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S4E10 Test Driving Model Identification

Uncategorized / 1 minute of reading

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, […]

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S4E09 Intensive Longitudinal Data: Be Careful What You Wish For

Uncategorized / 2 minutes of reading

In today’s episode Greg and Patrick talk about that sweet spot between panel designs and time series designs, intensive longitudinal data, both the logistical and analytical challenges but more importantly its tremendous potential. Along the way they also discuss camp fire face-plants, fear of horses, your spare eyelid, polishing your Nobel Prize, ripples in a pond,

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S4E08 Missing Data: The New State Of The Art With Craig Enders

Uncategorized / 2 minutes of reading

In this week’s episode Greg and Patrick get to explore modern methods for missing data analysis while belaboring quotes from Top Gun with their guest Craig Enders from the University of California at Los Angeles. Craig looks back over the past 20 years of developments in missing data analysis to discuss what has worked, what

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S4E07 The Quanti-House of Horror

Uncategorized / 1 minute of reading

In this week’s Halloween-themed episode, Greg and Patrick have fun talking about things in their academic jobs that scare them, from things that are a little scary to things that they find absolutely terrifying. Along the way they also discuss Mrs. Kopicky’s caramel popcorn balls, Wolf of Wall Street, Kenny T, forgetting you’ve told a

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S4E06 Building the Model You Want: With Laser Cannons!

Uncategorized / 2 minutes of reading

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

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S4E05 Moderated Nonlinear Factor Analysis: A Conversation with Dan Bauer

Uncategorized / 2 minutes of reading

In this week’s episode Greg and Patrick spend a wonderful, if not at times awkward, hour talking with Dan Bauer about the genesis, application, and future directions of the what may be the world’s worst acronym: MNLFA, or moderated nonlinear factor analysis. Along the way they also discuss unsolicited help from teenagers, gold stars, acronyms,

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S4E04 Partial Least Squares: Straight Outta Uppsala

Uncategorized / 3 minutes of reading

In this week’s episode Patrick and Greg talk about partial least squares, a technique that resembles structural equation modeling but with a lot of flexibility, including but not limited to its ability to accommodate both reflective and formative constructs. Along the way they also mention dark Red Bubble, Sheeps Kin, snorting SEM, the Coors Light

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S4E03 Two-Stage Least Squares Strikes Back

Uncategorized / 3 minutes of reading

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

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S4E02 Underachievers, Overachievers, & Maximum Likelihood Estimation

Uncategorized / 1 minute of reading

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

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S4E01 Ordinary Least Squares: Back Where It All Began

Uncategorized / 2 minutes of reading

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

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