Greg Hancock & Patrick Curran
Quantitude
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Greg Hancock & Patrick Curran
QuantitudeEpisodes
Greg Hancock & Patrick Curran
Quantitude
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Episodes of Quantitude
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In this week's episode Patrick and Greg explore the incredibly cool topic of survival analysis, which is a set of techniques that allows for powerful tests of predictors of the amount of time to experiencing an event; yet these models are not o
In this week's episode, Greg and Patrick talk about the challenges of combining confirmatory factor analysis and multilevel data, and the underappreciated but absolutely critical role that theory plays in choosing the proper model for your cons
In this week's episode Greg and Patrick discuss the assessment of global vs. local model fit and they argue that although global measures of fit can be useful, carefully assessing local fit may be of much greater importance in practice. Along t
In this week's episode Patrick and Greg provide an introduction to the Item Response Theory model: what it is, how it relates to traditional factor analysis, and how this modem approach improves upon some of the limitations of classical test th
In this week’s episode, Patrick and Greg play with some of the basics of probability in the context of some classic, fun, and often counterintuitive examples. Along the way they also discuss arguments with relatives, a feel for the roulette wh
In this week's episode Greg and Patrick are honored to visit with Yi Feng, a quantitative methodologist at UCLA, as she helps them understand classification and regression tree analysis. She describes the various ways in which these models can
In this week's episode Greg and Patrick talk about Simpson’s Paradox: what it is, examples of where it occurs in real life, and why we might not really need to think about it as a paradox at all. Along the way they also discuss Apple Vision, T
In this week's episode Greg and Patrick take a walk down memory lane to rediscover classical test theory, although they revisit this through the lens of modern latent variable models. They describe how these classical methods are actually high
In this week's episode Patrick and Greg launch a new occasional series called Stuff You Should Know. The topic for today is regression to the mean: what the heck is it, how does it arise in every day life, and what can we do about it. Along the
In this week's episode Greg and Patrick talk about confidence intervals: symmetric and asymmetric, asymptotic and bootstrapped, how to interpret them, and how not to interpret them. Along the way they also mention tire pressure gauge mysteries,
In this week's episode Patrick and Greg have great fun talking about meta-analysis with Paschal Sheeran, a social psychologist from the University of North Carolina at Chapel Hill. He describes what meta-analysis is, what it offers, and how to
In this week's episode, marking the fifth Quantitude Holiday Celebration, Greg and Patrick argue about their favorite holiday movies, including whether Die Hard counts as one or not; they then proceed to discuss several statistical ideas that a
In this week's episode Greg and Patrick explore alternative parameterizations of the SEM-based latent curve model to capture various forms of nonlinearity, some that are approximations and others that are exact. Along the way they also discuss
In today’s episode Greg and Patrick talk about regularization, which includes ridge, LASSO, and elastic net procedures for variable selection within the general linear model and beyond. Along the way they also mention Bowdlerizing, The Family S
In today’s episode, Greg and Patrick dig into Confirmatory Composite Analysis, a very clever way to get formative factors and their causal indicators into the traditional structural equation modeling framework, along with any other latent facto
In today’s episode, Patrick and Greg talk about the challenge of having causal indicators of formative factors within an analytical framework that is historically dominated by effect indicators and latent factors — and the critical importance o
In today’s episode, Patrick and Greg talk about fun extensions to the basic confirmatory factor model, including higher order models, bifactor or residualized models, and multitrait-multimethod models. Along the way they also mention microscope
In this week’s episode Greg and Patrick take advantage of the recent expiration of a statute of limitations that legally allows them to talk about the multilevel model: what it is, when we might use it, and extremely cool extensions that it all
In this week's episode Patrick and Greg enlist the help of six quantitative methodological scholars, who share a wide variety of fertile ground for quantitative research, which should be useful for students seeking dissertation topics as well a
In this week's episode Patrick and Greg plumb the depths of what is a dissertation and what purpose does it serve. They are aided in the use of an AI language interpreter to translate old man grousing to positive and supportive advice for stude
In this week’s episode, Greg and Patrick talk about the terrifying, the feared, the dreaded … Multicollinearity. Blamed for a multitude of general linear model problems, they dare to ask the question: “But should it be?” Along the way they also
In this week's episode, the first of Season 5, Patrick and Greg explore the very cool world of receiver operating characteristic, or ROC, curves: what they are, how they work, and why we can give partial thanks to Winston Churchill for their ex
In the last episode of season 4, Greg and Patrick discuss the very cool exploratory technique of cluster analysis — including concepts of multivariate distance, hierarchical and non-hierarchical methods, and how it differs from mixture models.
In this week's episode Patrick and Greg talk about the critical and often unmet assumptions underlying the use of measured variables at multiple time points, whether for simple analyses like tests of means or more complex analyses like modeling
In this week's episode Greg and Patrick are joined by Christen Priddie of Indiana University who will help them learn a bit about QuantCrit: its foundations, its purpose, and how it can enrich the quantitative methods process to which we might
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