The names of Quantitative Methods graduate students on these publications are in bold. * indicates that although the student has graduated, the work was completed while the individual was a student in the Quantitative Methods MA/PhD program.
2020 (or in press)
Hunter, A., Beribisky, N., Farmus, L., & Cribbie, R. (in press). Multiplicity control vs replication: Making an obvious choice even more obvious. Meta-Psychology.
Farmus, L. & Cribbie, R. A. (in press). Contextualizing statistical suppression within pretest-posttest designs. The Quantitative Methods for Psychology.
Beribisky, N., Davidson, H., & Cribbie, R. A. (in press). What is the smallest meaningful association among two variables? PeerJ.
Counsell, A.*, Chalmers, R. P., & Cribbie, R. A. (in press). Comparing means under heteroscedasticity and nonnormality: Further exploring robust means modeling. Journal of Modern Applied Statistical Methods.
Counsell, A.*, Cribbie, R. A., & Flora, D. (in press). Evaluating equivalence testing methods for measurement invariance. Multivariate Behavioral Research.
Chalmers, R. P., & Adkins, M., C. (2020) Writing Effective and Reliable Monte Carlo Simulations with the SimDesign Package. The Quantitative Methods for Psychology, 16(4), 248-280. doi: 10.20982/tqmp.16.4.p248
Price, H. L., Bruer, K. C., & Adkins, M. C. (2020). Using machine learning analyses to explore relations between eyewitness lineup looking behaviors and suspect guilt. Law and Human Behavior.
Davidson, H. & Cribbie, R. A. (2019). A more powerful familywise error controlling procedure for evaluating mean equivalence. Communications in Statistics: Simulation and Computation. DOI: https://doi.org/10.1080/03610918.2018.1530783.
Hoyda, J., Counsell, A., & Cribbie, R. A. (2019). Traditional and Bayesian approaches for testing mean equivalence and a lack of association. The Quantitative Methods for Psychology, 15, 12-24. DOI: 10.20982/tqmp.15.1.p012.
Farmus, L., Arpin-Cribbie, C. A., & Cribbie, R. A. (2019). Continuous predictors of pretest-posttest change: Highlighting the impact of the regression artifact. Frontiers in Quantitative Psychology and Measurement, 4, 1-8. 4:64. DOI: 10.3389/fams.2018.00064.
Davidson, H., Jabbari, Y., Peters, K., Patton, H., O’Hagan, F. & Cribbie, R. A. (2019). Statistical software in Canadian university psychology courses. Teaching of Psychology, 46, 246–250. DOI: https://doi.org/10.1177/0098628319853940.
Ng, V. K. & Cribbie, R.A. (2019). The gamma generalized linear model, log transformation, and the robust Yuen-Welch test for analyzing group means with skewed and heteroscedastic data. Communications in Statistics: Simulation and Computation, 48, 2269-2286. DOI: 10.1080/03610918.2018.1440301.
Shiskina, T., Farmus, L., & Cribbie, R. A. (2018). Testing for a lack of relationship among categorical variables. The Quantitative Methods for Psychology, 14, 167-179.
Kim, Y. J. & Cribbie, R. A. (2018). The variance homogeneity assumption and the traditional ANOVA: Exploring a better gatekeeper. British Journal of Mathematical and Statistical Psychology, 71, 1-12. DOI: 10.1111/bmsp.12103.
Mara, C. & Cribbie, R. A. (2017). Equivalence of population variances: Synchronizing the objective and analysis. Journal of Experimental Education, 86, 442-457. DOI: 10.1080/00220973.2017.1301356
Counsell, A., & Cribbie, R. A. (2017). Using the errors-in-variables method in two-group pretest-posttest design. Methodology, 13, 1-8. https://doi.org/10.1027/1614-2241/a000122
Ng, V. K. & Cribbie, R.A. (2017). Modeling continuous, skewed and heteroscedastic outcomes in psychology: Is generalized modeling the best 'fit'? Current Psychology, 36, 225-235. https://doi.org/10.1007/s12144-015-9404-0
Counsell, A., & Harlow, L. L. (2017). Reporting Practices and Use of Quantitative Methods in Canadian Journal Articles in Psychology. Canadian Psychology, 58, 140-147. 10.1037/cap0000074
Chalmers, R. P., & Ng, V. (2017). Plausible-Value Imputation Statistics for Detecting Item Misfit. Applied psychological measurement, 41(5), 372–387. https://doi.org/10.1177/0146621617692079
Chalmers, R., P., Pek, J., & Liu, Y. (2017). Profile-likelihood confidence intervals in item response theory models. Multivariate Behavioral Research, 52, 533-550. 10.1080/00273171.2017.1329082
Counsell, A., Furtado, M., Iorio, C., Anand, L., Canzonieri, A., Fine, A., …, & Katzman, M. A. (in press). Intolerance of uncertainty, social anxiety, and generalized anxiety: Differences by diagnosis and symptoms. Psychiatry Research, 252, 63-69. doi: 10.1016/j.psychres.2017.02.046
Chalmers, R. P. (2016). Generating Adaptive and Non-Adaptive Test Interfaces for Multidimensional Item Response Theory Applications. Journal of Statistical Software, 71, 1-38.
Chalmers, R. P., Counsell, A., & Flora, D. B. (2016). It might not make a big DIF: Improved Differential Test Functioning statistics that account for sampling variability. Educational and Psychological Measurement, 1, 114-140.
Counsell, A., Cribbie, R. A, & Harlow, L. L. (2016) Increasing Literacy in Quantitative Methods: The Key to the Future of Canadian Psychology. Canadian Psychology, 57, 193-201.
Cribbie, R. A., Ragoonanan, C., & Counsell, A. (2016). Testing for negligible interaction: A coherent and robust approach. British Journal of Mathematical and Statistical Psychology, 69, 159-174.
Pek, J., Chalmers, R. P., & Monette, G. (2016). On the Relationship Between Confidence Regions and Exchangeable Weights in Multiple Linear Regression. Multivariate Behavioral Research, 51, 719-739.
Sigal, M. J., & Chalmers, R. P. (2016). Play It Again: Teaching Statistics with Monte Carlo Simulation. Journal of Statistics Education, 24, 1-21.
Chalmers, R. P., & Flora, D. (2015). faoutlier: An R package for detecting influential cases in exploratory and confirmatory factor analysis. Applied Psychological Measurement, 39, 573-574.
Chalmers, R. P. (2015). Extended Mixed-Effects Item Response Models with the MH-RM Algorithm. Journal of Educational Measurement, 52, 200–222.
Friendly, M., & Sigal, M. J. (2015). The Milestones Project: A database for the history of data visualization. In M. A. Kimball & C. Kostelnick (Eds.), Visible Numbers. Ashgate Publishing.
Pek, J., Chalmers, R. P., Kok, B. E., & Losardo, D. (2015). Visualizing Confidence Bands for Semiparametrically Estimated Nonlinear Relations among Latent Variables. Journal of Educational and Behavioral Statistics. doi: 10.3102/1076998615589129 (Online first publication).
Pek, J., & Chalmers, R. P. (2015). Diagnosing nonlinearity with confidence envelopes for a semiparametric approach to modeling bivariate nonlinear relations among latent variables. Structural Equation Modeling, 22(2), 288–293. doi:10.1080/10705511.2014.93779
Chalmers, R. P., & Flora, D. (2014). Maximum-likelihood estimation of noncompensatory IRT models with the MH-RM algorithm. Applied Psychological Measurement, 38, 339-358.
Counsell, A., & Cribbie, R. A. (2014). Equivalence tests for comparing correlation and regression coefficients. British Journal of Mathematical and Statistical Psychology, 68, 292-309.
Friendly, M., & Sigal, M. J. (2014). Some prehistory of CARME: Visual language and visual thinking. In J. Blasius & M. Greenacre (Eds.), Visualization and verbalization of data (chap. 1). Chapman and Hall.
Smith, C. E., & Cribbie, R. A. (2014). Factorial anova with unbalanced data: A fresh look at the types of sums of squares. Journal of Data Science, 12, 1-17.
Mara, C.A., Cribbie, R.A., Flora, D.B., LaBrish, C., Mills, L., & Fiksenbaum, L. (2012). A simplified model for evaluating change in randomized pretest, posttest, follow-up designs. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 8, 97-103.
Smith, C. E., & Cribbie, R. A. (2013). Multiplicity control in structural equation modeling: Incorporating parameter dependencies. Structural Equation Modeling, 20, 79-85.
Waldman, J., & Smith, C. E. 2013). Hybrid learning in a Canadian college environment. Toronto. Higher Education Quality Council of Ontario.
Weiss, J. A., Robinson, S., Fung, S., Tint, A., Chalmers, R. P., & Lunsky, Y.
(2013). Family hardiness, social support, and self-efficacy in mothers of individuals
with Autism Spectrum Disorders. Research in Autism Spectrum Disorders, 7,
Counsell, A., Hadjistavropoulos, H. D., Kehler, M. D., & Asmundson, G. J. G. (2013). Posttraumatic stress disorder symptoms in individuals with multiple sclerosis. Psychological Trauma: Theory, Research, Practice, and Policy, 5, 448-452.
Banks, J.B., Ng, V., & Jones-Gotman, M. (2012). Does good+good=better? A pilot study on the effect of combining hedonically valenced smells and images, Neuroscience Letters, 514(1), 71-76
Chalmers, R. P. (2012). mirt: A multidimensional item response theory package for the R environment. Journal of Statistical Software, 48, 1–29.
Flora, D. B., LaBrish, C., & Chalmers, R. P. (2012). Old and new ideas for data screening and assumption testing for exploratory and confirmatory factor analysis. Frontiers in Quantitative Psychology and Measurement, 3, 55.
Mara, C.A. & Cribbie, R.A. (2012). Paired-samples tests of equivalence. Communication Statistics: Simulation and Computation, 41, 1928-1943.
Sigal, M. J., & Pettit, M. (2012). Information overload, professionalization, and the origins of the Publication Manual of the American Psychological Association. General Review of Psychology, 16, 357-363.
Sigal, M. J., & McKelvie, S. J. (2012). Is exposure to visual media related to cognitive ability? Testing Neisser's hypothesis for the Flynn effect. Journal of Articles in Support of the Null Hypothesis, 9, 23-50.