## Blogging with blogdown

Blogdown R

Building a site with blogdown, RStudio, and Updog has made my online existence SO much more pleasant!

## Measurement error as an endogeneity problem

We can think of measurement error as an endogeneity problem—that means we must address it in latent variable models to yield consistent effect size estimates.

Headline grabbing effect sizes with sexy variables in complicated models are, over the long run, far more likely to be found wanting than vindicated as possibly right. Science progresses with small, incremental contributions to our knowledge base.

## The Grand Theory of Entrepreneurship Fallacy

We characterize our human experience with uncertainty and with variance. Don’t expect anything better from data science on that human experience.

## Selection models and weak instruments

Causal inference

Weak instruments are a problem in any method dealing with endogeneity where an instrument varible is a proxy for random selection. Heckman selection models share a similar problem of weak instruments, and it has to do with the exclusion restriction.

## Interpreting logistic regression - Part II

R

In the case of logit models with continuous predictors,it takes some extra work to make sense of and really get a handle on the relationship the predictors and the outcome. Marginal effects and predicted probabilities are, to me, a must have in logit model analysis, particularly with continuous predictors.

## Interpreting logistic regression -- Part I

R

I’m a big fan of logit models. Because we can turn the results of a logit model into a set of predicted probabilities, they let us answer questions that are really interesting to academics and entrepreneurs.

## Credibility in strategic management research

What limits our impact on management practice is a lack of rigor, and not an excess of it.

## Another take on p-values

Causal inference P-values

The bottom line is that there is no substitute for using your own judgement when evaluating a study. Ask yourself just how likely it is that the null hypothesis is to be true, particularly when evaluating research purporting to offer surprising, novel, and counterintuitive findings.

## Bad statistics and theoretical looseness

I am actually a big fan of theory; I amm just not wild about the ways in which we (management and entrepreneurship scholars) test it. The driving reason is theoretical looseness; the ability to offer any number of theoretical explanations for a phenomenon of interest.