Older adults are the fastest-growing segment of computer and Internet users, but little is known about this segment of users. Are older adult Internet users a relatively homogenous population, as suggested in early research on Internet use in later life? Conversely, might the growing population of older users be becoming more diverse? Since agencies, governments, and businesses are providing an increasing number of online services to older adults, it is useful to know how older adults use online resources. An article in the Journals of Gerontology reports on a survey of 216 users of English-language websites intended for older adult users, identifying three subtypes of online older adults.
The author recruited participants from 16 English-language online forums intended for older adult users. Participants were administered an online questionnaire that asked about their patterns of online participation; their interest in 13 different popular topics on older-adult–targeted websites (topics identified through an earlier survey by the researcher about online communities for older adults); how participants benefited from online participation; and demographic information such as age, health, and education.
The author conducted a factor analysis of the participants’ reported interests. (A factor analysis is a statistical method of identifying statistical relationships among survey items.) Based on this, the topics of interest were grouped into four factors: aging-specific issues (interest in topics such as retirement or health), intellectual interest (e.g., gaining knowledge on topics such as social issues and research), light entertainment (such as shopping and leisure activities), and advanced tasks (e.g., technology and travel).
The author then conducted a cluster analysis based on these factors. Akin to how a factor analysis groups survey items, a cluster analysis identifies groups (or clusters) of participants who gave similar responses to the identified interest factors. Based on this, the investigator categorized respondents into three clusters: “information swappers” (participants who were primarily interested in exchanging information with other online community members), “aging-oriented” (mostly interested in reading about aging-related topics), and “socializers” (who had a mostly social motivation for engaging online). Information swappers were more interested in advanced topics such as finances and technology. Aging-oriented participants were less interested in using websites for entertainment value or general intellectual stimulation, and mostly focused on aging-specific topics. Socializers were most interested in using online forums for intellectual discussion and entertainment.
Finally, the author compared these clusters to identify demographic differences and different frequency of Internet usage across the groups of users, and to examine whether these groups perceived different benefits to their online participation. The group of information swappers had relatively more men, and members were likely to report higher incomes and better health than the other groups. Aging-oriented users were likely to be older and less likely to report excellent health, while the group of socializers had relatively more women than the other groups, and reported lower income.
The author notes that this sample should not be viewed as representative of the general older adult population of computer users, because it was a convenience sample among only English-language sites. Despite such limitations, the fact that this sample contained different subtypes of users—subtypes that varied demographically and in their use of online resources—suggests that further research should consider how different demographic groups of older adults make use of online communities and resources.
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