This week Patrick and Greg talk about maximum likelihood estimation: what it is, where it comes from, how it works, what it can do, and what it can’t do. Along they way they also discuss tour-bombing your kid, licking the turtle, Van Halen and AC/DC, orange mustaches, brandy snifter pong, bus #27, Ronnie Fisher, why […]
S4E02 Underachievers, Overachievers, & Maximum Likelihood Estimation Read More »
In the opening episode to Season 4, Greg and Patrick delve into ordinary least squares estimation: where it came from, what it attempts to achieve, and where it can take us from here. Along the way they also discuss Golden Retrievers who are neither gold nor can retrieve, Olivia Newton John, sub-conning out their own
S4E01 Ordinary Least Squares: Back Where It All Began Read More »
In this week’s episode Patrick and Greg get to hang out with Noah Greifer from the Institute for Quantitative Social Sciences at Harvard University to delve into the fascinating world of propensity scores: what they are, how we obtain them, and how they can be thoughtfully used to strengthen our causal inferences. Along the way
S3E27: Propensity Scores — I Meant To Do That! Read More »
In this week’s episode Greg and Patrick discuss the sometimes terrifying issue of fungible weights in multiple regression and structural equation modeling in which selecting a trivially worse criterion of fit can often lead to radical changes in the corresponding parameter estimates. Along the way they also discuss competitive family Wordle, disambiguation, inflammability, perpitty, being
S3E25: Fungible Parameter Estimates and Earthquake Waller Read More »
In this week’s episode Greg and Patrick explore Sewall Wright’s path tracing rules as an alternative to covariance and matrix algebra, including how the rules work and the tremendous insights they can provide toward understanding a model. Along the way they also discuss the Unabomber, Crate and Barrel, grocery lane profiling, tedious as poop, throwing
S3E24: The Wright Stuff – The Beauty of Path Tracing Rules Read More »
In this week’s episode Greg and Patrick discuss the critical distinction between sample distributions and sampling distributions and we explore all the different ways in which sampling distributions are foundational to how we conduct research. Along the way they also discuss Starbucks jazz, one item tests, hot pockets, delusions of grandeur, Tetris and Pong, drawing
S3E21: A Low-Resolution Discussion of Sampling Distributions Read More »
Patrick and Greg discuss the rise of machine learning in the social sciences with guest Doug Steinley who is a professor in the Department of Psychology at the University of Missouri at Columbia and is the current editor of the APA journal Psychological Methods. Along the way they also discuss funeral expenses, Swedish massage, Amy
S3E20: The Rise of Machine Learning in the Social Sciences with Doug Steinley Read More »
In this week’s episode Greg and Patrick have a wonderfully engaging conversation with social network analysis expert Tracy Sweet who is an Associate Professor in the Department of Human Development and Quantitative Methodology at the University of Maryland. Tracy patiently helps us understand what social network analysis is, and how it can be used to
S3E19: Social Network Analysis: Making Connections with Tracy Sweet Read More »
In this week’s episode Greg and Patrick discuss latent means models to conduct group mean comparisons while controlling for measurement error, which gives you more power and more accurate standardized effect size estimates. Along the way, they also mention Aunt Roz, table 8, naughty pigs, crossing the streams, big twinkies, asbestos, It’s a Small World,
S3E18: Aunt Roz’s Guide to Latent Means Models Read More »
Greg and Patrick explore the generalized linear model as a powerful framework for building regression models for binary and other discretely distributed dependent variables. Along the way they also discuss stealing property, statistical conspiracy theories, mic drops, coming uncorked, getting punched by biostatisticians, big logistic, tapping out, the Oakland Raiders, being 8.5 feet tall, sheep
S3E17: Logistic Regression: 2 Logit 2 Quit Read More »
