IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2023i15p6470-d1204965.html
   My bibliography  Save this article

Measuring Depression in Young Adults: Preliminary Development of an English Version of the Teate Depression Inventory

Author

Listed:
  • Linda Ruan-Iu

    (Department of Psychological Studies in Education and Human Development, College of Education, Temple University, Philadelphia, PA 19122, USA)

  • Laura L. Pendergast

    (Department of Psychological Studies in Education and Human Development, College of Education, Temple University, Philadelphia, PA 19122, USA)

  • Pei-Chun Liao

    (Division of Child & Adolescent Psychiatry, Columbia University Irving Medical Center, New York, NY 10032, USA)

  • Paul Jones

    (Department of Psychological Studies in Education and Human Development, College of Education, Temple University, Philadelphia, PA 19122, USA)

  • Nathaniel von der Embse

    (College of Education, University of South Florida, Tampa, FL 33620, USA)

  • Marco Innamorati

    (Department of Human Sciences, European University of Rome, 00163 Roma, Italy)

  • Michela Balsamo

    (Department of Psychological Sciences, Humanities and Territory, “G. d’Annunzio” University of Chieti-Pescara, 66100 Chieti, Italy)

Abstract

Depression is a common and debilitating condition that impacts individuals with various cultural backgrounds, medical conditions, and life circumstances. Thus, assessment tools need to be useful among different cultural groups. The 21-item Teate Depression Inventory (TDI) was developed in Italy, is designed to assess major depression, and focuses on cognitive and affective rather than somatic symptoms. This study aims to examine the factor structure and concurrent validity of the TDI English version among a non-clinical population in the United States. Participants included 398 adults (mean age 19.89 years, SD = 2.72, range: 18 to 46 years old) who completed the TDI and The Center for Epidemiologic Studies Depression Scale-Revised (CESD-R). The results supported a three-factor bifactor structure of the TDI (Positive Affect, Negative Affect, and Daily Functioning), which largely corresponds to the Tripartite Model of affective disorders. These findings support the use of TDI scores as measures of depressive symptoms among U.S. young adults, offering researchers and practitioners a brief and useful tool.

Suggested Citation

  • Linda Ruan-Iu & Laura L. Pendergast & Pei-Chun Liao & Paul Jones & Nathaniel von der Embse & Marco Innamorati & Michela Balsamo, 2023. "Measuring Depression in Young Adults: Preliminary Development of an English Version of the Teate Depression Inventory," IJERPH, MDPI, vol. 20(15), pages 1-11, July.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:15:p:6470-:d:1204965
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/15/6470/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/20/15/6470/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. John Schmid & John Leiman, 1957. "The development of hierarchical factor solutions," Psychometrika, Springer;The Psychometric Society, vol. 22(1), pages 53-61, March.
    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. Mohan, Geetha & Sivakumaran, Bharadhwaj & Sharma, Piyush, 2012. "Store environment's impact on variety seeking behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 19(4), pages 419-428.
    2. Meng-Tsang Hsieh & Jung-Sheng Chen & Chung-Ying Lin & Cheng-Fang Yen & Mark D. Griffiths & Yu-Te Huang, 2021. "Measurement Invariance of the Sexual Orientation Microaggression Inventory across LGB Males and Females in Taiwan: Bifactor Structure Fits the Best," IJERPH, MDPI, vol. 18(20), pages 1-12, October.
    3. Giofrè, David & Pastore, Massimiliano & Cornoldi, Cesare & Toffalini, Enrico, 2019. "Lumpers vs. splitters: Intelligence in children with specific learning disorders," Intelligence, Elsevier, vol. 76(C), pages 1-1.
    4. Kan, Kees-Jan & van der Maas, Han L.J. & Levine, Stephen Z., 2019. "Extending psychometric network analysis: Empirical evidence against g in favor of mutualism?," Intelligence, Elsevier, vol. 73(C), pages 52-62.
    5. Carlo Cavicchia & Maurizio Vichi & Giorgia Zaccaria, 2020. "The ultrametric correlation matrix for modelling hierarchical latent concepts," 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. 14(4), pages 837-853, December.
    6. Jeanne A. Teresi & Katja Ocepek-Welikson & John A. Toner & Marjorie Kleinman & Mildred Ramirez & Joseph P. Eimicke & Barry J. Gurland & Albert Siu, 2017. "Methodological Issues in Measuring Subjective Well-Being and Quality-of-Life: Applications to Assessment of Affect in Older, Chronically and Cognitively Impaired, Ethnically Diverse Groups Using the F," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 12(2), pages 251-288, June.
    7. Emanuele Maria Giusti & Giovanni Veronesi & Camilla Callegari & Gianluca Castelnuovo & Licia Iacoviello & Marco Mario Ferrario, 2022. "The North Italian Longitudinal Study Assessing the Mental Health Effects of SARS-CoV-2 Pandemic Health Care Workers—Part II: Structural Validity of Scales Assessing Mental Health," IJERPH, MDPI, vol. 19(15), pages 1-16, August.
    8. David J. Hessen, 2017. "Lower Bounds to the Reliabilities of Factor Score Estimators," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 648-659, September.
    9. P. Bentler, 1986. "Structural modeling and psychometrika: An historical perspective on growth and achievements," Psychometrika, Springer;The Psychometric Society, vol. 51(1), pages 35-51, March.
    10. Harold Bechtoldt, 1961. "An empirical study of the factor analysis stability hypothesis," Psychometrika, Springer;The Psychometric Society, vol. 26(4), pages 405-432, December.
    11. Kin Cheung & Tsz Leung Yip & C L Johnny Wan & Hilda Tsang & Lillian Weiwei Zhang & Anna Parpala, 2020. "Differences in study workload stress and its associated factors between transfer students and freshmen entrants in an Asian higher education context," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-23, May.
    12. Watson, Dorothy & Maître, Bertrand & Whelan, Christopher T., 2012. "Understanding Childhood Deprivation in Ireland," Research Series, Economic and Social Research Institute (ESRI), number BKMNEXT125, June.
    13. Carlo Cavicchia & Maurizio Vichi & Giorgia Zaccaria, 2022. "Gaussian mixture model with an extended ultrametric covariance structure," 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. 16(2), pages 399-427, June.
    14. Bongiorno, Christian & Miccichè, Salvatore & Mantegna, Rosario N., 2022. "Statistically validated hierarchical clustering: Nested partitions in hierarchical trees," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    15. Petr Květon & Martin Jelínek & Iva Burešová, 2022. "Psychometric Properties of the Sport Multidimensional Perfectionism Scale-2 in Czech Adolescent Athletes: An Exploratory Approach," SAGE Open, , vol. 12(3), pages 21582440221, July.
    16. Carlo Cavicchia & Maurizio Vichi, 2021. "Statistical Model-Based Composite Indicators for Tracking Coherent Policy Conclusions," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 449-479, August.
    17. Robert Jennrich & Peter Bentler, 2012. "Exploratory Bi-factor Analysis: The Oblique Case," Psychometrika, Springer;The Psychometric Society, vol. 77(3), pages 442-454, July.
    18. Reynolds, Matthew R. & Keith, Timothy Z., 2017. "Multi-group and hierarchical confirmatory factor analysis of the Wechsler Intelligence Scale for Children—Fifth Edition: What does it measure?," Intelligence, Elsevier, vol. 62(C), pages 31-47.
    19. Li Cai, 2015. "Lord–Wingersky Algorithm Version 2.0 for Hierarchical Item Factor Models with Applications in Test Scoring, Scale Alignment, and Model Fit Testing," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 535-559, June.
    20. Minjeong Jeon & Frank Rijmen & Sophia Rabe-Hesketh, 2018. "CFA Models with a General Factor and Multiple Sets of Secondary Factors," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 785-808, December.

    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:jijerp:v:20:y:2023:i:15:p:6470-:d:1204965. 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.