In this week’s episode Patrick and Greg discuss the dark art of using regression diagnostics to assess how well assumptions are met in the general linear model, with applications to the wide array of related techniques. Along the way they also mention Big Pharma, Merriam-Webster, free-radical opioids, baguettes, antisocial personality disorder, Elon Musk, yoinked, Trinity […]
S4E19 The Dark Art of Regression Diagnostics Read More »
In this week’s episode Greg and Patrick discuss variable transformations, both linear and non-linear, and explore the potential pros and cons of using these in applied research settings. Along the way the also discuss prank phone calls, English drama, rapping Dr. Seuss, persnickety estimation, weekly banana intake, it don’t make no never mind, Jeffry Bezos,
S4E17 Variable Transformations: Box-Cox, Fox in Socks Read More »
In this week’s episode Greg and Patrick discuss how we might flip the traditional null and alternative hypothesis testing procedures to move us from tests of literal equality to tests of practical equivalence. Along the way they also discuss tough love, horseshoes and hand-grenades, Patrick’s Driving School, Cheyenne Mountain, So Long and Thanks For All
S4E15 Flipping Our Hypotheses to Test Equivalence Read More »
In this week’s episode Greg and Patrick revisit a topic they addressed in their 2nd-ever episode: statistical power. Here they continue their discussion by attempting to clarify the power of what, and they explore ways of obtaining meaningful power estimates using the structural equation modeling framework. Along the way they also discuss tearing arms off, German
S4E13 Model-Based Power Analysis…The Power of *What* Read More »
In today’s episode, Patrick and Greg talk about those critical distributions that often go un-discussed — noncentral distributions — including their relation to the more familiar central distributions, their role in power and sample size determination, and how they are used in assessing data-model fit. Along the way they also mention back porch reading, latent
S4E11 The Centrality of Noncentral Distributions Read More »
In today’s episode, Greg and Patrick talk about model under-, just-, and over-identification, starting with the algebraic foundations and moving into implications for the practice of structural equation modeling. Along the way they also mention shagging cars, new Ferraris, bank statements, prerecorded reactions, Quantitude implied contract, necessary & sufficient, walk of shame, Patrick’s Roz impression,
S4E10 Test Driving Model Identification Read More »
In this week’s episode Greg and Patrick talk about the frequent gap between research questions and methods and ways to think about reengineering your variables, your parameters, or your entire model to help get what you want. Along the way they also discuss living in the dorms, Greek god sculpture rockets, not living in the
S4E06 Building the Model You Want: With Laser Cannons! Read More »
In this week’s episode Greg and Patrick explore the often neglected method of two stage least squares; they take a walk down memory lane to explore its origins and then drag it kicking and screaming into the 21st century for much promising use within the latent variable model. Along the way they also discuss magic
S4E03 Two-Stage Least Squares Strikes Back Read More »
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 »
