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Statistics and Data Analysis

Statistics and Data Analysis Resources
Statistics and data analysis are important tools for social and personality psychologists.

Below you can find information and links from SPSP tutorials, webinars, convention deep dive workshops, and more.


SPSP Tutorials
SPSP Webinars

Bayesian Analysis with JASP: A Fresh Way of Doing Statistics
SPSP2017 – Alexander Etz
The main purpose of this workshop is to provide participants with a gentle introduction to key Bayesian concepts in estimation and hypothesis testing, as well as familiarize them with the free statistics software JASP.
 
Theory and Practice of Bayesian Influence Using JASP
SPSP2019 – Alexander Etz, Quentin Gronau, Johnny van Doorn
This workshop will provide attendees with a friendly, gentle introduction to Bayesian statistics, as well as demonstrate how to perform Bayesian analyses using JASP statistical software. Attendees will come away understanding the "why" and "how" of Bayesian estimation and hypothesis testing. This workshop is relevant to any student or researcher who wishes to draw conclusions from empirical data.
 
Theory and Practice of Bayesian Influence Using JASP
SPSP Webinar – Alexander Etz, Julia Haaf, Johnny van Doorn
This webinar provides attendees with a friendly, gentle introduction to Bayesian statistics, and demonstrates how to perform Bayesian analyses using JASP statistical software. Attendees will come away understanding the "why" and "how" of Bayesian estimation and hypothesis testing. This workshop is relevant to any student or researcher who wishes to draw conclusions from empirical data.


SPSP2018 – Robert Ackerman & Deborah Kashy
This workshop provides an introduction to Dyadic Growth-Curve Models and Cross-Lagged dyadic models within the Actor-Partner Interdependence Model context (Kenny, Kashy, & Cook, 2006). Students will learn basic features of longitudinal dyadic data and how to estimate and interpret the results of these models using Multilevel Modeling.
 
Mediation with Repeated Measures and Multilevel Data
SPSP2019 – Amanda Montoya
This workshop provides and interactive introduction to mediation analysis for a variety of multilevel designs starting with a simple two-condition within subjects design. I will demonstrate implementation (using two freely available tools for SPSS and SAS) and interpretation for questions of mediation in these designs.
 
MEMORE: Mediation and Moderation in Repeated Measures Designs
SPSP2017 – Amanda Montoya
This workshop overviews mediation and moderation analysis in repeated-measures designs when the independent variable of interest is a within-participant factor. We will cover implementation (using a freely available tool for SPSS and SAS) interpretation for questions of mediation and moderation in these designs.
 
An Introduction to Social Network Analysis
SPSP2017 – Gregory Webster
Social network analysis is becoming the vanguard of methodological approaches to understanding individuals in social contexts. Because social networks integrate information about individuals (nodes) and their relationships (ties), they are ideal for understanding human social interaction. This workshop will provide a primer on social network analysis for social–personality psychologists.

Recommended reading:

Clifton, A., & Webster, G. D. (2017). An introduction to social network analysis for personality and social psychologists. Social Psychological and Personality Science, 8(4), 442–453.
Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing social networks (2nd ed.). Thousand Oaks, CA: SAGE.
 
A Practical Guide to Multilevel Modeling: Parts 1 & 2
SPSP Webinar – Amie M. Gordon
This two-part webinar is ideal for newbies as well as researchers who have been exposed to Multilevel Modeling through a prior class or workshop but still have lots of questions. Topics in Part 1 include:

1. Identifying if MLM is necessary and determining whether data violates assumptions of independence.

2. Figuring out the nested structure of your data (including cross-classified models); Identifying the sources of non-independence in your data, including the possibility of cross-classification.

3. Approaches to dealing with non-independence – when to deal with non-independence through random versus fixed factors.

Topics in Part 2 include:

1. The difference between fixed and random effects and what changes in the analysis process when random slopes are allowed in the model.

2. Grand-mean versus group centering – what they are and when to use them, unconfounding within and between person effects.

3. The residual and random effects of covariance matrices.

Additional Resources: A Guided Tour Through R


Experience Sampling Methods and Implementation
SPSP2020 – Sabrina Thai
This workshop provides a hands-on introduction to conducting experience sampling studies. I will demonstrate how to create your own experience sampling smartphone app using an open-source scaffold, ExperienceSampler, as well as how to integrate ExperienceSampler with existing survey software like Qualtrics. We will also discuss issues related to various stages of conducting an experience sampling studies: design decisions (i.e., signal frequency, type of design, sample size), best practices, data organization, and data analysis. Participants are strongly encouraged to bring laptops and their own experience sampling questionnaires.
 
Practical Best Practices in Psychological Science: Calibrate Your Confidence
SPSP2020 – Alison Ledgerwood
If you don't feel completely up to speed on cutting-edge developments in methods and practices, this workshop is for you. Topics and exercises are organized around the theme of calibrating confidence to the strength of results. You'll leave knowing how to think wisely about power, when and how to preregister, how to conduct sequential analyses to maximize power while conserving resources, etc.
 
I've Got the Power
SPSP2016 – Erin Hennes, Sean Lane
Recent discussions regarding replicability have stimulated increased emphasis on sufficiently powering studies to obtain effects that are robust to repeated investigation. However, appropriate methods for doing so have historically been under-explained, and many contemporary power analysis packages provide limited, black-box approaches that cannot accommodate commonly-used complex models. This workshop provides straightforward guidelines for designing flexible power simulations (using SPSS, SAS, MPlus, and R) that use researcher-specified parameters to maximize robust and replicable study results.


 
Sample Size Planning for Appropriate Statistical Power
SPSP2019 – Samantha Anderson
This workshop will cover sample size planning techniques when the goal is to achieve an adequately powered study. In addition to the conceptual and mathematical foundations of statistical power, attendees will learn about two major types of sample size planning for statistical power.


SPSP R Tutorial
SPSP Online Learning – Danney Rasco
This series provides an applied introduction to R for new users who conceptually understand statistics at an undergraduate level. It assumes no experience with R or Rstudio, and each video in the series builds on information covered in previous videos.
 
Creating Reproducible Research Reports Using R Markdown
SPSP Webinar – Michael Frank
R Markdown is a simple but very powerful way to mix R data analysis code and text. R Markdown documents are a great way to document your data analysis and create reproducible reports (e.g., that automatically render your graphs and tables and even your results section from your data). You can even use R Markdown to write your entire paper, avoiding copy-and-pasting your analyses, which can be a major source of errors in papers. The rendered documents look spiffy on the web and in print. In this workshop, we introduce R Markdown and show how it can be used as part of a reproducible writing workflow.
 
A Guided Tour Through R
SPSP2018 – Sean Murphy
This workshop will deliver a hands-on introduction to the statistical software package R, aimed at those with little to no prior experience. You'll work through examples that will demonstrate how to clean, visualize, and analyze your data in R. You'll also be shown some of the many ways that using R can make your research process easier, more efficient and more reproducible.

Additional Resources: I also have a longer course available here (though slightly older). I recommend any psychologists trying to learn R start with Dani Navarro's learning statistics with R (because it's one of the few resources apart from mine that are focused for psychologists), followed up with Hadley Wickham's R for Data Science (because it is the best resource for teaching the power of R for reporting and graphics, especially for those wanting to do open science).
 
Data Visualization in R
SPSP2017 – Frederik Aust & Maike Luhmann
This workshop provides an introduction to graphics in R using the R plot and ggplot2 packages. Various methods for data visualization will be demonstrated and actively practiced. Graphical functions for both quick data visualization and publication purposes will be covered. No prior knowledge of R is required.
 
Reproducible Data Analysis and Paper Writing in R
SPSP2019 – Jessica Kosie & Sara Weston
In recent years, a variety of free tools have gained in popularity, making reproducible research practices easier. The goal of this workshop is to provide training to interested researchers so that they can begin using these tools in their own work. Topics to be covered include: basic R, tidyverse, ggplot, and an intro to writing papers in RMarkdown. No previous programming experience is required.


An Introduction to Drift Diffusion Modeling
SPSP2018 – David Johnson
This workshop provides a primer on the drift diffusion model. It will cover the theory by which the drift diffusion model translates decision and response time data into cognitive processes. We will walkthrough how to estimate the model hierarchically and interpret results using the free software R and JAGS.
 
Data Visualization in R
SPSP2017 – Frederik Aust & Maike Luhmann
This workshop provides an introduction to graphics in R using the R plot and ggplot2 packages. Various methods for data visualization will be demonstrated and actively practiced. Graphical functions for both quick data visualization and publication purposes will be covered. No prior knowledge of R is required.