special 

supplements on computer and Internet use. In October 2003, supplementary questions regarding 

computer and Internet use were asked about eligible household members 3 years old and older. 

Most interviews were conducted by phone using computer-assisted telephone interviewing. 

Standard Errors and the Accuracy and Precision of Estimates 

The estimates in this report are derived from samples and are subject to two broad classes 

of error—sampling and nonsampling error. Sampling errors can occur when the data are 

collected from a sample of a population rather than from the entire population. To the extent that 
 

46

the sample differs from the population it is supposed to represent, estimates based on a sample 

can differ from the values that would have been obtained from a universe survey using the same 

instruments, instructions, and procedures. Nonsampling errors come from a variety of sources 

and affect all types of surveys, universe as well as sample surveys. Examples of sources of 

nonsampling error include item wording, data processing error, and reporting error by 

respondents. In the CPS computer and Internet use supplement, reporting error may occur when 

household respondents are not fully informed about the computer and Internet use activities of 

other members of their households. Nonsampling errors may be greater or less than sampling 

error, but unlike sampling error, the effects of nonsampling error cannot be predicted by 

statistical theory. As much as possible, procedures are built into surveys in order to minimize 

nonsampling errors. These may include cognitive tests of survey item wording, designed to 

ensure that questions will be easily understood by respondents, and quality-control procedures 

during data editing, designed to catch data processing errors. 

 The standard error is a measure of the variability due to sampling when estimating a 

parameter. It indicates how much variance there is in the population of possible estimates of a 

parameter for a given sample size. Standard errors can be used as a measure of the precision 

expected from a particular sample. The chances that a sample estimate would differ from a 

population parameter by less than the standard error are about 68 out of 100; the chances that the 

difference would be less than 1.96 times the standard error are about 95 out of 100. 

 Since the CPS sample is not a simple random sample, the methods that are most 

frequently used to compute standard errors must be adjusted to account for the effects of the 

sample design. Standard errors for percentages based on CPS data were calculated using the 

following formula: 

 s.e. = (b / N)( p)(100 − p)
 

47

 where b = the parameter associated with the characteristic,1

N = the population on which the percentage is based, and 

p = the percentage (0<p<100). 

 For example, where table 1 shows an estimate that 91 percent of students use computers, 

the standard error is calculated as follows. The variable b is 4,867 (this can be found in the 

Census Bureau technical report previously cited); the N is 58,273,000, and the percentage is 91. 

Using the formula given above, the standard error is .3. 

 .3 = (4867 / 58,273,000)(91)(100 − 91)

Response Rates 

 The unweighted response rate for the October 2003 core CPS was 92.7 percent, and the 

response rate for the computer and Internet use supplement was 93.7 percent, for an overall 

response rate on the supplement of 86.9 percent. 

Method and Statistical Procedures for the Comparison of Estimates 

 The comparisons in the text have all been tested for statistical significance to ensure that 

the differences are larger than those that might be expected due to sampling error. Four types of 

comparisons have been made in the text. All statistical comparisons in this report were tested for 

significance at the 95 percent confidence level (p<.05), and all reported differences are 

statistically significant, unless otherwise noted. One type of comparison tests the significance of 

a statistic’s relationship to a specified value, such as 50 percent. When the specified value falls 

outside the confidence interval for the statistic, the statistic is said to be statistically different 

from the specified value. The confidence interval for an estimate is determined by the following 

formula: 

 CI = x ± t (s.e.) 

 

1

 These parameters and their use are explained in U.S. Census Bureau, Source and Accuracy Statement for the 

October 2003 CPS Microdata File for Internet and Computer Use in the U.S., Washington, DC, 2004. The general 

purpose of the parameters is to adjust the sampling error calculation to account for the complex sampling procedures 

used in the Current Population Survey. 
 

48

 where x = the estimate for which the confidence interval is desired 

 t = the critical value, which is 1.96 for a 95 percent interval 

 s.e. = the standard error of the estimate 

 For example, the statement on page 27 of this report that “more than half of [singlelocation] Internet users rely solely on home access” was tested as follows: 

 CI = x ± t (s.e.) 

 CI = 56 ± 1.96 (1.0) 

 54 to 58 = 56 ± 1.96 

 In repeated sampling, the population parameter (i.e., the “true” value) can be expected to 

fall within the range of the 95 percent confidence interval 95 percent of the time. In the previous 

example, the true value is expected to be between 54 and 58, so the statement in the text that 

“more than half of [single-location] Internet users rely solely on home access” is supported. Had 

the confidence interval included the range 49 to 53, for example, the statement would not have 

been supported because the range includes the value 50. 

The second type of comparison tests the hypothesis that there is a linear relationship 

between two variables. A bivariate linear regression model is specified, and a statistically 

significant regression coefficient supports the hypothesis that there is a linear association 

between the dependent and independent variables. To account for the complex sampling 

methods of the CPS, a weighted least squares (WLS) regression is used in which the model (e.g., 

the percentage of children who use computers) is weighted by the variance of the dependent 

variable (Gujarati 1995). 

The third type of comparison tests the statistical significance of the difference of two 

statistics. The Student’s t statistic can be used to test the likelihood that the differences between 

two percentages are larger than would be expected by sampling error alone. 
 

49

2

2

2

1

1 2

se se

P P

t

+

− =

where P1 and P2 are the estimates to be compared and se1 and se2 are their respective standard 

errors. 

 For example, the statement on page 7 of this report that “[students] living in households 

where a language other than Spanish is spoken are more likely to use computers ... than those 

living in Spanish monolingual homes” was tested as follows. The estimate for the percentage of 

students living in Spanish monolingual homes who use computers is 80 percent, and the standard 

error of this estimate is 1.6 percent; the estimate and standard error for students not living in 

Spanish monolingual homes are 91 percent and 0.3 percent, respectively (table 1). The t value is 

-6.76, as shown below. 

6.76

1.6 0.3

80 91

2 2 = −

+

− t =

 Since the absolute value of t exceeds the critical value of 1.96, readers can conclude that 

students who are not living in Spanish monolingual homes are more likely to use computers than 

those living in Spanish monolingual homes. 

 The fourth type of comparison is an equivalence test (Rogers, Howard, and Vessey 

1993), which determines whether there is any substantive difference between two statistics. This 

test requires an a priori determination of the minimum difference considered substantively 

important (delta). Equivalence tests in this report were conducted with a delta value of 5 

percentage points, meaning that differences smaller than this are not considered meaningful. A 

delta of 5 percentage points is commensurate with the statistical properties of the data presented 

in this report and the topics under consideration; considering the sources of non-sampling error 

that affect survey data, including proxy reporting errors and data processing errors, and the issues 

addressed in this report, it may not be reasonable to attribute substantive importance to 

differences smaller than 5 percentage points. 
 

50

The equivalence test uses one-tailed t-tests to construct a confidence interval for the 

difference between two statistics. This confidence interval is compared to the minimum 

substantively significant difference described by delta and negative delta. If the confidence 

interval is within the range of negative delta and delta—that is, if the upper bound of the 

confidence interval is less than delta and the lower bound of the interval is more than negative 

delta—this indicates that the difference between the two statistics is less than the smallest 

difference that can be considered important, so the two statistics are equivalent. 

The formula for the confidence interval of the difference is as follows: 

( . .) 1 2 CI = p − p ± t s e

 where p1 = the first statistic being compared 

 p2 = the second statistic being compared 

 t = the critical value for the desired confidence level2

 s.e. = the standard error of the difference of p1 and p2 

In this report the estimate of the percentage of female students who use the Internet is 61 

percent (s.e. = 0.6) and the estimate for males is 58 percent (s.e. = 0.6). The difference is 2.2 

percent (s.e. = 0.9). To test the equivalence of these two statistics when the minimum 

substantively important difference, delta, is 5 percentage points, compare the confidence interval 

for the difference to delta. In this case the confidence interval for the difference is –3.1 to –1.3. 

Since this interval is within the delta interval of –5 to 5, the percentages of male and female 

students who use computers are about the same. 

 

2

 For 85 percent confidence in equivalency, using a one-tailed test, the critical value is 1.036. Equivalence tests in 

this report are conducted at the 85 percent confidence level (while other statistical tests use 95 percent confidence) 

in order to reduce the likelihood of falsely rejecting the hypothesis that two estimates are equivalent. No single 

confidence threshold is appropriate for all equivalence tests (Rogers, Howard, and Vessey 1993). 
 

51

Logistic Regression Analysis 

Regression is a procedure that uses one or more independent variables to predict the 

values of a dependent variable and to reveal each independent variable’s association with the 

variable being predicted. In the two analyses presented in table 2, computer use and Internet use 

(the dependent variables) were analyzed using several independent variables that have previously 

been found to be associated with the use of information technologies. 

Logistic regression is a form of regression used when the dependent variable is 

dichotomous (that is, when it can take only two different values, such as “computer user” or 

“computer non-user”). In logistic regression, the equation predicts the natural log of the odds 

(the “log odds”) of an event occurring, such as the sampled individual being a computer user.3

 

The form of the equation is as follows: 

Log[P/(1-P)] = B0 + B1X1 + ... + BpXp

In this equation, the value B0 is a constant. The X values are the observed values of 

independent variables such as age or income, and the corresponding B values are parameters 

indicating the effect of a one-unit change in X on the log odds of the event. The B parameters 

indicate the association between the independent variable and the dependent variable when all 

the other independent variables are statistically controlled. 

 

Dichotomous independent variables and the “reference category.” Most of the 

independent variables in the logistic regression equations are treated as dichotomous. For 

example, the six categories of race/ethnicity are included in the regressions as five variables: 

Black, non-Hispanic; Hispanic; Asian or Pacific Islander, non-Hispanic, American Indian, Aleut, 

or Eskimo, non-Hispanic; and more than one race, non-Hispanic. White, non-Hispanic is the 

“reference category.” (The largest group is usually used as the reference category for categorical 

 

3

 Odds are the probability of an event occurring divided by the probability of it not occurring. For example, 

consider the data reported in table 1 indicating that 59 percent of students use the Internet. The odds of someone 

from this population using the Internet are (.59)/(1-.59) = 1.44 to one. This means that a student is 1.44 times more 

likely to use the Internet than to not use the Internet. 
 

52

variables, while the lowest-ranked group is usually used as the reference category for ordinal 

variables. The reference category is coded 0 and other characteristics are coded 1.) The 

indicated parameter estimate and odds ratio (explained below) are interpreted in comparison with 

the reference category. For example, since the Hispanic parameter estimate in the computer use 

analysis is negative (or, since the odds ratio is less than one), this indicates that Hispanics are 

less likely than Whites (the reference category) to use computers. Similarly, the negative 

parameter estimate for “Black, non-Hispanic” (or the odds ratio less than one) indicates that a 

Black child is less likely to use computers than a White child. 

Odds ratio. The results of the regression analysis include an “odds ratio” for each 

independent variable. The odds ratio is a widely used measure of effect size for dichotomous 

data in general (Haddock, Rindskopf, and Shadish 1998) and for logistic regression in particular 

(Mason et al. 2000). The odds ratio shows each independent variable’s statistical relationship to 

the dependent variable when all of the other independent variables are held constant. More 

specifically, it shows how the odds of the dependent variable event occurring (i.e., the odds of a 

child being a computer user or being an Internet user) change when the independent variable 

changes. For example, table 2 shows that a child from a family in the highest income group is 

about twice as likely (in terms of odds, not probability) to use the Internet as a child from a 

family in the lowest income group, all else being equal. This is indicated by the odds ratio of 

1.89 for the highest income category. Table 2 also shows that the odds of a Hispanic child using 

a computer are about two-thirds as great as the odds of a non-Hispanic White child doing so, as 

indicated by the odds ratio of .64. 

Continuous independent variables. Continuous variables do not have a reference 

category. Grade level is the only continuous variable in this analysis. The odds ratio and 

parameter estimate for this variable describe the statistical effect of a one-unit change in grade on 

the dependent variable. For example, the odds ratio shows that a student who is one grade ahead 

of another student is estimated to be 1.28 times more likely to use computers than the student in 

the lower grade (table 2), all else being equal. 
 

53

Significance tests and CPS sample design. Significance tests were conducted after 

adjusting for the effects of the CPS sample design. Since the CPS sample is not a simple random 

sample, the methods that are normally used to compute the significance of parameter estimates in 

regression must be adjusted to account for the sample design. Adjustments were made to the 

weight variable to reflect design effects before the regression analysis was conducted. The new 

weight variable used in the regression analyses was calculated as follows: 

w DEFF

w

wn × =

where wn = the new final weight for a case used in regression 

w = the original final weight for the case 

w = the mean of the original final weight, or 1828.2756 

 DEFF = the design effect for the case 

The DEFF is the square of the factor by which standard errors are inflated due to the 

characteristics of the complex sample design used in the Current Population Survey. The DEFF 

equals the product of two parameters that account for race/ethnicity and metropolitan status 

(which are factors in the CPS sample design) divided by the survey sampling interval, which is 

2,128. The parameter for White, non-Hispanic, is 4,687. The parameter for Hispanic is 11,347. 

The parameter for Black, non-Hispanic; Asian or Pacific Islander, non-Hispanic; American 

Indian and Alaska Native, non-Hispanic, and More than one race, is 6,733. These parameters are 

increased by a factor of 1.5 for persons living in non-metropolitan areas and are unchanged for 

persons living in metropolitan areas. The DEFF ranges from 2.20 for a White non-Hispanic 

child living in a metropolitan area (4,687/2,128 = 2.20) to 8.00 for a Hispanic child living in a 

non-metropolitan area (11,347 × 1.5 / 2,128 = 8.00). 

Multicollinearity. Multicollinearity means that independent variables in a regression 

model are correlated with one another. A strong correlation between independent variables 

increases the variances in parameter estimates and consequently can lead to incorrect results. 

Multicollinearity in the regression models presented in this report was examined because there is 

a moderate association between income category and poverty status. These analyses, not shown 
 

54

here, indicate that multicollinearity does not cause misleading results in the reported regression 

models. 

Variables Used in the Analysis 

Computer use. Computer users are identified by three CPS questions that ask if the 

subject uses computers at home, at school, or at his or her main job. “Computer” is not defined 

for the respondent. Use of computers at any other location, such as community centers, public 

libraries, or friends’ homes, is not ascertained. Due to this limitation, CPS estimates may 

slightly understate the total number of people who use computers at any location. Data on the 

intensity or frequency of use are not available from CPS, so individuals are classified 

dichotomously as users or non-users. 

Internet use. Internet users are identified using a variable on the October 2003 CPS file, 

PRNET1, that the Census Bureau derived from several CPS questions that ask if the subject 

connects to the Internet or uses e-mail at home or at work, uses the Internet at school, or uses the 

Internet at any location. In the September 2001 CPS, the variable PRNET1 was derived slightly 

differently; questions about using e-mail at home and about using the Internet at any location 

were not included. As a result, a small number of people who should have been counted as 

Internet users were not counted by PRNET1 on the September 2001 file. Previous publications 

presenting September 2001 data (e.g., DeBell and Chapman 2003) used the Internet-use variable 

originally furnished by the Census Bureau. This report’s estimates of Internet use in 2001 (table 

B-4) are corrected and are calculated in the same way as 2003 estimates. As a result, 2001 

estimates of Internet use in this report may differ slightly from previously published estimates. 

For example, while the percentage of children and adolescents age 5–17 who used the Internet in 

2001 was previously estimated to be 59 percent (DeBell and Chapman 2003), this report puts the 

figure at 60 percent (table B-4). Data on the intensity or frequency of use are not available from 

CPS, so individuals are classified dichotomously as users or non-users. 

Race/ethnicity. The race/ethnicity variable is derived from two CPS questions that ask the 

subject’s race (classified as White, Black, American Indian/Aleut/Eskimo, Asian or Pacific 

Islander, or any combination of the previous categories) and whether or not he or she is Hispanic. 
 

55

These data were used to derive a race/ethnicity variable with six mutually exclusive categories: 

White, non-Hispanic; Black, non-Hispanic; Asian or Pacific Islander, non-Hispanic; American 

Indian, Aleut, or Eskimo, non-Hispanic; More than one race, non-Hispanic; and Hispanic. 

Persons of Hispanic origin may be of any race. 

Physical disability status. Each student is classified as disabled or not disabled. Subjects 

who were reported to have any one or more of the following “long-lasting physical conditions” 

were classified as disabled: “blindness or a severe vision impairment even with glasses or contact 

lenses;” “deafness or a severe hearing impairment even with a hearing aid;” “a physical 

condition that substantially limits [the person’s] ability to walk or climb stairs;” or “a condition 

that makes it difficult to type on an ordinary typewriter or traditional computer keyboard.” Note 

that it is not possible to infer that children and adolescents identified as disabled in this report 

receive any services or accommodation as a result of disability. 

School enrollment. Students were reported as attending either a public school or a private 

school. 

Parent educational attainment. The CPS collects data about the highest level of school 

completed or the highest degree received for each person over the age of 15, as well as 

information about graduate or professional coursework since completing a bachelor’s degree. 

These data were collapsed into an education variable with categories corresponding to five levels 

of educational attainment: less than a high school diploma, a high school diploma or equivalent 

(GED), some college education but no bachelor’s degree (including people with an associate’s 

degree), college degree (i.e., people with a bachelor’s degree), and graduate education beyond a 

bachelor’s degree, including people who have taken graduate coursework but have not earned a 

degree. These data were then grouped by parent-child relationship and the highest level of 

education achieved by either parent residing with the child was assigned as the level of parent 

educational attainment. Those children and adolescents who do not reside with a parent are 

dropped from the analysis of parental education, and parents who do not live with their children 

are not included in the computation of parent educational attainment. 
 

56

Family/household type. Each household in the sample is classified as one of four types. 

In a “two-parent married household” the student lives with a married couple. In “male 

householder” or “female householder” households the student lives with an unmarried male or 

female adult—usually the father or mother, respectively. Children and adolescents whose 

housing arrangements fit none of these categories are classified as an “other arrangement.” 

Household language. The CPS household language variable HUSPNISH classifies 

households according to whether or not Spanish is the only language spoken by all members of 

the household who are 15 years of age or older. 

Family income. Family income is derived from a single question asked of the household 

respondent. Income includes money from all sources including jobs, business, interest, rent, 

social security payments, and so forth that was received in the preceding 12 months. The income 

of all family members 15 years old and over is included. Income is reported in sixteen categories 

ranging from “less than $5,000” to “$150,000 or more.” For convenience in tabular presentation, 

these categories were collapsed to five: under $20,000, $20,000-$34,999, $35,000-49,999, 

$50,000-$74,999, and $75,000 or more. 

Poverty status. Poverty status is deduced from household size and reported household 

income categories. Households with reported income in categories below the poverty threshold 

for their household size (as currently defined by the U.S. Census Bureau for 2003) were 

classified as poor, and those households with income categories above the poverty threshold 

were classified as not poor. Some households reported income in a range that straddles the 

poverty threshold. It is likely that some of these households meet the Census Bureau definition 

of poverty and that some do not, but the CPS data do not allow discrimination between poor and 

non-poor status for these households. For the purposes of this analysis, all households in an 

income category that straddles a poverty threshold were classified as poor. For example, the 

poverty threshold for a family of three persons including one related child is currently $14,680, 

and a family of three that reported an income in the category “$12,500 to $14,999” is classified 

as poor in this analysis. 
 

57

Metropolitan status. The location of each subject’s residence is considered metropolitan 

and part of a central city, metropolitan and not part of a central city, or non-metropolitan. 

Metropolitan status is defined by the Office of Management and Budget and indicates that an 

area has a total population of at least 75,000 in New England or 100,000 elsewhere and also 

contains a place with a population of at least 50,000 or an area that meets the Census Bureau’s 

definition of an “urbanized” area. Metropolitan areas often include several counties and may 

include territory in more than one state. Central cities usually are one or more of the most 

populous areas in each metropolitan area. 
 

58

Appendix B: Supplemental Tables 

Table B-1.

Percent s.e. Percent s.e.

Total (persons age 18 and over) 213,426 64 0.5 59 0.5

Adult characteristic

Age

18-29 46,146 73 0.4 69 0.5

30-39 41,291 73 0.5 69 0.5

40-49 44,427 72 0.5 67 0.5

50-64 47,179 64 0.5 59 0.5

65 and over 34,385 28 0.5 25 0.5

Sex

Female 110,966 64 0.3 60 0.3

Male 102,460 63 0.3 59 0.3

Race/ethnicity1

White 150,508 69 0.3 65 0.3

Hispanic 26,565 41 1.0 36 1.0

Black 23,685 52 0.8 46 0.8

Asian 9,366 69 1.2 65 1.3

American Indian 1,029 54 4.0 51 4.0

More than one race 2,273 69 2.5 64 2.6

Physical disability status

Disabled 19,133 30 0.7 28 0.7

Not disabled 161,625 66 0.3 63 0.3

Educational attainment

Less than high school credential 32,558 24 0.5 20 0.5

High school credential 68,720 52 0.4 47 0.4

Some college 57,601 77 0.4 72 0.4

Bachelor's degree 27,180 86 0.5 84 0.5

Graduate education 27,367 90 0.4 89 0.4

Family & household characteristic

Household language

Spanish-only 7,954 23 1.0 18 0.9

Not Spanish-only 205,472 65 0.2 61 0.2

Poverty status

In poverty 23,321 35 1.0 32 0.9

Not in poverty 148,815 71 0.4 67 0.4

Family income

Under $20,000 32,165 33 0.6 31 0.6

$20,000–$34,999 34,427 51 0.6 46 0.6

$35,000–$49,999 26,720 69 0.6 63 0.6

$50,000–$74,999 34,143 79 0.5 75 0.5

$75,000 or more 44,770 89 0.3 86 0.4

Metropolitan status

Metropolitan, central city 51,336 59 0.5 55 0.5

Metropolitan, not central city 93,018 69 0.3 65 0.3

Non-metropolitan 39,072 56 0.7 51 0.7

Percentage of adults who use computers and the Internet, by adult and family/household 

characteristics: 2003

1

 White, Black, Asian, More than one race, and American Indian respectively indicate White, nonHispanic; Black, non-Hispanic; Asian or Pacific Islander, non-Hispanic; More than one race, nonHispanic; and American Indian, Aleut, or Eskimo, non-Hispanic. Hispanics may be of any race.

NOTE: s.e. is standard error. Detail may not sum to totals because of rounding or missing data. 

The adult population described here includes all persons age 18 or older, including students 

enrolled in grade 12 or below who are 18 or older.

SOURCE: U.S. Census Bureau, Current Population Survey, October 2003.

Number of

adults 

(in thousands)

Percent using 

computers

Percent using the 

Characteristic Internet
 

59

Table B-2.

Percent s.e. Percent s.e.

58,273 91 0.3 59 0.4

4,928 66 1.5 23 1.3

3,719 80 1.4 32 1.7

4,120 86 1.2 35 1.6

3,902 90 1.1 44 1.7

4,045 92 0.9 50 1.7

3,881 94 0.9 56 1.7

4,096 94 0.8 63 1.6

4,149 95 0.8 67 1.6

4,212 94 0.8 68 1.6

4,161 96 0.6 76 1.4

4,457 96 0.6 77 1.4

4,118 97 0.6 80 1.3

4,126 97 0.6 80 1.3

4,361 97 0.5 81 1.3

NOTE: s.e. is standard error. Detail may not sum to total because of rounding. 

SOURCE: U.S. Census Bureau, Current Population Survey, October 2003.

Grade 8

Grade 9

Grade 10

Grade 11

Grade 5

Grade 6

Grade 7

Grade 12

Total

Grade level

Nursery school

Grade 4

Kindergarten

Grade 1

Grade 2

Grade 3

Percentage of children in nursery school and students in grades K-12 

using computers and the Internet, by grade level: 2003

Number of 

children 

(in thousands)

Percent using 

computers

Percent using the 

Internet Characteristic
60 

Table B-3. 

Percent s.e. Percent s.e. Percent s.e. Percent s.e. Percent s.e. Percent s.e. Pe

39,364 83 0.4 70 0.5 66 0.5 47 0.5 47 0.5 — — 

2,502 84 1.6 16 1.6 29 2.0 10 1.3 9 1.2 — — 2,214 88 1.5 20 1.8 36 2.2 13 1.5 11 1.4 — — 

12,802 87 0.6 55 1.0 53 1.0 30 0.9 25 0.8 — — 

8,983 85 0.8 87 0.8 76 1.0 59 1.1 55 1.1 — — 

12,863 76 0.8 92 0.5 85 0.7 68 0.9 75 0.8 34 0.9 19,315 81 0.6 72 0.7 67 0.7 50 0.8 49 0.8 11 0.5 20,050 85 0.5 68 0.7 65 0.7 44 0.8 43 0.8 11 0.5 27,535 84 0.5 69 0.6 69 0.6 48 0.7 50 0.7 12 0.4 

4,854 78 2.0 71 2.2 55 2.4 41 2.4 35 2.3 8 1.3 4,096 82 1.6 75 1.8 58 2.0 43 2.0 39 2.0 10 1.2 1,699 73 2.8 70 2.9 63 3.0 48 3.1 45 3.1 12 2.0 

150 69 9.8 63 10.2 50 10.6 44 10.5 38 10.3 12 6.9 

1,031 83 3.0 70 3.7 66 3.8 48 4.0 44 4.0 10 2.4 

358 84 4.2 59 5.6 64 5.5 37 5.5 43 5.7 12 3.7 

32,774 83 0.4 70 0.5 67 0.6 47 0.6 47 0.6 11 0.4 33,605 83 0.4 72 0.5 67 0.6 48 0.6 47 0.6 12 0.4 

5,759 84 1.0 57 1.4 59 1.4 41 1.4 39 1.4 8 0.8 

Percentage of home computer users among children in nursery school and students in grades K-12, using home computers for specific activities, by 

characteristics: 2003

Total number 

of students 

(in thousands)

Home computer activity

Play games1

Complete 

school 

assignments

Connect to the 

Internet

Word 

processing E-mail

Graphics, 

photos, images, 

audio, video2

Sp

or 

Computer user characteristic

Total

Student characteristic

PrivatePublic

School enrollment

Not disabledDisabled

Physical disability status

More than one raceMaleFemaleAmerican IndianAsianBlackHispanicKindergartenNursery school

Grade levelSex

9-126-81-5White

Race/ethnicity3

See notes at end of table.
61 

Table B-3. 

Percent s.e. Percent s.e. Percent s.e. Percent s.e. Percent s.e. Percent s.e. Pe

2,012 77 2.0 68 2.3 48 2.4 38 2.3 31 2.2 7 1.2 7,616 82 1.0 71 1.1 62 1.2 41 1.2 44 1.2 10 0.7 

11,991 84 0.7 71 0.9 67 0.9 45 1.0 46 1.0 12 0.6 

7,006 83 1.0 69 1.2 69 1.2 49 1.3 48 1.3 11 0.8 9,425 84 0.8 70 1.0 71 1.0 55 1.1 50 1.1 13 0.7 

30,327 83 0.5 69 0.6 67 0.6 47 0.6 46 0.6 11 0.4 

1,751 80 2.1 74 2.3 66 2.5 44 2.6 49 2.6 19 2.0 6,946 81 1.0 71 1.2 62 1.3 45 1.3 46 1.3 11 0.8 

341 84 4.3 79 4.8 76 5.0 48 5.9 57 5.8 12 3.8 922 79 2.9 73 3.2 42 3.5 38 3.5 26 3.1 5 1.6 

38,443 83 0.4 70 0.5 67 0.5 47 0.6 47 0.6 11 0.3 

3,973 82 1.9 67 2.4 49 2.5 39 2.4 32 2.3 7 1.3 

29,599 84 0.7 70 0.8 69 0.8 48 0.9 48 0.9 12 0.6 

3,301 82 1.4 67 1.8 50 1.9 40 1.8 33 1.8 8 1.0 5,056 82 1.2 68 1.4 59 1.5 40 1.5 41 1.5 9 0.9 5,333 84 1.1 69 1.4 63 1.4 45 1.5 43 1.5 10 0.9 7,819 83 0.9 69 1.1 68 1.1 46 1.2 47 1.2 12 0.8 

12,064 85 0.7 72 0.9 75 0.9 54 1.0 53 1.0 13 0.7 

7,550 81 1.0 71 1.1 62 1.2 45 1.2 43 1.2 10 0.7 

19,412 83 0.6 71 0.7 69 0.7 49 0.8 48 0.8 12 0.5 

Non-metropolitan 6,815 83 1.2 67 1.5 62 1.6 44 1.6 44 1.6 11 1.0 

Computer user characteristic

$75,000 or more

Metropolitan status

Metropolitan, central cityMetropolitan, not central cityUnder $20,000$20,000–$34,999$35,000–$49,999Not Spanish-only$50,000–$74,999

Poverty status

In povertyNot in poverty

Family income

Female householderOther arrangement

Household language

Spanish-only 

Family & household characteristic

Percentage of home computer users among children in nursery school and students in grades K-12, using home computers for specific activities, by studen

characteristics: 2003—Continued

— Not available. Data were not collected.# Rounds to zero.

1 Refers to playing games without using the Internet.

Parent educational attainment

Less than high sch. credentialHigh school credentialSome collegeBachelor's degreeGraduate education

Total number 

of students 

(in thousands)

Home computer activity

Play games1

Complete 

school 

assignments

Connect to the 

Internet

Word 

processing E-mail

3 White, Black, Asian, More than one race, and American Indian respectively indicate White, non-Hispanic; Black, non-Hispanic; Asian or Pacific Islander, non-Hispanic; More 

and American Indian, Aleut, or Eskimo, non-Hispanic. Hispanics may be of any race.

NOTE: s.e. is standard error. Detail may not sum to totals because of rounding, missing data, and multiple response. Estimates are of the percentage of computer users who use c

For estimates of all students age 3 and older in nursery school through grade 12, including those who do not have computers at home, see table 5.SOURCE: U.S. Census Bureau, Current Population Survey, October 2003.

Graphics, 

photos, images, 

audio, video2

S

o

2 Questions about noted computer activities were asked only about persons age 15 and older. Most of these students were in grades 9 and above.

Family/household type

Two-parent married householdMale householder
62

 

Table B-4.

Percent s.e. Percent s.e. Percent s.e. Percent s.e.

Total (persons age 5–17) 53,013 90 0.3 60 0.5 53,561 91 0.3 62 0.5

Child characteristic

Age

5–7 11,990 80 0.8 34 1.0 11,785 83 0.8 36 1.0

8–10 12,455 91 0.6 55 1.0 11,849 92 0.5 55 1.0

11–14 16,493 93 0.5 70 0.8 17,173 94 0.4 71 0.8

15–17 12,075 93 0.5 78 0.9 12,753 95 0.4 80 0.8

Sex

Female 25,835 90 0.4 60 0.7 26,139 91 0.4 63 0.6

Male 27,178 89 0.4 60 0.7 27,422 92 0.4 61 0.6

Race/ethnicity

2

White 33,433 93 0.3 68 0.6 32,279 94 0.3 70 0.6

Hispanic 8,400 79 1.6 39 1.9 9,503 85 1.2 45 1.7

Black 8,275 85 1.1 47 1.5 8,048 87 1.0 48 1.4

Asian 2,268 90 1.5 67 2.2 2,126 90 1.7 59 2.8

American Indian 637 90 2.7 55 4.5 332 87 4.7 45 7.1

More than one race — — — — — 1,273 92 2.0 67 3.4

Physical disability status

Disabled 626 80 3.7 50 4.6 594 80 3.5 49 4.4

Not disabled 45,416 90 0.3 61 0.5 44,100 92 0.3 63 0.5

Family & household characteristic

Parent educational attainment

Less than high school credential 5,450 76 1.3 33 1.5 5,344 81 1.2 37 1.4

High school credential 13,611 87 0.7 52 1.0 12,953 90 0.6 55 0.9

Some college 15,665 92 0.5 65 0.9 15,381 94 0.4 65 0.8

Bachelor's degree 6,712 94 0.7 71 1.3 7,891 94 0.6 70 1.1

Graduate education 9,114 96 0.4 76 1.0 9,463 97 0.4 77 0.9

Family/household type

Two-parent married household 37,230 91 0.3 64 0.6 37,525 93 0.3 65 0.5

Male householder 2,715 87 1.5 56 2.2 2,897 90 1.2 56 2.0

Female householder 12,440 86 0.7 50 1.0 12,522 89 0.6 54 1.0

Other arrangement 628 75 3.9 52 4.6 616 88 2.8 57 4.3

Household language

Spanish-only 2,549 70 2.1 30 2.1 2,680 80 1.7 29 1.9

Not Spanish-only 50,464 91 0.3 62 0.5 50,881 92 0.3 63 0.5

Poverty status

In poverty 9,277 80 1.3 39 1.6 9,303 85 1.1 41 1.6

Not in poverty 36,904 93 0.4 67 0.8 35,826 94 0.4 68 0.8

Family income

Under $20,000 8,344 80 1.0 38 1.2 8,027 85 0.9 42 1.2

$20,000–$34,999 8,852 86 0.8 50 1.2 8,615 88 0.8 52 1.2

$35,000–$49,999 7,438 92 0.7 64 1.3 6,993 93 0.7 64 1.2

$50,000–$74,999 9,530 94 0.6 69 1.1 9,053 94 0.5 69 1.1

$75,000 or more 12,018 96 0.4 77 0.9 12,441 96 0.4 78 0.8

Metropolitan status

Metropolitan, central city 12,249 85 0.7 51 1.0 12,060 88 0.6 53 1.0

Metropolitan, not central city 23,566 91 0.4 64 0.7 24,516 92 0.4 66 0.7

Non-metropolitan 9,609 91 0.8 61 1.4 9,679 92 0.7 61 1.3

— Not available. Data were not collected.

Percentage of children and adolescents age 5–17 who use computers and the Internet, by child and family/household characteristics: 2001, 2003

NOTE: s.e. is standard error. Detail may not sum to totals because of rounding or missing data. 

SOURCE: U.S. Census Bureau, Current Population Survey, September 2001 and October 2003.

Percent using 

computers

Percent using the 

Internet

Characteristic

1

 Revised from previously published estimates. This report's estimates of Internet use in 2001 are calculated in the same way as the 2003 estimates. For details, 

see the Methodological and Technical Notes section.

2

 White, Black, Asian, More than one race, and American Indian respectively indicate White, non-Hispanic; Black, non-Hispanic; Asian or Pacific Islander, nonHispanic; More than one race, non-Hispanic; and American Indian, Aleut, or Eskimo, non-Hispanic. Hispanics may be of any race. Estimates for race/ethnicity 

categories in 2001 and 2003 may differ due to differences in question wording.

Percent using 

computers

Percent using the 

Internet

Number of

children 

(in thousands)

Number of

children 

(in thousands)

September 2001 October 2003 1