Acquiring Knowledge From the Media in the Internet Age

Abstract

Democratic foundations rest on public support and a reasonably broad distribution of knowledge. The media have traditionally been assigned responsibility for providing much of that knowledge, but they do not inform all citizens equally; and communication scholars have, thus, studied the threat represented by the knowledge gap phenomenon. This study examines several factors on which the knowledge gap perspective is based in the online environment, including knowledge about international affairs, the local community, and the Internet. Results confirm gaps in all 3 knowledge types by education and income groups, including positive relationships between several media use and knowledge measures.

Keywords:

• Channel Influence
• Internet
• Knowledge Domains
• Knowledge Gap
• Learning
• Media Effects

Notes

Note. The table includes bivariate correlations between the media use variables and the knowledge indexes based on the entire sample unless otherwise indicated.

*p < .05. **p < .01. ***p < .001. †p < .10.

Note. For each model, the level of education was entered as a variable with three groupings corresponding to that described in the text, with the frequency of newspaper reading or number of years on the Internet as random factors. A similar pattern is found when the media variables are entered as covariates with education to predict knowledge indexes.

Note. For gender, 1 = male, 2 = female; for ethnicity, White = 1, others = 2.

*p < .05. **p < .01. ***p < .001. †p < .10.

Note. N = 340. For gender, 1 = male, 2 = female; for ethnicity, White = 1, others = 2. *p < .05. **p < .01. ***p < .001. †p < .10.

A Nielsen ranking of the most popular Web sites (listed by the owning company) lists the following: Microsoft®, Time Warner, Yahoo!®, Google™, eBay®, the U.S. government, Amazon.com®, the Walt Disney group, Ask Jeeves, and InterActiveCorp (see www.Nielsen.com, November 2004 monthly panel).

For examples, see Chaffee, Zhao, and Leshner (1994); Drew and Weaver (1990, 1991); Eveland and Scheufele (2000); Hofstetter, Sticht, and Hofstetter (1999); Kwak (1999); Lee and Cappella (2001); Lemert (1993); Martinelli and Chaffee (1995); McLeod et al. (1996); Vincent and Basil (1997); Weaver and Drew (1995).

Contact us for a copy of a table reporting results of studies focusing on knowledge acquisition and the knowledge gap in recent years.

For example, Scheufele, Nisbet, Brossard, and Nisbet (2004) used four items tapping correct identification of public figures and knowledge of current events. Norris and Sanders (2003) used true/false factual statements about whether the rates of unemployment, crime, income tax, and currencies were rising or declining. Eveland (2004) asked respondents to identify office holders, to recall names of candidates, and to identify candidates by ideology. Baum (2003) asked two open-ended questions about where General Manuel Noriega took refuge to escape U.S. troops and how control of the Panama Canal would change in the future. Althaus and Tewksbury (2000) added together correct responses to eight factual knowledge questions.

Average Internet knowledge index for men = 2.54 (N = 156) and women = 2.04 (N = 195), t(349) = 3.0, p < .01.

Averages were international affairs knowledge: Whites = 2.11, others = 1.85, t(347) = 2.07, p < .05; community knowledge: Whites = 2.25, others = 1.57, t(347) = 4.53, p < .001; and Internet knowledge: Whites = 2.40, others = 1.97, t(347) = 2.40, p < .02. The sample included 234 Whites and 115 others.

Age was broken down into three groups of almost equal sizes (117 aged 18–33; 115 aged 34–47; and 115 aged 48 +). Knowledge differences were international public affairs: youngest = 1.82, middle = 1.94, oldest = 2.33, F(2, 344) = 7.02, p < .001; community knowledge: youngest = 1.50, middle = 2.17, oldest = 2.41, F(2, 344) = 15.70, p < .001; and Internet knowledge: youngest = 2.43, middle = 2.63, oldest = 1.77, F(2, 344) = 10.10, p < .001.

Community knowledge averages were married = 2.32 (N = 183); divorced, widowed, or separated = 1.63 (N = 65); and never married = 1.78 (N = 101), F(2, 346) = 9.12, p < .001. The differences for Internet knowledge by marital status approached statistical significance: married = 2.43; divorced, widowed, or separated = 1.91; and never married = 2.21, F(2, 346) = 2.81, p < .06.

Bivariate correlations show the same pattern: correlations between education and international public affairs knowledge (r = .30, p < .001), Internet knowledge (r = .40, p < .001), and community knowledge (r = .26, p < .001); household income and international public affairs knowledge (r = .27, p < .001), Internet knowledge (r = .26, p < .05), and community knowledge (r = .30, p < .01). Age is correlated with the knowledge indexes as expected (international public affairs knowledge: r = .16, p < .001; Internet knowledge: r = −.24, p < .001; and community knowledge: r = .24, p < .001), with a similar pattern found for gender, where high = female (international public affairs knowledge: r = −.12, p < .01; Internet knowledge: r = − .16, p < .001; and community knowledge: r = − .14, p < .01). When both age and gender are controlled, the positive relationships between status and knowledge persist, and even slightly increase in most cases: education with international public affairs knowledge (partial r = .32, p < .001), Internet knowledge (partial r = .40, p < .001), and community knowledge (partial r = .29, p < .001); income with international public affairs knowledge (partial r = .27, p < .001), Internet knowledge (partial r = .24, p < .001), and community knowledge (partial r = .31, p < .001).

Contact us for a complete list of bivariate correlations between social categories and media use.

The R ² change is .047 (F change = 17.2, p < .001) for age as a predictor of community knowledge with the following variables already in the equation: education, income, gender, magazine readership, hours watched television, frequency read newspaper, and years on Internet.

The R ² change is .036 (F change = 12.8, p < .001) for age as a predictor of knowledge of international public affairs with the following variables already in the equation: education, income, gender, magazine readership, hours watched television, frequency read newspaper, and years on the Internet.

The R ² change is .003 (F change = 1.40, ns) for age as a predictor of community knowledge with the following variables already in the equation: education, income, gender, magazine readership, hours watched television, frequency read newspaper, and years on the Internet.

The R ² change is .014 (F change = 5.20, p < .024) for age as a predictor of community knowledge with the following variables already in the equation: education, income, gender, magazine readership, hours watched television, and frequency read newspaper.

Additional information

Notes on contributors

Leo W. Jeffres

Leo W. Jeffres (Ph.D., University of Minnesota, 1976) is a Professor in the School of Communication at Cleveland State University.

Kimberly Neuendorf

Kimberly Neuendorf (Ph.D., Michigan State University, 1982) is a Professor in the School of Communication at Cleveland State University.

David J. Atkin

David J. Atkin (Ph.D., Michigan State University, 1986) is a Professor in the Department of Communication at the University of Connecticut.