Department of Commerce, , Table Descriptive analyses of Internet access also disclose gradients by age. From the available data, then, it is evident that older persons are less likely than their younger counterparts to live in households where a computer is present, and they are less likely to use a computer when it is available U. Bureau of the Census, ; U. Department of Commerce, It is also clear that many of the compositional characteristics of the older population are associated with lower rates of access and use.
Persons with a bachelor's degree or more are four times as likely to have a computer as persons with less than a high school diploma. Being married, being in the labor force, and living in multiple-person households are all related to access to a home computer U. Bureau of the Census, However, the extent to which age differentials in access to and use of home computers can be accounted for by such compositional characteristics remains an unanswered question.
On the basis of a sample of persons aged 40 or older in southeastern Michigan, Morrell and colleagues found that the effect of age on World Wide Web use was diminished, but still significant, after they controlled for access, demographic, and socioeconomic factors. The purpose of this article, therefore, is to shed additional light on questions of computer availability and computer use in old age and to bring findings from a rather substantial data set to bear on the issues. We contend that it is important to move beyond the descriptive zero-order analyses predominant in the literature in order to examine a number of potential influences simultaneously and to try to disentangle the effects of age from compositional factors characterizing diverse age cohorts.
Specifically, our intent is to utilize a large, nationally representative data set—the September Current Population Survey CPS and its supplement on home computers and Internet use—to examine patterns of home computer availability and use. Our primary research question is, to what extent do compositional differences account for the global relationship between age and availability and use of computers?
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Though our data set is cross-sectional, meaning we can only examine age differences, the robust nature of the CPS sample is such that even a cross-sectional analysis is informative as it is based on by far the largest, most recent, and most representative sampling of computer usage. Data for this study have been drawn from the September CPS and its associated supplement on computer and Internet utilization. The CPS is a monthly survey of approximately 56, U.
Housing units are selected by use of a multistage probability design. Information about the household and each of its members is collected from a single knowledgeable respondent by means of in-person or telephone interviews. Primarily intended to generate information on employment and unemployment, the CPS also includes a wide array of social, economic, and demographic measures see U.
Bureau of the Census, , for additional information about the CPS. The September CPS includes data on a total of , persons residing in 56, households. Because our interest is in home computer availability and use among adults, we restrict the analysis to persons who are 25 years of age and older. This is also consistent with the Census convention that reports level of educational attainment—one of our principal control variables—for the population 25 and older, or approximately the age when formal education has been completed for much of the population.
Limiting the analysis to individuals 25 and older drops the sample size to 93, persons in 52, households. When missing data on all variables are taken into account, the final sample for the analysis is further reduced to 71, individuals in 40, households. As is evident, the truncated analysis sample corresponds quite closely to the parent sample from which it is drawn.
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The gender distributions are identical, and the average of the absolute differences on race and education is less than. Thus, these distributional comparisons show only negligible discrepancies between the samples and indicate that bias stemming from missing data is not likely to be problematic. The dependent variables are based on responses to two questions. In order to allow for a detailed examination of age differences in home computer availability and use, we categorize age into fourteen 5-year groups, beginning with 25—29 and ending with 90 years of age and older.
Given our interest in determining whether zero-order age differences in home computer availability and use might be at least partially attributable to differences between age groups in terms of compositional characteristics, several control variables are used.
Employment status is coded as a four-category variable employed, unemployed, retired, or not in the labor force for other reasons , marital status is divided into six groups married, spouse present; married, spouse absent; widowed; divorced; separated; or never married , and Hispanic origin and race includes five groups Hispanic origin; White; Black; American Indian, Aleut, or Eskimo; or Asian or Pacific Islander. Finally, we have created a summary, additive measure of disabilities. Scores range from 0 no disabilities to 5 five disabilities , based on the presence or absence of four physical conditions 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 and on whether the person has difficulty going outside the home alone e.
We elected to use the additive index in the analysis rather than the individual disability measures after preliminary analyses showed that all measures were positively correlated with each other and all were negatively correlated with both home computer availability and use. This multivariate technique can be used to examine the relationship between a single predictor variable and a dependent variable e. No assumption of linearity is required. To determine the relationship between an independent and a dependent variable, MCA yields gross or unadjusted mean scores on the dependent variable for respondents in each category of the independent variable.
With multiple predictors, MCA provides adjusted net scores, which are equivalent to the mean value of the dependent variable for each category of a predictor after the effects of the remaining predictors are controlled for.
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Eta and beta coefficients are available to assess the strength of relationships at the bivariate and multivariate levels, respectively, as are F tests to determine whether any given predictor e. We begin by examining the patterns of age differences in the compositional characteristics of interest in our analysis.
The data in Table 2 present the relationships between age and each control variable. For presentation purposes and to conserve space, we use a collapsed, seven-category measure of age as well as truncated measures of several of the control variables with a focus on subsets of categories that are of particular relevance. The results of these analyses point to clear and significant differences in the compositional characteristics of the age groups. The prevalence of retirement is greater with advancing age, as is widowhood; at the same time, members of minority groups comprise a smaller percentage of persons at the older ages.
Increasing age brings with it a higher percentage of women, generally higher percentages of persons at the lower levels of family income and lower percentages at higher income levels, as well as higher percentages of persons living alone. Older persons are less likely than younger persons to have graduated from high school or to hold college degrees.
They are more likely to report having one or more disabilities. Relationships between home computer availability, age, and our control variables are presented in Table 3. Among those who do have a computer at home, the relationships between personal use of the computer, age, and the control variables are shown in Table 4. In light of the 0 or 1 coding of the dependent variables, the unadjusted and adjusted mean scores are equivalent to the proportion of persons in each category who have a computer in their home and the proportion of persons in each category who use that computer.
Focusing first on living in a household where a computer is present, one sees that the bivariate data in Table 3 show a clear pattern of age differences in home computer availability—an increase from ages 25—29 through ages 40—44, followed by a steady decline through ages 85— Peak availability occurs in the 40—44 age range, in which nearly three of four persons have computers at home, dropping to less than half at ages 65—69 and to a low of approximately one out of six persons among those over 85 years of age.
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These data also indicate that computer availability is lower in the homes of persons who are retired or widowed and in the homes of persons who are of Hispanic origin or who are Black, American Indians, Eskimos, or Aleuts. Women are less likely than men to reside in households where computers are available, as are people with lower family incomes or who live in households composed of one or two people. Finally, low levels of education and having disabilities are also associated with not having a computer in the home.
As indicated earlier, our primary interest is to determine whether, and to what extent, lower rates of computer availability and use among the older population are due to compositional characteristics that serve to dampen the likelihood that a computer is at hand and is used by older persons. We have shown in Table 2 that age is associated with a variety of compositional characteristics e.
It is reasonable to assert that the effect of such compositional characteristics, then, is to depress rates of computer availability among the older population. At the same time, several of the control variables are, themselves, intercorrelated e. Thus, we now seek to determine the patterns of age and other differences after we have controlled for the effects of the remaining variables.
It is clear from the column of data presenting adjusted mean scores in Table 3 that a portion, but not all, of the zero-order pattern of age differences in home computer availability is attributable to compositional effects, with the greatest impact being at the oldest ages. Among persons 90 years of age and older, for example, only The overall magnitude of the reduction in age differences is also evident from a comparison of eta and beta; although still significant, the beta for the adjusted effects is.
Even though all of the adjusted effects of the control variables remain significant, the magnitude of those relationships decreases and several of the major differences observed at the bivariate level are appreciably reduced.
For example, after the remaining variables are controlled for, retirees are only marginally less likely than persons in other employment status categories to reside in households where a computer is available, and only a minor difference now separates widows from persons who are married with a spouse present.
Ethnic and racial differences persist, albeit in diminished magnitude, and women are now slightly more likely than men to reside in households where a computer is available. Family income, household size, and education differences remain evident, although the betas are consistently smaller than the zero-order etas. With the effects of other variables controlled, only minor differences occur by number of disabilities.
Overall, age proves to be the third strongest predictor of the availability of computers in the household, following family income and education, and, taken together, all of the variables explain The data in Table 4 present unadjusted and adjusted relationships between age, the control variables, and computer use for persons who reside in households where a computer is available. Taken at face value, the bivariate relationships show clear age differences in computer use.
Advancing age is associated with lower levels of home computer use, with more precipitous declines occurring after ages 70— Retirees, persons who are widowed, and those of Hispanic origin are least likely to make use of computers in their homes, but women are slightly more likely than men to take advantage of computer availability. Use generally increases with family income and education but declines with increasing household size, which is a relationship running in the opposite direction of that seen for availability. Finally, the percentage of persons using a home computer declines as the number of disabilities rises.
An examination of age differences in computer use adjusted for the effects of the control variables shows that only a small portion of the original bivariate age effects is due to variation in compositional characteristics. This is particularly the case among the oldest age groups, whereas the effects of the control variables cease to dampen rates of use prior to ages 75— That the effects of the compositional characteristics play a limited role in explaining age differences in computer use is evident from the fact that the multivariate beta.
Comparisons of the etas and betas show that differences in computer use by employment status are reduced appreciably, as are differences by marital status and Hispanic origin or race, although to a lesser extent. If the effects of other variables are taken into account, computer use among retirees and widows is considerably higher than was the case when only unadjusted effects were looked at. Women continue to have higher rates of use than men, family income differences are greatly diminished, and the effect of household size—with lower rates of use associated with increasing household size—is strengthened.
Level of educational attainment continues to have an effect on computer use, although it is clear that a substantial portion of the bivariate effects of disabilities can be explained by the other controls.
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A cross-sectional analysis has intrinsic limitations, but the quality of the data presented here enables us to generate important insights into the reasons for age differences in computer availability and use over the life course. It has generally been presumed that older persons are on the far side of a digital divide because of attitudinal and comfort factors inhibiting utilization of the new technologies.
Our contention is that the role of composition factors defining older cohorts also has to be unraveled to arrive at a more complete understanding about age differences in the use of computer technology. The very characteristics that set older persons apart from their younger counterparts i. The unadjusted mean scores for computer availability shown in Table 3 might lead one to conclude that there is a long and fairly level plateau from ages 30—34 to 50—54, followed by a decline that becomes even more pronounced beginning at ages 60— At first glance, then, these data appear to confirm what others have suggested.
However, once key compositional factors such as gender, education, income, size of household, disabilities, employment and marital status, and race are controlled, a subtle but important shift occurs. A long, slow, linear decline does take place, but it is neither as rapid nor as severe as the bivariate analysis suggests.