In this week’s episode Greg and Patrick explore the incredibly cool possibilities offered by the time-varying effects model as a non-parametric alternative to the more restrictive time-varying covariate model. Along the way we also discuss North Dakota, Cal Ripken Jr., pre-gaming, not trying to be funny, left lane guy, Walker Texas Ranger, smelling toast, Bob […]
S7E13 Time-Varying Effect Models Read More »
In this week’s episode, Patrick and Greg discuss the benefits and limitations of generalized estimating equations as an approach to dealing with data that violate the assumption of independence. Along the way they also mention: goat rodeos, haunted houses, jump scares, liberos, Haikyu!!, Whoa Ace!, dadenfreude, Otto, cutting the baguette, this just in, American Idiot,
S6E08 The Statenfreude of Generalized Estimating Equations Read More »
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 constructs. Along the way they also discuss learning in a second language, torn meniscuseseses, concert C trumpets, nosy neighbors,
S5E21 Multilevel Factor Analysis: But What Do The Factors Mean?! Read More »
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 the way the also discuss cheap beach house rentals, misplaced sand dunes, Mrs. Lincoln,
S5E20 Local fit…Because Global Fit Measures Suck! Read More »
In this week’s episode Greg and Patrick take advantage of a 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 allows. Along the way they also discuss hostile federal judges, McNeish, airing of
S5E05 Multilevel Models Unleashed Read More »
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,
S4E09 Intensive Longitudinal Data: Be Careful What You Wish For Read More »
In today’s episode, Greg and Patrick talk about the rich array of models for testing hypotheses about mutual influences between people within a dyadic relationship. Along the way they also discuss bones to pick, red herrings, bad influences, cow nutrition, playing both sides of the ball, Deion Sanders, the mind’s eye, it’s not me it’s
S3E13: The Actor-Partner Interdependence Model Read More »
Patrick and Greg talk about the potential advantages and disadvantages of alternative approaches to analyzing two time-point data. They discuss traditional models for raw and residualized change scores, and describe how each of these remains baked into the soul of contemporary models for repeated measures data. Along the way they also mention: 24 inch pizza
S3E04: Two Time Point Data: What Is Your Quest? Read More »
Greg and Patrick talk about how, although textbooks almost exclusively talk about simple random samples, in practice they almost never actually occur. To try to sort this out, they enlist the help of Dr. Laura Stapleton from the University of Maryland, who lived a prior life as an economist and knows a thing or two
S2E32: Lies, Damned Lies, and Random Samples with Laura Stapleton Read More »
Greg and Patrick explore the many interesting issues related to centering predictor variables prior to analysis, both in the standard regression model and in the multilevel model. Along the way they also discuss honking Diet Coke through your nose, rashes, hostage negotiation, reading at seven months of age, dealer’s choice, Coors Light, Galapagos tortoises, friendly
S2E30: ‘Always Center Your Predictors!’ And Other Sh*t My Advisor Says Read More »
