MethodsX (Jan 2022)

Conceptual comparison of constructs as first step in data harmonization: Parental sensitivity, child temperament, and social support as illustrations

  • Marije L. Verhage,
  • Carlo Schuengel,
  • Annaleena Holopainen,
  • Marian J. Bakermans-Kranenburg,
  • Annie Bernier,
  • Geoffrey L. Brown,
  • Sheri Madigan,
  • Glenn I. Roisman,
  • Mette S. Vaever,
  • Maria S. Wong,
  • Marian J. Bakermans-Kranenburg,
  • Lavinia Barone,
  • Kazuko Y. Behrens,
  • Johanna Behringer,
  • Annie Bernier,
  • Ina Bovenschen,
  • Geoffrey L. Brown,
  • Rosalinda Cassibba,
  • Jude Cassidy,
  • Gabrielle Coppola,
  • Alessandro Costantini,
  • Mary Dozier,
  • Karin Ensink,
  • R. M. Pasco Fearon,
  • Brent Finger,
  • Airi Hautamaki,
  • Nancy L. Hazen,
  • Elena Ierardi,
  • Inês Jongenelen,
  • Simo Køppe,
  • Francesca Lionetti,
  • Sheri Madigan,
  • Sarah Mangelsdorf,
  • Mirjam Oosterman,
  • Cecilia S. Pace,
  • K. Lee Raby,
  • Cristina Riva Crugnola,
  • Glenn I. Roisman,
  • Carlo Schuengel,
  • Alessandra Simonelli,
  • Gottfried Spangler,
  • George M. Tarabulsy,
  • Mette S. Væver,
  • Marije L. Verhage,
  • Maria S. Wong,
  • Bronia Arnott,
  • Heidi Bailey,
  • Patrick J. Brice,
  • Karl-Heinz Brisch,
  • Germana Castoro,
  • Elisabetta Costantino,
  • Chantal Cyr,
  • Carol George,
  • Gabriele Gloger-Tippelt,
  • Sonia Gojman,
  • Susanne Harder,
  • Carollee Howes,
  • Heidi Jacobsen,
  • Deborah Jacobvitz,
  • Mi Kyoung Jin,
  • Femmie Juffer,
  • Miyuki Kazui,
  • Esther M. Leerkes,
  • Karlen Lyons-Ruth,
  • Catherine McMahon,
  • Elizabeth Meins,
  • Salvador Millán,
  • Lynne Murray,
  • Katja Nowacki,
  • David R. Pederson,
  • Lynn Priddis,
  • Avi Sagi-Schwartz,
  • Sarah J. Schoppe-Sullivan,
  • Judith Solomon,
  • Anna Maria Speranza,
  • Miriam Steele,
  • Howard Steele,
  • Doug M. Teti,
  • Marinus H. van IJzendoorn,
  • W. Monique van Londen-Barentsen,
  • Mary J. Ward

Journal volume & issue
Vol. 9
p. 101889

Abstract

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This article presents a strategy for the initial step of data harmonization in Individual Participant Data syntheses, i.e., making decisions as to which measures operationalize the constructs of interest - and which do not. This step is vital in the process of data harmonization, because a study can only be as good as its measures. If the construct validity of the measures is in question, study results are questionable as well. Our proposed strategy for data harmonization consists of three steps. First, a unitary construct is defined based on the existing literature, preferably on the theoretical framework surrounding the construct. Second, the various instruments used to measure the construct are evaluated as operationalizations of this construct, and retained or excluded based on this evaluation. Third, the scores of the included measures are recoded on the same metric. We illustrate the use of this method with three example constructs focal to the Collaboration on Attachment Transmission Synthesis (CATS) study: parental sensitivity, child temperament, and social support. This process description may aid researchers in their data pooling studies, filling a gap in the literature on the first step of data harmonization. • Data harmonization in studies using combined datasets is of vital importance for the validity of the study results. • We have developed and illustrated a strategy on how to define a unitary construct and evaluate whether instruments are operationalizations of this construct as the initial step in the harmonization process. • This strategy is a transferable and reproducible method to apply to the data harmonization process.

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