Data in brief 28 (2020) 104953 Contents lists available at ScienceDirect Data in brief journal homepage: www.elsevier.com/locate/dib Data Article Data on adult skills formation Rosario Scandurra a, *, Jorge Calero b a noma de Barcelona, Spain Universitat Auto b Universitat de Barcelona, Spain a r t i c l e i n f o a b s t r a c t Article history: This article features supplementary data related to the article Received 20 August 2019 “How are adult skills configured?” [1]. The tables show the Received in revised form 16 October 2019 descriptive statistics of the variables included in the model Accepted 15 November 2019 together with the measurement model and the measure of overall Available online 6 December 2019 model fit. Moreover, the data article describes the procedures used and can be beneficial for the research community for further Keywords: research on adult skills. For further information please consult Adult skills Education linked data. OECD © 2019 The Author(s). Published by Elsevier Inc. This is an open Structural equation model access article under the CC BY license (http://creativecommons. PIAAC org/licenses/by/4.0/). 1. Data This article provides additional data on the configuration of adult skills in five OECD countries. The data contain 142 items for a total of 13,825 respondents aged between 26 and 55 years. These data were used in a recent article [1] based on the theoretical model proposed by Desjardins [2] and further developed in a recent paper [3]. The data were extracted from the Program for the International Assessment of Adult Competencies (PIAAC), released in October 2013 and updated in March 2015. The data are made available on the OECD webpage and were retrieved in April 2017. The first wave1 of PIAAC provides direct measures of skills together with rich information on the individual social DOI of original article: https://doi.org/10.1016/j.ijer.2019.06.004. * Corresponding author. E-mail address: rosario.scandurra@uab.cat (R. Scandurra). 1 More countries were added in the successive round of PIAAC including over 40 economies. https://doi.org/10.1016/j.dib.2019.104953 2352-3409/© 2019 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/). 2 R. Scandurra, J. Calero / Data in brief 28 (2020) 104953 Specifications Table Subject area Social sciences & Education Specific subject area Adult skills Type of data Tables and raw data How data were acquired The data were retrieved from the OECD webpage Data format raw Parameters for data collection Detailed and comparable measures of adult skills Description of data collection Data collect report on adult skills for respondents between 16 and 65. Data source location The data is available through OECD webpage and can be downloaded here: http://www. oecd.org/skills/piaac/publicdataandanalysis/ Data accessibility Scandurra, Rosario (2018), “PIAAC_SII”, Mendeley Data, v1 https://doi.org/10.17632/ vbtc8f92wc.1 Related research article R. Scandurra, J. Calero, How are adult skills configured?, International Journal of Educational Research, (2019). https://doi.org/10.1016/j.ijer.2019.06.004 [1] Value of the Data  This article provides additional data and describes the procedure adopted for examining adult skills using PIAAC data  These data can be used as an example for comparative analysis of adult skills which employs Structural Equation Models (SEM).  Readers can benefit of additional data on adult skills configuration in five OECD countries.  These data can be used for further development and research on adult skills. environment for adults aged between 16 and 65 in 24 countries, mostly OECD members. We employed a Structural Equation Model (SEM) to explore skills configuration for the United States, Japan, Germany, Spain and Denmark. Table 1 provides the information of all the variables included in the model. Table 2 Table 1 Latent and observed variables used in the model. Latent variables Observed variables Symbol Label Abbreviation Symbol Description Type x1 Gender x1 Gender dichotomous x2 Age x2 Age ordinal x3 Foreign born x3 Born in country dichotomous h1 Family background F1 y1 Father Higher Education ordinal h2 Education F2 y4 Highest Level of Education continuous y5 Age of obtaining hi. education qual. ordinal h3 Use of skills in the workplace F3 y6 Use of Reading Skills at Work ordinal y7 Use of Numeracy Skills at Work ordinal y8 Use of Writing Skills at Work ordinal y9 Use of Influencing Skills at Work ordinal h4 Use of skills at home F4 y10 Use of Reading Skills at Home ordinal y11 Use of Numeracy Skills at Home ordinal y12 Use of Writing Skills at Home ordinal y13 Use of ICT Skills at Home ordinal h5 Literacy proficiency F5 y14 Plausible value Literacy pvlit1 continuous y15 Plausible value Literacy pvlit2 continuous y16 Plausible value Literacy pvlit3 continuous y17 Plausible value Literacy pvlit4 continuous y18 Plausible value Literacy pvlit5 continuous y19 Plausible value Literacy pvlit6 continuous y20 Plausible value Literacy pvlit7 continuous y21 Plausible value Literacy pvlit8 continuous y22 Plausible value Literacy pvlit9 continuous y23 Plausible value Literacy pvlit10 continuous R. Scandurra, J. Calero / Data in brief 28 (2020) 104953 3 Table 2 Descriptive statistics. Denmark Germany Japan Spain United States Age Recoded 5-Year Groups 25-29 10.38 13.58 12.88 13.10 17.42 30-34 14.59 14.28 15.38 16.99 16.43 35-39 16.18 12.95 20.17 18.78 15.81 40-4 20.07 19.43 18.04 19.11 15.85 45-49 20.76 21.07 17.62 18.00 16.68 50-5 18.02 18.70 15.91 14.03 17.80 Missing 0.00 0.00 0.00 0.00 0.00 Background - Born In Country Yes 75.72 88.27 99.66 86.42 84.43 No 24.19 11.70 0.34 13.58 15.52 Missing 0.09 0.03 0.00 0.00 0.04 Father Higher Education In 3 Categories ISCED 1, 2, and 3C Short 35.79 9.99 27.27 72.58 21.10 ISCED 3 (Excluding 3C Short) and 4 36.63 52.54 42.84 14.25 44.55 ISCED 5 and 6 26.56 32.45 26.02 11.39 31.54 Missing 1.03 5.01 3.87 1.78 2.81 Gender Men 50.56 50.87 52.91 53.32 49.26 Women 49.44 49.13 47.09 46.68 50.74 Missing 0.00 0.00 0.00 0.00 0.00 Age of Obtaining Education (AOE)- Hi. Qualification Aged 15 or Younger 2.37 2.30 3.30 20.56 3.43 Aged 16-19 13.97 29.42 37.22 30.46 27.58 Aged 20-24 32.67 35.06 53.82 29.17 34.97 Aged 25-29 28.96 20.93 3.76 11.80 16.85 Aged 30-34 10.35 7.94 1.14 3.15 8.42 Aged 35 or Older 11.16 3.83 0.65 3.23 7.93 Missing 0.53 0.52 0.11 1.63 0.83 Index Of Use Of Reading Skills At Work All Zero Response 2.49 3.90 3.76 13.95 3.39 Lowest to 20% 10.10 12.64 12.53 19.89 10.94 More than 20%e40% 14.15 14.73 19.37 17.59 17.51 More than 40%e60% 22.63 19.25 19.83 15.40 19.12 More than 60%e80% 25.31 24.03 19.83 13.65 21.47 More than 80% 25.16 25.42 24.42 19.15 27.46 Missing 0.16 0.03 0.27 0.37 0.12 Index Of Use Of Numeracy Skills At Work All Zero Response 15.68 15.11 9.57 26.90 12.96 Lowest to 20% 16.52 16.64 15.00 12.88 10.90 More than 20%e40% 15.71 15.32 25.29 14.99 12.14 More than 40%e60% 17.49 15.29 18.91 12.84 17.34 More than 60%e80% 17.58 16.64 15.65 15.47 21.76 More than 80% 16.86 20.96 15.31 16.55 24.86 Missing 0.16 0.03 0.27 0.37 0.04 Index Of Use Of Writing Skills At Work All Zero Response 7.11 9.16 6.84 23.12 11.60 Lowest to 20% 12.38 12.05 10.03 13.58 12.68 More than 20%e40% 21.66 18.04 14.28 15.44 13.34 More than 40%e60% 22.85 21.48 18.31 13.58 15.57 More than 60%e80% 19.92 20.89 24.08 16.18 21.02 More than 80% 15.93 18.35 26.21 17.74 25.76 Missing 0.16 0.03 0.27 0.37 0.04 Index Of Use Of Influencing Skills At Work All Zero Response 4.99 9.26 7.14 16.47 4.75 Lowest to 20% 11.10 16.43 20.17 23.45 12.14 More than 20%e40% 15.74 19.67 22.71 16.03 15.98 More than 40%e60% 19.76 22.11 19.03 15.40 16.02 More than 60%e80% 24.41 19.78 17.28 13.58 20.89 More than 80% 23.85 12.67 13.41 14.73 30.10 Missing 0.16 0.07 0.27 0.33 0.12 (continued on next page) 4 R. Scandurra, J. Calero / Data in brief 28 (2020) 104953 Table 2 (continued ) Denmark Germany Japan Spain United States Index Of Use Of Reading Skills At Home All Zero Response 0.31 0.14 0.46 1.45 1.07 Lowest to 20% 7.98 8.81 16.45 23.86 8.88 More than 20%e40% 19.64 15.11 27.23 21.82 13.83 More than 40%e60% 27.65 21.41 24.57 18.22 19.61 More than 60%e80% 24.84 26.50 18.88 15.66 22.67 More than 80% 19.45 28.03 12.42 18.96 33.94 Missing 0.12 0.00 0.00 0.04 0.00 Index Of Use Of Numeracy Skills At Home All Zero Response 5.14 5.57 15.99 14.69 4.42 Lowest to 20% 16.74 16.09 29.70 22.52 10.03 More than 20%e40% 19.45 17.69 24.69 18.70 13.91 More than 40%e60% 22.04 20.89 15.38 14.69 20.23 More than 60%e80% 21.51 23.96 9.68 16.03 25.93 More than 80% 15.02 15.81 4.56 13.36 25.47 Missing 0.09 0.00 0.00 0.00 0.00 Index Of Use Of Writing Skills At Home All Zero Response 3.40 2.44 7.25 15.81 9.29 Lowest to 20% 21.23 16.99 22.71 28.57 18.37 More than 20%e40% 14.15 10.13 21.08 15.66 10.86 More than 40%e60% 26.47 31.86 24.31 20.15 20.15 More than 60%e80% 18.98 22.08 14.09 9.24 18.04 More than 80% 15.65 16.50 10.56 10.58 23.29 Missing 0.12 0.00 0.00 0.00 0.00 Index Of Use Of ICT Skills At Home All Zero Response 0.25 0.63 1.79 0.71 0.58 Lowest to 20% 10.44 15.46 32.66 16.03 10.57 More than 20%e40% 14.59 17.37 24.84 16.33 15.52 More than 40%e60% 21.73 19.95 14.01 14.55 17.51 More than 60%e80% 24.41 20.72 6.80 13.36 18.79 More than 80% 24.75 16.33 3.91 13.21 20.85 Missing 3.83 9.54 15.99 25.83 16.18 Highest Level of Education (years) Mean 13.65 14.44 13.75 12.34 14.24 Standard deviation 2.63 2.72 2.26 3.48 2.92 n 3206 2871 2632 2694 2133 Maximum 22 22 22 22 22 Minimum 3 3 3 3 3 Index Of Use Of Writing Skills At Home All Zero Response 3.40 2.44 7.25 15.81 9.29 Lowest to 20% 21.23 16.99 22.71 28.57 18.37 More than 20%e40% 14.15 10.13 21.08 15.66 10.86 More than 40%e60% 26.47 31.86 24.31 20.15 20.15 More than 60%e80% 18.98 22.08 14.09 9.24 18.04 More than 80% 15.65 16.50 10.56 10.58 23.29 Missing 0.12 0.00 0.00 0.00 0.00 Index Of Use Of ICT Skills At Home All Zero Response 0.25 0.63 1.79 0.71 0.58 Lowest to 20% 10.44 15.46 32.66 16.03 10.57 More than 20%e40% 14.59 17.37 24.84 16.33 15.52 More than 40%e60% 21.73 19.95 14.01 14.55 17.51 More than 60%e80% 24.41 20.72 6.80 13.36 18.79 More than 80% 24.75 16.33 3.91 13.21 20.85 Missing 3.83 9.54 15.99 25.83 16.18 Highest Level of Education (years) Mean 13.65 14.44 13.75 12.34 14.24 Standard deviation 2.63 2.72 2.26 3.48 2.92 n 3206 2871 2632 2694 2133 Maximum 22 22 22 22 22 Minimum 3 3 3 3 3 Source: PIAAC 2013, Authors' calculations. R. Scandurra, J. Calero / Data in brief 28 (2020) 104953 5 Table 3 Goodness of fit measures for literacy SEM. c2 c2/df df n RMSEA 90% C.I. RMSEA CFI TLI WRMR United States 874.081 3.2 273 2421 0.034 0.031e0.036 0.965 0.959 1.102 Japan 828.144 3.03 273 2633 0.031 0.029e0.033 0.973 0.969 1.202 Spain 508.566 1.86 273 2695 0.021 0.018e0.023 0.989 0.987 0.79 Germany 1000.218 3.66 273 2871 0.034 0.032e0.036 0.969 0.964 1.204 Denmark 1125.514 4.12 273 3205 0.035 0.033e0.037 0.968 0.962 1.252 Comparative Fit index (CFI), Tucker e Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA). Source: PIAAC 2013, Authors' calculations. shows the descriptive statistics. Table 3 provides information of the measures of model-fit. Finally, Table 4 details the measurement model. 2. Experimental design, materials, and methods To test the hypothesized relationships between the constructs and to evaluate the theoretical model, we used a Structural Equation Model (SEM). This is a broadly flexible set of statistical tech- niques, which allows the representation of the constructs of interest and the measurement of the extent to which the data are consistent with a proposed theoretical model. Table 1 provides a list of the observed and latent variables included in the model. We have measured the four components of skills acquisition as follows: family background using the father's highest level of educational attainment; education using two items (the highest level of education attainment in years and the age of obtaining the highest education qualification); and the practice of skills in the workplace and in the home using four items. We also controlled for age, for being born outside the test country and for gender. For a matter of clarity, Fig. 1 in Ref. [1] shows the path diagram of the model. Finally, the latent construct of literacy and numeracy comprises ten plausible values. The PIAAC framework evaluate literacy and numeracy using 58 and 56 items, respectively, distributed across three main task characteristics (medium, context and aspect) and differentiated between paper and computer-based questions [4]. As in other standardized international educational assessments, PIAAC uses Item-Response Techniques (IRT) to generate ten plausible values of each domain examined. Table 3 reports the goodness-of-fit measures of the model for numeracy. The estimator selected was the robust weighted least squares (WLSMV), created to deal especially with a combination of ordinal, discrete and continuous data and a small to medium sample size. The estimates were produced using Mplus 7.4. We then scrutinized the modification indices and performed J-Rule using Jrule [5] which implements the method described in Ref. [6]. We performed sensitivity tests including missing data and recoding the zero category of observed indicators in the latent constructs of use of skills into missing data. Bootstrap estimation was performed using 2000 iterations, yielding the same results as the WLSMV estimation. The model fit indexes were consistent across all countries, with respect to the standard CFI and TLI thresholds (above 0.95). The RMSEA was also below 0.05, pointing to the plausibility of the model. In conclusion, we can reject the null hypothesis of a divergent structure of configuration of skills across the five countries considered. Therefore, following the standard procedure in the SEM literature, our two-step modelling process included i) a measurement model, describing the way observed variables load onto latent constructs, and ii) a structural model, which estimates the pathways among all the variables, including the latent constructs [7]. Table 4 reports the factor loading of each unobserved latent variable. We performed a confirmatory factor analysis (CFA) of the measurement model specifying the established relationships of the observed variables to the latent constructs. A confirmatory factor analysis (CFA) was performed to check for the consistency of each latent variable (measurement model). 6 Table 4 Measurement model. United States Japan Spain Germany Denmark Estimate S.E. P-Value Estimate S.E. P-Value Estimate S.E. P-Value Estimate S.E. P-Value Estimate S.E. P-Value R. Scandurra, J. Calero / Data in brief 28 (2020) 104953 F1 Fated 0.725 0.004 0.000 0.735 0.004 0.000 0.723 0.004 0.000 0.721 0.003 0.000 0.724 0.003 0.000 F2 Yrsqual 0.995 0.015 0.000 0.909 0.011 0.000 0.933 0.011 0.000 0.973 0.012 0.000 0.951 0.016 0.000 AOE 0.682 0.015 0.000 0.906 0.013 0.000 0.760 0.013 0.000 0.695 0.014 0.000 0.636 0.016 0.000 F3 Readh_C 0.800 0.014 0.000 0.818 0.016 0.000 0.830 0.013 0.000 0.787 0.015 0.000 0.776 0.014 0.000 Numh_C 0.718 0.017 0.000 0.707 0.018 0.000 0.655 0.017 0.000 0.718 0.018 0.000 0.702 0.015 0.000 Writh_C 0.836 0.014 0.000 0.623 0.018 0.000 0.796 0.014 0.000 0.652 0.017 0.000 0.761 0.013 0.000 Icth_C 0.733 0.017 0.000 0.683 0.020 0.000 0.716 0.018 0.000 0.687 0.019 0.000 0.752 0.015 0.000 F4 Readw_C 0.872 0.014 0.000 0.866 0.013 0.000 0.911 0.010 0.000 0.870 0.011 0.000 0.858 0.013 0.000 Numw_C 0.688 0.019 0.000 0.716 0.016 0.000 0.708 0.016 0.000 0.753 0.015 0.000 0.712 0.016 0.000 Writw_C 0.804 0.014 0.000 0.725 0.015 0.000 0.797 0.013 0.000 0.683 0.016 0.000 0.640 0.016 0.000 Inflw_C 0.608 0.020 0.000 0.642 0.017 0.000 0.687 0.016 0.000 0.721 0.015 0.000 0.641 0.017 0.000 F5 Pvlit1 0.952 0.003 0.000 0.888 0.005 0.000 0.949 0.003 0.000 0.939 0.003 0.000 0.938 0.003 0.000 Pvlit2 0.947 0.004 0.000 0.894 0.004 0.000 0.939 0.004 0.000 0.938 0.004 0.000 0.938 0.003 0.000 Pvlit3 0.947 0.004 0.000 0.894 0.004 0.000 0.943 0.003 0.000 0.938 0.003 0.000 0.940 0.003 0.000 Pvlit4 0.959 0.003 0.000 0.905 0.004 0.000 0.934 0.004 0.000 0.938 0.003 0.000 0.933 0.003 0.000 Pvlit5 0.951 0.004 0.000 0.893 0.004 0.000 0.946 0.003 0.000 0.932 0.004 0.000 0.939 0.003 0.000 Pvlit6 0.941 0.004 0.000 0.900 0.004 0.000 0.943 0.003 0.000 0.938 0.003 0.000 0.934 0.003 0.000 Pvlit7 0.948 0.004 0.000 0.899 0.004 0.000 0.935 0.004 0.000 0.940 0.003 0.000 0.936 0.003 0.000 Pvlit8 0.948 0.004 0.000 0.900 0.004 0.000 0.942 0.003 0.000 0.942 0.003 0.000 0.940 0.003 0.000 Pvlit9 0.956 0.003 0.000 0.904 0.004 0.000 0.937 0.003 0.000 0.943 0.003 0.000 0.934 0.003 0.000 Pvlit10 0.942 0.004 0.000 0.897 0.005 0.000 0.942 0.003 0.000 0.945 0.003 0.000 0.936 0.003 0.000 Source: PIAAC 2013, Authors' calculations. R. Scandurra, J. Calero / Data in brief 28 (2020) 104953 7 Acknowledgments Rosario Scandurra acknowledges the support of the Juan de la Cierva Grants Programme (Ref. FJCI- 2016-28588). Conflict of Interest The authors declare that they have no known competing financial interests or personal relation- ships that could have appeared to influence the work reported in this paper. References [1] R. Scandurra, J. Calero, How are adult skills configured? Int. J. Educ. Res. (2019) https://doi.org/10.1016/j.ijer.2019.06.004. [2] R. Desjardins, Determinants of literacy proficiency: a lifelong-lifewide learning perspective, Int. J. Educ. Res. 39 (2003) 205e245, https://doi.org/10.1016/j.ijer.2004.04.004. [3] R. Scandurra, J. Calero, Modelling adult skills in OECD countries, Br. Educ. Res. J. 43 (3) (2017) 781e804, https://doi.org/10. 1002/berj.3290. [5] D.L. Oberski, Jrule for Mplus, 2009, https://doi.org/10.5281/zenodo.10657. Software. [4] OECD, Technical Report of the Survey of Adult Skills (PIAAC), 2003. Paris. [6] W.E. Saris, A. Satorra, W.M. van der Veld, Testing structural equation models or detection of misspecifications? Struct. Equ. Model. 16 (4) (2009) 561e582, https://doi.org/10.1080/10705510903203433. [7] R.B. Kline, Principles and Practices of Structural Equation Modelling, fourth ed., Guilford Press, London, 2016.