Analysis of the Relationships Among Factors Affecting Educational Competitiveness : An Application of FS/QCA method
Author(s):
Minhee KIM (presenting / submitting) Youngchool Choi (presenting)
Conference:
ECER 2016
Format:
Paper

Session Information

09 SES 03 A, Research into the Predictive Validity of Individual and Contextual Characteristics for Academic Success and Returns on Education

Paper Session

Time:
2016-08-23
17:15-18:45
Room:
NM-F101
Chair:
Monica Rosén

Contribution

This study is conducted in order to investigate the relationships between different factors affecting educational competitiveness, which is crucial to enhancing national competitiveness in every country, and to put forward policy implications whereby each country may raise the level of its educational competitiveness. It is generally accepted that educational competitiveness can greatly affect national competitiveness. International institutions such as the International Institute of Management and Development (IMD) and the World Economic Forum (WEF) have published reports on the national competitiveness of different countries. Educational competitiveness, as a sub-branch of national competitiveness, is regarded as an important element in national development. Hence, researchers and practitioners have primarily concentrated on which factors are most strongly associated with enhancing educational competitiveness, and on how to strengthen it.

The international institutions which evaluate students’ achievements from a comparative perspective are the International Association for the Evaluation of Educational Achievement (IEA) and the OECD. The former publishes Trends in International Mathematics and Science Study (TIMSS), while the latter publishes the reports of the Programme for International Student Assessment (PISA). TIMSS and PISA are concerned with evaluating students’ achievements in, respectively, mathematics and science, and reading, mathematics, and science. This study selects PISA score as a final indicator to represent educational competitiveness.

It is generally understood that many factors can affect the educational competitiveness of a country, and many studies have indicated that a number of factors can be involved in improving the educational sector in one country. Here, we address the potential factors associated with educational competitiveness and their interrelationships.

First, we hypothesize that per capita GDP is associated with total expenditure on education.The expenditure of OECD member countries on education increased by 28 percent between 2000 and 2006, reaching an average annual growth rate of 4 percent. In spite of the fact that expenditure on education nowadays accounts for a large proportion of GDP, and also has been increasing constantly, there have been few studies proving that growth in education spending leads to growth in educational quality. In the meantime, some studies (Choi, 2008; Shin and Joo, 2013) have concluded that accumulated per capita expenditure on education has positively affected PISA score. On the basis of these research findings, this study hypothesizes that per capita GDP, total expenditure on education, and total per capita expenditure on education affect educational competitiveness, and that per capita GDP also affects total expenditure on education as a percentage of GDP, and total per capita expenditure on education.

Second, we hypothesize that parents’ concerns about education is associated with educational competitiveness. It is important, in relation to educational competitiveness, whether parents are strongly concerned about a student’s future career or not. This is more important in Asian than in Western societies. Parental concerns about children’s education can be represented by total expenditure on education burdened by the private sector. There have been few studies examining the relationships between total expenditure on education burdened by the private sector and educational competitiveness. Here, following the work of some scholars (Choi, 2008; KEDI, 2010), we hypothesize that private-source expenditure on education as a percentage of GDP is positively associated with educational competitiveness.

Third, we hypothesize that pupil.teacher ratio can affect educational competitiveness. The ratio of students to teaching staff is an important issue as regards the quality of education worldwide. It is assumed that the smaller the number of students a teacher can teach, the greater will be the effectiveness of the teaching.

On the basis of the theoretical discussion above, we suggest the following research questions: Which configurations can affect educational competitiveness as a dependent variable?

Method

The countries to be included in this analysis are OECD member countries. The variables analyzed in this research consist of five independent variables and one dependent variable. 1. Educational competitiveness - Average of 2015 PISA scores including three subjects (reading, mathematics, and science). 2 . Per capita GDP - Per capita GDP, 2015 IMD. 3. Total expenditure on education - Total expenditure on education as a percentage of GDP, 2015 IMD. 4. Total per capita expenditure on education - Total per capita expenditure on education , 2015 IMD. 5. Pupil.teacher ratio - Ratio of students to teaching staff, 2015 OECD. 6. Parental concerns about children’s education - Ratio of private-source expenditure on education to GDP, 2015 OECD We used to QCA method. QCA is a case-oriented analytic technique that can systematically deal with small number of cases by applying “Boolean algebra to implement principles of comparison used by scholars engaged in the qualitative study of macro social phenomena. The first state in a QCA, like other methods is to show descriptive statistics of the variables included in the analysis. Then, it is necessary to standardize the original values of each variable in order to address the problems relating to mean and standard deviation of each variable occurring in the analysis process. The next stage is to produce fuzzy set membership score of each variable. For this, we used the fs/QCA calibrate function. This function requires three values of the variable as anchor points that indicate (1) full membership in the set; (2) full non membership in the set; ant (3) the point of maximum ambiguity. Conventionally, a membership of 0.95 or greater indicates an item; a membership of 0.05 or less indicates an item, and a membership of 0.5 indicates the point of maximum of ambiguity as to membership in the set . And then, the next step is to build a truth table with data for selected cases regarding the causal conditions and the outcome variables. Next, investigation of a truth table by itself allows for a study of diversity, showing which configurations are common and which ones do not happen or happen very seldom. Finally, we produce configuration explaining educational competitiveness.

Expected Outcomes

The main purpose of this study is to demonstrate specific configuration models explaining educational competitiveness in OECD countries and to put forward policy implications whereby each country can strengthen its educational competitiveness. Following the requirements of the FS/QCA model specification, we converted actual value of each variable to fuzzy set membership scores, produced truth table, and derived the three configurations explaining educational competitiveness.The research will be expected outcomes show that there are three significant combinations of variables affecting educational competitiveness (PISA score). Model 1 is a configuration of four variables (high total expenditure on education as a percentage of GDP, high total per capita expenditure on education, high ratio of private-source expenditure on education to GDP, and high GDP). Model 2 is a configuration of five variables (low total expenditure on education, low total per capita expenditure on education, low ration of students to teaching staff, low private-source expenditure on education, and low GDP. Model 3 is a configuration of five variables (low total expenditure on education, low total per capita expenditure on education, high private-source expenditure on education, high ratio of students to teaching staff, and high GDP). Finally, the study suggests that each country should endeavour to enhance its own educational competitiveness, considering how the factors associated with this relate to each other. FS/QCA is an alternative approach to analysis in educational competitiveness that involves truth tables, The use of QCA has been rarely reported in educational competitiveness studies, and is likely to be conceptual and paradigmatic challenges to its adoption in some settings.

References

Biever, T. and Martens, K. (2011). The OECD PISA study as a soft power in education? Lessons from Switzerland and US, European Journal of Education, 46(1), part 1. Borgonovi, F., Montt, G. (2012). Parental involvement in selected PISA countries and economies. OECD Education working paper no. 73. Choi, Y. C. (2008). Relationships between national competitiveness and decentralization, Korean Association of Local Government Studies Summer Conference Proceedings. Choi, Y. C. and Lee, J. H. (2015). What most matters in strengthening educational competitiveness?: An Application of FS/QCA method. 7th World Conference on Educational Sciences Proceedings. Holzinger, K. and Knill, C. (2008). Theoretical framework: causal factors and convergence expectations, in K. Holzinger, C. Knill and B. Arts (eds), Environmental Policy Convergence in Europe: The impact of international institutions and trade. Cambridge: Cambridge University Press. . IMD (2015). World Competitiveness Yearbook. Geneva: IMD. KEDI (2010). Analysis of Effects of Education on National Competitiveness. Seoul: KEDI. Lee, C. and Lee, K. H. (2006). Analysis of the Conditions of Korea Education Competiveness Index of IMD World Competitiveness Yearbook. The Journal of Korean Education, 33(1), 173-197. Lingard, B. and Grek, S. (2007). The OECD, indicators and PISA: an exploration of events and theoretical perspectives. Edinburgh, ESRC/ESP Research Project. OECD (2015). Education at a Glance. Paris: OECD. OECD (2015). PISA 2014 Results in Focus. Paris: OECD. Ragin, C. (2000). Fuzzy-Set Social Science. Chicago: The University of Chicago Press. Shin, H. S. and Joo, Y. H. (2013). Global governance and educational policy in Korea, Korean Journal of Educational Research, 51(3), 133-159. Tanzi, V. and Schuknecht, L. (1998). Can small governments secure economic and social well being?, in Grubel, H. (ed.), How To Spend the Fiscal Dividend: What is the optimal size of government? Vancouver: Fraser Institute. Thygeson, M.M., Solberg, L., Asche, S.E., Fontaine, P., Pawlson, L. G., Scholle, S.H. (2011). Using Fuzzy Set Qualitative Comparative Analysis to Explore the Relationship between Medical Homenss and Quality. Health Research and Education Trust. DOI: 10.1111/J.1475. Research Article. Yavuz, M. (2009). Factors that affect mathematics-science (MS) scores in the secondary education institutional exam: an application of structural equation modeling, Educational Sciences: Theory and Practice, 9(3), 1557-1572.

Author Information

Minhee KIM (presenting / submitting)
Daegu University
College of Education
Kyoungsan-si
Youngchool Choi (presenting)
Chungbuk National University, Republic of South Korea

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