ABCDEFGHIJKLMNOPQRSTUVWXY
1
General TopicSpecific Topic(s)FormatLinkDescription
2
Structural Equation ModelingIntroduction to Structural Equation ModelingVideohttps://www.youtube.com/watch?v=wHFrgp3SQMI&list=PLQGe6zcSJT0XCCmKAxqiA8AGl-z6HYn5BPatrick begins with a general introduction of the SEM and describes the various kinds of statistical models that can be considered special cases of the SEM. He then builds a conceptual path model linking parent alcoholism to adolescent substance and uses this to describe different strengths of the SEM. He then introduces the six core components of any SEM: specification, identification, estimation, evaluation, respecification, and interpretation. He concludes by describing topics that additional episodes in the series will explore.
3
Structural Equation ModelingIntroduction to Growth ModelingVideohttps://www.youtube.com/watch?v=2hV7MyEX2UA&list=PLQGe6zcSJT0VxMZUN6DBuhIoCRZNoA2VzGrowth curve models go by a variety of names (e.g., multilevel models, mixed effects models, latent curve models) but share a common focus on individual change over time. In this video, Patrick introduces the basic features of a growth curve model...
4
Linear RegressionIntroduction to Linear RegressionVideohttps://www.youtube.com/watch?v=u2sKbCfviFw&list=PLQGe6zcSJT0V4xC1NDyQePkyxUj8LWLnDDan Bauer provides an overview of regression as most commonly applied within the social, behavioral, and health sciences
5
Intensive Longitudinal DataIntroduction to Intensive Longitudinal Data
Multilevel Modeling
Dynamic Structural Equation Modeling		Videohttps://centerstat.org/apa-ild/ To introduce participants to the design and analysis of research with ILD, the American Psychological Association (APA) hosted a series of free online Science Training Sessions on The Collection and Analysis of Intensive Longitudinal Data from August 31 to October 11, 2022. Within this series, CenterStat instructors Jean-Philippe Laurenceau, Daniel Bauer, and Patrick Curran provided four webinars focused on the conceptualization and implementation of ILD methods and analyses within multilevel modeling and dynamic structural equation frameworks
6
Matrix AlgebraReview of Matrix AlgebraVideohttps://centerstat.org/matrix-review/
7
General ResourceToo many to list!Podcasthttps://quantitudepod.org/Quantitude is a podcast dedicated to all things quantitative, ranging from the relevant to the highly irrelevant. Co-hosts Patrick Curran and Greg Hancock talk about serious statistical topics, but without taking themselves too seriously. Think: CarTalk hi-jacked by the two grumpy old guys from the Muppets, grousing about quantitative methods, statistics, and data analysis, all presented to you with the production value of a 6th grade school project. But in a good way.
8
Longitudinal Data AnalysisGrowth Modeling
Power Analysis
Bayesian Methods
Dynamic Dyadic Systems
And more!		Blog Post
Video		https://thechangelab.stanford.edu/tutorials/The Change Lab @ Stanford, directed by Professor Nilam Ram, studies change and attempts to invoke change.
We study how people change, how media change, how the world changes, and how all those changes interact and influence each other over time. We develop novel techniques for measuring change (often using mobile technologies), cool data visualizations (and sonifications), and novel methods for analysis of longitudinal data. We occasionally forward new theory about the dynamics of human behavior and human interaction.
9
General ResourceToo many to list!Blog Postshttps://quantdev.ssri.psu.edu/tutorialsWe develop new methods for the study of human behavior – measurement, study design, and analysis techniques – and use them to study behavioral change.
10
Machine LearningClassification
Resampling Methods
Regularization
Tree-Based Methods
Support Vector Machines
Deep Learning
And More!		Free Bookhttps://www.statlearning.com/Statistical learning refers to a set of tools for making sense of complex datasets. In recent years, we have seen a staggering increase in the scale and scope of data collection across virtually all areas of science and industry. As a result, statistical learning has become a critical toolkit for anyone who wishes to understand data — and as more and more of today’s jobs involve data, this means that statistical learning is fast becoming a critical toolkit for everyone.
11
CodingMPlus
Lavaan		Mini-Courseshttps://www.goquantfish.com/collections/free-courses Most health & social scientists don’t have access to world-class stats training. Either the timing doesn’t fit, the cost is prohibitive, or the quality is lacking. Quantfish is here to fix that. Our expert-led courses teach you what you need now to do great research. And our courses are built using our signature microlearning method - not just recordings of livestreams.
12
CodingBasic introduction to RFree Bookhttps://rstudio-education.github.io/hopr/This is the website for “Hands-On Programming with R”. This book will teach you how to program in R, with hands-on examples. I wrote it for non-programmers to provide a friendly introduction to the R language. You’ll learn how to load data, assemble and disassemble data objects, navigate R’s environment system, write your own functions, and use all of R’s programming tools. Throughout the book, you’ll use your newfound skills to solve practical data science problems.
13
CodingR for Data ScienceFree Bookhttps://r4ds.hadley.nz/This is the website for the 2nd edition of “R for Data Science”. This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it and visualize. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualizing, and exploring data.
14
CodingData visualization in R (ggplot2)Blog Posthttps://r-statistics.co/Complete-Ggplot2-Tutorial-Part1-With-R-Code.htmlThis is a complete and full fledged tutorial. I start from scratch and discuss how to construct and customize almost any ggplot. It goes into the principles, steps and nuances of making the plots effective and more visually appealing. So, for practical purposes I hope this tutorial serves well as a bookmark reference that will be useful for your day-to-day plotmaking.
15
Multilevel ModelingIntroduction to Multilevel ModelingFree Bookhttps://www.learn-mlms.com/Bonjour/hi, and welcome to learn-mlms.com. This website will teach you the fundamentals about multilevel modelling, from why and when you would use them and how to do so for various research questions and data structures.
16
Multilevel ModelingIntroduction to Multilevel ModelingVideohttps://spsp.org/professional-development/learning-online/practical-guide-multilevel-modeling#item-accordion-item--6477__panelThis 2-part multilevel modeling (MLM) tutorial is designed for newbies as well as researchers who have been exposed to it through a prior class or workshop but still have lots of questions.
17
Data ManagementData Mangement in Large-Scale Education ResearchFree Bookhttps://datamgmtinedresearch.com/This book begins, like many other books in this subject area, by describing the research life cycle and how data management fits within the larger picture. The remaining chapters are then organized by each phase of the life cycle, with examples of best practices provided for each phase. Considerations on whether you should implement and how to integrate those practices into your workflow will be discussed.
18
CodinglavaanSlideshttps://users.ugent.be/~yrosseel/lavaan/lavaan2.pdf
19
20
21
22
23
24
25