Decision Tree-Based Classification Model for Identification of Effective Leadership Indicators

Svajone Bekesiene, Sarka Hoskova-Mayerova

Abstract


This study was aimed at identifying effective leadership abilities as appreciated by soldiers in the Lithuanian armed forces. Leader behavior was measured using an adapted version of the Leader Behavior Description Questionnaire (LBDQ), which was originally developed by Andrew W. Halpin from Ohio State University. Data were collected from soldiers holding different ranks and doing professional military service in all units of the Lithuanian armed forces and were analyzed using the IBM SPSS version 20 software application. For our data analysis, the Chi-square Automatic Interaction Detector (CHAID) decision tree growing method was used with three class dependent variables. The CHAID algorithm helped in specifying the best splits for each of twelve potential predictors and then select the predictors whose splits presented the most serious differences in the sub-populations of the sample. In the Chi-squared significance test, the lowest p-value was achieved. The model structures obtained after analysis are presented.


Keywords


CHAID growing method; decision tree model; leadership; leadership style; leader behavior

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References


Higgs, M. & Rowland, D., All Changes Great and Small: Exploring Approaches to Change and its Leadership, Journal of Change Management, 5(2), pp. 121-151, 2000.

Stoner, J., Freeman, R. & Gilbert, D., UAB: Polygraphy and Informatics, Vadyba, Kaunas, 2000. (In Lithuanian)

Urban, R. & Urbanová, N., Dynamics of Human Resources in the Military Organization, Knowledge Based Organization International Conference, pp. 894-897, 2011.

Popper, M. Leadership in Military Combat Units and Business Organizations: A Comparative Psychological Analysis, Journal of Managerial Psychology, 11(1), pp. 15-23, 1996.

Bekesiene, S., Hoskova-Mayerova, S. & Diliunas, P., Structural Equation Modeling Using the Amos and Regression of Effective Organizational Commitment Indicators in Lithuanian Military Forces, In: Aplimat – 16th Conference on Applied Mathematics Proceedings, Bratislava, pp. 91-102, 2017.

Bekesiene, S., Hoskova-Mayerova, S. & Diliunas, P., Identification of Effective Leadership Indicators in the Lithuania Army Forces, Studies in Systems, Decision and Control, Springer International Publishing AG, 104, pp. 107-122, 2017. DOI:10.1007/978-3-319-54819-7_9

Vališ, D. Žák, L. Hasilová, K. & Vintr, Z., Applying Regression Diagnostics for Identifying Non-standard Behaviour of a Technical System, In: International Conference on Military Technologies (ICMT). Brno: University of Defence, pp. 142-148, 2017.

Stogdill, R. , Handbook of Leadership: A Survey of Theory and Research, Free Press, New York, pp. 411, 1974.

Stogdill, R.M., Goode, O.S. & Day, D.R., The Leader Behavior of Corporation Presidents, Personnel Psychology, 16(2), pp. 127-132, 1963.

Newman, T., Vital Statistics, Community Care, pp. 18-24, 2002.

Salkind, N., Exploring Research, 4th ed., Prentice Hall (Upper Saddle River), 2000.

Fornell, C. & Larcker, D.F., Evaluating Structural Equation Models with Unobservable Variables and Measurement Error, Journal of Marketing Research, 18(1), pp. 39-50, 1981. DOI:10.2307/3151312

Hair, J.F., Black, W.C., Balin, B.J. & Anderson, R.E., Multivariate Data Analysis, Maxwell Macmillan International Editions, 2010.

Bollen, K.A. & Long, J.S., Testing Structural Equation Models, Newbury Park, CA, Sage, 1993.

Garson, G.D., Structural Equation Modelling, 2011, http://www.statisticalassociates.com/sem_p.pdf, (10 February 2017).

The Ohio State Leadership Studies, Leader Behavior Description Questionnaire – Form XII Self, Fisher College of Business, The Ohio State University, https://cyfar.org/sites/default/files/LBDQ_1962_Self_ Assessment.pdf, (6 February 2017).

International Business Machine, IBM SPSS AMOS, Available at: http://www-03.ibm.com/software/ products/en/spss-amos, (6 February 2017).

Ritschard, G., CHAID and Earlier Supervised Tree Methods, in: McArdle, J.J. & Ritschard, G. (eds.). Contemporary Issues in Exploratory Data Mining in the Behavioral Sciences, Routledge, London, pp. 48-74, 2013.

Maturo, A. & Maturo, F., Research in Social Sciences: Fuzzy Regression and Causal Complexity, Studies in Fuzziness and Soft Computing, 305, pp. 237-249, 2013. DOI:10.1007/978-3-642-35635-3-18

Urban, R. Urbanova, N. A New Requests Implementation into the Military Professionals Preparation, XXX International Colloquium On The Management Of Educational Process, Proceedings Social Science & Humanities, pp.173-177, 2012.

Kubicek, P. Mulickova, E. Konecny, M. & Kučerová, J., Flood Management and Geoinformation Support within the Emergency Cycle (EU Example), J. Hřebíček, G. Schimak, and R. Denzer (Eds.): ISESS 2011, IFIP AICT 359, pp. 77–86, 2011.

Maturo, F. & Ventre, V., Consensus in Multiperson Decision Making using Fuzzy Coalitions, Studies in Fuzziness and Soft Computing, 357, pp. 451-464, 2018. DOI:10.1007/978-3-319-60207-3_26




DOI: http://dx.doi.org/10.5614%2Fj.math.fund.sci.2018.50.2.2

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