Development and Psychometric Validation of Geography Interest Inventory for Secondary School Students

Erutujiro Goodluck
Federal Government Girls College Onitsha, Nigeria
DOI –
http://doi.org/10.37502/IJSMR.2021.4603

Abstract 

In recent times, the number of students’ academic performance in geography in secondary schools in Delta State, Nigeria has been on the declined. Therefore, it becomes imperative to know the student’s interest level in the subject. The study is on development and psychometrics validation of geography interest inventory. The instrument has 27 items.  The instrument was administered to a sample of 494 secondary students in Delta State, Nigeria. The sample was randomly split in two halves. The first sample 247 was used for Exploratory Factor Analysis (EFA). The results of the Exploratory Factor Analysis resulted in Four Factor Solution that is emotion, value, Knowledge, and engagement. To validate the structure of factors obtained from the EFA, confirmatory factor analysis was conducted using a second sample of 247. The result of model fit in terms of TLI (0.904), CFI (0.917), RMSEA (0.066) and SRMR (0.63) were above the recommended cut of o.90 for CFI, TLI, and less than 0.08 for RMSEA and SRMR respectively.  Furthermore, the study also indicates that items loading in the respective dimension were significant. Therefore, the geography interest inventory is valid and reliable.

Keywords: Geography, psychometrics, validation, inventory, and interest.

References

  • Akintade BO (2011). Considering the determinants of selecting geography as a discipline: The case of Senior Secondary School Students in Ilorin, Nigeria. Ozean J. of Social Sci., (4)3: 131-138.
  • Arifin, W. N. (2015). The graphical assessment of multivariate normality using SPSS. Education in Medicine Journal, 7 (2), e71–e75.
  • Bandalos, D.L. and Boehm-Kaufman, M.R. (2009), “Four common misconceptions in exploratory factor analysis”, in Lance, C.E. and Vandenberg, R.J. (Eds), Statistical and Methodological Myths and Urban Legends, Routledge, New York, NY, pp. 61-88.
  • Bangbade JO (2004). Effects of Subject Matter Knowledge in the Teaching and Learning of Biology and Physics: Teaching and Teacher education; pp. 109-102.
  • Barak A, & Cohen L (2002). Empirical Examination of the Self-Direct Search. J. Career Assess., 10: 387-400.
  • Bartlett, M. S. (1951). The effect of standardization on a χ 2 approximation in factor analysis. Biometrika, 38 (3/4), 337–344.
  • Brown, T. A. (2015). Confirmatory factor analysis for applied research. New York: The Guilford Press.
  • Courtney, M. G. R. (2013). Determining the number of factors to retain in EFA: Using the spss r-menu v2. 0 to make more judicious estimations. Practical Assessment, Research & Evaluation, 18 (8), 1–14.
  • DeVellis, R. F. (2012). Scale development: Theory and applications (3rd ed). California: Sage publications.
  • EGUNJOBI, A. O. (2014). Effect of tutorial mode of computer instruction on student academic performance in secondary school pratical geography in Nigeria. AFRRN STECH, 3(1), 150-166.
  • Elochukwu CC (2001). Teachers Attributes in Secondary Schools in Nigeria as Hindrance to Educational Development. Proceedings of the National Conference on Standards in Education, which held at the University of Benin, Benin city: University of Benin Press.
  • Estawu, S.S. Sababa, L.K. &Filgona, J. (2016). Effects of field trips strategy on senior secondary school students academic achievement in geography in Numan Educational Zone of Adamawa, Nigeria. European Journal of Educational Studies, 2(12), 138-154.
  • Fabrigar, L., & Wegener, D. (2012). Exploratory factor analysis. New York: Oxford University Press.
  • Fornell, C. and Larcker, D.F. (1981), Evaluating structural equation models with unobservable variables and measurement error”, Journal of Marketing Research, Vol. 18 No. 1, pp. 39-50.
  • Gorsuch, R. L. (2014). Exploratory factor analysis. New York: Routledge.
  • Guilford, J.P. (1959). Personality. U.S.A, McGraw Hill Book Company.
  • Hair, J.F. Jr, Black, W.C., Babin, B.J. and Anderson, R.E. (2010), Multivariate Data Analysis: A Global Perspective, 7th ed., Prentice Hall, Upper Saddle River, NJ.
  • Hinkin, T.R. (1998), A brief tutorial on the development of measures for use in survey questionnaires, Organizational Research Methods, Vol. 1 No. 1, pp. 104-121.
  • Obika, G.A.(2004). Development and preliminary validation of economics interest scale for secondary schools. University of Nigeria Nsukka Thesis.
  • Okunrotifa PO (2008). Geography in Nigerian High School. New Zealand J. of Geo. 55 (1): 16-19. Olanipekun AO (1988). Economic and Social Implications of the New Geography Curriculum: in F. C. Okafor, A. R. O. Jibunoh, M. A. Abegunde, O. D. Awaritefe and Akinbode (eds) The New Geography: A Handbook for Practising Teachers.Warri: Nigerian Geography Teachers Association (SWZ): 69-92.
  • Rena U (2000). Who will Teach: A case Study of Teacher Education Reform. Caddo California Gap press: p. 381
  • Rilwani, M.C., Akahomen, D.O & Gbakeji, J.O. (2014). Secondary school student’s attrition in geography in Essan West G. g. A of Edo state. Sky Journal of Educational Research Vol. 2(4), 028 – 036,
  • Snow, G. M. (2011). Development of mathematics interest inventory to identify gifted children from underrepresented and diverse population.
  • Tabachnick, B. G., & Fidell, L. S. (2012). Using multivariate statistics. Boston, MA: Allyn & Bacon/Pearson Education.
  • WAEC (2015). West African Examinations Council, Chief Examiners ‘Report May/June SSSCE pp. 14-16.
  • WAEC (2016). West African Examinations Council, Chief Examiners ‘Report May/June SSSCE pp. 14-16.
  • WAEC (2017). West African Examinations Council, Chief Examiners ‘Report May/June SSSCE pp. 14-16.
  • WAEC (2018). West African Examinations Council, Chief Examiners ‘Report May/June SSSCE pp. 14-16.
  • WAEC (2019). West African Examinations Council, Chief Examiners‘Report May/June SSSCE pp. 14-16.