Analysis of Paired Dichotomous Data: A Gentle Introduction to the McNemar Test in SPSS

Main Article Content

Omolola A. Adedokun
Wilella D. Burgess
https://orcid.org/0000-0001-9737-6407

Abstract

Background: Although McNemar Test is the most appropriate tool for analyzing pre-post differences in dichotomous items (e.g., “yes” or “no”, “correct” or “incorrect”, etc.), many scholars have noted the inappropriate use of Pearson’s Chi-square Test by researchers, including social scientists and evaluators, for the analysis of related or dependent dichotomous variables.


Purpose: The goal of this paper is to promote the use of McNemar Test among evaluators by providing a gentle introduction to the method. 


Setting: Not applicable.


Intervention: Not applicable.


Research Design: Not applicable.


Data Collection and Analysis: Using data from 506 6th grade students’ responses to a pre-post science test; this contribution illustrates how to conduct McNemar Test in SPSS.


Findings: This contribution provides a non-technical introduction to McNemar test and illustrates its use in an applied research/evaluation context.

Downloads

Download data is not yet available.

Article Details

How to Cite
Adedokun, O. A., & Burgess, W. D. (2012). Analysis of Paired Dichotomous Data: A Gentle Introduction to the McNemar Test in SPSS. Journal of MultiDisciplinary Evaluation, 8(17), 125–131. https://doi.org/10.56645/jmde.v8i17.336
Section
Ideas to Consider in Evaluation

References

Berenson, M. L., & Koppel, N. B. (2005). Why McNemar's procedure needs to be included in the Business Statistics Curriculum. Decision Sciences Journal of Innovative Education, 3(1), 125-136. https://doi.org/10.1111/j.1540-4609.2005.00056.x DOI: https://doi.org/10.1111/j.1540-4609.2005.00056.x

Ciechalski, J. C., Pinkney, J. W., & Weaver, F. S. (2002). A method for assessing change in attitude: The McNemar Test [Poster presentation]. Annual Meeting of the American Educational Research Association, New Orleans, LA.

Heck, R. H., Thomas, S. L., & Tabata, L. N. (2010). Multilevel and longitudinal modeling with IBM SPSS. Routledge. https://doi.org/10.4324/9780203855263 DOI: https://doi.org/10.4324/9780203855263

Hoffman, J. I. E. (1976). The Incorrect use of Chi-square analysis for paired data. Clinical Experiment Immunology, 24, 227-229.

Howell, D. C. (2008). Testing change over two measurements in two independent groups. http://www.uvm.edu/~dhowell/methods7/Supplements/Testing%20Dependent%20Proportions.pdf

Lachenbruch, P. A. (2005). McNemar test. In P. Armitage & T. Colton (Eds.), Encyclopedia of biostatistics (pp. 3062-3063). John Wiley and Sons. https://doi.org/10.1002/0470011815.b2a10035 DOI: https://doi.org/10.1002/0470011815.b2a10035

Lehr, R. G. (2010). McNemar Test. In S. Chow (Ed.), Encyclopedia of biopharmaceutical statistics (pp. 740-744). Informa Healthcare. https://doi.org/10.3109/9781439822463.121 DOI: https://doi.org/10.3109/9781439822463.121

Levin, J. R., & Serlin, R. C. (2000). Changing students' perspectives of McNemar's test of change. Journal of Statistics Education, 8(2). https://www.amstat.org/publications/jse/secure/v8n2/levin.cfm https://doi.org/10.1080/10691898.2000.12131289 DOI: https://doi.org/10.1080/10691898.2000.12131289

Marascuilo, L. A., Omelich, C. L., & Gokhale, D. V. (1988). Planned and post hoc methods for multiple-sample McNemar (1947) tests with missing data. Psychological Bulletin, 103, 238-245. https://doi.org/10.1037/0033-2909.103.2.238 DOI: https://doi.org/10.1037//0033-2909.103.2.238

Marascuilo, L. A., & Serlin, R. C. (1979). Tests and contrasts for comparing change parameters for a multiple sample McNemar data model. British Journal of Mathematical and Statistical Psychology, 32, 105-112. https://doi.org/10.1111/j.2044-8317.1979.tb00755.x DOI: https://doi.org/10.1111/j.2044-8317.1979.tb00755.x