#205 - Chris Nicklin (Temple University)

In this week’s podcast, it was Chris-on-Chris action: Chris Cooper interviewed Chris Nicklin about Mr. Nicklin’s new book on statistics in language research. This was Dr. Nicklin’s first appearance as an interviewee on the podcast, but it is not the first time his work has been featured. In Interview #156, Joe Vitta discussed his paper “Academic word difficulty and multidimensional lexical sophistication: An English-for-academic-purposes-focused conceptual replication of Hashimoto and Egbert (2019),” co-authored in 2023 with Chris Nicklin and Simon W. Albright. It appears that working with others is a core part of Chris’s approach to academic work. Chris and Chris spoke on the importance of collaboration:

(20:16) On this list, there's a name, Joe Vitta. I was like, that's a small world; like, it couldn't be him. I thought this guy was like a proper brain working at a decent university. And then, sure enough, on the first day, I get a tap on the shoulder, and it's like, "Are you Chris Nicklin? I'm like, "Joe, what are you doing here? He was, yeah, so right from that we just started talking about statistics and analysis, right from that very moment, and it wasn't very, very long before we were working together on a paper that eventually got published in MLJ, like the one about effect sizes, and yeah, since then I think we've constantly had something. 

The theme of having a continuous conversation with yourself, the data, and a team of motivated companions runs throughout the interview. 

Without giving too much away about the context of the investigation (you will have to listen to the interview to get the full background and findings), you can find examples of Chris mentioning, with much enthusiasm, how projects can spin up when collaborative spirit and support are present. Here is another occasion in the interview of this kind of proactive investigation with the support of similarly minded academics:

(40:14) If you square root it, you get an R, and then we were finding that these R's were much smaller, so we asked Stuart, who's working on that data, can we look at your PhD data, which was the stuff published in language testing, and we were just curious, does it work with that, and yet it worked, so we, me and Joe, decided at that point this is a paper in its own right, I think the other vocab testing, very, still hasn't been written up, I don't think, but me and Joe, like, hang on, Stuart's data is kind of perfect for this, it's an excellent data set from an excellent study, but this is showing what we found in our MLJ paper on a more important level with more implications, and I was, I was concerned, like only having one replicate one data reanalysis, and it was around that time I'd remembered reading Laufer's paper in SSLA…

As I say, the whole story is even more complex and interesting, but it was the spirit of working together and sharing ideas and energy towards a common goal that is truly the theme of this interview.

Go on - have a listen!