As a field, we should put more emphasis on interpreting the magnitude of coefficient estimates rather than only assessing statistical significance. To support this claim, I demonstrate how focusing only on statistical significance can lead to incorrect and incomplete conclusions in many common applications of the linear regression model. Moreover, I demonstrate why interpreting coefficient estimates in common non-linear estimators (e.g., probit, logit, Poisson, and negative binomial estimators) requires additional care compared to the linear regression model.
|Original language||English (US)|
|Title of host publication||Research Methodology in Strategy and Management|
|Editors||David Ketchen, Donald Bergh|
|Number of pages||21|
|State||Published - 2007|
|Name||Research Methodology in Strategy and Management|
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