Quantitization refers to transforming qualitative data into numerical format. Approaches to quantitization include dichotomizing qualitative themes and counting qualitative codes. Statistical analysis of themes and code counts has the potential to produce valuable information for mixed methods researchers. Examples of quantitized statistical analysis are presented along with ways in which the results can provide useful insights. With regard to analyzing code counts, due to limitations associated with traditional parametric and nonparametric tests of significance, permutation testing is offered as a rigorous alternative. A detailed example of permutation testing using readily available resampling software is provided.
BalasubramaniG. K.WisniewskiS. R.ZhangH.EngH. F. (2005). Development of an efficient SAS macro to perform permutation tests for two independent samples. Computer Methods and Programs in Biomedicine, 79, 179-187.
2.
BazeleyP. (2009). Editorial: Integrating data analyses in mixed method research. Journal of Mixed Methods Research, 3(3), 203-207.
3.
BergerV. W. (2000). Pros and cons of permutation tests in clinical trials. Statistics in Medicine, 19, 1319-1328.
4.
BrymanA. (2007). Barriers to integrating quantitative and qualitative research. Journal of Mixed Methods Research, 1(1), 8-22.
5.
ChauW.McIntoshA. R.RobinsonS. E.SchulzM.PantevC. (2004). Improving permutation test power for group analysis of spatially filtered MEG data. Neuroimage, 23, 983-996.
6.
ChiharaL. M.HesterbergT. C. (2011). Mathematic statistics with resampling and R. Hoboken, NJ: Wiley.
7.
CollingridgeD. S.GanttE. E. (2009). The quality of qualitative research. American Journal of Medical Quality, 23, 389-395.
8.
CreswellJ. W. (2008). Qualitative inquiry and research design: Choosing among the five traditions. Thousand Oaks, CA: Sage.
9.
CreswellJ. W.FettersM. D.IvankovaN. V. (2004). Designing a mixed methods study in primary care. Annals of Family Medicine, 2(1), 7-12.
10.
CreswellJ. W.Plano ClarkV. L. (2007). Designing and conducting mixed methods research. Thousand Oaks, CA: Sage.
11.
DavisonA. C.HinkleyD. V. (1997). Tests. In DavisonA. C.HinkleyD. V. (Eds.), Bootstrap methods and their application (pp. 136-190). New York, NY: Cambridge University Press.
12.
EdgingtonE. S.OnghenaP. (2007). Between-subjects designs. In EdgingtonE. S.OnghenaP. (Eds.), Randomization tests (4th ed., pp. 55-88). Boca Raton, FL: Taylor & Francis.
13.
EfronB.TibshiraniR. J. (1994). Permutation tests. In EfronB.TibshiraniR. J. (Eds.), An introduction to the bootstrap (pp. 202-219). New York, NY: Chapman & Hall.
14.
ErnstM.D. (2004). Permutation methods: A basis for exact inference. Statistical Science, 19, 676-685.
15.
EudleyT.L.KerrJ.D.TrumboB.E. (2010). Using R to simulate permutation distributions for some elementary experimental designs. Journal of Statistics Education, 18 (1), 1-30. Retrieved from http://www.amstat.org/publications/jse/v18n1/eudey.pdf.
16.
FinchW. H.DavenportT. (2009). Performance of Monte Carlo permutation and approximate tests for multivariate means comparisons with small sample sizes when parametric assumptions are violated. Methodology, 5(2), 60-70.
17.
FisherR. A. (1990). Statistical methods, experimental design, and scientific inference. Oxford, England: Oxford University. (Original work published in 1935)
18.
FreidlinB.GastwirthJ. L. (2000). Should the median test be retired from general use?American Statistician, 54, 161-164.
19.
GebhardJ.SchmitzN. (1998). Permutation tests: A revival?Statistical Papers, 39, 75-85.
20.
GoodP. I. (2005). Resampling methods: A practical guide to data analysis. Huntington Beach, CA: Birkhauser.
21.
GoodP. I. (2010). Permutation, parametric, and bootstrap tests of hypotheses. Huntington Beach, CA: Springer.
22.
GreeneJ. (2007). Mixed methods in social inquiry. San Francisco, CA: Jossey-Bass.
23.
GreeneJ. (2008). Is mixed methods social inquiry a distinctive methodology?Journal of Mixed Methods Research, 2(1), 7-22.
24.
HesterbergT.MooreD. S.MonaghanS.ClipsonA.EpsteinR. (2003). Bootstrap methods and permutation tests. In The practice of business statistics (pp. 18.1-18.74). Retrieved from http://bcs.whfreeman.com/pbs/cat_160/PBS18.pdf
25.
HopeA. C. (1968). A simplified Monte Carlo significance test procedure. Journal of the Royal Statistical Society, B, 30, 582-298.
26.
HowellD. C. (2010). Statistical methods for psychology. Belmont, CA: Wadsworth.
27.
JohnsonD. H. (1995). Statistical sirens: The allure of nonparametrics. Ecology, 76, 1998-2000.
28.
KennedyM. M. (1979). Generalizing from single case studies. Evaluation Quarterly, 3, 661-678.
29.
KvaleS. (1996). Interview: An introduction to qualitative research interviewing. Thousand Oaks, CA: Sage.
30.
LaFleurB. J.GreevyR. A. (2009). Introduction to permutation and resampling-based hypothesis tests. Journal of Clinical Child & Adolescent Psychology, 38, 286-294.
31.
LudbrookJ.DudleyH. (1998). Why permutation tests are superior tot and F tests in biomedical research. American Statistician, 52, 127-132.
32.
ManlyB. F. J. (1997). Randomization and Monte Carlo methods in biology (2nd ed.). London, England: Chapman Hall.
33.
MayR. B.HunterM. A. (1993). Some advantages of permutation tests. Canadian Psychology, 34, 401-407.
34.
MorganD. L. (2007). Paradigms lost and pragmatism regained: Methodological implications of combining qualitative and quantitative methods. Journal of Mixed Methods Research, 1(1), 48-76.
35.
Nagy Hesse-BiberS. (2010). A qualitative approach to mixed methods design, analysis, interpretation, write up, and validity. In Nagy Hesse-BiberS. (Ed.), Mixed methods research: Merging theory with practice (pp. 63-103). New York, NY: Guildford Press.
36.
NeymanJ.PearsonE. S. (1928). On the use and interpretation of certain test criteria for purposes of statistical inference (Part I). Biometrika, 20A, 175-240.
37.
OnwuegbuzieA. J. (2003). Effect sizes in qualitative research: A prolegomenon. Quality & Quantity: International Journal of Methodology, 37, 393-409.
38.
OnwuegbuzieA. J.TeddlieC. B. (2003). A framework for analyzing data in mixed method research. In TashakkoriA.TeddlieC. B. (Eds.), Handbook of mixed methods in social and behavioral research (pp. 351-384). Thousand Oaks, CA: Sage.
39.
PolitD. F.BeckC. T. (2010). Generalization in quantitative and qualitative research: Myths and strategies. International Journal of Nursing Studies, 47, 1451-1458.
40.
RosnerB. (2010). Fundamentals of biostatistics. Boston, MA: Brooks & Cole.
41.
RoutledgeR. D. (1997). P-values from permutation and f-tests. Computational Statistics & Data Analysis, 24, 379-386.
42.
SandelowskiM. (2001). Real qualitative researchers don’t count: The use of numbers in qualitative research. Research in Nursing & Health, 24, 230-240.
43.
SandelowskiMVoilsC. I.KnaflG. (2009). On quantitizing. Journal of Mixed Methods Research, 3(3), 208-222.
ZhangS. (2009). The split sample permutationt-tests. Journal of Statistical Planning and Inference, 139, 3512-3524.
49.
ZimmermanD. W. (1994). A note on the influence of outliers on parametric and nonparametric tests. Journal of General Psychology, 121, 391-401.
50.
ZimmermanD. W. (2000). Statistical significance levels of nonparametric tests biased by heterogeneous variances of treatment groups. Journal of General Psychology, 127, 354-364.
