Gender and the Internet: Causes of Variation in Access ... - English

Gender and the Internet: Causes of Variation in Access, Level, and Scope of Use n Ira M. Wasserman, Eastern Michigan University Marie Richmond-Abbott, Eastern Michigan University Objective. The article examines differences in the use of the Internet by gender, with a consideration of access to the web, use of communication facilities related to email and chat rooms, frequency of use, and types of websites used. The study considers the impact of socioeconomic status and social, geographic, racial, and ethnic variables for explaining variations in the use of the web by men and women, and how these factors are mediated by knowledge of how to use the web. Methods. The study employs data collected by the General Social Survey (GSS) in 2000, and relates access, communication levels, frequency of use, and types of sites used to gender and other relevant variables. The relevant variables are analyzed by multivariate analysis. Results. Access to the web was independent of gender, but was related to education, race, income, age, and marital status. Women were less likely than men to chat on the web, but were slightly more likely to use email, and they utilized different types of sites than men. Conclusions. Women access the web as frequently as men, but they communicate on the Internet differently than men, are online less than men, and utilize different types of websites than men. Knowledge related to web use is an important independent variable that influences Internet use by men and women.

Prior to the 1990s, the scientific and military communities developed a form of the Internet for their own purpose (ARPANET), but it was only in the 1990s, with the development of hyper-text language, that the Internet became a mass tool that could be employed by economic, political, religious, and social groups that developed their own websites to communicate with the larger public (Norris, 2001:27). The use of this new technology in an effective manner required a home computer, and/or access to a computer at work, or in a public setting (e.g., a public library or a school), funds (private or public) to purchase monthly access to a service provider, and knowledge of the computer in relation to web use. One would expect that the lower socioeconomic class and culturally deprived minorities (e.g., racial and ethnic minorities, rural residents) would be limited in their use of this new n Direct correspondence to Ira M. Wasserman, Department of Sociology, Eastern Michigan University, Ypsilanti, MI 48197 [email protected]. The authors will share coding procedures for purposes of replication.

SOCIAL SCIENCE QUARTERLY, Volume 86, Number 1, March 2005 r2005 by the Southwestern Social Science Association

Gender and the Internet

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technology (Wilhelm, 2000; Martin, 2003). These social groups would be less likely to have a home computer and/or be able to pay the monthly fee for access to such a service provider (Wilson, 2000). One status group whose level of web use has been problematic has been women. In the early 1990s women had less experience with computers than men. They were more likely than men to use computers at work (Kaplan, 1994), but this use was for routine office activities, such as word processing and spreadsheet work. Women saw men as being better able to comprehend the Internet (Newton, 2001), possessed less self-efficacy toward the computer, and had high levels of computer anxiety (Durndell and Haag, 2002). All these technophobic factors led to a gender gap in Internet use in the 1990s (Dhalokia, Dhalokia, and Pedersen, 1994). The accelerated growth of this new technology in the 1990s narrowed the gap between men and women with regard to Internet access, and by 2000 this access gap had almost disappeared (Nie and Erbring, 2000; Norris, 2001:82–84; U.S. Department of Commerce, 2000). Since 1995, numerous survey studies (Chilsolm, 1996; Clemente, 1998; Flagg, 1999; Cummings and Krout, 2002) have found that the new users of the Internet tend to be women. Proportionately more women are attending medical and law schools as well as entering science and engineering professions. Women have also increased their participation in office administrative activities and have become knowledgeable about word processing, spreadsheets, and email communication (Morahan-Martin, 1998), although women in lower-status occupational positions are less likely to have access to the web. Women are also more likely than men to purchase products for home use (e.g., kitchen products, decorative products, books), often on the web (Hilts, 1997; Raymond, 2000). There is also evidence that women are more likely than men to employ the web to maintain social contacts (Howard, Rainee, and Jones, 2001; Jackson et al., 2001). Email is the most common form of Internet social activity, and women tend to employ it slightly more than men (Nie and Erbring, 2000). Chat-room communication is a more specialized form of social communication, since it stresses anonymous communication. All the changes just mentioned have influenced Internet use by gender and recent studies (Bimber, 2000; Ono and Zavodny, 2003) find no gender variation in access to the web, although these studies do find that women use fewer websites, and also employ the Internet less frequently, than men. It would be useful to extend this analysis of the gender gap with regard to the Internet by considering possible causes for the variation in the scope and frequency of website use. Theoretical Perspective on the Gender Gap on the Internet

In considering the gender gap on the Internet, it is necessary to define three aspects of this gap: (1) access to the Internet, (2) frequency of use of

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Social Science Quarterly

the Internet, and (3) scope of use of the Internet. Access refers to the opportunity for individuals to use the web because they can utilize a computer in a public or private setting and have connections to the Internet. The frequency of use refers to the amount of time that an individual devotes to the use of the Internet. The scope of use refers to the variety of websites (e.g., financial, health, hobby, commercial) used by an individual. With regard to scope of use, it is necessary to determine whether some sets of sites are more likely to be utilized by certain categories of Internet users. With regard to access, current studies (Bimber, 2000; Ono and Zavodney, 2003) have found little significant variation in access by gender. In the early stages of home computer use, the new technology was popularly portrayed as a male domain; at that time, woman were more likely to be technophobic (Levine and Donista-Schmidt, 1998). However, recently, women have begun to use computers in home and office settings to make consumer purchases and to exchange email messages with friends and colleagues. Given their emotive role in family matters, women are more likely than men to use email messages to maintain long-distance social network ties with friends and relatives; by contrast, men and women differ little in their use of email communication at the local level (Boneva, Krout, and Frohlich, 2001). As they become more experienced with the Internet, women become increasingly competent in its use (Schumacher and Morahan-Martin, 2001). All these findings would lead one to believe that there is little gender variation in access to the web. Although one can access the Internet through free sources at universities and public libraries, for most individuals access usually requires the ownership of a home computer and the payment of a monthly service fee to an Internet provider. Given this fact, one would expect socioeconomic and cultural factors to influence Internet access. Thus, one would expect those with lower incomes (Martin, 2003), and those individuals with less formal education to have less access to the web. One might also expect African Americans (Hoffman, Novak, and Schlosser, 2001) and Hispanics to have less access, given their socioeconomic disadvantages, which limit their web connectivity at home, work, and at public facilities not located in their neighborhood. In the case of Hispanics, language might serve as a barrier to their use of the web (Wilhelm, 2000). Geographic location may also serve as a barrier to Internet use. One would expect economic barriers to limit rural access to the web. Strover (1999) and Bell, Reddy, and Rainie (2004) found that rural residents are limited with regard to the quality of Internet providers. Regional geography may also influence Internet use. Spooner (2003) found that web use was highest in the New England states,1 with 66 percent of the residents having

1 These states are Connecticut, Maine, Massachusetts, New Hampshire, Vermont, and Rhode Island.


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Internet access, and the Pacific Northwest,2 with 68 percent of these residents having web access. By contrast, Internet access was lowest in the south,3 with only 48 percent of the residents having web access. It is likely that regional variations in education and income may partially explain the regional variation in web access. The frequency of use of the Internet involves the amount of time an individual uses the web for social and/or professional activity. Many individuals use the Internet for social entertainment, to play games, and for hobby interests. By contrast, other individuals use this new technology for business and commercial activities, such as banking and stock transactions. One would expect individuals of higher socioeconomic status, and those involved in the economic system (often men, for financial reasons) to make greater use of the Internet. The scope of use of the Internet involves the employment of millions of websites currently available, with the number of these websites expanding at an astronomical rate. In principle, it is possible to categorize all these different websites into various broad groupings (Web Bound, 2002). Government websites would include the sites created by the federal, state, and local governments. Websites related to science, including biology, chemistry, geology, physics, and weather forecasting, have also expanded on the web. Some of these sites (e.g., sports, sexually explicit materials) (Mehta, 2001) are more likely to be male oriented; others (e.g., cooking, religious) can be classified as female oriented, while a vast majority of them (e.g., health and fitness, games) might be classified as androgynous. One would expect the use of these various types of sites to vary among men and women. Individuals are unlikely to utilize all of these sites, but are more likely to employ sets of sites for their activities. The variation in the scope and frequency of use of these various sites by gender may be caused by (1) socioeconomic differences between men and women and (2) gender-specific differences related to the Internet (Bimber, 2000:870–71). Socioeconomic differences between men and women are related to the fact that men have higher income levels and, at present, slightly higher educational levels. These differences may also be related to work and home activity by men and women that influence the availability of the web. Being at work full time, and especially in professional and administrative work, gives men greater access to the web, as well as to technical experts who can effectively advise them on its use—a resource that women who are at home lack. A second reason for any variation in frequency and scope of use may be gender-specific differences caused by lifetime experience with technology. Men have been more familiar with computers and the Internet than women, 2

These states are Oregon and Washington. These states are Alabama, Arkansas, Kentucky, Louisiana, Mississippi, Tennessee, and West Virginia. 3

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and therefore they possess more information and skills about how to use this new technology (Goulding, 2003). At present, women are less experienced with regard to this new technology and this may influence their level of use of it. Under situations (e.g., classrooms, work activity) where men and women both use the web, studies (e.g., Martin, 1998; Wei, 1998) have found that women are just as proficient as men in its use. Thus, genderspecific differences appear to be related to historical differences between men and women with regard to the use of technology and female preferences for face-to-face social interaction. One would predict that controlling for socioeconomic differences between men and women, as well as gender-specific differences in experience, should reduce the influence of gender on the frequency and scope of Internet use. Methodology

The data for this study are drawn from the General Social Survey (GSS) for the year 2000 (Davis and Smith, 2000). The GSS has been conducted for a number of years between 1972 and 2000, with the survey results being accumulated over the years. The survey was administered to a representative national sample between 1972 and 2000. For the year 2000, the GSS included, for the first time, a series of questions concerned with computer and Internet use; these computer and Internet questions were administered to 2,362 randomly selected individuals. From this total sample for the year 2000 it was possible to determine whether an individual had access to the World Wide Web (WWW). For those individuals who answered the questions related to web use, it was found that 992 individuals had access, whereas 1,318 individuals did not. Independent of this web use, the 2000 GSS questioned 1,098 respondents regarding their level of use of email and the locations (i.e., home, work, public facilities) where they utilized email. The extent of email use was determined from the hours per week that the respondents used this technology. Since this variable was skewed, it was transformed (Neter et al., 1996:129–34) to: emailtr ¼ lne ð1 þ emailÞ: ð1Þ Among web users, the 2000 GSS determined the extent of their use of this new technology. The number of web users who were questioned was reduced by one-third by only surveying those individuals who used the web at some time during the previous week. Frequency of web use was determined by ascertaining how many hours per week the respondent used the Internet. Since this variable was skewed like email use, it was transformed in a manner similar to Equation (1). To ascertain the scope of use of the Internet, the GSS considered a series of 21 generic websites (e.g., health, sports, sexually explicit materials), which will be examined in the analysis section of this article, and determined whether a respondent used the various


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types of websites within the past 30 days and their degree of use of that type of site (National Opinion Research Center, 2001:794–97). For each type of website, the respondent was asked whether in the past 30 days they had not used the site, used it one to two times, three to five times, or more than five times. The 21 generic categories do not cover all websites, but they do include a large proportion of them and it is unlikely that excluded sites would be used more frequently by women than men, thereby biasing our findings. For example, one might think that excluding commercial sites would bias the findings in favor of men, but other studies ( Jorgensen, 2001; Kennedy, Wellman, and Klement, 2003:85) have found that men shop online more than women. To determine the scope of use of the Internet, a factor analysis was performed on the 21 websites. Factor analysis (Harman, 1976), also known as latent variable analysis, examines a set of variables, and considers the underlying and unobserved factors that may explain and summarize complex phenomenon. For example, one might expect a social conservative to strongly oppose abortion, gay marriage, and affirmative action. Exploratory factor analysis (Nunnally, 1978:327–404), the type utilized in this study, considers the 21 variables, which have a score of 0 if the respondent did not utilize them in the past 30 days and 1 if they did use them in that time, and determines which factors belong to which groups. Employing a varimax rotation technique, the most widely used orthogonal rotation method (Nie et al., 1975:485), a set of factor loadings was computed using the SPSS Factor program. For each factor loading, the study selected only variables with factor scores greater than 0.50 (the usual cut-off point), and then created a factor score using the following formula (Nie et al., 1975:488): Fi ¼ f 1 Z1 þ f 2 Z2 þ . . . ¼ f i Zi ;

ð2Þ

where fi 5 factor loading greater than 0.50, Zi 5 standard score for Variable I 5 (Variable I mean Variable I)/(SD Variable I). In the analysis section of this article, four factor scores will be created and related to the other independent variables. Past studies (Bimber, 2000; Ono and Zavodny, 2003) have employed education, income, housewife status, and working full time as measures of SES. However, most studies (Nie and Erbring, 2000; Wilhelm, 2000:25; Mossberger, Tolbert, and Stansbury, 2001; National Telecommunications and Information Administration, 2001; Lenhart, 2003) use education and income as measures of socioeconomic status. A problem with using income as a measure of SES for this study is that the relation between income and web use is not linear but curvilinear, with low-income individuals having little use of the web, and higher-income individuals having significant use. To account for this curvilinear relation, it is necessary to relate income and income2 to web use, creating a second-order nonlinear model. A difficulty with doing this is that there is a high correlation between income and

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income2, introducing multicollinearity into the model. To reduce this multicollinearity in the model, the income variable is transformed to: Incometr ¼ Income Mean Income:

ð3Þ

This centering technique substantially reduces multicollinearity in the model, and allows these two measures of income to be utilized in the study. For this article, Education (years) and Incometr and Income2tr will be utilized as a measure of SES. In relation to geography, it was first ascertained whether an individual lived in a rural area or not, being given a score of 1 if he or she resided there, and 0 otherwise. Since Internet use was highest in the New England and Northwest Pacific states,4 and lowest in the south,5 three regional geographic variables will be created, including New England states, Pacific states, and southern states. For each of these three categories of states, an individual will be given a score of 1 if he or she resides in the respective regions, and a score of 0 otherwise. Other studies have found that knowledge of the computer and the Internet influences its use. Mossberger, Tolbert, and Stansbury (2001:15–37) found that there was a computer skill divide in relation to Internet use, while Hargittai (2002) found that there were differences in people’s online skills that influenced their proficient use of the Internet. Given these findings, an Internet knowledge score, titled Knowledge, was created for each respondent from a set of questions related to the utilization of the web. Four items related to Internet use were considered, and they concerned whether the respondent (1) knew how to download information, (2) knew how to upload information, (3) knew how to use a hyperlink, and (4) knew the name of five search engines (e.g., Google, Yahoo, Copernicus, AltaVista). For each of the four items, the respondent was given a score of 0 of he or she did not know how to accomplish each of the tasks, and 1 if he or she did know how. A combined Knowledge score was then created for each respondent by adding together the scores for the four items. The Knowledge variable had a range of values from 0 to 4, and an alpha value of 0.6250, where the alpha value is a measure of the consistency of answers to the four items. An alpha value above 0.60 indicates that the items form a consistent scale. A series of other control variables were also created from the 2000 GSS data set. The respondent’s age in years was determined, and six qualitative variables were created. If a respondent was African American, Asian or other related group, Latino, female, married, or divorced, he or she was given a score of 1, and 0 otherwise. 4 The 2000 GSS includes California in the Pacific states, not separating the northwest states from California. Our measure will include all three Pacific states. 5 The GSS does not include all the southern states in the Spooner (2003) study, but has most of his states in the south central area of the nation, including the states of Alabama, Kentucky, Mississippi, Tennessee, and West Virginia. This study will equate the south central states with the southern states in the Spooner (2003) study.


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Analysis of Results

To determine access to the web, a qualitative variable was created from the 2000 GSS data, where a respondent was given a score of 1 if he or she used the web in any setting, including at home, work, school, and/or public location, and 0 otherwise. Since this measure concerns web and nonweb use, it was not possible to relate the measure to Internet knowledge. Web access was statistically related to a set of variables that involve SES, social and geographical variables, and racial and ethnic variables, using logistic regression to estimate the coefficients in the model. The coefficients provide an indication as to whether individuals in all the previous social categories are more or less likely to use the web (Hosmer and Lemeshow, 1989). Table 1 indicates the estimates for the various control variables. The computed Exp (B) is an odds ratio, where a value of 1 specifies that the variable has no impact on web use, a value less than 1 shows that individuals with those social characteristics are less likely to use the web, and a value greater than 1 demonstrates that individuals with those social characteristics are more likely to use the web. The findings in Table 1 are consistent with previous findings related to gender and access, since they point out that there is no gender gap with regard to web access. With regard to SES, Nie and Erbring (2000) found TABLE 1 Logistic Regression Analysis Relating a Set of Independent Variables to Web Access for 2000 General Social Survey Variables Socioeconomic Variables Education (years) R’s Income 1998tr R’s Income 19982tr Social and Geographic Variables Female Age Married Divorced Rural New England South central Pacific Racial and Ethnic Variables African American Asian and other Latino Constant

Slope B

Exp(B)

0.352 n n 0.037 n n 0.002

1.423 1.037 1.002

0.029 0.040 n 0.434 n n 0.231 0.357 n 0.052 0.349 0.120

0.972 0.961 1.539 1.260 0.700 1.054 0.706 1.128

0.670 n n 0.110 0.395 n 3.243 n n

0.512 0.896 0.675 0.039

N 5 2,310; model chi-square 5 383.140; po0.01;

po0.01; n0.01opo0.05.

nn

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education and age to be the most important determinants of access, while Mossberger, Tolbert, and Stansbury (2001:35), examining three other recent studies, found a significant educational and income gap. The findings in Table 1 are consistent with these previous findings. Age and marital status are statistically related to access, with younger and married individuals having more access. As predicted earlier in the article, geographical rural location significantly influenced access. Regional geographical location, although it is statistically related to access by itself, does not influence access when controls are introduced into the model. Consistent with the findings of Mossberger, Tolbert, and Stansbury (2001:35), African Americans and Latinos have less access to the Internet than whites, even with controls for education and income. One would expect that cultural lags in web use by racial and ethnic minorities would explain this discrepancy and should diminish over time. However, findings by Lenhart (2003:8) between 2000 and 2002 suggest that this gap may persist, especially for African Americans. Before examining web use in relation to the content of the web, it will be useful to study communication on the web in relation to email and chatroom use. A chat-room variable that determines the amount of hours per week that respondents spend in chat-room activity is utilized, and is transformed like the email variable (Equation (1)) to take account of skewness in this measure. These two communication measures on the Internet are related to the independent variables in the study by using ordinary least squares (OLS) to determine their influence on the two dependent variables (Table 2). With regard to email communication, previous studies (Boneva, Krout, and Frohlich, 2001; Nie and Erbring, 2000) have shown that women are more likely to use email for long-distance personal communication, but not for other forms of communication. The findings in Table 2 are consistent with these results, since women use email more than men, but the differences in use are not significant. The socioeconomic variables are the most significant for explaining email use, with those individuals with higher levels of education and income using email more frequently. Email is used for business and professional activities to main social contacts. Internet users and email users are wealthier and more highly educated (Hoffman, Novak, and Schlosser, 2001), and the findings in Table 2 reflect this fact. With regard to chat-room use, previous studies (e.g., Nie and Erbring, 2000) have found users to be young and anonymous. The results in Table 2 support this conclusion, although it shows that these users are not significantly younger. The findings in the table suggest that they are more likely to be males with lower income levels, and with a significantly higher probability of being divorced. Chat rooms are used for a variety of purposes, including social and sexual contacts, the swapping of news and political information, and the interaction of professionals (e.g., librarians). When social groups of men and women interact, men tend to dominate the


261 TABLE 2

Ordinary Least Square Estimates of Independent Variables Related to Email Use and Chat-Room Use on the Internet Email Use Variable

Slope B

Socioeconomic Variables Education 0.081 n n R’s Income 1998tr 0.020 n n R’s Income 19982tr 0.002 n n Social and Geographic Variables Female 0.069 Age 0.001 Married 0.080 Divorced 0.034 Rural 0.100 New England 0.037 South central 0.149 Pacific 0.063 Racial and Ethnic Variables African American 0.156 Asian and other 0.170 Latino 0.222 Constant 0.252 N 1,098 R2 0.083 nn

Chat-Room Use (S.E.)

Slope B

(S.E.)

(0.012) (0.006) (0.001)

0.015 0.016 n n 0.001

(0.013) (0.006) (0.001)

(0.060) (0.003) (0.071) (0.088) (0.111) (0.124) (0.128) (0.082)

0.174 n n 0.004 0.109 0.219 n 0.022 0.071 0.199 0.017

(0.062) (0.003) (0.074) (0.093) (0.117) (0.134) (0.134) (0.082)

(0.089) (0.159) (0.115) (0.204)

0.126 0.160 0.016 0.882 555 0.080

(0.106) (0.160) (0.115) (0.212)

po0.01; n0.01opo0.05.

conversation because they are likely to use their social power to control the social interaction (Carroll, 2002). This social domination is also present in many chat rooms, which discourages women from participating in them. The findings in Table 2 support the previous findings that chat rooms are male dominated. Women have attempted to increase their chat-room use by forming feminine online chat groups. Next, we examine the frequency of web use by considering the number of hours per week that respondents are online, transforming the measure like the previous two measures to take account of skewness. Since the knowledge variable was also skewed, it was transformed in a similar manner. Table 3 indicates the OLS estimates for the independent variables in the study. Consistent with previous studies (Nie and Erbring, 2000; Ono and Zavodny, 2003), it is found that women use the Internet less frequently than men. Socioeconomic status does not significantly influence frequency of use, nor does geography. The lack of any causal relationship between socioeconomic status and frequency of use is surprising, but understandable when we realize that there are so many types of web users. Asians and others tend to use the web more frequently than whites and other racial and ethnic

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Social Science Quarterly TABLE 3

Ordinary Least Square Estimates of Independent Variables for Level of Web Use on the Internet for the 2000 GSS Variable Socioeconomic Variables Education R’s Income 1998tr R’s Income 19982tr Social and Geographic Variables Female Age Married Divorced Rural New England South central Pacific Racial and Ethnic Variables African American Asian and other Latino Internet Skills Knowledgetr Constant N R2

Slope B

(S.E)

0.020 0.001 0.000

(0.015) (0.007) (0.001)

0.163 n 0.002 0.128 0.009 0.193 0.187 0.126 0.154

(0.074) (0.003) (0.087) (0.109) (0.137) (0.164) (0.165) (0.094)

0.102 0.399 n n 0.076

(0.123) (0.178) (0.131)

0.661 n 0.816 n 553 0.107

(0.123) (0.326)

po0.01; n0.01opo0.05.

nn

groups. Internet knowledge does significantly influence Internet use, with those who are more knowledgeable using it more frequently. The causal direction is difficult to ascertain with this cross-sectional data, since it is hard to know whether Internet knowledge increases level of use, or whether use of the web increases expertise in this new technology. The issue of causal direction will be explored further when the article considers the scope of web use. The influence of lifetime experience on the frequency of Internet use by gender was further explored by defining three age groups (i.e., 18–35, 36–60, and 61–89), and determining the slope and standard error estimates for these three age groups for the variables in Table 3. In calculations not shown here, it was found that only for the elderly age group were females significantly less likely to use the Internet less frequently. For the other two age groups, women did use the web less frequently than men, but the results were not significant. The findings suggest that it is greater lifetime experience with technology that explains the greater use of the web by men, since there was no significant variation in frequency of use by gender for the young and middle-age sample.


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Previous studies of the Internet have shown that websites tend to be male oriented, female oriented, or androgynous. For example, an Australian study in 2000 (Australian Broadcast Authority, 2001) found that men were more likely to use the web for financial trading (23 percent vs. 14 percent), accessing the news (58 percent vs. 38 percent), and looking at sexually explicit materials (25 percent vs. 6 percent). Using this a priori information, the 21 websites were classified into these three categories, and their use by gender was examined for the 2000 GSS data (Table 4). The findings indicate the level of use of the Internet for the various specific websites by males and females, and compute a chi-square value for each of the sites to determine variation in use by gender, with higher chi-square values indicating greater gender variation in use. Male, female, and androgynous sites are classified in terms of whether there is a significant gender difference in use, with androgynous sites being classified as those where there is no significant difference in use by gender. The male- and female-oriented sites are those that one would expect from prior information. Men were more likely to use websites that provided financial information, government information, news and current events, and sexually explicit information. By contrast, women were significantly more likely to use religious and church sites, as well as cooking and recipe sites. In general, men were more likely than women to use most of the 21 categories of websites, a finding that one would expect, given the findings in Table 3. Using factor analysis, it is possible to construct four factors related to these 21 types of website.6 The first factor involves entertainment and personal interaction, with the variables that constitute the factor being humor, sexually explicit materials, and personal home page sites. The second factor involves government and politics, with the variables that constitute the factor being government information, news, and political information. The third factor involves art and education, with the variables that constitute this factor being art, music, school, and other educational sites. The fourth factor involves hobbies and practical information, with the variables that constitute this factor being cooking sites, hobby sites, and health sites. The first factor explains 20.72 percent of the variance in total scores, the second factor 8.11 6

The following formulas were used to compute the four factors: 1. Factor 1 5 0.629 n((Humor Site 0.4027)/0.4905)10.640 n((Sexually Explicit Materials 0.1353)/0.3423)10.573 n((Personal Home Page 0.3176)/0.4659) 5 Entertainment and Personal Interaction Sites. 2. Factor 2 5 0.727 n((Government Information 0.4605)/0.4988)10.551 n((News 0.7705)/0.4208)10.683 n((Political Information 0.2948)/0.4583) 5 Government and Political Information Sites. 3. Factor 3 5 0.588 n((Art 0.2776)/0.4477)10.518 n((Music 0.4529)/0.4982)10.595 n ((School Information 0.2371)/0.4526)10.623 n((Other Educational Sites 0.5653)/0.4961) 5 Educational and Art Sites. 4. Factor 4 5 0.722 n((Cooking 0.3328)/0.4716)10.516 n((Hobby 0.5002)/0.5004)1 0.556 n((Health 0.5046)/0.5004) 5 Hobby and Practical Activity Sites.

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Social Science Quarterly TABLE 4 Variation in Type of Website Used by Gender in 2000 GSS

Website Male-Oriented Websites Financial News and current events Government Sports Sexually explicit information Science Humor Personal home page Hobbies, crafts Sites related to work Female-Oriented Websites Religious and church related Cooking, recipes Androgynous Websites School you or children attend Other educational sites Travel Music, concerts Visual art/art museums Television/movie Health and fitness Games on computer Political information

% Male Use (N1) % Female Use (N2) Chi-Square 62.5 84.3 53.5 58.7 23.9 49.8 45.3 36.6 54.7 66.1

(331) (331) (331) (329) (331) (331) (331) (331) (331) (330)

47.0 70.0 38.9 25.2 4.2 37.5 35.2 26.4 46.3 55.5

(338) (337) (337) (337) (337) (337) (335) (331) (331) (331)

16.207 n n 19.231 n n 14.239 n n 76.553 n n 54.144 n n 10.339 n 7.054 n 7.076 n 4.704 n 7.817 n

6.3 23.9

(331) (331)

23.7 42.4

(337) 5.704 n (337) 25.941 n n

23.0 55.2 67.7 48.3 26.3 29.3 48.6 42.7 33.0

(330) (330) (331) (331) (331) (331) (331) (337) (330)

24.3 57.9 63.6 42.4 29.1 33.2 51.9 43.6 27.0

(337) (337) (338) (337) (337) (337) (337) (337) (337)

0.156 0.499 1.221 2.349 0.657 2.637 0.722 0.054 2.885

po0.01; n0.01opo0.05.

nn

percent of the variance, the third factor 6.37 percent, and the fourth factor 5.59 percent of the variance in total scores. OLS is used to relate these four factors individually to our independent variables. Table 5 shows the estimates for the four factors for the various independent variables. Gender is significant only for the first and fourth factor, with women being less likely to use the web for entertainment and personal interaction, and more likely to use it for hobbies and practical matters. With regard to Factor 1, entertainment and personal interaction, it is the less educated, males, African Americans, and those with more Internet knowledge who tend to use these type of sites. For Factor 2, related to government and news information, only Internet knowledge was significant. For Factor 3, related to art and education, it was the more educated, younger, and those with more Internet knowledge who were more likely to use these sites. For Factor 4, related to hobbies and practical activities, gender, being married, and Internet knowledge were positively related to this factor. For all these constructed types of sites, Internet knowledge significantly explains variation in the constructed scores.


265 TABLE 5

Ordinary Least Square Estimates of Independent Variables Related to Four Factors Linked with the Scope of Web Use Entertainment and Government and Personal Activity Political Art and Education Slope B

(S.E.)

Socioeconomic Variables Education 0.068 n n (0.023) R’s Income 1998tr 0.014 (0.011) R’s Income 19982tr 0.002 (0.001) Social and Geographic Variables Female 0.582 n n (0.112) Age 0.009 (0.005) Married 0.369 (0.131) Divorced 0.057 (0.166) Rural 0.253 (0.208) New England 0.016 (0.248) South central 0.173 0(.250) Pacific 0.200 (0.142) Racial and Ethnic Variables African American 0.372 n (0.186) Asian and other 0.279 (0.269) Latino 0.020 (0.198) Internet Skills 0.871 n n (0.186) Knowledgetr Constant 0.221 (0.494) N (552) 0.167 R2 nn

Slope B

(S.E.)

Slope B

(S.E.)

Hobbies and Practical Slope B

(S.E.)

0.058 0.013 0.002

(0.026) 0.068 n n (0.027) 0.036 (0.023) (0.013) 0.028 (0.013) 0.015 (0.011) (0.002) 0.001 (0.002) 0.003 n n (0.001)

0.198 0.011 0.030 0.066 0.241 0.275 0.313 0.177

(0.127) (0.006) (0.149) (0.188) (0.236) (0.283) (0.285) (0.161)

0.344 0.185 0.072

0.236 (0.133) 0.261 n (0.114) 0.023 n n (0.006) 0.007 (0.005) 0.106 (0.156) 0.331 n n (0.134) 0.357 (0.196) 0.082 (0.169) 0.125 (0.246) 0.098 (0.211) 0.172 (0.294) 0.058 (0.253) 0.386 (0.297) 0.242 (0.255) 0.022 (0.169) 0.046 (0.144)

(0.212) 0.381 (0.307) 0.349 (0.225) 0.200

(0.223) 0.041 (0.319) 0.517 (0.235) 0.051

(0.190) (0.275) (0.202)

1.326 n n (0.212) 1.287 n n (0.220) 0.733 n n (0.189) 3.778 n n (0.561) 2.914 n n (0.585) 1.391 n n (0.502) (552) (551) (553) 0.153 0.126 0.066

po0.01; n0.01opo0.05.

To determine the direction of the causal relation between Internet knowledge and frequency and scope of web use, it will be useful to use OLS estimates to relate Internet knowledge to these two types of web use, as well as the other independent variables in the study (Table 6). With regard to gender and race, it is clear from Table 6 that women possess less Internet knowledge than do men, as do African Americans in relation to whites and other racial groups. For many of the dependent variables, race and gender have an indirect effect on them through web knowledge. For the factor scores, rural location has an inverse impact on Internet knowledge. Frequency of web use has a significant impact on web knowledge, suggesting that the frequent use of the web increases Internet knowledge— certainly not an unexpected finding. Only Factors 2 and 3 significantly influence web knowledge. Thus, people who access these categories of sites acquire increased web knowledge, which is not the case for Factors 1 and 4. These type of sites may require greater technical sophistication to sift through the multiple layers of information. Using sites related to Factors 1 and 4, which concern entertainment and personal interaction, as well as

266

Social Science Quarterly TABLE 6 Ordinary Least Squares Estimates Relating Internet Knowledge to Other Independent Variables and Level of Web Use and Factor Scores for 2000 GSS Frequency of Web Use

Variable

Slope B

Socioeconomic Variables Education 0.027 n n R’s Income 1998tr 0.005 n 2 R’s Income 1998tr 0.000 Social and Geographic Variables Female 0.106 n n Age 0.001 Married 0.021 Divorced 0.021 Rural 0.091 New England 0.028 South central 0.100 Pacific 0.055 Racial and Ethnic Variables African American 0.090 n Asian and other 0.040 Latino 0.027 Level of Internet Use Web use (hrs/week) 0.077 n n Entertainment and personal — Government and political — Art and educational — Hobbies and practical — Constant 1.564 n n N (553) R2 0.230

(S.E.)

Factor Scores Slope B

(S.E.)

(0.005) (0.003) (0.000)

0.022 n n 0.006 n 0.000

(0.005) (0.002) (0.000)

(0.025) (0.001) (0.030) (0.037) (0.047) (0.056) (0.056) (0.032)

0.102 n n 0.001 0.026 0.009 0.110 n 0.026 0.066 0.040

(0.026) (0.001) (0.030) (0.037) (0.046) (0.055) (0.055) (0.032)

(0.042) (0.061) (0.005)

0.112 n n 0.013 0.026

(0.042) (0.060) (0.044)

(0.014) — — — — (0.089)

— 0.019 0.033 n n 0.028 n n 0.008 1.750 n n (549) 0.269

— (0.010) (0.009) (0.009) (0.010) (0.087)

po0.01; n0.01opo0.05.

nn

hobbies and practical information, has no significant impact on web knowledge, perhaps because these sites are more user friendly. It is probably the case that the use of these latter categories of sites does not require that the individual user master this new technology to the same degree as the second and third categories of sites. Discussion

This article has been concerned with the causes for gender variation in Internet use. Most prior studies of this question (e.g., Bimber, 2000; Ono and Zavodny, 2003) have focused on issues related to web access and frequency of web use. This study, employing nationwide GSS data for the year


267

2000, extended the analysis to consider email and chat-room use, as well as the scope of web use by employing four constructed factors from 21 categories of websites. Unlike previous studies, this one considered the importance of web knowledge for explaining variation in the frequency and scope of Internet use. With regard to web access, the findings in the article are consistent with other studies that found no gender variation in access. The web has become an integral part of the home and work environment, which has acted to give women access to this new technology. As men purchase home computers and obtain web access related to their work activity, it gives married persons in the household equal access to the web. Race and rural location did influence access, with African Americans and rural residents having lower levels of access. The racial disparity in access is likely caused by the work, home, and educational disadvantages suffered by African Americans in American society. The rural difference is due to the higher costs of access in rural areas, as well as the level of choice of providers. With regard to types of use, email and chat-room use involves personal communication between social actors, social professionals (e.g., social scientists), and social organizations (e.g., corporation actors), with email communication involving all three types of personal communication, and chatroom interaction involving mainly personal, anonymous communication. Women were more likely to use email communication than men, but the difference in use was not statistically significant. It is likely that men are more likely to use email communication for professional and commercial communication, while women use it more for personal, long-distance communication. This issue can be explored in future research by examining variations in types of email use. With regard to chat-room use, it tends to be male dominated, probably due to the fact that in society men use their higher social and economic power to dominate conversations, and chat-room use on the Internet reflects these conditions in the larger society (Carroll, 2002). The greater use of chat rooms by divorced individuals may reflect their need for increased social interaction after the rupturing of their family ties. Consistent with previous studies, the article found that men use the web more frequently than women, and this level of use was related to web knowledge. It was also found that socioeconomic status had no significant impact on the frequency of web use, probably because of the multiple uses of the Internet for personal and professional activities. The findings raise the question as to whether women are historically disadvantaged with their knowledge of this new technology and if their disadvantage explains their lower level of use. The article further explored the issue by considering the scope of Internet use. Descriptive data, using chi-square statistics, showed that men dominated certain types of websites (e.g., government, financial, sexually explicit materials), and in general used more types of sites than women. The article then utilized factor analysis to differentiate four types of websites from among the 21 types of sites examined.

268


The findings in Table 5 show that Internet knowledge increases use of all four types of sites. However, Table 6, examining the relation in the other direction, showed that only two types of sites (i.e., governmental and educational) increased Internet knowledge, probably because the users of these types of sites needed to know how to upload and download relevant information. Differences in Internet knowledge persist by gender, and may continue to do so because of the different types of websites accessed by each sex. The findings suggest that the higher level of use of a variety of websites by men increases their web knowledge, which in turn causes them to utilize this new technology more frequently than women. As women expand their use of different types of websites, their web knowledge and their use of the web should expand. It is likely that prior to the introduction of web technology in the 1990s women engaged less frequently in social activities (e.g., financial, scientific, governmental) that influenced relative use of the various websites. The growth of the web allowed women to more efficiently engage in these social activities (e.g., trading stocks), which should expand their use of the web and decrease the gender gap with regard to Internet knowledge. Future research is needed to specify the activities involved in the use of the various categories of websites and to identify the settings (e.g., work, home, school, public facilities) where these different types of social activities occur.

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