IDEAS home Printed from https://ideas.repec.org/a/gam/jsoctx/v13y2022i1p10-d1020879.html
   My bibliography  Save this article

Analysis of Profiles of Family Educational Situations during COVID-19 Lockdown in the Valencian Community (Spain)

Author

Listed:
  • Jesús Miguel Jornet-Meliá

    (Evaluation and Measurement Group (GemEduco): Education for Social Cohesion, included in the Register of Research Groups of the University of Valencia (GIUV2016-290), 46010 Valencia, Spain)

  • Carlos Sancho-Álvarez

    (Evaluation and Measurement Group (GemEduco): Education for Social Cohesion, included in the Register of Research Groups of the University of Valencia (GIUV2016-290), 46010 Valencia, Spain)

  • Margarita Bakieva-Karimova

    (Evaluation and Measurement Group (GemEduco): Education for Social Cohesion, included in the Register of Research Groups of the University of Valencia (GIUV2016-290), 46010 Valencia, Spain)

Abstract

Due to the pandemic (COVID-19), the education system in Spain was forced to close for three months, creating an unprecedented situation: improvised distance schooling. Family characteristics and their life situations with Information and Communication Technology use would be aspects to be studied as educational conditioning factors. This paper presents the ways in which a representative sample of families in the Valencian Community (Spain) assumed the education of their children during the lockdown. Mixed methods (quantitative -surveys-/qualitative -focus groups-) are used. Multivariate profiles are studied (k-means cluster) that summarise the life circumstances, represented by composite indicators resulting from the families’ responses to specific items describing their way of life and educational performance. Associated variables, such as demographic or life situation characteristics, are analyzed for each profile. Some gaps (described by indicators that synthesize the functioning of the families) are observed due to life circumstances that correspond not only to vulnerable groups but also to upper-middle-level families.

Suggested Citation

  • Jesús Miguel Jornet-Meliá & Carlos Sancho-Álvarez & Margarita Bakieva-Karimova, 2022. "Analysis of Profiles of Family Educational Situations during COVID-19 Lockdown in the Valencian Community (Spain)," Societies, MDPI, vol. 13(1), pages 1-20, December.
    • Handle: RePEc:gam:jsoctx:v:13:y:2022:i:1:p:10-:d:1020879
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2075-4698/13/1/10/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2075-4698/13/1/10/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sugar, Catherine A. & James, Gareth M., 2003. "Finding the Number of Clusters in a Dataset: An Information-Theoretic Approach," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 750-763, January.
    2. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    3. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Julian Rossbroich & Jeffrey Durieux & Tom F. Wilderjans, 2022. "Model Selection Strategies for Determining the Optimal Number of Overlapping Clusters in Additive Overlapping Partitional Clustering," Journal of Classification, Springer;The Classification Society, vol. 39(2), pages 264-301, July.
    2. Li, Pai-Ling & Chiou, Jeng-Min, 2011. "Identifying cluster number for subspace projected functional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2090-2103, June.
    3. Kondo, Yumi & Salibian-Barrera, Matias & Zamar, Ruben, 2016. "RSKC: An R Package for a Robust and Sparse K-Means Clustering Algorithm," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i05).
    4. J. Fernando Vera & Rodrigo Macías, 2021. "On the Behaviour of K-Means Clustering of a Dissimilarity Matrix by Means of Full Multidimensional Scaling," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 489-513, June.
    5. J. Vera & Rodrigo Macías & Willem Heiser, 2013. "Cluster Differences Unfolding for Two-Way Two-Mode Preference Rating Data," Journal of Classification, Springer;The Classification Society, vol. 30(3), pages 370-396, October.
    6. Véronique Cariou & Stéphane Verdun & Emmanuelle Diaz & El Qannari & Evelyne Vigneau, 2009. "Comparison of three hypothesis testing approaches for the selection of the appropriate number of clusters of variables," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 3(3), pages 227-241, December.
    7. Yi Peng & Yong Zhang & Gang Kou & Yong Shi, 2012. "A Multicriteria Decision Making Approach for Estimating the Number of Clusters in a Data Set," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-9, July.
    8. Z. Volkovich & Z. Barzily & G.-W. Weber & D. Toledano-Kitai & R. Avros, 2012. "An application of the minimal spanning tree approach to the cluster stability problem," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(1), pages 119-139, March.
    9. Fischer, Aurélie, 2011. "On the number of groups in clustering," Statistics & Probability Letters, Elsevier, vol. 81(12), pages 1771-1781.
    10. Fang, Yixin & Wang, Junhui, 2012. "Selection of the number of clusters via the bootstrap method," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 468-477.
    11. Anis Hoayek & Didier Rullière, 2024. "Assessing clustering methods using Shannon's entropy," Post-Print hal-03812055, HAL.
    12. Koltcov, Sergei, 2018. "Application of Rényi and Tsallis entropies to topic modeling optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1192-1204.
    13. Ertl, Antal & Horn, Dániel & Kiss, Hubert János, 2024. "Economic Preferences across Generations and Family Clusters: A Comment," I4R Discussion Paper Series 105, The Institute for Replication (I4R).
    14. Jane L. Harvill & Priya Kohli & Nalini Ravishanker, 2017. "Clustering Nonlinear, Nonstationary Time Series Using BSLEX," Methodology and Computing in Applied Probability, Springer, vol. 19(3), pages 935-955, September.
    15. Z. Volkovich & D. Toledano-Kitai & G.-W. Weber, 2013. "Self-learning K-means clustering: a global optimization approach," Journal of Global Optimization, Springer, vol. 56(2), pages 219-232, June.
    16. J. Fernando Vera & Rodrigo Macías, 2017. "Variance-Based Cluster Selection Criteria in a K-Means Framework for One-Mode Dissimilarity Data," Psychometrika, Springer;The Psychometric Society, vol. 82(2), pages 275-294, June.
    17. Lingsong Meng & Dorina Avram & George Tseng & Zhiguang Huo, 2022. "Outcome‐guided sparse K‐means for disease subtype discovery via integrating phenotypic data with high‐dimensional transcriptomic data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 352-375, March.
    18. Christophe Genolini & Bruno Falissard, 2010. "KmL: k-means for longitudinal data," Computational Statistics, Springer, vol. 25(2), pages 317-328, June.
    19. Mark Chiang & Boris Mirkin, 2010. "Intelligent Choice of the Number of Clusters in K-Means Clustering: An Experimental Study with Different Cluster Spreads," Journal of Classification, Springer;The Classification Society, vol. 27(1), pages 3-40, March.
    20. Fujita, André & Takahashi, Daniel Y. & Patriota, Alexandre G., 2014. "A non-parametric method to estimate the number of clusters," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 27-39.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsoctx:v:13:y:2022:i:1:p:10-:d:1020879. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.