Abstract
In contrast to qualitative research, quantitative research focuses primarily on the testing of hypotheses using variables that are measured numerically and analyzed using statistical procedures. If appropriately designed, quantitative approaches provide the ability to establish causal relationships between variables. Hypothesis testing is a critical component of quantitative methods, and requires appropriately framed research questions, knowledge of the appropriate literature, and guidance from relevant theoretical frameworks. Within the field of simulation, two broad categories of quantitative research exist: studies that investigate the use of simulation as a variable and studies using simulation to investigate other questions and issues. In this chapter we review common study designs and introduce some key concepts pertaining to measurement and statistical analysis. We conclude the chapter with a survey of common errors in quantitative study design and implementation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Babbie ER. The practice of social research. 12th ed. Belmont: Wadsworth Cengage; 2010.
Mujis D. Doing quantitative research in education with SPSS. 2nd ed. London: SAGE Publications; 2010.
Vogt WP. Dictionary of statistics and methodology. 2nd ed. London: SAGE Publications; 1999.
Kessler D, Pusic M, Chang TP, Fein DM, Grossman D, Mehta R, et al. Impact of just-in-time and just-in-place simulation on intern success with infant lumbar puncture. Pediatrics. 2015;135(5):e1237–46.
Calhoun AW, Sutton ERH, Barbee AP, McClure B, Bohnert C, Forest R, et al. Compassionate options for pediatric EMS (COPE): addressing communication skills. Prehosp Emerg Care. 2017;21(3):334–43.
Sullivan GM, Sargeant J. Qualities of qualitative research: part I. J Grad Med Educ. 2011;3(4):449–52.
Crandall SJ, Caelleigh AS, Steinecke A. Reference to the literature and documentation. Acad Med. 2001;76(9):925–7.
Picho K, Artino AR Jr. 7 deadly sins in educational research. J Grad Med Educ. 2016;8(4):483–7.
Cook DA, Beckman TJ, Bordage G. Quality of reporting of experimental studies in medical education: a systematic review. Med Educ. 2007;41(8):737–45.
Bernhard HR. Social research methods, qualitative and quantitative approaches. 2nd ed. London: SAGE Publications; 2013.
Evans BC, Coon DW, Ume E. Use of theoretical frameworks as a pragmatic guide for mixed methods studies: a methodological necessity? J Mix Methods Res. 2011;5(4):276–92.
Morgan DL. Paradigms lost and pragmatism regained: methodological implications of combining qualitative and quantitative methods. J Mixed Methods Res. 2007;1(1):48–76.
Tavakol M, Sandars J. Quantitative and qualitative methods in medical education research: AMEE Guide No 90: part II. Med Teach. 2014;36(10):838–48.
Ingham-Broomfield RA. Nurse’s guide to quantitative research. Aust J Adv Nurs. 2014;32(2):32–8.
Neuman WL. Social research methods: qualitative and quantitative approaches. 7th ed. Edinburgh Gate: Pearson Education Limited; 2014.
Lopreiato JO, editor. Healthcare simulation dictionary. Rockville: Agency for Healthcare Research and Quality; 2016.
Issenberg SB, McGaghie WC, Petrusa ER, Lee Gordon D, Scalese RJ. Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review. Med Teach. 2005;27(1):10–28.
Calhoun AW, Bhanji F, Sherbino J, Hatala R. Simulation for high-stakes assessment in pediatric emergency medicine. Clin Pediatr Emerg Med. 2016;17(3):212–23.
Calhoun AW, Donoghue A, Adler M. Assessment in pediatric simulation. In: Grant V, Cheng A, editors. Comprehensive healthcare simulation: pediatrics. Cham: Springer International; 2016. p. 77–94.
Cook DA, Brydges R, Ginsburg S, Hatala R. A contemporary approach to validity arguments: a practical guide to Kane’s framework. Med Educ. 2015;49(6):560–75.
Downing SM. Validity: on meaningful interpretation of assessment data. Med Educ. 2003;37(9):830–7.
Messick S. Meaning and values in test validation: the science and ethics of assessment. Educ Res. 1989;18(2):5–11.
Vetter TR. Fundamentals of research data and variables: the devil is in the details. Anesth Analg. 2017;125:1375–80.
Sullivan GM, Feinn R. Using effect size-or why the P value is not enough. J Grad Med Educ. 2012;4(3):279–82.
Sullivan GM. Is there a role for spin doctors in Med Ed research? J Grad Med Educ. 2014;6(3):405–7.
Feise RJ. Do multiple outcomes measures require P-value adjustment? BMC Med Res Methodol. 2002;2(8):1–4.
Noble WS. How does multiple testing correction work? Nat Biotechnol. 2009;27(12):1135–7.
Quertemont E. How to statistically show the absence of an effect. Psychol Belg. 2011;51(2):109–27.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Calhoun, A.W., Hui, J., Scerbo, M.W. (2019). Quantitative Research in Healthcare Simulation: An Introduction and Discussion of Common Pitfalls. In: Nestel, D., Hui, J., Kunkler, K., Scerbo, M., Calhoun, A. (eds) Healthcare Simulation Research. Springer, Cham. https://doi.org/10.1007/978-3-030-26837-4_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-26837-4_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26836-7
Online ISBN: 978-3-030-26837-4
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)