My research interests include methods for analysis of longitudinal and non-normal data, with an emphasis on individual differences. Specifically, I have focused on methods for data with a high proportion of zeros, and categorical and continuous positively skewed data. I study methods best-suited for handling such data, including random-effects and multi-level models. I also have expertise in survival analysis, factor analysis, and missing data. Because of my methods and statistics expertise, I often collaborate on substantive research projects, as well. In this vein, I specialize and often collaborate on projects that include applications to social and political empowerment as well as health outcomes.
Blozis, S.A., McTernan, M., Harring, J., & Zheng, Q. (2020). Two-part mixed-effects location scale models. Behavior Research Methods, 52, 1836–1847.
McTernan, M., Yokoo, K., & Tong, W. (2020). A comparison of gender-affirming chest surgery on non-binary versus transmasculine patients. Annals of Plastic Surgery, 84(4), S323-S328.
McTernan, M. (2017). An advanced study of methods for categorical and continuous data with many zeros (Doctoral dissertation, University of California, Davis).
Davoudzadeh, P., McTernan, M., & Grimm, K. J. (2015). Early school readiness predictors of grade retention from kindergarten through eighth grade: A multilevel discrete-time survival analysis approach. Early Childhood Research Quarterly, 32, 183-192.
McTernan, M., Love, P., & Rettinger, D. (2014). The influence of personality on the decision to cheat. Ethics & Behavior, 24(1), 53-72.
McTernan, M., & Blozis, S. User-specified likelihood expressions using NLMIXED and the GENERAL statement (White paper for a conference presentation at the Western Users of SAS Software 2013 Regional Convening).
As a Quantitative Psychologist, I typically teach statistics and research methods courses. Teaching these courses has always been a welcomed challenge for me. I understand that many students come to the psychology discipline with little interest in statistics. Throughout my time with the students, I strive to 1) impart a deep understanding of statistical concepts, 2) draw connections from the material that will incite excitement about the subject matter, and 3) to empower the students to use statistics as a tool to explore research questions of their own. I structure my classroom to reflect these goals.
PSYCHOLOGY 121 @ Sac State:
METHODS AND STATISTICS FOR PSYCHOLOGICAL RESEARCH
Project-based experience of Psychology. Study of scientific processes in research such as literature reviews; developing testable hypotheses; design; IRB review; data collection, analysis, and interpretation; critical analysis of studies; APA paper preparation; and issues in dissemination. Study of some advanced statistical processes such as factorial ANOVAs, planned and post hoc comparisons, and multiple regression. Study of statistical software programs used in the analysis of data. Prerequisite: PSYC 9
PSYCHOLOGY 9 @ Sac State:
STATISTICS FOR PSYCHOLOGY
Introduction to descriptive and inferential statistics as tools for evaluating data from Psychological research. Topics include: measures of central tendency, measures of variability, correlation and regression, sampling distributions, hypothesis testing procedures including t-tests and analysis of variance, and selected other topics. Application of hand computation will be emphasized to include the interpretation and significance of statistical findings. Prerequisite: Passing score on ELM; PSYC 2, PSYC 4, PSYC 8. PSYC majors only.
PSYCHOLOGY 302 @ Clark:
GRADUATE STATISTICAL METHODS
This is the second semester of a 2-semester course that provides graduate students with an introduction to statistical methods, concepts, and analyses. This semester we will focus on advanced statistics for psychological research. The goal is to give you tools for analyzing your own data for your graduate research projects and beyond. Statistical tests and frameworks covered may include a review of the general linear model, multiple regression, logistic regression, ANOVA and ANCOVA, principal components and CFA, SEM, and an introduction to hierarchical models. We will also cover approaches for handling real data that may include missing data solutions and non-parametric methods. We will primarily use R statistical software in this course.
Note: I enjoy teaching and mentoring all students. To facilitate my ability to create a safe learning space for my students, I have completed the following trainings, in addition to :
LGBTQIA Safe Zone Training (CSUS)
Trans* Safe Zone Training (UC Davis)
Dreamer Ally Training (CSUS)