1Institute for Anthropological Research, Zagreb, Croatia
Two Aspects of Bias in Multivariate Studies: Mixing Specific with General Concepts and “Comparing Apples and Oranges”
This paper presents two types of bias that occur relatively often when using multivariate analysis. For both types of bias, it is characteristic that the number and choice of different types of variables are not balanced by application of clear methodological rules. Following the interpretation of broader theoretical positions, which include "confirmation bias" ( of initial hypothesis) and "mis¬specification bias", a description of two types of bias characteristic of multivariate analysis are given: "mixed-level bias" (in terms of specificity - generality) and "mixed-constructs bias" . Both types of bias further enhance the disparity in the number and ratio of different types of variables in the same multivariate analysis. Details of situations, when these two types of bias appear, are presented and displayed in four different examples. Several strategies are proposed as to how these types of bias can try to be avoided, during the preparation of studies, during the statistical analyses and their interpretation.
Mixed-constructs bias, Mixed-level bias, Multivariate analysis
1. KUZON WM JR, URBANCHEK MG, MCCABE S, The seven deadly sins of statistical analysis. Ann Plast Surg 37 (1996) 265. - 2. POHL Y, Invalid results because of inappropriate statistical analyses. Treatment recommendations still in question. Dental Traumatology, 25 (2009) 350. – 3. BOWER KM, On The Use of Indicator Variables in Regression Analysis. International Society of Six Sigma Professionals EXTRAOrdinary Sense, 2 (2001) 1. – 4. WULDER MA, A Practical Guide to the Use of Selected Multivariate Statistics. (Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre, Victoria, 1998). – 5. NICKERSON RS, Confirmation Bias; A Ubiquitous Phenomenon in Many Guises. Review of General Psychology, 2 (1998) 175. – 6. DARLEY JM, GROSS PH, A Hypothesis-Confirming Bias in Labelling Effects. Journal of Personality and Social Psychology, 44 (1983) 20. – 7. SHAFIR E, Choosing versus rejecting: why some options are both better and worse than others", Memory and Cognition 21 (1993) 546. – 8. ECKMAN S, KREUTER F, Confirmation Bias in Housing Unit Listing. Public Opin Q 75 (2011) 139. – 9. TRNNES PC, Consistency in Application of Auditing Standards: The Impact of Auditors’ Confirmation Bias toward Prior Year Audit Opinion. University of New South Wales Presentation at NHH (2010). accessed 2.1.2013. Available from: URL: http://www.nhh.no/en/research-faculty/department-of-business-and-management-science/seminars/accounting-and-management-science-seminars-spring-2010.aspx - 10. HERGOVICH A, SCHOTT R, BURGER C, Biased Evaluation of Abstracts Depending on Topic and Conclusion: Further Evidence of a Confirmation Bias Within Scientific Psychology. Current Psychology, 29 (2010) 188. – 11. CALIKLI G, BENER A, Influence of confirmation biases of developers on software quality: an empirical study. Software Qual J, 21 (2013) 377. – 12. AGUIRRE-URRETA MI, MARAKAS GM, Revisting bias due to construct misspecification: different results from considering coefficients in standardized form. MIS Quarterly, 36 (2012) 123. – 13. PETTER S, RAI A, STRAUB D. The Critical Importance of Construct Measurement Specification: A Response to Aguirre-Urreta and Marakas. MIS Quarterly, 36 (2012) 147. – 14. BURNHAM KP, ANDERSON DR. Data-Based Selection of an Appropriate Biological Model: The Key to Modern Data Analysis. In McCullough DR, Barrett RH (Eds) Wildlife 2001 - Populations (Springer Netherlands, Amsterdam, 1992). – 15. LIGHT GL, Regression, Model Misspecification and Causation, with Pedagogical Demonstration. Applied Mathematical Sciences, 4 (2010) 225. – 16. XYCOON, Online Econometrics Textbook - Regression Extensions - Assumption Violations of Linear Regression - Misspecification in Linear Regression, Office for Research Development and Education (2013). accessed 2.11.2013. Available from: URL: http://www.xycoon. com/. – 17. DAVIS G, KANAGO B. Misspecification bias in models of the effect of inflation uncertainty. Economics Letters, 38 (1992) 325. – 18. CLARKE KA, KENKEL B, RUEDA MR, Misspeciffication and the Propensity Score: The Possibility of Overadjustment. (2011). accessed 2.11.2013. Available from: URL: http://www.rochester.edu/college/gradstudents/mrueda/ documents/Clarke_Kenkel_Rueda.pdf. - 19. STREINER DL. Figuring out factors: the use and misuse of factor analysis. Can J Psychiatry, 39 (1994) 135. – 20. BRAY BC, LANZA ST, TAN X. An Introduction to Eliminating Bias in Classify-Analyze Approaches for Latent Class Analysis. (The Methodology Center Technical Report, The Pennsylvania State University, 2012). – 21. IVANOV L, PENEZIĆ Z, Burnsova skala perfekcionizma. In Proroković A, Lacković-Grgin A, Ćubela V, Penezić Z (Eds) Zbirka psihologijskih skala i upitnika 2 (Filozofski fakultet, Zadar, 2004). – 22. SINDIK J. Povezanost manifestnog i doživljajnog perfekcionizma sa situacijskim parametrima učinkovitosti košarkaša. Hrvatski športskomedicinski vjesnik, 24 (2009) 98. – 23. FRANCEŠKO M, MIHIĆ V, BALA G, Struktura motiva postignuća merena skalom MOP2002. In Čukić B, Franceško M (Eds) Ličnost u višekulturnom društvu: Organizacijska multikulturalnost i Evropski identitet (Filozofski fakultet, Zadar, Novi Sad, 2002). - 24. LILLIENFIELD SO, AMMIRATI R, LANDFIELD K, Giving debiasing away: Can psychological research on correcting cognitive errors promote human welfare? Perspectives on Psychological Science, 4 (2009), 390.