IDEAS home Printed from https://ideas.repec.org/a/vrs/ecothe/v54y2016i4p563-586n7.html
   My bibliography  Save this article

CHAID Decision Tree: Methodological Frame and Application

Author

Listed:
  • Milanović Marina

    (Faculty of Economics, University of Nis, Serbia)

  • Stamenković Milan

    (Faculty of Economics, University of Kragujevac, Serbia)

Abstract

Technological advancement across human activities has brought about accelerated generation of huge amounts of data. Consequently, researchers are faced with the problem how to determine adequate ways of turning the available data mass into useful knowledge. Data analysis adapted to these changes when data mining was developed as an approach to data analysis from different perspectives which reveals significant hidden regularities. This paper presents conceptual characteristics of decision tree, an important data mining method which is, due to its explorative nature, exceptionally suitable for detection of data structure when analysing various problem situations. The empirical section of the paper demonstrates applicative characteristics of this method using CHAID algorithm in leadership studies: an interdependence of selected personal characteristics and the manager’s leadership style has been investigated. The aim of the paper is to develop a classification model for identification of the dominant leadership style. The study was conducted on the sample of 417 managers of privately owned small-sized enterprises in Serbia, using a specially designed questionnaire. The classification model identified the set of six statistically significant personal characteristics as predictors of dominant leadership style.

Suggested Citation

  • Milanović Marina & Stamenković Milan, 2016. "CHAID Decision Tree: Methodological Frame and Application," Economic Themes, Sciendo, vol. 54(4), pages 563-586, December.
    • Handle: RePEc:vrs:ecothe:v:54:y:2016:i:4:p:563-586:n:7
    DOI: 10.1515/ethemes-2016-0029
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/ethemes-2016-0029
    Download Restriction: no
    File URL: https://libkey.io/10.1515/ethemes-2016-0029?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    2. Horvat Ivan & Pejić Bach Mirjana & Merkač Skok Marjana, 2014. "Decision Tree Approach to Discovering Fraud in Leasing Agreements," Business Systems Research, Sciendo, vol. 5(2), pages 61-71, September.
    3. Kim, Samuel Seongseop & Timothy, Dallen J. & Hwang, Jinsoo, 2011. "Understanding Japanese tourists’ shopping preferences using the Decision Tree Analysis method," Tourism Management, Elsevier, vol. 32(3), pages 544-554.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rike Stotten & Michaela Maurer & Hannes Herrmann & Markus Schermer, 2019. "Different Forms of Accommodation in Agritourism: The Role of Decoupled Farmer-Based Accommodation in the Ötztal Valley (Austria)," Sustainability, MDPI, vol. 11(10), pages 1-27, May.
    2. Bacon, Victoria R. & Kearney, Christopher A., 2020. "School climate and student-based contextual learning factors as predictors of school absenteeism severity at multiple levels via CHAID analysis," Children and Youth Services Review, Elsevier, vol. 118(C).
    3. Aleksandar Đukić & Milorad K. Banjanin & Mirko Stojčić & Tihomir Đurić & Radenka Đekić & Dejan Anđelković, 2024. "An Ensemble of Machine Learning Models for the Classification and Selection of Categorical Variables in Traffic Inspection Work of Importance for the Sustainable Execution of Events," Sustainability, MDPI, vol. 16(22), pages 1-38, November.
    4. Guy Assaker & Wassim Shahin, 2022. "What Drives Faculty Publication Citations in the Business Field? Empirical Results from an AACSB Middle Eastern Institution," Publications, MDPI, vol. 10(4), pages 1-29, November.
    5. Arnold Tóth & Tímea Juhász & Botond Kálmán, 2020. "Determinants Of Financial Habits," Economy & Business Journal, International Scientific Publications, Bulgaria, vol. 14(1), pages 237-256.
    6. Gholamreza Shiran & Reza Imaninasab & Razieh Khayamim, 2021. "Crash Severity Analysis of Highways Based on Multinomial Logistic Regression Model, Decision Tree Techniques, and Artificial Neural Network: A Modeling Comparison," Sustainability, MDPI, vol. 13(10), pages 1-23, May.
    7. Francisco Javier Rondán-Cataluña & Patricio E. Ramírez-Correa & Jorge Arenas-Gaitán & Muriel Ramírez-Santana & Elizabeth E. Grandón & Jorge Alfaro-Pérez, 2020. "Social Network Communications in Chilean Older Adults," IJERPH, MDPI, vol. 17(17), pages 1-17, August.
    8. Iram Parvez & Jianjian Shen & Ishitaq Hassan & Nannan Zhang, 2021. "Generation of Hydro Energy by Using Data Mining Algorithm for Cascaded Hydropower Plant," Energies, MDPI, vol. 14(2), pages 1-28, January.
    9. Antônio Carlos Pacagnella Júnior & Henrique Luiz da Silva & Wagner Wilson Bortoletto & Paulo Sergio de Arruda Ignacio, 2023. "Financial and environmental efficiency of CDM projects: Analysis and classification for investment decisions," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(2), pages 926-941, March.

    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. Fernanda Oliveira & Pedro Pintassilgo & Patrícia Pinto & Isabel Mendes & João Albino Silva, 2017. "Segmenting visitors based on willingness to pay for recreational benefits," Tourism Economics, , vol. 23(3), pages 680-691, May.
    2. Legohérel, Patrick & Hsu, Cathy H.C. & Daucé, Bruno, 2015. "Variety-seeking: Using the CHAID segmentation approach in analyzing the international traveler market," Tourism Management, Elsevier, vol. 46(C), pages 359-366.
    3. do Valle, Patrícia Oom & Pintassilgo, Pedro & Matias, António & André, Filipe, 2012. "Tourist attitudes towards an accommodation tax earmarked for environmental protection: A survey in the Algarve," Tourism Management, Elsevier, vol. 33(6), pages 1408-1416.
    4. Marquet, Oriol & Miralles-Guasch, Carme, 2014. "Walking short distances. The socioeconomic drivers for the use of proximity in everyday mobility in Barcelona," Transportation Research Part A: Policy and Practice, Elsevier, vol. 70(C), pages 210-222.
    5. Strobl, Carolin & Boulesteix, Anne-Laure & Augustin, Thomas, 2007. "Unbiased split selection for classification trees based on the Gini Index," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 483-501, September.
    6. Hache, Emmanuel & Leboullenger, Déborah & Mignon, Valérie, 2017. "Beyond average energy consumption in the French residential housing market: A household classification approach," Energy Policy, Elsevier, vol. 107(C), pages 82-95.
    7. Ghosh, Atish R. & Qureshi, Mahvash S. & Kim, Jun Il & Zalduendo, Juan, 2014. "Surges," Journal of International Economics, Elsevier, vol. 92(2), pages 266-285.
      • Mahvash S Qureshi & Mr. Atish R. Ghosh & Mr. Juan Zalduendo & Mr. Jun I Kim, 2012. "Surges," IMF Working Papers 2012/022, International Monetary Fund.
    8. Tomàs Aluja-Banet & Eduard Nafria, 2003. "Stability and scalability in decision trees," Computational Statistics, Springer, vol. 18(3), pages 505-520, September.
    9. I. Albarrán & P. Alonso-González & J. M. Marin, 2017. "Some criticism to a general model in Solvency II: an explanation from a clustering point of view," Empirical Economics, Springer, vol. 52(4), pages 1289-1308, June.
    10. Schwartz, Ira M. & York, Peter & Nowakowski-Sims, Eva & Ramos-Hernandez, Ana, 2017. "Predictive and prescriptive analytics, machine learning and child welfare risk assessment: The Broward County experience," Children and Youth Services Review, Elsevier, vol. 81(C), pages 309-320.
    11. Yousaf Muhammad & Dey Sandeep Kumar, 2022. "Best proxy to determine firm performance using financial ratios: A CHAID approach," Review of Economic Perspectives, Sciendo, vol. 22(3), pages 219-239, September.
    12. Ralf Elsner & Manfred Krafft & Arnd Huchzermeier, 2003. "Optimizing Rhenania's Mail-Order Business Through Dynamic Multilevel Modeling (DMLM)," Interfaces, INFORMS, vol. 33(1), pages 50-66, February.
    13. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.
    14. Osman Taylan & Abdulaziz S. Alkabaa & Mustafa Tahsin Yılmaz, 2022. "Impact of COVID-19 on G20 countries: analysis of economic recession using data mining approaches," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-30, December.
    15. Archana R. Panhalkar & Dharmpal D. Doye, 2020. "An approach of improving decision tree classifier using condensed informative data," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 47(4), pages 431-445, December.
    16. Bas Donkers & Richard Paap & Jedid‐Jah Jonker & Philip Hans Franses, 2006. "Deriving target selection rules from endogenously selected samples," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 549-562, July.
    17. Vicente-Cera, Isaías & Acevedo-Merino, Asunción & Nebot, Enrique & López-Ramírez, Juan Antonio, 2020. "Analyzing cruise ship itineraries patterns and vessels diversity in ports of the European maritime region: A hierarchical clustering approach," Journal of Transport Geography, Elsevier, vol. 85(C).
    18. Edward Kozłowski & Anna Borucka & Andrzej Świderski & Przemysław Skoczyński, 2021. "Classification Trees in the Assessment of the Road–Railway Accidents Mortality," Energies, MDPI, vol. 14(12), pages 1-15, June.
    19. Javad Hassannataj Joloudari & Edris Hassannataj Joloudari & Hamid Saadatfar & Mohammad Ghasemigol & Seyyed Mohammad Razavi & Amir Mosavi & Narjes Nabipour & Shahaboddin Shamshirband & Laszlo Nadai, 2020. "Coronary Artery Disease Diagnosis; Ranking the Significant Features Using a Random Trees Model," IJERPH, MDPI, vol. 17(3), pages 1-24, January.
    20. Lea Piscitelli & Annalisa De Boni & Rocco Roma & Giovanni Ottomano Palmisano, 2023. "Carbon Farming: How to Support Farmers in Choosing the Best Management Strategies for Low-Impact Food Production," Land, MDPI, vol. 13(1), pages 1-16, 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:vrs:ecothe:v:54:y:2016:i:4:p:563-586:n:7. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.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.