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Denne artikel demonstrerer, hvordan en analyse og formidling af resultater fra en multilevelanalyse med fordel kan suppleres med grafiske illustrationer.Ved brug af data fra European Social Survey demonstreres det, hvordan man grafisk kan forenkle både fortolkning og formidling af ellers forholdsvist komplicerede resultater som eksempelvis Random Intercept-, Random Slope- og cross-level interaktionseffekter.Demonstrationen foretages gennem en analyse af sammenhængen mellem individuel social kapital og jobtilfredshed på tværs af 23 europæiske lande. Det undersøges hertil, hvordan denne sammenhæng varierer alt efter de specifikke kulturelle forhold, der er til stede i de enkelte lande

Et billede siger mere end 1000 ord

Denne artikel demonstrerer, hvordan en analyse og formidling af resultater fra en multilevelanalyse med fordel kan suppleres med grafiske illustrationer.Ved brug af data fra European Social Survey demonstreres det, hvordan man grafisk kan forenkle både fortolkning og formidling af ellers forholdsvist komplicerede resultater som eksempelvis Random Intercept-, Random Slope- og cross-level interaktionseffekter.Demonstrationen foretages gennem en analyse af sammenhængen mellem individuel social kapital og jobtilfredshed på tværs af 23 europæiske lande. Det undersøges hertil, hvordan denne sammenhæng varierer alt efter de specifikke kulturelle forhold, der er til stede i de enkelte lande

CEO personality traits, strategic flexibility, and firm dynamics

Reexamining CEO personality traits from a real options theory perspective, we suggest that the firm's strategic flexibility can be worsened by CEO conscientiousness and neuroticism. We use a measure of strategic flexibility as the firm's ability to take advantage of heightened volatility, which then results in superior stock returns. Our results suggest that strategic adaptability is impeded by rigid planning, resistance to change (conscientiousness) and lack of emotional stability (neuroticism). For firms that experience a decrease in volatility, the opposite holds. In line with trait activation theory, our results imply that the effect of specific CEO personality traits on firm dynamics and performance is contingent and context-specific. Our findings are economically significant and have important implications concerning CEO selection and management

Generalized two-part fractional regression with cmp

Researchers who model fractional dependent variables often need to consider whether their data were generated by a two-part process. Two-part mod- els are ideal for modeling two-part processes because they allow us to model the participation and magnitude decisions separately. While community-contributed commands currently facilitate estimation of two-part models, no specialized com- mand exists for fitting two-part models with process dependency. In this article, I describe generalized two-part fractional regression, which allows for dependency between models’ parts. I show how this model can be fit using the community- contributed cmp command (Roodman, 2011, Stata Journal 11: 159–206). I use a data example on the financial leverage of firms to illustrate how cmp can be used to fit generalized two-part fractional regression. Furthermore, I show how to obtain predicted values of the fractional dependent variable and marginal effects that are useful for model interpretation. Finally, I show how to compute model fit statistics and perform the RESET test, which are useful for model evaluation

Generalized two-part fractional regression with cmp

Researchers who model fractional dependent variables often need to consider whether their data were generated by a two-part process. Two-part mod- els are ideal for modeling two-part processes because they allow us to model the participation and magnitude decisions separately. While community-contributed commands currently facilitate estimation of two-part models, no specialized com- mand exists for fitting two-part models with process dependency. In this article, I describe generalized two-part fractional regression, which allows for dependency between models’ parts. I show how this model can be fit using the community- contributed cmp command (Roodman, 2011, Stata Journal 11: 159–206). I use a data example on the financial leverage of firms to illustrate how cmp can be used to fit generalized two-part fractional regression. Furthermore, I show how to obtain predicted values of the fractional dependent variable and marginal effects that are useful for model interpretation. Finally, I show how to compute model fit statistics and perform the RESET test, which are useful for model evaluation

How and why alpha should depend on sample size: A Bayesian-frequentist compromise for significance testing

The use of fixed alpha levels in statistical testing is prevalent in management research, but can lead to Lindley's paradox in highly powered studies. In this article, we propose a sample size-adjusted alpha level approach that combines the benefits of both frequentist and Bayesian statistics, enabling strict hypothesis testing with known error rates while also quantifying the evidence for a hypothesis. We present an R-package that can be used to set the sample size-adjusted alpha level for generalized linear models, including linear regression, logistic regression, and Poisson regression. This approach can help researchers stop relying on mindless defaults and avoid situations where they reject the null hypothesis when the evidence in the test actually favors the null hypothesis, improving the accuracy and robustness of statistical analysis in management research

A multilevel Bayesian framework for predicting municipal waste generation rates

Prediction of waste production is an essential part of the design and planning of waste management systems. The quality and applicability of such predictions depend heavily on model assumptions and the structure of the collected data. Ordinarily, municipal waste generation data are organized in hierarchical structures with municipal or county levels, and multilevel models can be used to generalize linear regression by directly incorporating the structure into the model. However, small amounts of data can limit the applicability of multilevel models and provide biased estimates. To cope with this problem, Bayesian estimation is often recommended as an alternative to frequentist estimation, such as least squares or maximum likelihood estimation. This paper proposes a multilevel framework under a Bayesian approach to model municipal waste generation with hierarchical data structures. Using a real-world dataset of municipal waste generation in Denmark, the predictive accuracy of multilevel models is compared to aggregated and disaggregated Bayesian models using socio-economic external variables. Results show that Bayesian multilevel models outperform the other models in prediction accuracy, based on the leave-one-out information criterion. A comparison of the Bayesian approach with its frequentist alternative shows that the Bayesian model is more conservative in coefficient estimation, with estimates shrinking to the grand mean and broader credible intervals, in contrast with narrower confidence intervals produced by the frequentist models.(c) 2021 Elsevier Ltd. All rights reserved