Using Two-Step Cluster Analysis and Latent Class Analysis to Construct a Typology of Internet Users Among Community-Dwelling Older People

References

• Abeele, M. V., Antheunis, M. L., & Schouten, A. P. (2014). Me, myself and my mobile: A segmentation of youths based on their attitudes towards the mobile phone as a status instrument. Telematics and Informatics, 31(2), 194–208. https://doi.org/10.1016/j.tele.2013.04.004
   Web of Science ®Google Scholar
• Arthanat, S., Vroman, K. G., Lysack, C., & Grizzetti, J. (2019). Multi-stakeholder perspectives on information communication technology training for older adults: Implications for teaching and learning. Disability and Rehabilitation Assistive Technology, 14(5), 453–461. https://doi.org/10.1080/17483107.2018.1493752
   PubMed Web of Science ®Google Scholar
• Benassi, M., Garofalo, S., Ambrosini, F., Sant’angelo, R. P., Raggini, R., De Paoli, G., Ravani, C., Giovagnoli, S., Orsoni, M., & Piraccini, G. (2020). Using two-step cluster analysis and latent class cluster analysis to classify the cognitive heterogeneity of cross-diagnostic psychiatric inpatients. Frontiers in Psychology, 11, 1085. https://doi.org/10.3389/fpsyg.2020.01085
   PubMed Web of Science ®Google Scholar
• Berner, J., Rennemark, M., Jogreus, C., Anderberg, P., Skoldunger, A., Wahlberg, M., Elmstahl, S., & Berglund, J. (2015). Factors influencing Internet usage in older adults (65 years and above) living in rural and urban Sweden. Health Informatics Journal, 21(3), 237–249. https://doi.org/10.1177/1460458214521226
   PubMed Web of Science ®Google Scholar
• Blank, G., & Groselj, D. (2014). Dimensions of Internet use: Amount, variety, and types. Information, Communication & Society, 17(4), 417–435. https://doi.org/10.1080/1369118X.2014.889189
   Web of Science ®Google Scholar
• Borg, K., & Smith, L. (2018). Digital inclusion and online behaviour: Five typologies of Australian internet users. Behaviour & Information Technology, 37(4), 367–380. https://doi.org/10.1080/0144929x.2018.1436593
   Web of Science ®Google Scholar
• Boz, H., & Karatas, S. E. (2015). A review on Internet use and quality of life of the elderly. Cypriot Journal of Educational Sciences, 10(3), 182–191. https://doi.org/10.18844/cjes.v1i1.64
   Google Scholar
• Brandtzæg, P. B., Heim, J., & Karahasanović, A. (2011). Understanding the new digital divide—A typology of Internet users in Europe. International Journal of Human-Computer Studies, 69(3), 123–138. https://doi.org/10.1016/j.ijhcs.2010.11.004
   Web of Science ®Google Scholar
• Calhoun, D., & Lee, S. B. (2019). Computer usage and cognitive capability of older adults: Analysis of data from the health and retirement study. Educational Gerontology, 45(1), 22–33. https://doi.org/10.1080/03601277.2019.1575026
   Web of Science ®Google Scholar
• Celeux, G., & Soromenho, G. (1996). An entropy criterion for assessing the number of clusters in a mixture model. Journal of Classification, 13(2), 195–212. https://doi.org/10.1007/BF01246098
   Web of Science ®Google Scholar
• Chiu, C.-J. (2019). Relationship between internet behaviors and social engagement in middle-aged and older adults in Taiwan. International Journal of Environmental Research and Public Health, 16(3), 416. https://doi.org/10.3390/ijerph16030416
   PubMed Web of Science ®Google Scholar
• Coneus, K., & Schleife, K. (2010). Online but still divided: Inequality in private internet use in Germany, ZEW Discussion Papers, No. 10-042. Mannheim: Zentrum für Europäische Wirtschaftsforschung (ZEW).
   Google Scholar
• Damodaran, L., Gilbertson, T., Olphert, W., Sandhu, J., & Craig, M. (2015). Digital inclusion-the vision, the challenges and the way forward. International Journal on Advances in Internet Technology, 8(3/4), 78–92.
   Google Scholar
• Dequanter, S., Gorus, E., Van Laere, S., De Witte, N., Verté, D., Steenhout, I., Fobelets, M., & Buyl, R. (2022). Internet use and cognitive frailty in older adults: A large-scale multidimensional approach. European Journal of Ageing, 19(4), 1–10. https://doi.org/10.1007/s10433-022-00686-2
   PubMed Web of Science ®Google Scholar
• De Witte, N., Gobbens, R., De Donder, L., Dury, S., Buffel, T., Schols, J., & Verté, D. (2013). The comprehensive frailty assessment instrument: development, validity and reliability. Geriatric Nursing, 34(4), 274–281. https://doi.org/10.1016/j.gerinurse.2013.03.002
   PubMed Web of Science ®Google Scholar
• De Witte, N., Hoeyberghs, L., Verte, E., Donder, L. D., Dierckx, E., Verte, D., Kempen, G. I. J. M., & Schols, J. M. G. A. (2018). The comprehensive frailty assessment instrument enables to detect multidimensional frailty in community dwelling older people. Healthy Aging Research, 07(3). https://doi.org/10.12715/har.2018.7.13
   Google Scholar
• Dutton, W. H., & Reisdorf, B. C. (2016). The internet through the ages. In P. G. Nixon, R. Rawal, & A. Funk (Eds.), Digital media usage across the life course (pp. 16–28). Taylor and Francis Group.
   Google Scholar
• Eurostat. (2023). Internet access and use statistics - households and individuals. https://ec.europa.eu/eurostat/statistics-explained/index.php?oldid=379591
   Google Scholar
• Ferreira, S. M., Sayago, S., & Blat, J. (2016). Going beyond telecenters to foster the digital inclusion of older people in Brazil: Lessons learned from a rapid ethnographical study. Information Technology for Development, 22(sup1), 26–46. https://doi.org/10.1080/02681102.2015.1091974
   Web of Science ®Google Scholar
• Field, A. (2017). Discovering statistics using IBM SPSS statistics. Sage Publications Ltd.
   Google Scholar
• Gallistl, V., Rohner, R., Hengl, L., & Kolland, F. (2021). Doing digital exclusion–technology practices of older internet non-users. Journal of Aging Studies, 59, 100973. https://doi.org/10.1016/j.jaging.2021.100973
   PubMed Web of Science ®Google Scholar
• Gallistl, V., Rohner, R., Seifert, A., & Wanka, A. (2020). Configuring the older non-user: Between research, policy and practice of digital exclusion. Social Inclusion, 8(2), 233–243. https://doi.org/10.17645/si.v8i2.2607
   Web of Science ®Google Scholar
• Gallistl, V., & Wanka, A. (2022). The internet multiple: How internet practices are valued in later life. International Journal of Ageing & Later Life, 15(2), 103–126. https://doi.org/10.3384/ijal.1652-8670.3563
   Google Scholar
• Garfein, A. J., & Herzog, A. R. (1995). Robust aging among the young-old, old-old, and oldest-old. Journal of Gerontology: Social Science, 50B(2), S77–S87. https://doi.org/10.1093/geronb/50B.2.S77
   Google Scholar
• Gariboldi, M. I., Chen, M., Wei, Y., Xu, S., Galea, G., & Lee, S. (2023). Towards digital healthy ageing: The case of Agatha and priorities moving forward. The Lancet Regional Health–Western Pacific, 35, 35. https://doi.org/10.1016/j.lanwpc.2022.100649
   Google Scholar
• Genoe, R., Kulczycki, C., Marston, H., Freeman, S., Musselwhite, C., & Rutherford, H. (2018). E-leisure and older adults: Findings from an international exploratory study. Therapeutic Recreation Journal, 52(1), 1. https://doi.org/10.18666/TRJ-2018-V52-I1-8417
   Web of Science ®Google Scholar
• Granero, R., Jiménez-Murcia, S., Del Pino-Gutiérrez, A., Mena-Moreno, T., Mestre-Bach, G., Gómez-Peña, M., Moragas, L., Aymamí, N., Giroux, I., Grall-Bronnec, M., Sauvaget, A., Codina, E., Vintró-Alcaraz, C., Lozano-Madrid, M., Camozzi, M., Agüera, Z., Martín-Romera, V., Sánchez-González, J., Menchón, J. M., … Sánchez, I. (2020). Gambling phenotypes in older adults. Journal of Gambling Studies, 36(3), 809–828. https://doi.org/10.1007/s10899-019-09922-4
   PubMed Web of Science ®Google Scholar
• Gródek-Szostak, Z., Siguencia, L. O., Niemczyk, A., & Seweryn, R. (2021). Digital exclusion of elderly citizens: Polish experiences based on the project adult social inclusion in a digital environment (ASIDE). Ekonomia, 27(4), 53–62. https://doi.org/10.19195/2658-1310.27.4.4
   Google Scholar
• Hargittai, E., & Dobransky, K. (2017). Old dogs, new clicks: Digital inequality in skills and uses among older adults. Canadian Journal of Communication, 42(2), 195–212. https://doi.org/10.22230/cjc.2017v42n2a3176
   Web of Science ®Google Scholar
• Hargittai, E., Piper, A. M., & Morris, M. R. (2019). From internet access to internet skills: Digital inequality among older adults. Universal Access in the Information Society, 18(4), 881–890. https://doi.org/10.1007/s10209-018-0617-5
   Web of Science ®Google Scholar
• Helsper, E. J. (2021). The Digital Disconnect: the social causes and consequences of digital inequalities. SAGE Publications Ltd .
   Google Scholar
• Helsper, E. J., & Reisdorf, B. C. (2013). A quantitative examination of explanations for reasons for internet nonuse. Cyberpsychology, Behavior, and Social Networking, 16(2), 94–99. https://doi.org/10.1089/cyber.2012.0257
   PubMed Web of Science ®Google Scholar
• Helsper, E. J., & Reisdorf, B. C. (2016). The emergence of a “digital underclass” in Great Britain and Sweden: Changing reasons for digital exclusion. New Media & Society, 19(8), 1253–1270. https://doi.org/10.1177/1461444816634676
   Web of Science ®Google Scholar
• Heponiemi, T., Gluschkoff, K., Leemann, L., Manderbacka, K., Aalto, A.-M., & Hyppönen, H. (2021). Digital inequality in Finland: Access, skills and attitudes as social impact mediators. New Media & Society.
   Google Scholar
• Heponiemi, T., Virtanen, L., Kaihlanen, A.-M., Kainiemi Päivikki Koponen, E., & Koskinen, S. (2022). Use and changes in the use of the internet for obtaining services among older adults during the COVID-19 pandemic: A longitudinal population-based survey study. New Media & Society, 14614448221097000, 146144482210970. https://doi.org/10.1177/14614448221097000
   Web of Science ®Google Scholar
• Hunsaker, A., & Hargittai, E. (2018). A review of Internet use among older adults. New Media & Society, 20(10), 3937–3954. https://doi.org/10.1177/1461444818787348
   Web of Science ®Google Scholar
• Kappeler, K., Festic, N., & Latzer, M. (2020). Who remains offline and why? Growing social stratification of internet use in the highly digitized Swiss society. University of Zurich.
   Google Scholar
• König, R., Seifert, A., & Doh, M. (2018). Internet use among older Europeans: An analysis based on SHARE data. Universal Access in the Information Society, 17(3), 621–633. https://doi.org/10.1007/s10209-018-0609-5
   Web of Science ®Google Scholar
• Krug, R. D. R., Xavier, A. J., & d’Orsi, E. (2018). Fatores associados à manutenção do uso da internet, estudo longitudinal EpiFloripa Idoso. Revista de Saúde Pública, 52, 37. https://doi.org/10.11606/S1518-8787.2018052000216
   PubMedGoogle Scholar
• Leukel, J., Schehl, B., & Sugumaran, V. (2021). Digital inequality among older adults: Explaining differences in the breadth of Internet use. Information, Communication & Society, 26(1), 1–16. https://doi.org/10.1080/1369118X.2021.1942951
   Web of Science ®Google Scholar
• Li, Y., Han, W. J., & Hu, M. (2022). Does internet access make a difference for older adults’ cognition in urban China? The moderating role of living arrangements. Health & Social Care in the Community, 30(4), e909–e920. https://doi.org/10.1111/hsc.13493
   PubMed Web of Science ®Google Scholar
• Lo Siou, G., Yasui, Y., Csizmadi, I., McGregor, S. E., & Robson, P. J. (2011). Exploring statistical approaches to diminish subjectivity of cluster analysis to derive dietary patterns: The Tomorrow project. American Journal of Epidemiology, 173(8), 956–967. https://doi.org/10.1093/aje/kwq458
   PubMed Web of Science ®Google Scholar
• Marques, D. R., Gomes, A. A., Clemente, V., Drake, C. L., Roth, T., Morin, C. M., & de Azevedo, M. H. P. (2021). Typologies of individuals vulnerable to insomnia: A two-step cluster analysis. Sleep and Biological Rhythms, 19(1), 33–44. https://doi.org/10.1007/s41105-020-00285-7
   Web of Science ®Google Scholar
• McCosker, A., Critchley, C., Walshe, J., Tucker, J., & Suchowerska, R. (2021). Accounting for diversity in older adults’ digital inclusion and literacy: The impact of a national intervention. Ageing & Society, 43(11), 1–21. https://doi.org/10.1017/S0144686X21001550
   Web of Science ®Google Scholar
• McCutcheon, A. L. (1987). Latent class analysis. Sage.
   Google Scholar
• McHugh, M. L. (2012). Interrater reliability: The kappa statistic. Biochemia Medica, 22(3), 276–282. https://doi.org/10.11613/BM.2012.031
   PubMed Web of Science ®Google Scholar
• Munguía, L., Jiménez-Murcia, S., Granero, R., Baenas, I., Agüera, Z., Sánchez, I., Codina, E., Del Pino-Gutiérrez, A., Testa, G., Treasure, J., & Fernández-Aranda, F. (2021). Emotional regulation in eating disorders and gambling disorder: A transdiagnostic approach. Journal of Behavioral Addictions, 10(3), 508–523. https://doi.org/10.1556/2006.2021.00017
   PubMed Web of Science ®Google Scholar
• Nelissen, S., Van den Bulck, J., & Beullens, K. (2017). A typology of cancer information seeking, scanning and avoiding: Results from an exploratory cluster analysis. Information Research: An International Electronic Journal, 22(2), n2.
   Web of Science ®Google Scholar
• Neves, B. B., Fonseca, J. R. S., Amaro, F., Pasqualotti, A., & Lozano, S. (2018). Social capital and Internet use in an age-comparative perspective with a focus on later life. Public Library of Science ONE, 13(2), e0192119. https://doi.org/10.1371/journal.pone.0192119
   Google Scholar
• Nimrod, G. (2020). Changes in internet use when coping with stress: Older adults during the COVID-19 pandemic. The American Journal of Geriatric Psychiatry, 28(10), 1020–1024. https://doi.org/10.1016/j.jagp.2020.07.010
   PubMed Web of Science ®Google Scholar
• Norusis, M. (2010). PASW Statistics Guide 18 Guide to Data Analysis. Pearson Education International Guilford Press.
   Google Scholar
• Nylund, K., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A monte carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535–569. https://doi.org/10.1080/10705510701575396
   Web of Science ®Google Scholar
• Nylund, K., Bellmore, A., Nishina, A., & Graham, S. (2007). Subtypes, severity, and structural stability of peer victimization: What does latent class analysis say? Child Development, 78(6), 1706–1722. https://doi.org/10.1111/j.1467-8624.2007.01097.x
   PubMed Web of Science ®Google Scholar
• Oktar, Y., & Turkan, M. (2018). A review of sparsity-based clustering methods. Signal Processing, 148, 20–30. https://doi.org/10.1016/j.sigpro.2018.02.010
   Web of Science ®Google Scholar
• Pantelaki, E., Maggi, E., & Crotti, D. (2023). Who is online? A latent class analysis of internet activities and determinant characteristics of older people. Computers in Human Behavior, 147, 107830. https://doi.org/10.1016/j.chb.2023.107830
   Web of Science ®Google Scholar
• Petrovčič, A., Reisdorf, B. C., Grošelj, D., & Prevodnik, K. (2022). A typology of aging internet users: Exploring digital gradations in internet skills and uses. Social Science Computer Review, 41(5), 1921–1940. https://doi.org/10.1177/08944393221117753
   Web of Science ®Google Scholar
• Reisdorf, B., & Rhinesmith, C. (2020). Digital inclusion as a core component of social inclusion. Social Inclusion, 8(2), 132–137. https://doi.org/10.17645/si.v8i2.3184
   Web of Science ®Google Scholar
• Robinson, L., Cotten, S. R., Ono, H., Quan-Haase, A., Mesch, G., Chen, W., Schulz, J., Hale, T. M., & Stern, M. J. (2015). Digital inequalities and why they matter. Information Communication and Society, 18(5), 569–582. https://doi.org/10.1080/1369118X.2015.1012532
   Web of Science ®Google Scholar
• Schehl, B., Leukel, J., & Sugumaran, V. (2019). Understanding differentiated internet use in older adults: A study of informational, social, and instrumental online activities. Computers in Human Behavior, 97, 222–230. https://doi.org/10.1016/j.chb.2019.03.031
   Web of Science ®Google Scholar
• Sghaier, S., Asiamah, N., Danquah, E., Opuni, F. F., & Hatsu, S. (2022). Information technology ability mediates the association between older adults’ subjective age and social activity: A STROBE-compliant cross-sectional analysis. Archives of Gerontology and Geriatrics, 103, 104790. https://doi.org/10.1016/j.archger.2022.104790
   PubMed Web of Science ®Google Scholar
• Tsai, H. S., Shillair, R., & Cotten, S. R. (2017). Social support and “playing around”: An examination of how older adults acquire digital literacy with tablet computers. Journal of Applied Gerontology: The Official Journal of the Southern Gerontological Society, 35(1), 29–55. https://doi.org/10.1177/0733464815609440
   Google Scholar
• van Boekel, L. C., Peek, S. T., & Luijkx, K. G. (2017). Diversity in Older Adults’ use of the Internet: Identifying subgroups through latent class analysis. Journal of Medical Internet Research, 19(5), e180. https://doi.org/10.2196/jmir.6853
   PubMedGoogle Scholar
• van Deursen, A. J. A. M., & Helsper, E. J. (2015). A nuanced understanding of Internet use and non-use among the elderly. European Journal of Communication, 30(2), 171–187. https://doi.org/10.1177/0267323115578059
   Web of Science ®Google Scholar
• van Deursen, A. J., van der Zeeuw, A., de Boer, P., Jansen, G., & van Rompay, T. (2021). Digital inequalities in the Internet of Things: Differences in attitudes, material access, skills, and usage. Information, Communication & Society, 24(2), 258–276. https://doi.org/10.1080/1369118X.2019.1646777
   Web of Science ®Google Scholar
• van Deursen, A. J., & Van Dijk, J. A. (2014). The digital divide shifts to differences in usage. New Media & Society, 16(3), 507–526. https://doi.org/10.1177/1461444813487959
   Web of Science ®Google Scholar
• Van Dijk, J. A. (2017). Digital divide: Impact of access. The International Encyclopedia of Media Effects, 1–11. https://doi.org/10.1002/9781118783764.wbieme0043
   Google Scholar
• Vulpe, S., & Craciun, A. (2020). Silver surfers from a European perspective: Technology communication usage among European seniors. European Journal of Ageing, 17(1), 125–134. https://doi.org/10.1007/s10433-019-00520-2
   PubMed Web of Science ®Google Scholar
• Wallinheimo, A.-S., & Evans, S. L. (2021). More frequent internet use during the COVID-19 pandemic associates with enhanced quality of life and lower depression scores in middle-aged and older adults. Healthcare.
   Google Scholar
• Wang, H., Wang, J., & Zhu, L. (2021). A new validity function of FCM clustering algorithm based on intra-class compactness and inter-class separation. Journal of Intelligent & Fuzzy Systems, 40(6), 12411–12432. https://doi.org/10.3233/JIFS-210555
   Web of Science ®Google Scholar
• Woodward, A. T., Freddolino, P. P., Wishart, D. J., Bakk, L., Kobayashi, R., Tupper, C., Panci, J., & Blaschke-Thompson, C. M. (2013). Outcomes from a peer tutor model for teaching technology to older adults. Ageing & Society, 33(8), 1315–1338. https://doi.org/10.1017/S0144686X12000530
   Web of Science ®Google Scholar
• Yang, H., Chen, H., Pan, T., Lin, Y., Zhang, Y., & Chen, H. (2022). Studies on the digital Inclusion among older adults and the quality of life—A Nanjing example in China. Frontiers in Public Health, 10, 811959. https://doi.org/10.3389/fpubh.2022.811959
   PubMed Web of Science ®Google Scholar
• Yates, S. J., Carmi, E., Lockley, E., Pawluczuk, A., French, T., & Vincent, S. (2020). Who are the limited users of digital systems and media? An examination of UK evidence. First Monday, 25(7). https://doi.org/10.5210/fm.v25i7.10847
   Google Scholar