Modeling Reputational and Informational Influences in Threshold Models of Bandwagon Innovation Diffusion

Bandwagon innovation diffusion is characterized by a positive feedback loop where adoptions by some actors increase the pressure to adopt for other actors. In particular, when gains from an innovation are difficult to quantify, such as implementing quality circles or downsizing practices, diffusion is likely to occur through a bandwagon process. In this paper we extend Abrahamson and Rosenkopf&2018;s (1993) model of bandwagon diffusion to examine both reputational and informational influences on this process. We find that the distribution of reputations among the set of potential adopters affects the extent of bandwagon diffusion under conditions of moderate ambiguity, and we find that bandwagons occur even when potential adopters receive information about others&2018; unprofitable experiences with the innovation

Our Cities as Economic Engines

Focusing on newly released data from the Bureau of Economic Analysis, Sougstad and Abrahamson unpack the trends and look at Rapid City and Sioux Falls in the context of the northern Great Plains region

Beyond Charity: A Century of Philanthropic Innovation

This book shows how the evolution of the Foundation's innovative practices have helped to shape the direction and pattern of philanthropy over the course of one hundred years

Researchers Should Make Thoughtful Assessments Instead of Null-Hypothesis Significance Tests

Null-hypothesis significance tests (NHSTs) have received much criticism, especially during the last two decades. Yet, many behavioral and social scientists are unaware that NHSTs have drawn increasing criticism, so this essay summarizes key criticisms. The essay also recommends alternative ways of assessing research findings. Although these recommendations are not complex, they do involve ways of thinking that many behavioral and social scientists find novel. Instead of making NHSTs, researchers should adapt their research assessments to specific contexts and specific research goals, and then explain their rationales for selecting assessment indicators. Researchers should show the substantive importance of findings by reporting effect sizes and should acknowledge uncertainty by stating confidence intervals. By comparing data with naïve hypotheses rather than with null hypotheses, researchers can challenge themselves to develop better theories. Parsimonious models are easier to understand and they generalize more reliably. Robust statistical methods tolerate deviations from assumptions about samples

2017-2018 Mostly Music: Ludwig van Beethoven

https://spiral.lynn.edu/conservatory_mostlymusic/1030/thumbnail.jp

2017-2018 Mostly Music: Franz Joseph Haydn

https://spiral.lynn.edu/conservatory_mostlymusic/1029/thumbnail.jp

Application of robust regression in translational neuroscience studies with non-Gaussian outcome data

Linear regression is one of the most used statistical techniques in neuroscience, including the study of the neuropathology of Alzheimer’s disease (AD) dementia. However, the practical utility of this approach is often limited because dependent variables are often highly skewed and fail to meet the assumption of normality. Applying linear regression analyses to highly skewed datasets can generate imprecise results, which lead to erroneous estimates derived from statistical models. Furthermore, the presence of outliers can introduce unwanted bias, which affect estimates derived from linear regression models. Although a variety of data transformations can be utilized to mitigate these problems, these approaches are also associated with various caveats. By contrast, a robust regression approach does not impose distributional assumptions on data allowing for results to be interpreted in a similar manner to that derived using a linear regression analysis. Here, we demonstrate the utility of applying robust regression to the analysis of data derived from studies of human brain neurodegeneration where the error distribution of a dependent variable does not meet the assumption of normality. We show that the application of a robust regression approach to two independent published human clinical neuropathologic data sets provides reliable estimates of associations. We also demonstrate that results from a linear regression analysis can be biased if the dependent variable is significantly skewed, further indicating robust regression as a suitable alternate approach

Population Structure and Spatial Pattern in the Dioecious Shrub Ceratiola ericoides

The dioecious shrub Ceratiola ericoides (Florida rosemary) dominates xeric, infrequently burned Florida scrub vegetation, often to the near-exclusion of other woody species. We studied the spatial pattern, age, sex and size structure of four populations in Florida, USA: two coastal scrub populations subject to recurrent local disturbances due to sand movement, and two inland scrub populations in sites periodically burned by stand-replacing fires. The age structure of individual genets was estimated from node counts and used to describe the age structure of the populations. The sex ratio of males to females was not significantly different from 1:1, except within a female- biased coastal population subject to frequent sand movement. Node counts indicated that the mean age for reproductive individuals was 15 - 16 yr for the inland populations and 13 - 16 yr for the coastal populations. In all sites, there was no difference in mean age between males and females. Vegetative reproduction was uncommon except for the least-disturbed coastal population where 72 % of the reproductive individuals originated through layering. Individuals were generally randomly dispersed at the coastal sites, whereas significant aggregation of males and females occurred in the inland sites where the populations were initiated following fire. Seedling recruitment was continuous in the disturbed coastal scrub site, where 35 % of the individuals were juveniles. Most juveniles were dispersed from 0.5 to 0.75 m around females. At one of the inland sites, where juveniles comprised 11 % of the population, juveniles were clustered at 0.25 to 5.75 m around females. Coastal populations were all-aged, while inland populations were uneven-aged. Recruitment appears to follow periods of disturbance; infrequent fire in the inland populations and continuous sand movement on the coast are factors initiating recruitment