What do We Know About Semantic Verbal Fluency in Turkish?
November 5, 2020
The semantic (or categorical) verbal fluency test involves asking a participant to generate as many words from a given category as they can for 60-90 seconds. Previously, I have collaborated with colleagues with the aim of proposing simple automatic scoring methods for verbal fluency data. My PhD student Rabia Yasa Kostas, co-supervised by Sarah MacPherson, is working on developing such an automated scoring tool for Turkish.
As our first step, we wanted to see what data there is on Turkish, what metrics had been used, and whether there were any norms that our tool might draw on. This led us to perform a systematic review of what is known about semantic verbal fluency in Turkish, the protocol for which has been registered and is available from Prospero. It turns out that there is a lot more data than we originally thought, but most of it is in unpublished Turkish dissertations. We are about to finish the full text screening stage of the review, and hope to complete data extraction and write up before Christmas.
So far, almost all of the relevant papers that we have found report results from the classic Semantic Verbal Fluency design (name as many animals as you can in 60 seconds), and use word counts to score them, not analysis techniques that uncover potential internal structure, such as Troyer’s clustering and switching approach.
I will blog again once our analysis is complete – in the mean time, wish us luck as we delve into data extraction!
PDF of Powerpoint
R1: Semantic fluency as neuropsychological test
Cecato, J. F., Martinelli, J. E., Izbicki, R., Yassuda, M. S., & Aprahamian, I. (2015). A subtest analysis of the Montreal cognitive assessment (MoCA): Which subtests can best discriminate between healthy controls, mild cognitive impairment and Alzheimer’s disease? International Psychogeriatrics / IPA, 1–8. https://doi.org/10.1017/S1041610215001982
Mioshi, E., Dawson, K., Mitchell, J., Arnold, R., & Hodges, J. R. (2006). The Addenbrooke’s Cognitive Examination Revised (ACE-R): A brief cognitive test battery for dementia screening. Int J Geriatr Psychiatry, 21(11), 1078–1085. https://doi.org/10.1002/gps.1610
R2: Scoring Semantic Fluency
Mayr, U. (2002). On the dissociation between clustering and switching in verbal fluency: Comment on Troyer, Moscovitch, Winocur, Alexander and Stuss. Neuropsychologia, 40(5), 562–566. https://doi.org/10.1016/S0028-3932(01)00132-4
Troyer, A. K., Moscovitch, M., & Winocur, G. (1997). Clustering and switching as two components of verbal fluency: Evidence from younger and older healthy adults. Neuropsychology, 11(1), 138–146.
R3: norms for English
These are two of several papers that exist
Tombaugh, T. N., Kozak, J., & Rees, L. (1999). Normative data stratified by age and education for two measures of verbal fluency: FAS and animal naming. Archives of Clinical Neuropsychology, 14(2), 167–177. https://doi.org/10.1016/S0887-6177(97)00095-4
Troyer, A. K. (2000). Normative data for clustering and switching on verbal fluency tasks. Journal of Clinical and Experimental Neuropsychology, 22(3), 370–378. https://doi.org/10.1076/1380-3395(200006)22:3;1-V;FT370
R4: common tool in neuropsychological assessment for Turkish patients
This is evidenced by numerous dissertations; we give two examples here.
Kiliç, Muhsin Koray (2015). Panic disorder: Neurocognitive functions and impulsivity. Unpublished dissertation
Yazici, Melik (2019). Neuropsychological Evaluation of the Population of Eastern and South-Eastern Anatolia on Executive Functions and Complex Attention. Unpublished dissertation.
R5: normative studies
Şentürk, T. (2019). Turkish Normative Data Of Semantic And Phonemic Verbal Fluency Tests. Dokuz Eylul University. Unpublished Masters Thesis
Tuncer, A. M. (2012). VERBAL FLUENCY PERFORMANCE OF TURKISH SPEAKING ADULTS. Anadolu University. Unpublished PhD thesis
Kastenbaum, J. G., Bedore, L. M., Peña, E. D., Sheng, L., Mavis, I., Sebastian-Vaytadden, R., … & Kiran, S. (2019). The influence of proficiency and language combination on bilingual lexical access. Bilingualism, 22(2), 300-330.
Nielsen, T. R., & Waldemar, G. (2016). Effects of literacy on semantic verbal fluency in an immigrant population. Aging, Neuropsychology, and Cognition, 23(5), 578-590.
Uysal, A. A., & Maviş, I. (2018). Assessing Executive Functions of Turkish-German Bilinguals, Turkish Speaking Children with S/LI and Turkish Speaking Monolingual Children. Int Arch Commun Disord, 1(008).
Does Cognitive Function Predict Self-Reported Ability to Use Computers?
November 5, 2020
The world is rapidly becoming digital. But what about those who, for whatever reason, find it hard to access digital services? In order to understand how best to support this group of people, we need to first of all seek to find out who they are.
In this study, we used computer self-efficacy (people’s self-reported ability to use computers) as a proxy marker – those who feel less able to cope with computers are more likely to feel “digitally left behind”. We leveraged the large, well-curated SHARE data set, a comprehensive survey of health, ageing, and retirement which includes data on socioeconomic circumstances as well as health, IT use, and cognition. We were particularly interested in the extent to which lower cognitive abilities (assessed using memory and semantic fluency) might be associated with feeling “left behind”.
However, in our analysis, we found that the single most important predictor of feeling digitally left behind, feeling unable to use technology, was prior experience with using computers in their job before retirement. We also saw effects of gender (women feeling more left behind than men), location (people in rural areas feeling more left behind than people in urban areas), and ability to make ends meet (poorer people feeling more left behind).
As these findings suggest, there are no simple causal connections here. First of all, this is self-rated ability, which always diverges from actual ability, secondly, well-educated male city dwellers who are well off are more likely to have had white collar jobs that require IT use, and finally, the model we used for our analysis did not include any interactions.
Our next steps are to refine the model, and to construct country-specific models. Initial investigations by Yifeng Gao in his Master’s Thesis suggest that the contributions of each of the factors may vary quite substantially between countries.
PDF of Powerpoint
R1: digitally left behind:
Bailey, Dan, Perks, M., & Winter, Chris. (2018). Supporting the Digitally Left Behind. Ingenia, 76.
Cotten, S. R., Francis, J., Kadylak, T., Rikard, R. V., Huang, T., Ball, C., & DeCook, J. (2016). A Tale of Two Divides: Technology Experiences Among Racially and Socioeconomically Diverse Older Adults. In J. Zhou & G. Salvendy (Eds.), Human Aspects of IT for the Aged Population. Design for Aging (pp. 167–177). Springer International Publishing.
Quan-Haase, A., Martin, K., & Schreurs, K. (2016). Interviews with digital seniors: ICT use in the context of everyday life. Information, Communication & Society, 19(5), 691–707. https://doi.org/10.1080/1369118X.2016.1140217
R2: trend towards digital
Smith, A. C., Thomas, E., Snoswell, C. L., Haydon, H., Mehrotra, A., Clemensen, J., & Caffery, L. J. (2020). Telehealth for global emergencies: Implications for coronavirus disease 2019 (COVID-19). Journal of Telemedicine and Telecare, 26(5), 309–313. https://doi.org/10.1177/1357633X20916567
R3: reasons for problems with using technology
Gilleard, C., & Higgs, P. (2008). Internet use and the digital divide in the English longitudinal study of ageing. European Journal of Ageing, 5(3), 233. https://doi.org/10.1007/s10433-008-0083-7
Börsch-Supan, A., J. Bristle, K. Andersen-Ranberg, A. Brugiavini, F. Jusot, H. Litwin, G. Weber (Eds.) (2019). Health and Socio-Economic Status over the Life Course. First Results from SHARE Waves 6 and 7. Berlin: De Gruyter
Bergmann, M., A. Scherpenzeel and A. Börsch-Supan (Eds.) (2019). SHARE Wave 7 Methodology: Panel Innovations and Life Histories. Munich: Munich Center for the Economics of Aging (MEA).
König, R., & Seifert, A. (2020). From Online to Offline and Vice Versa: Change in Internet Use in Later Life Across Europe. Frontiers in Sociology, 5, 4. https://doi.org/10.3389/fsoc.2020.00004
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
Tavares, A. I. (2020). Self-assessed health among older people in Europe and internet use. International Journal of Medical Informatics, 141, 104240. https://doi.org/10.1016/j.ijmedinf.2020.104240
R5: normalisation method
Gelman, A. (2008). Scaling regression inputs by dividing by two standard deviations. Stat Med, 27(15), 2865–2873. https://doi.org/10.1002/sim.3107
R6: people may not realise how well they are doing
Choi, J. S., Betz, J., Deal, J., Contrera, K. J., Genther, D. J., Chen, D. S., Gispen, F. E., & Lin, F. R. (2015). A Comparison of Self-Report and Audiometric Measures of Hearing and Their Associations With Functional Outcomes in Older Adults. Journal of Aging and Health, 28(5), 890–910. https://doi.org/10.1177/0898264315614006