# A New Podcast on All Things Quantitative: Quantitude

Our very own Patrick Curran has teamed up with Greg Hancock (Professor, College of Education, University of Maryland) to launch a new podcast called Quantitude. It is dedicated to all things quantitative, ranging from the relevant to the highly irrelevant. Picture a cross between the Car Talk guys, the two old men from the Muppets, and a graduate statistics course.

Quantitude explores serious issues but in a sometimes grousing and irreverent way. Episodes address current topics in quantitative methodology, data analysis, and research methods; interviews with professionals in the field; responses to listener questions; quantitative puzzlers; and much more. Episodes are posted every-other week, notably on “Quanti-Tudesday”.

So if you’re interested, please check Quantitude out. The podcast can be found at https://www.buzzsprout.com/639103 (or wherever you listen to your favorite podcasts), and the Quantitude home page is http://quantitudethepodcast.org/. Finally, Quantitude is on Twitter at @quantitudepod

## What exactly qualifies as intensive longitudinal data and why am I not able to use more traditional growth models to study stability and change over time?

This post considers the unique features of intensive longitudinal data (ILD) relative to other more traditional data structures and how we can appropriately analyze ILD given these features

## My advisor told me I should group-mean center my predictors in my multilevel model because it might “make my effects significant” but this doesn’t seem right to me. What exactly is involved in centering predictors within the multilevel model?

How to specify multilevel models to obtain within- and between-group effects through centering lower-level predictors.

## What’s the best way to determine the number of latent classes in a finite mixture analysis?

Selecting the number of classes (or components) is one of the most challenging decisions to make when fitting a finite mixture model (including latent class analysis and latent profile analysis). In this post, we talk through the conventional wisdom on class enumeration, as well as when this breaks down.

## I’m reporting within- and between-group effects in from a multilevel model, and my reviewer says I need to address “sampling error” in the group means. What does this mean, and what can I do to address this?

Why between-group effects estimating in MLMs are sometimes biased, and what to do about it

## Quantitude Podcast Episode Dedicated to Diversity in Academia

Patrick Curran and Greg Hancock have dedicated a recent episode of their quantitative methods podcast, Quantitude, to diversity and equity in academia. As part of…