The Impact of Adults' Used Skills on Their Self-Evaluated Skills and Social Lives Over Time
Previous research focused on individuals’ background, contexts and cognitive performance in education, work, and life. Given the increasing numb.
- Pub. date: June 15, 2023
- Pages: 97-118
- 234 Downloads
- 439 Views
- 0 Citations
Previous research focused on individuals’ background, contexts and cognitive performance in education, work, and life. Given the increasing number of people living alone temporarily, the question arises whether the frequent use of skills, including social skills, relates to individuals’ later positively self-evaluated skills and social lives. Based on an integrated framework, the current analysis aimed to disentangle these relationships with longitudinal data from Germany over three years. The target sample consisted of n = 3263 working adults. A Bayesian structural equation model included adults’ frequent use of skills, self-evaluated skills, household size, close friends, and seven covariates (e.g., numeracy and literacy test scores, weekly working hours. The results suggested positive relationships between adults’ frequent use of numeracy, literacy, and social skills and later self-evaluations (except literacy used on self-evaluated numeracy). Those who less frequently used social skills three years earlier were also less likely to have a larger household size than those who reporting frequently using their social skills. Adults who frequently used literacy skills three years earlier reported higher numbers of close friends than those who less frequently used literacy. The findings highlight the importance of adults’ social skills and frequently used skills for self-evaluated numeracy and literacy.
Keywords: Adults, frequently used skills, literacy, numeracy, PIAAC-L, social lives.
0
References
American Psychological Association. (n.d.). Learning. In APA dictionary of psychology. Retrieved November 28, 2022 from https://dictionary.apa.org/learning
Braun, H. (2018). How long is the shadow? The relationships of family background to selected adult outcomes: Results from PIAAC. Large-Scale Assessments in Education, 6, Article 4. https://doi.org/10.1186/s40536-018-0058-x
Braun, H., & von Davier, M. (2017). The use of test scores from large ‑ scale assessment surveys: Psychometric and statistical considerations. Large-Scale Assessments in Education, 5, Article 17. https://doi.org/10.1186/s40536-017-0050-x
Broadbent, H., & Mareschal, D. (2019). Neuroconstructivism. In S. Hupp & J. Jewell (Eds.), The Encyclopedia of Child and Adolescent Development (pp. 1–11). Wiley Online Library. https://doi.org/10.1002/9781119171492.wecad104
Brooks, S. P., & Gelman, A. (1998). General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7(4), 434–455. https://doi.org/10.1080/10618600.1998.10474787
Brun-Schammé, A., & Rey, M. (2021). A new approach to skills mismatch (OECD Productivity Working Papers No. 24). OECD iLibrary. https://bit.ly/3CCzSDY
Burkhardt, L., Silbermann, T., & Bartsch, S. (2018). Weighting in PIAAC-L 2016. Social Science Open Access Repository. https://doi.org/10.21241/ssoar.57697
Choi, A., Guio, J., & Escardibul, J. -O. (2020). The challenge of mapping overeducation and overskilling across countries: A critical approach using PIAAC. Compare: A Journal of Comparative and International Education, 50(2), 237–256. https://doi.org/10.1080/03057925.2019.1600400
Cinamon, R. G. (2016). Integrating work and study among young adults: Testing an empirical model. Journal of Career Assessment, 24(3), 527–542. https://doi.org/10.1177/1069072715599404
Čopková, R., Gróf, M., Zausinová, J., & Siničáková, M. (2021). Adaptation of the entrepreneurship competences questionnaire based on EntreComp framework. Advance online publication. https://bit.ly/42XcPyM
Crusius, J., Corcoran, K., & Mussweiler, T. (2022). Social comparison: Theory, research, and applications. In D. Chadee (Ed.), Theories in social psychology (2nd ed., pp. 165–187). John Wiley & Sons. https://bit.ly/42qVZI6
Depaoli, S., & van de Schoot, R. (2017). Improving transparency and replication in Bayesian statistics: The WAMBS-checklist. Psychological Methods, 22(2), 240–261. https://doi.org/10.1037/met0000065
Dunn, T. J., Baguley, T., & Brunsden, V. (2014). From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. The British Journal of Psychology, 105(3), 399–412. https://doi.org/10.1111/bjop.12046
Engelhardt, L., & Goldhammer, F. (2018). Number series study (DIPF): Technical report (GESIS Papers, 2018/01). Social Science Open Access Repository. https://doi.org/10.21241/ssoar.55737
European Commission. (2021, September 15). Shaping Europe’s Digital Future: Digital decade. https://digital-strategy.ec.europa.eu/en/policies
European Commission. (2023, March 30). National qualifications framework. European Commission- Eurydice. https://bit.ly/3XjRqOR
Felstead, A., Gallie, D., Green, F., & Zhou, Y. (2007). Skills at work, 1986 to 2006. ESRC Centre on Skills, Knowledge and Organisational Performance. https://bit.ly/45TZr0L
Festinger, L. (1954). A theory of social comparison processes. Human Relations, 7(2), 117–140. https://doi.org/10.1177/001872675400700202
Freund, P. A., & Kasten, N. (2012). How smart do you think you are? A meta-analysis on the validity of self-estimates of cognitive ability. Psychological Bulletin, 138(2), 296–321. https://doi.org/10.1037/a0026556
Gal, I. (2002). Adults’ statistical literacy: Meanings, components, responsibilities. International Statistical Review, 70(1), 1–25. https://doi.org/10.1111/j.1751-5823.2002.tb00336.x
Garnier-Villarreal, M., & Jorgensen, T. D. (2020). Adapting fit indices for Bayesian structural equation modeling: Comparison to maximum likelihood. Psychological Methods, 25(1), 46–70. https://doi.org/10.1037/met0000224
Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7(4), 457–472. https://doi.org/10.1214/ss/1177011136
Goethals, G. R. (1986). Social comparison theory: Psychlogy from the lost and found. Personality and Social Psychology Bulletin, 12(3), 261–278. https://doi.org/10.1177/0146167286123001
Goldhammer, F., Martens, T., & Lüdtke, O. (2017). Conditioning factors of test-taking engagement in PIAAC: An exploratory IRT modelling approach considering person and item characteristics. Large-Scale Assessments in Education, 5, Article 18. https://doi.org/10.1186/s40536-017-0051-9
Goldhammer, F., Naumann, J., Stelter, A., Tóth, K., Rölke, H., & Klieme, E. (2014). The time on task effect in reading and problem solving is moderated by task difficulty and skill: Insights from a computer-based large-scale assessment. Journal of Educational Psychology, 106(3), 608–626. https://doi.org/10.1037/a0034716
Gorges, J., Maehler, D. B., Koch, T., & Offerhaus, J. (2016). Who likes to learn new things: Measuring adult motivation to learn with PIAAC data from 21 countries. Large-Scale Assessments in Education, 4, Article 9. https://doi.org/10.1186/s40536-016-0024-4
Greiff, S., Wüstenberg, S., Molnár, G., Fischer, A., Funke, J., & Csapo, B. (2013). Complex problem solving in educational contexts — something beyond g: Concept, assessment, measurement invariance, and construct validity. Journal of Educational Psychology, 105(2), 364–379. https://doi.org/10.1037/a0031856
Hahnel, C., Kroehne, U., Goldhammer, F., Schoor, C., Mahlow, N., & Artelt, C. (2019). Validating process variables of sourcing in an assessment of multiple document comprehension. British Journal of Educational Psychology, 89(3), 524–537. https://doi.org/10.1111/bjep.12278
Hall, J. A., Andrzejewski, S. A., & Yopchick, J. E. (2009). Psychosocial correlates of interpersonal sensitivity: A meta-analysis. Journal of Nonverbal Behavior, 33, 149–180. https://doi.org/10.1007/s10919-009-0070-5
Holling, H., & Preckel, F. (2005). Self-estimates of intelligence––methodological approaches and gender differences. Personality and Individual Differences, 38(3), 503–517. https://doi.org/10.1016/j.paid.2004.05.003
Hoofs, H., van de Schoot, R., Jansen, N. W. H., & Kant, Ij. (2018). Evaluating model fit in Bayesian confirmatory factor analysis with large samples: Simulation study introducing the BRMSEA. Educational and Psychological Measurement, 78(4), 537–568. https://doi.org/10.1177/0013164417709314
Kaplan, D. (2016). Causal inference with large ‑ scale assessments in education from a Bayesian perspective: A review and synthesis. Large-Scale Assessments in Education, 4, Article 7. https://doi.org/10.1186/s40536-016-0022-6
Kaplan, D., & Lee, C. (2018). Optimizing prediction using Bayesian model averaging: Examples using large-scale educational assessments. Evaluation Review, 42(4), 423–457. https://doi.org/10.1177/0193841X18761421
Karmiloff-Smith, A. (2012). From constructivism to neuroconstructivism: The activity-dependent structuring of the human brain. In E. Martí & C. Rodríguez (Eds.), After Piaget (pp. 1–14). Routledge. https://doi.org/10.4324/9781315082899-1
Khorramdel, L., von Davier, M., Gonzalez, E., & Yamamoto, K. (2020). Plausible values: Principles of item response theory and multiple imputations. In D. B. Maehler & B. Rammstedt (Eds.), Large-scale cognitive assessment (pp. 27–47). Springer. https://doi.org/10.1007/978-3-030-47515-4_3
Király, I. (2022). Changes in the focus of developmental models: From social contexts to social cognition. In J. Gervain, G. Csibra, & K. Kovács (Eds.), A life in cognition. Language, cognition, and mind (Vol. 11, pp. 307–321). Springer. https://doi.org/10.1007/978-3-030-66175-5_22
Kitanova, M. (2020). Youth political participation in the EU: Evidence from a cross-national analysis. Journal of Youth Studies, 23(7), 819–836. https://doi.org/10.1080/13676261.2019.1636951
Kluemper, D. H., Mossholder, K. W., Ispas, D., Bing, M. N., Iliescu, D., & Ilie, A. (2019). When core self-evaluations influence employees’ deviant reactions to abusive supervision: The moderating role of cognitive ability. Journal of Business Ethics, 159, 435–453. https://doi.org/10.1007/s10551-018-3800-y
Konrath, S. H., O’Brien, E. H., & Hsing, C. (2011). Changes in dispositional empathy in American college students over time: A meta-analysis. Personality and Social Psychology Review, 15(2), 180–198. https://doi.org/10.1177/1088868310377395
Krath, J., Schürmann, L., & Von Korflesch, H. F. O. (2021). Revealing the theoretical basis of gamification: A systematic review and analysis of theory in research on gamification, serious games and game-based learning. Computers in Human Behavior, 125, Article 106963. https://doi.org/10.1016/j.chb.2021.106963
Kunina-Habenicht, O., & Goldhammer, F. (2020). ICT Engagement: A new construct and its assessment in PISA 2015. Large-Scale Assessments in Education, 8, Article 6. https://doi.org/10.1186/s40536-020-00084-z
Maehler, D. B., Martin, S., & Rammstedt, B. (2017). Coverage of the migrant population in large-scale assessment surveys. Experiences from PIAAC in Germany. Large-Scale Assessments in Education, 5, Article 9. https://doi.org/10.1186/s40536-017-0044-8
Marsh, H. W. (1987). The big-fish-little-pond effect on academic self-concept. Journal of Educational Psychology, 79(3), 280–295. https://doi.org/10.1037/0022-0663.79.3.280
Marsh, H. W., Abduljabbar, A. S., Morin, A. J. S., Parker, P., Abdelfattah, F., Nagengast, B., & Abu-Hilal, M. M. (2015). The Big-Fish-Little-Pond Effect: Generalizability of social comparison processes over two age cohorts from Western, Asian, and Middle Eastern Islamic countries. Journal of Educational Psychology, 107(1), 258–271. https://doi.org/10.1037/a0037485
Martin, S., Zabal, A., Maehler, D. B., & Rammstedt, B. (2022). Data from PIAAC Germany and its longitudinal follow-up, PIAAC-L. Journal of Open Psychology Data, 10(1), Article 20. https://doi.org/10.5334/jopd.74
Mayer, R. E. (1998). Cognitive, metacognitive, and motivational aspects of problem solving. Instructional Science, 26, 49–63. https://doi.org/10.1023/A:1003088013286
Merkle, E. C., Fitzsimmons, E., Uanhoro, J., & Goodrich, B. (2021). Efficient Bayesian structural equation modeling in Stan. Journal of Statistical Software, 100(6), 1-22. https://doi.org/10.18637/jss.v100.i06
Merkle, E. C., & Rosseel, Y. (2018). Blavaan: Bayesian structural equation models via parameter expansion. Journal of Statistical Software, 85(4), 1-30. https://doi.org/10.18637/jss.v085.i04
Merkle, E. C., Rosseel, Y., Garnier-Villarreal, M., & Jorgensen, T. D. (2019). Blavaan: Bayesian latent variable analysis. The Comprehensive R Archive Network. https://bit.ly/45ROSLF
Merkle, E. C., & Wang, T. (2018). Bayesian latent variable models for the analysis of experimental psychology data. Psychonomic Bulletin and Review, 25, 256–270. https://doi.org/10.3758/s13423-016-1016-7
Möller, J., Pohlmann, B., Koller, O., & Marsh, H. W. (2009). A meta-analytic path analysis of the internal/external frame of reference model of academic achievement and academic self-concept. Review of Educational Research, 79(3), 1129–1167. https://doi.org/10.3102/0034654309337522
Möller, J., Zitzmann, S., Helm, F., Machts, N., & Wolff, F. (2020). A meta-analysis of relations between achievement and self-concept. Review of Educational Research, 90(3), 376–419. https://doi.org/10.3102/0034654320919354
Mund, M., Freuding, M. M., Möbius, K., Horn, N., & Neyer, F. J. (2020). The stability and change of loneliness across the life span: A meta-analysis of longitudinal studies. Personality and Social Psychology Review, 24(1), 24–52. https://doi.org/10.1177/1088868319850738
Mussweiler, T. (2003). Comparison processes in social judgment: Mechanisms and consequences. Psychological Review, 110(3), 472–489. https://doi.org/10.1037/0033-295X.110.3.472
Organization for Economic Co-operation and Development. (2013). OECD skills outlook 2013: First results from the survey of adult skills. https://bit.ly/45LqsDF
Organization for Economic Co-operation and Development. (2015). About the survey of adult skills. OECD iLibrary. https://doi.org/10.1787/9789264236844-3-en
Organization for Economic Co-operation and Development. (2016). Skills matter: Further results from the survey of adult skills, tables of results. OECD iLibrary. https://doi.org/10.1787/9789264258051-9-en
Organization for Economic Co-operation and Development. (2021). AI and the future of skills, Volume 1: Capabilities and assessments. OECD iLibrary. https://doi.org/10.1787/5ee71f34-en
Oyserman, D. (2017). Culture three ways: Culture and subcultures within countries. Annual Review of Psychology, 68(1), 435–463. https://doi.org/10.1146/annurev-psych-122414-033617
Papen, U. (2009). Literacy, learning and health–A social practices view of health literacy. Literacy and Numeracy Studies, 16(2), 19–34. https://search.informit.org/doi/10.3316/aeipt.179045
Paulick, I., Großschedl, J., Harms, U., & Möller, J. (2017). How teachers perceive their expertise: The role of dimensional and social comparisons. Contemporary Educational Psychology, 51, 114–122. https://doi.org/10.1016/j.cedpsych.2017.06.007
Perry, A., Maehler, D. B., & Rammstedt, B. (2018). Introduction to the special issue on results, methodological aspects, and advancements of the Programme for the International Assessment of Adult Competencies (PIAAC). Large-Scale Assessments in Education, 6, Article 14. https://doi.org/10.1186/s40536-018-0066-x
PIAAC Literacy Expert Group. (2009). PIAAC literacy: A conceptual f ramework (OECD Education Working Papers No. 34). https://doi.org/10.1787/220348414075
PIAAC Numeracy Expert Group. (2009). PIAAC numeracy: A conceptual framework (OECD Education Working Papers No. 35). https://doi.org/10.1787/220337421165
R Core Team. (2013). R: A language and environment for statistical computing. http://www.r-project.org/index.html
Rammstedt, B., Martin, S., & Tausch, A. (2017). PIAAC-Longitudinal (PIAAC-L), Germany. GESIS Data Archive. https://doi.org/10.4232/1.12925
Rammstedt, B., Martin, S., Zabal, A., Carstensen, C., & Schupp, J. (2017). The PIAAC longitudinal study in Germany: Rationale and design. Large-Scale Assessments in Education, 5, Article 4. https://doi.org/10.1186/s40536-017-0040-z
Revelle, W. (2017). An introduction to the psych package: Part I. Data entry and description. Personality Project. http://personality-project.org/r/intro.pdf
Riekhoff, A. -J. (2018). Extended working lives and late-career destabilisation: A longitudinal study of Finnish register data. Advances in Life Course Research, 35, 114–125. https://doi.org/10.1016/j.alcr.2018.01.007
Rohrmann, S., Bechtoldt, M. N., & Leonhardt, M. (2016). Validation of the impostor phenomenon among managers. Frontiers in Psychology, 7, Article 821. https://doi.org/10.3389/fpsyg.2016.00821
Roshid, M. M., Webb, S., & Chowdhury, R. (2022). English as a business lingua franca: A discursive analysis of business e-mails. International Journal of Business Communication, 59(1), 83–103. https://doi.org/10.1177/2329488418808040
Sabatini, J. P., & Bruce, K. M. (2009). PIAAC reading component: A conceptual framework (OECD Education Working Papers No. 33). https://doi.org/10.1787/220367414132
Santiago-Vela, A., & Hall, A. (2022). Distinguishing challenging and overchallenging jobs: Cognitive and affective skills mismatches and their impact on job satisfaction. Research in Comparative and International Education, 18(1), 55–78. https://doi.org/10.1177/17454999221116486
Schneider, S. L. (2018). Education in OECD’s PIAAC study: How well do different harmonized measures predict skills? Methods, Data, Analyses, 12(1), 151–176. https://doi.org/10.12758/mda.2017.15
Schweizer, K. (2011). On the changing role of Cronbach’s α in the evaluation of the quality of a measure. European Journal of Psychological Assessment, 27(3), 143–144. https://doi.org/10.1027/1015-5759/a000069
Şenol, F. B. (2022). Investigation of the relationship between academic competencies and social information processing of 60–72 month-old children. International Electronic Journal of Elementary Education, 14(3), 283–294. https://doi.org/10.26822/iejee.2022.244
Sharma, G., Mallick, Z., Ahmad, S., Khan, Z. A., James, A. T., & Asjad, M. (2022). An integrated multi-criteria decision-making approach for estimating the importance of the cognitive function impairment risk factors. Decision Analytics Journal, 4, Article 100107. https://doi.org/10.1016/j.dajour.2022.100107
Sherman, A. M., de Vries, B., & Lansford, J. E. (2000). Friendship in childhood and adulthood: Lessons across the life span. The International Journal of Aging and Human Development, 51(1), 31–51. https://doi.org/10.2190/4QFV-D52D-TPYP-RLM6
Skovholt, K., Grønning, A., & Kankaanranta, A. (2014). The communicative functions of emoticons in workplace e-mails: :-). Journal of Computer-Mediated Communication, 19(4), 780–797. https://doi.org/10.1111/jcc4.12063
Solano, C. H. (1986). People without friends: Loneliness and its alternatives. In V. J. Derlega & B. A. Winstead (Eds.), Friendship and social interaction (pp. 227–246). Springer. https://doi.org/10.1007/978-1-4612-4880-4_12
Stan Development Team. (2017). Stan modeling language users guide and reference manual version 2.17.0. https://bit.ly/43Lpkhk
Stigler, S. M. (2013). The true title of Bayes’s essay. Statistical Science, 28(3), 283–288. https://doi.org/10.1214/13-STS438
van de Schoot, R., Kaplan, D., Denissen, J., Asendorpf, J. B., Neyer, F. J., & van Aken, M. A. G. (2014). A gentle introduction to Bayesian analysis: Applications to developmental research. Child Development, 85(3), 842–860. https://doi.org/10.1111/cdev.12169
Virtanen, M., Jokela, M., Madsen, I. E. H., Magnusson Hanson, L. L., Lallukka, T., Nyberg, S. T., Alfredsson, L., Batty, G. D., Bjorner, J. B., Borritz, M., Burr, H., Dragano, N., Erbel, R., Ferrie, J. E., Heikkilä, K., Knutsson, A., Koskenvuo, M., Lahelma, E., Nielsen, M. L., … Kivimäki, M. (2018). Long working hours and depressive symptoms: Systematic review and meta-analysis of published studies and unpublished individual participant data. Scandinavian Journal of Work, Environment & Health, 44(3), 239–250. https://doi.org/10.5271/sjweh.3712
Virtanen, M., Singh-Manoux, A., Ferrie, J. E., Gimeno, D., Marmot, M. G., Elovainio, M., Jokela, M., Vahtera, J., & Kivimäki, M. (2009). Long working hours and cognitive function: The Whitehall II Study. American Journal of Epidemiology, 169(5), 596–605. https://doi.org/10.1093/aje/kwn382
Westermann, G., Mareschal, D., Johnson, M. H., Sirois, S., Spratling, M. W., & Thomas, M. S. C. (2007). Neuroconstructivism. Developmental Science, 10(1), 75–83. https://doi.org/10.1111/j.1467-7687.2007.00567.x
Wirth, J., & Klieme, E. (2003). Computer-based assessment of problem solving competence. Assessment in Education: Principles, Policy & Practice, 10(3), 329–345. https://doi.org/10.1080/0969594032000148172
Zabal, A., Martin, S., Massing, N., Ackermann, D., Helmschrott, S., Barkow, I., & Rammstedt, B. (2014). PIAAC Germany 2012: Technical report. Social Science Open Access Repository. https://bit.ly/3qouba1
Zabal, A., Martin, S., & Rammstedt, B. (2016). PIAAC-L data collection 2014: Technical report follow-up to PIAAC Germany 2012 (GESIS Papers, 2016/17). Social Science Open Access Repository. https://doi.org/10.21241/ssoar.49665
Zabal, A., Martin, S., & Rammstedt, B. (2017). PIAAC-L data collection 2015: Technical report (GESIS Papers, 2017/29). Social Science Open Access Repository. https://shorturl.at/zBDRY
Zell, E., & Krizan, Z. (2014). Do people have insight into their abilities? A metasynthesis. Perspectives on Psychological Science, 9(2), 111–125. https://doi.org/10.1177/1745691613518075
Zell, E., & Lesick, T. L. (2022). Big five personality traits and performance: A quantitative synthesis of 50+ meta‐analyses. Journal of Personality, 90(4), 559–573. https://doi.org/10.1111/jopy.12683
Zell, E., Strickhouser, J. E., Sedikides, C., & Alicke, M. D. (2020). The better-than-average effect in comparative self-evaluation: A comprehensive review and meta-analysis. Psychological Bulletin, 146(2), 118-149. https://doi.org/10.1037/bul0000218