The names of Quantitative Methods graduate students on these publications are in bold.
2023 (or in press)
Beribisky, N., Alter, U., & Cribbie, R. A. (in press). A multi-faceted mess: A review of statistical power analysis in psychology journal articles. Meta-Psychology.
2022
Cribbie, R. A., Alter, U., Beribisky, N., Chalmers, R. P., Counsell, A., Farmus, L., Martinez Gutierrez, N., Ng, V. (2022). negligible: R package for negligible effect/equivalence testing. https://cran.r-project.org/web/packages/negligible/index.html
Farmus, L., Beribisky, N., Martinez Gutierrez, N., Alter, U., Panzarella, E., & Cribbie, R. A. (2022). Effect size reporting and interpretation in social personality research. Current Psychology. https://doi.org/10.1007/s12144-021-02621-7
Martinez Gutierrez, N., & Cribbie, R. (2022). Effect sizes for equivalence testing: Incorporating the equivalence interval. PsyArXiv. 10.31234/osf.io/5buz9
2021
Beribisky, N., Alter, U., & Cribbie, R. A. (2021). A multi-faceted mess: A review of statistical power analysis in psychology journal articles. Meta-Psychology. https://doi.org/10.31234/osf.io/3bdfu
Panzarella, E., Beribisky, N., & Cribbie, R. A. (2021). Denouncing the use of field-specific effect size distributions to inform magnitude. PeerJ, 9, e11383. https://doi.org/10.7717/peerj.11383
Tajik-Parvinchi, D., Farmus, L., Tablon-Modica1, P., Cribbie, R. A., & Weiss, J. A. (2021). The role of cognitive control and emotion regulation in predicting mental health problems in children with neurodevelopmental disorders. Child: Care, Health and Development, 47, 608-617. 10.1111/cch.12868
Martinez Gutierrez, N., & Cribbie, R. (2021). Incidence and interpretation of statistical suppression in psychological research. Canadian Journal of Behavioural Science, 53, 480–488. https://doi.org/10.1037/cbs0000267
2020
Beribisky, N., Mara, C., & Cribbie, R. A. (2020). An equivalence-based approach to assessing substantial mediation. The Quantitative Methods for Psychology, 16, 424-441. https://doi.org/10.20982/tqmp.16.4.p424
Davidson, H. & Cribbie, R. A. (2020). A more powerful familywise error controlling procedure for evaluating mean equivalence. Communications in Statistics: Simulation and Computation, 49, 2914-2929. https://10.1080/03610918.2018.1530783.
Counsell, A., & Cribbie, R. A. (2020). Student attitudes toward learning statistics with R. Psychology Teaching Review, 24. https://shop.bps.org.uk/psychology-teaching-review-vol-26-no-2-2020.
Farmus, L. & Cribbie, R. A. (2020). Contextualizing statistical suppression within pretest-posttest designs. The Quantitative Methods for Psychology, 16, 21-32. https://doi.org/10.20982/tqmp.16.1.p021
Farmus, L., Rotondi, M., & Cribbie, R. A. (2020). The flipped classroom improves performance in introductory statistics: A systematic review and meta-analysis. Journal of Statistics Education, 28, 316-325. 10.1080/10691898.2020.1834475
Tajik-Parvinchi, D., Farmus, L., Cribbie, R. A., Albaum, C., Weiss, J. A. (2020). Clinical and parental predictors of emotion regulation following cognitive behaviour therapy in children with autism. Autism: The International Journal of Research and Practice, 24, 851-866. https://doi.org/10.1177/1362361320909178
Hunter, A., Beribisky, N., Farmus, L., & Cribbie, R. (2020). Multiplicity control vs replication: Making an obvious choice even more obvious. Meta-Psychology, 4. https://doi.org/10.15626/MP.2019.1992
Counsell, A., Cribbie, R. A., & Flora, D. (2020). Evaluating equivalence testing methods for measurement invariance. Multivariate Behavioral Research, 52, 312-328. https://doi.org/10.1080/00273171.2019.1633617
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.
2019
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.
2018
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.
2017
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
2016
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.
