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Samantha Anderson

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Asst Professor
Faculty, TEMPE Campus, Mailcode 1104
Biography: 

Samantha F. Anderson is an Assistant Professor of Psychology at Arizona State University, focusing on Quantitative Methods. Broadly speaking, her research investigates questions pertaining to research design and meta-science, with a focus on developing approaches that are both rigorous and practical. More specifically, her work centers on the following themes: sample size planning for high statistical power, methods for addressing replication, the impact of multiplicity on Type I error rates and power, and missing data in causal inference. In an effort to make her work accessible and user-friendly, she has co-developed open-source software for conducting sample size planning, available at https://designingexperiments.com/shiny-r-web-apps/ under the heading “Bias and Uncertainty Corrected Sample Size for Power”.

Education: 

Ph.D. (Quantitative) Psychology, University of Notre Dame

M.A. (Clinical) Psychology, University of Notre Dame

B.S. Psychology, University of Wisconsin-Madison

Research Interests: 

Several of my research interests pertain to the often overlooked design elements of psychological studies, particularly regarding study design, statistical power, and sample size. In this area, I have developed an approach to sample size planning that uses information from a prior published or pilot study to plan the sample size for a future study, adjusting the prior study effect size estimate for publication bias and/or uncertainty. This method, "BUCSS", is accompanied by open-source software, both as an R package and series of web applications, that allow researchers to implement the approach for multiple regression and ANOVA designs with continuous outcomes. 

I also am interested in meta-science topics that investigate how methodological issues can affect the scientific literature. In this area, I study methodology pertaining to replication studies, which have recently become more common in psychology. My research has centered on taxonomies for defining what it means to successfully replicate prior work, along with appropriate analyses to achieve various replication goals. I am also interested in the influence of various "researcher degrees of freedom", such as multiple testing, which have conseuquences for replicability.

Another area of active interest involves methods for handling missing data and dropout in studies aiming to detect a causal treatment effect. Two-wave randomized pretest posttest studies can offer unique challenges for estimating treatment effects under different missing data conditions, particularly when the data violate statistical assumptions and the model is misspecified. Finally, I conduct quantitatively-informed substantive research in the area of stress and depression, and I enjoy working with intensive longitudinal data. 

Fall 2021
Course NumberCourse Title
PSY 530Intermed Statistics
PSY 598Special Topics
Spring 2021
Course NumberCourse Title
PSY 399Supervised Research
PSY 499Individualized Instruction
PSY 598Special Topics
Fall 2020
Course NumberCourse Title
PSY 330Statistical Methods
PSY 530Intermed Statistics
PSY 598Special Topics
Spring 2020
Course NumberCourse Title
PSY 330Statistical Methods
PSY 399Supervised Research
PSY 499Individualized Instruction
PSY 598Special Topics
Fall 2019
Course NumberCourse Title
PSY 530Intermed Statistics
PSY 598Special Topics
Spring 2019
Course NumberCourse Title
PSY 330Statistical Methods
PSY 399Supervised Research
PSY 499Individualized Instruction
Fall 2018
Course NumberCourse Title
PSY 530Intermed Statistics
Expertise Areas: