I rarely get besotted (a old English word) with a handbook even though I have contributed to three of them over the years. But I must admit that for positivist behavioral accounting researchers, one should “bit the bullet” and purchase what I consider a somewhat overpriced product (especially if you want it in hard copy format and when the ebook option is more expensive than what a softcover should be).
What am I talking about? Libby and Thorne’s 2017 The Routledge Companion to Behavioural Accounting Research 1st Edition (available at https://www.routledge.com/The-Routledge-Companion-to-Behavioural-Accounting-Research/Libby-Thorne/p/book/9781138890664 if the embedded hotlink does not work). I cannot recall the last time I loaned a book to a PhD student and had to call it back to look up a reference – but I did with this book.
The book contains several chapters of interest including an elaboration on Bill Kinney Jr.’s most famous footnote (Footnote 23 of Kinney Jr. 1986) that talks about how to plan your research through a relatively straightforward mnemonic. As a Doctoral Student Advisor/Supervisor and as an Editor for most of the last decade, it is so obvious to me that many of us teach this but it goes in one ear and out the other for our students!! The quality of behavioral accounting research would improve immensely if BAR researchers followed its precepts.
The book also contains a reprint of Libby’s 1982 Chapter 1 explaining the predictive validity model. The original is so much better than the echo of it that he published in AOS in 2002, not to mention the original does not focus on an example article that has been retracted as likely based on fake data.
This is truly a reference book that should stand the test of time.
PLEASE note this is an uncompensated endorsement but also note that I have a chapter in the book so there is a potential COI. Now that I said that I have moral license to exaggerate but those who know me know that would not matter!
In a day and age of multiple statistical methods being readily available through software packages, and a decline in the number of methods courses being required of PhD students in behavioral accounting, it is little surprise when a reviewer encounters a statistical analysis in a paper where they have substantive expertise. [ Aside: When I was a boy I took two classes in probability and statistical theory, three applied methods courses and that was just the requirements. I took another three methods courses beyond what was the minimum for credit and audited two others. ]
So what does the reviewer do? Do they assume the author(s) has picked an obscure statistical methods for data analysis correctly? OR Do they educate themselves on the method and consider whether it is appropriate for the type of data?
Of course, this assumes that the reviewer is well enough trained in statistical methods to recognize what assumptions are needed to be fulfilled in order to use any given method. While the previous generation of researchers certainly knew how to check for gross violations of underlying statistical assumptions in ANOVA and other linear models, some understood the assumptions underlying SEM and its variants, when it gets to hazard rate models, logit and probit models, and other such models only a handful new. I wonder today how many newly trained behavioral PhD’s could say the same????
The beauty is that if a reviewer has any basic knowledge of model assumptions that need to be fulfilled in general, our good friend “google” can help you find what the assumptions are for models that you many not have heard of. For example, this week I had a paper that employed Poisson log linear regression models in my doctoral class. While I had heard of Poisson distribution and I had heard of log linear regressions, this is the first time I had heard this combination. Yep, after 30 years in the business, a new one.
So what did I do, I went to my trusty “google” and looked for “Assumptions underlying Poisson loglinear regression models.” The first thing that popped up was that the dependent variable is suppose to be count data that was theoretically unbounded at the upper end of the distribution. (“If the data are anything but non-negative integers that are (in principle, at least) unbounded, Poisson regression is the wrong model to use. “) Whoops, the data had only three values 0, 1, 2. Well that suggests further research is necessary by the reviewer and at least a strongly worded review note should result. [Second aside: I will find out whether my doctoral students read this blog when I see whether they bring up the point in class!]
Unfortunately this is a real example of a paper that is in the later rounds of review at a major journal (NOT BRIA of course). It will be interesting to see if the journal review process catches it and whether the data stand up to proper statistical analysis (likely an ordered probit but I could be wrong)!
Today as I prepared to teach a governance class on executive compensation, I came across a Canadian think tank study on the Double Glass Ceiling. https://www.policyalternatives.ca/publications/reports/double-pane-glass-ceiling The double glass refers to the fight women have in breaking into top executive and board ranks and then in executive ranks getting equal pay for equal work!
At the same time I read in the Globe & Mail, the closest thing Canada has to the NY Times or Times of London, about the results of the American Economic Association survey https://www.aeaweb.org/news/press-release-climate-survey-results-march-2019 on discrimination nation among current and former AEA members – predominately academic economists. The response rate was huge 25 % of current members responded. What they reported was a hugely misogynistic culture that devalued contributions of women and racial minority economists.
As I think about the difficulty I run into convincing many tenured Associate Professors that are female to go up for promotion to full prof, I wonder if the set of experiences they encountered might be similar to those in the AEA. Maybe the various accounting associations should find out!