Risks of Error in the Quantitative Sociology Research
Article Sidebar
Main Article Content
Abstract
This research aimed at exploring research designs and analyzing risks of error in quantitative sociology research. The samples were drawn from research papers in sociology published in the digital database of ThaiLIS-Thai Library Integrated System. The data were collected using search engine, extensible markup language (XML), and a digital questionnaire. Then the data were analyzed using percentage, cluster analysis, binomial proportion test, and multiple logistic regression. The results showed that most of the samples were research studies with hypothesis testing and non-parametric statistics. There was no difference between the proportion of research at risk and the one without risks of error. The results based on multiple logistic regression indicated that research without instrumentation verification and statistical methods for calculating the sample size was more likely to have risks (odds) 93% of research error. The research with invalidated instruments had 14 times higher risk than the one with validated instruments. The research without statistical calculation for sample size also had a twice higher risk than the one with statistical calculation for sample size.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
บทความทุกบทความเป็นลิขสิทธิ์ของวารสารวิชาการมนุษยศาสตร์และสังคมศาสตร์ มหาวิทยาลัยบูรพาเท่านั้น
References
Allison, P. D. (2002). Missing data. SAGE.
Arnab, R. (2017). Survey sampling theory and applications. Academic Press.
Aven, T. (2011). Quantitative risk assessment: The scientific platform. Cambridge University Press.
Babble, E. (2013). The practice of social research (13rd ed.). Wadsworth Cengage Learning.
Berendsen, H. J. C. (2011). A student’s guide to data and error analysis. Cambridge University Press.
Bevington, P. R., & Robinson, D. K. (1992). Data reduction and error analysis for the physical sciences. McGraw-Hill.
Biemer, P. P., Groves, R. M., Lyberg, L. E., Mathiowetz, N. A., & Sudman, S. (Eds.). (1991). Measurement errors in surveys. Wiley.
Blair, E., & Blair, J. (2015). Applied survey sampling. SAGE
Bollinger, C. R. (1998). Measurement error in the current population survey: A nonparametric look. Journal of Labor Economics, 16(3), 576-594. https://doi.org/10.1086/209899
Buonaccorsi, J. P. (2010). Measurement error: Models, methods, and applications. CRC Press.
Christopher, A. N. (2017). Interpreting and using statistics in psychological research. SAGE.
Cochran, W. G. (1977). Sampling techniques. John Wiley & Sons.
Curran-Everett, D., & Benos, D. J. (2007). Guidelines for reporting statistics in journals published by the American Physiological Society. Advances in Physiology Education, 31(4), 295-298. https://doi.org/10.1152/ advan.00022.2007
Debra, W. (2011). Analyzing quantitative data: An introduction for social researchers. John Wiley & Sons.
Deming, W. E. (1944). On errors in surveys. American Sociological Review, 9(4), 359-369. https://doi.org/10.2307/2085979
Drost, E. A. (2011). Validity and reliability in social science research. Education Research and Perspectives, 38(1), 105-123.
Field, A. (2009). Discovering statistics using SPSS (sex and drugs and rock ‘n’ roll). SAGE.
Flynn, B. B., Sakakibara, S., Schroeder, R. G., Bates, K. A., & Flynn, E. J. (1990). Empirical research methods in operations management. Journal of Operations Management, 9(2), 250-284. https://doi.org/10.1016/0272-6963(90)90098-X
Garson, G. D. (2012). Testing statistical assumptions. Statistical Publishing Associates.
Ghasemi, A., & Zahediasl, S. (2012). Normality tests for statistical analysis: A guide for non-statisticians. International Journal of Endocrinology & Metabolism, 10(2), 486-489. https://doi.org/10.5812/ijem.3505
Goldstein, R. (1989). Power and sample size via MS/PC-DOS computers. The American Statistician, 43(4), 253-260. https://doi.org/10.2307/2685373
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis. Pearson/Prentice Hall.
Henry, G. T. (1990). Practical sampling. SAGE.
Holmes, T. H. (2004). Ten categories of statistical errors: a guide for research in Endocrinology and metabolism. Am J Physiol Endocrinol Metab, 286(4), E495-501. https://doi.org/10.1152/ajpendo.00484.2003
Israel, G. D. (1992). Determining sample size. https://www.tarleton.edu/ academicassessment/documents/Samplesize.pdf
Kennedy, J. E. (2015). Beware of inferential errors and low power with bayesian analyses: Power analysis is needed for confirmatory research. Journal of Parapsychology, 79(1), 53-64. https://psycnet.apa.org/record/2015-39112-004
Kimberlin, C. L., & Winterstein, A. G. (2008). Validity and reliability of measurement instruments used in research. Am J Health Syst Pharm, 65(23), 2276-2284. https://doi.org/10.2146/ajhp070364
Kumar, R. (2014). Research methodology: A step-by-step guide for beginners (4th ed.). SAGE.
Lash, T. L., Fox, M. P., & Fink, A. K. (2009). Applying quantitative bias analysis to Epidemiologic data. Springer.
Lash, T. L., Fox, M. P., Cooney, D., Lu, Y., & Forshee, R. A. (2016). quantitative bias analysis in regulatory settings. American Journal of Public Health, 106(7), 1227-1230. https://doi.org/10.2105/AJPH.2016.303199
Lavrakas, P. J. (2008). Encyclopedia of survey research methods (Vols. 1-10). SAGE.
Lenth, R. V. (2001). Some practical guidelines for effective sample size determination. American Statistician, 55(3), 187-193. https://doi.org/ 10.1198/000313001317098149
Osborne, J. W., & Waters, E. (2002). Four assumptions of multiple regression that researches should always test. Practical Assessment, Research & Evaluation, 8(2), 1-5. https://pareonline.net/getvn.asp?v=8&n=2
Pritchard, C. L. (2010). Risk management: Concepts and guidance. ESI International.
Ranganathan, P., Aggarwal, R., & Pramesh, C. S. (2015). Common pitfalls in statistical analysis: Odds versus risk. Perspect Clin Res., 6(4), 222-224. https://doi.org/10.4103/2229-3485.167092
Rice, S. A. (1929). Contagious bias in the interview: A methodological note. American Journal of Sociology, 35(3), 420-423. https://doi.org/ 10.1086/215055
Scheaffer, R. L., Mendenhall III, W., & Ott, R.L. (2006). Elementary survey sampling. Thomson Brooks/Cole.
Schennach, S. M. (2012). Measurement error in nonlinear models: A review. https://doi.org/10.1017/CBO9781139060035.009
Taherdoost, H. (2016). Validity and reliability of the research instrument: How to test the validation of a questionnaire/survey in a research. International Journal of Academic Research in Management (IJARM), 5(3), 28-36. https://doi.org/10.2139/ssrn.3205040
Thompson, S. K. (2012). Sampling. Wiley.
Thye, S. R. (2000). Reliability in experimental sociology. Social Forces, 78(4), 1277-1309. https://doi.org/10.1093/sf/78.4.1277
Verma, J. P. G., & Abdel-Salam, A. G. (2019). Testing statistical assumptions in research. Wiley.
Yamane, T. (1967). Elementary sampling theory. Prentice-Hall.
Ye, X., Xiao, X., Shi, J., & Ling, M. (2016). The new concepts of measurement error theory. Measurement, 83(1), 96-105. https://doi.org/10.1016/ j.measurement.2016.01.038