Ansiedad Futura en Adultos Jóvenes Españoles: Propiedades Psicométricas de la Dark Future Scale.
Resumen
Background/Objective: The Dark Future Scale (DFS) is a self-report instrument which assesses the tendency to think about the future with anxiety, fear, and uncertainty. Although it has been applied in different populations, instrumental studies are scarce, and there is no validated Spanish version. The aim was therefore to develop a Spanish version of the scale (DFS-S) and to analyze its psychometric properties in a sample of young adults. Method: Participants were 1,019 individuals aged from 18 to 24 years. They completed the DFS-S and the IPIP-BFM-20. Validity evidence based on the internal structure, including measurement invariance across gender, as well as on relationships with personality traits was obtained. Reliability and gender differences in DFS-S scores were also examined. Results: Results supported a single-factor structure, χ2(5) = 10.79, CFI = .999, RMSEA = .034, SRMR = .016, that was invariant across gender. Reliability of test scores was satisfactory (ω = .92). In the correlation analysis, future anxiety showed a strong positive correlation with neuroticism (.42) and a moderate negative correlation with extraversion (-.25). Females scored higher than males on future anxiety. Conclusions: The DFS-S has satisfactory psychometric properties and it is an adequate tool for measuring future anxiety among young adults.
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American Educational Research Association, American Psychological Association, & National Council on Measurement in Education (2014). Standards for educational and psychological testing. AERA Publications. https://www.testingstandards.net/open-access-files.html
Armağan, E., & Durukal, E. A. (2021). The antecedents of the consumers’ mobile learning intention during the Covid-19 pandemic. International Journal of Social Science and Economics Invention, 7, 178-182. https://doi.org/10.23958/ijssei/vol07-i09/315
Browne M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136-162). Sage.
Byrne, B. (2008). Testing for multigroup equivalence of a measuring instrument: A walk through the process. Psicothema, 20(4), 872-882.
Campo-Arias, A., & Oviedo, H. C. (2008). Psychometric properties of a scale: Internal consistency. Revista de Salud Pública, 10, 831–839. https://doi.org/10.1590/S0124-00642008000500015
Carelli, M. G., Wiberg, B., & Åström, E. (2015). Broadening the TP profile: Future negative time perspective. In M. Stolarski, N. Fieulaine, & W. van Beek (Eds.), Time perspective theory; Review, research, and application (pp. 87-97). Springer. https://doi.org/10.1007/978-3-319-07368-2_5
Carelli, M. G., Wiberg, B., & Wiberg, M. (2011). Development and construct validation of the Swedish Zimbardo Time Perspective Inventory. European Journal of Psychological Assessment. https://doi.org/10.1027/1015-5759/a000076
Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14(3), 464-504. https://doi.org/10.1080/10705510701301834
Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233-255. https://doi.org/10.1207/S15328007SEM0902_5
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Dadaczynski, K., Okan, O., Messer, M., & Rathmann, K. (2021). University students’ sense of coherence, future worries and mental health: Findings from the German COVID-HL-survey. Health Promotion International, 00, 1-11. https://doi.org/10.1093/heapro/daab070
De Vaus, D. (2002). Analyzing social science data: 50 key problems in data analysis. Sage.
Dimitrov, D. M. (2010). Testing for factorial invariance in the context of construct validation. Measurement and Evaluation in Counseling and Development, 43(2), 121-149. https://doi.org/10.1177/0748175610373459
Donnellan, M. B., Oswald, F. L., Baird, B. M., & Lucas, R. E. (2006). The Mini-IPIP Scales: Tiny-yet-effective measures of the Big Five factors of personality. Psychological Assessment, 18(2), 192–203. https://doi.org/10.1037/1040-3590.18.2.192
Duplaga, M., & Grysztar, M. (2021). The association between future anxiety, health literacy and the perception of the COVID-19 pandemic: A cross-sectional study. Healthcare, 9, 43. https://doi.org/10.3390/healthcare9010043
Faravelli, C., Scarpato, M. A., Castellini, G., & Lo Sauro, C. (2013). Gender differences in depression and anxiety: The role of age. Psychiatric Research, 210(3), 1301-1303. https://doi.org/10.1016/j.psychres.2013.09.027
Goldberg, L. R. (1999). A broad-bandwidth, public domain, personality inventory measuring the lower-level facets of several five-factor models (Vol. 7). In I. Mervielde, I. Deary, F. De Fruyt, & F. Ostendorf, Personality psychology in Europe (pp. 7-28). Tilburg University Press.
Grant, S. (2011). Neuroticism: The personality risk factor for stress and impaired health and well-being. In R. G. Jackson (Ed.), The psychology of neuroticism and shame (pp. 1-36). Nova Science Publishers, Inc.
Graves, B. S., Hall, M.E., Dias-Karch, C., Haischer, M. H., & Apter, C. (2021). Gender differences in perceived stress and coping among college students. PLOS ONE, 16(8), Article e0255634. https://doi.org/10.1371/journal.pone.0255634
Han, K., Colarelli, S. M., & Weed, N. C. (2019). Methodological and statistical advances in the consideration of cultural diversity in assessment: A critical review of group classification and measurement invariance testing. Psychological Assessment, 31(12), 1481–1496. https://doi.org/10.1037/pa s0000731
Hantsoo, L., & Epperson, C. N. (2017). Anxiety disorders among women: A female lifespan approach. Focus, 15, 162-172. https://doi.org/10.1176/appi.focus.20160042
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for the indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55. https://doi.org/10.1080/10705519909540118
International Test Commission (2017). The ITC guidelines for translating and adapting tests (2nd edition). www.InTestCom.org
Lanz, M., Sorgente, A., Vosylis, R., Fonseca, G., Lep, Z., Li, L., Zupančič, M, Crespo, C., Relvas, A. P., & Serido, J. (2021). A cross-national study of COVID-19 impact and future possibilities among emerging adults: The mediating role of intolerance of uncertainty. Emerging Adulthood, 0, 1-16. https://doi.org/10.1177/21676968211046071
Leung, A. Y. M., Parial, L. L., Tolabing, M. C., Sim, T., Mo, P., Okan, O., & Dadaczynski, K. (2021). Sense of coherence mediates the relationship between digital health literacy and anxiety about the future in aging population during the COVID-19 pandemic: A path analysis. Aging & Mental Health, 26(3), 544-553. https://doi.org/10.1080/13607863.2020.1870206
Li, C. (2016). The performance of ML, DWLS, and ULS estimation with robust corrections in structural equation models with ordinal variables. Psychological Methods, 21(3), 369-387. https://doi.org/10.1037/met0000093
MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149. https://doi.org/10.1037/1082-989X.1.2.130
Martínez-Molina, A., & Arias, V. B. (2018). Balanced and positively worded personality short-forms: Mini-IPIP validity and cross-cultural invariance. PeerJ, 6, e5542. https://doi.org/10.7717/peerj.5542
Marques, A. A., Bevilaqua, M. C. N., da Fonseca, A. M. P., Nardi, A. E., & Thuret, S. (2016). Gender differences in the neurobiology of anxiety: Focus on adult hippocampal neurogenesis. Neural Plasticity, ID 5026713. https://doi.org/10.1155/2016/5026713
Mîndrilă, D. (2010). Maximum likelihood (ML) and diagonally weighted least squares (DWLS) estimation procedures: A comparison of estimation bias with ordinal and multivariate non-normal data. International Journal of Digital Society, 1(1), 60-66. https://doi.org/10.20533/ijds.2040.2570.2010.0010
Rapelli, G., Lopez, G., Donato, S., Pagani A. F., Parise, M., Bertoni, A., & Iafrate, R. (2020). A postcard from Italy: Challenges and psychosocial resources of partners living with and without a chronic disease during COVID-19 epidemic. Frontiers in Psychology, 11, 567522. https://doi.org/10.3389/fpsyg.2020.567522
Rosellini, A. J., & Brown, T. A. (2011). The NEO Five-Factor Inventory: Latent structure and relationships with dimensions of anxiety and depressive disorders in a large clinical sample. Assessment, 18(1), 27-38. https://doi.org/10.1177/1073191110382848
Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. https://doi.org/10.18637/jss.v048.i02
Scandurra, C., Bochicchio, V., Dolce, P., Valerio, P., Muzii, B., & Maldonato, N. M. (2021). Why people were less compliant with public health regulations during the second wave of the Covid-19 outbreak: The role of trust in governmental organizations, future anxiety, fatigue, and Covid-19 risk perception. Current Psychology. https://doi.org/10.1007/s12144-021-02059-x
Shabahang, R., Aruguete, M. S., & Shim, H. (2021). Online news addiction: Future anxiety, fear of missing out on news, and interpersonal trust contribute to excessive online news consumption. Online Journal of Communication and Media Technologies, 11(2), e202105. https://doi.org/10.30935/ojcmt/10822
Sobol, M., Blachnio, A., & Przepiórka, A. (2020). Time of pandemic: Temporal perspectives related to compliance with public health regulations concerning the COVID-19 pandemic. Social Science & Medicine, 265, 113408. https://doi.org/10.1016/j.socscimed.2020.113408
Stolarski, M., & Matthews, G. (2016). Time perspectives predict mood states and satisfaction with life over and above personality. Current Psychology, 35, 516-526. https://doi.org/10.1007/s12144-016-9515-2
Viladrich, C., Angulo-Brunet, A., & Doval, E. (2017). A journey around alpha and omega to estimate internal consistency reliability. Annals of Psychology, 33(3), 755–782. https://doi.org/10.6018/analesps.33.3.268401
Watson, D. C. (2020). Well-Being, temporal orientation, and the dual nature of materialism. Imagination, Cognition and Personality: Consciousness in Theory, Research, and Clinical Practice, 40(1), 65-86. https://doi.org/10.1177/0276236620911602
Wenjuan, G., Siqing, P., & Xinqiao, L. (2020). Gender differences in depression, anxiety, and stress among college students: A longitudinal study from China. Journal of Affective Disorders, 263, 292-300. https://doi.org/10.1016/j.jad.2019.11.121
Zaleski, Z. (1996). Future anxiety: Concept, measurement, and preliminary research. Personality and Individual Differences, 21(2), 165-174. https://doi.org/10.1016/0191-8869(96)00070-0
Zaleski, Z. (2005). Future orientation and anxiety. In A. Strathman & J. Joireman (Eds.), Understanding behavior in the context of time: Theory, research, and application (pp. 125 – 139). Taylor & Francis Group.
Zaleski, Z., Sobol-Kwapinska, M., Przepiorka, A., & Meisner, M. (2019). Development and validation of the Dark Future scale. Time & Society, 28(1), 107-123. https://doi.org/10.1177/0961463X16678257
Zimbardo, P. G., & Boyd, J. N. (1999). Putting time in perspective: A valid, reliable individual-differences metric. Journal of Personality and Social Psychology, 77(6), 1271-1288. https://doi.org/10.1037/0022-3514.77.6.1271
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