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Henrik Nielsen - One of the best experts on this subject based on the ideXlab platform.
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Configuring a profile-deviation-analysis to Statistical Test complementarity effects from balanced management control systems in a configurational fit approach
Journal of Management Control, 2020Co-Authors: Thomas Borup Kristensen, Henrik NielsenAbstract:This paper develops novel Test formulas able to Test performance effects from balancing control forms in management control systems using the notion of complementarity. Extant research has underlined the importance of researching and understanding complementarity effects stemming from multiple control forms—i.e., management control systems. In configurations of management controls, a number of control forms work interdependently. In some cases, these interdependencies produce complementarity effects, which previous literature has not captured in full, as synergy effects from interdependencies in configurations are often treated implicitly or Tested too reductionistic. Previously used Statistical Test techniques and formulas have not been fully developed to Test performance effects using a configurational fit approach that accounts for complementarity effects from balancing multiple control forms and roles of management accountants (finance functions). In configurations of management control systems, control forms and/or roles of management accountants can be balanced to fit local optima for each control form, and simultaneously fitting the distance, i.e. balance, to other control forms/roles, in which the latter can produce complementarity effects. This balance type of complementarity is a subset of the broader notion of complementarity. To move research forward, new formulas are developed that is suited to Testing complementarity effects from balancing management control forms. In this way, the black box of how configurations of control forms produce performance is being opened, yet not completely, to better examine how they collectively produce performance. The developed Test formulas are illustrated using survey-data on whether multiple roles of management accountants can affect performance in a complementing manner given the strategy of the company they serve.
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Configuring a profile-deviation-analysis to Statistical Test complementarity effects from balanced management control systems in a configurational fit approach
Journal of Management Control, 2020Co-Authors: Thomas Borup Kristensen, Henrik NielsenAbstract:This paper develops novel Test formulas able to Test performance effects from balancing control forms in management control systems using the notion of complementarity. Extant research has underlined the importance of researching and understanding complementarity effects stemming from multiple control forms—i.e., management control systems. In configurations of management controls, a number of control forms work interdependently. In some cases, these interdependencies produce complementarity effects, which previous literature has not captured in full, as synergy effects from interdependencies in configurations are often treated implicitly or Tested too reductionistic. Previously used Statistical Test techniques and formulas have not been fully developed to Test performance effects using a configurational fit approach that accounts for complementarity effects from balancing multiple control forms and roles of management accountants (finance functions). In configurations of management control systems, control forms and/or roles of management accountants can be balanced to fit local optima for each control form, and simultaneously fitting the distance, i.e. balance, to other control forms/roles, in which the latter can produce complementarity effects. This balance type of complementarity is a subset of the broader notion of complementarity. To move research forward, new formulas are developed that is suited to Testing complementarity effects from balancing management control forms. In this way, the black box of how configurations of control forms produce performance is being opened, yet not completely, to better examine how they collectively produce performance. The developed Test formulas are illustrated using survey-data on whether multiple roles of management accountants can affect performance in a complementing manner given the strategy of the company they serve.
Thomas Borup Kristensen - One of the best experts on this subject based on the ideXlab platform.
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Configuring a profile-deviation-analysis to Statistical Test complementarity effects from balanced management control systems in a configurational fit approach
Journal of Management Control, 2020Co-Authors: Thomas Borup Kristensen, Henrik NielsenAbstract:This paper develops novel Test formulas able to Test performance effects from balancing control forms in management control systems using the notion of complementarity. Extant research has underlined the importance of researching and understanding complementarity effects stemming from multiple control forms—i.e., management control systems. In configurations of management controls, a number of control forms work interdependently. In some cases, these interdependencies produce complementarity effects, which previous literature has not captured in full, as synergy effects from interdependencies in configurations are often treated implicitly or Tested too reductionistic. Previously used Statistical Test techniques and formulas have not been fully developed to Test performance effects using a configurational fit approach that accounts for complementarity effects from balancing multiple control forms and roles of management accountants (finance functions). In configurations of management control systems, control forms and/or roles of management accountants can be balanced to fit local optima for each control form, and simultaneously fitting the distance, i.e. balance, to other control forms/roles, in which the latter can produce complementarity effects. This balance type of complementarity is a subset of the broader notion of complementarity. To move research forward, new formulas are developed that is suited to Testing complementarity effects from balancing management control forms. In this way, the black box of how configurations of control forms produce performance is being opened, yet not completely, to better examine how they collectively produce performance. The developed Test formulas are illustrated using survey-data on whether multiple roles of management accountants can affect performance in a complementing manner given the strategy of the company they serve.
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Configuring a profile-deviation-analysis to Statistical Test complementarity effects from balanced management control systems in a configurational fit approach
Journal of Management Control, 2020Co-Authors: Thomas Borup Kristensen, Henrik NielsenAbstract:This paper develops novel Test formulas able to Test performance effects from balancing control forms in management control systems using the notion of complementarity. Extant research has underlined the importance of researching and understanding complementarity effects stemming from multiple control forms—i.e., management control systems. In configurations of management controls, a number of control forms work interdependently. In some cases, these interdependencies produce complementarity effects, which previous literature has not captured in full, as synergy effects from interdependencies in configurations are often treated implicitly or Tested too reductionistic. Previously used Statistical Test techniques and formulas have not been fully developed to Test performance effects using a configurational fit approach that accounts for complementarity effects from balancing multiple control forms and roles of management accountants (finance functions). In configurations of management control systems, control forms and/or roles of management accountants can be balanced to fit local optima for each control form, and simultaneously fitting the distance, i.e. balance, to other control forms/roles, in which the latter can produce complementarity effects. This balance type of complementarity is a subset of the broader notion of complementarity. To move research forward, new formulas are developed that is suited to Testing complementarity effects from balancing management control forms. In this way, the black box of how configurations of control forms produce performance is being opened, yet not completely, to better examine how they collectively produce performance. The developed Test formulas are illustrated using survey-data on whether multiple roles of management accountants can affect performance in a complementing manner given the strategy of the company they serve.
David Bryant - One of the best experts on this subject based on the ideXlab platform.
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a simple and robust Statistical Test for detecting the presence of recombination
Genetics, 2006Co-Authors: Trevor C Bruen, Herve Philippe, David BryantAbstract:Recombination is a powerful evolutionary force that merges historically distinct genotypes. But the extent of recombination within many organisms is unknown, and even determining its presence within a set of homologous sequences is a difficult question. Here we develop a new statistic, Φw, that can be used to Test for recombination. We show through simulation that our Test can discriminate effectively between the presence and absence of recombination, even in diverse situations such as exponential growth (star-like topologies) and patterns of substitution rate correlation. A number of other Tests, Max χ2, NSS, a coalescent-based likelihood permutation Test (from LDHat), and correlation of linkage disequilibrium (both r2 and |D′|) with distance, all tend to underestimate the presence of recombination under strong population growth. Moreover, both Max χ2 and NSS falsely infer the presence of recombination under a simple model of mutation rate correlation. Results on empirical data show that our Test can be used to detect recombination between closely as well as distantly related samples, regardless of the suspected rate of recombination. The results suggest that Φw is one of the best approaches to distinguish recurrent mutation from recombination in a wide variety of circumstances.
Alex R Piquero - One of the best experts on this subject based on the ideXlab platform.
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using the correct Statistical Test for the equality of regression coefficients
Criminology, 1998Co-Authors: Raymond Paternoster, Robert Brame, Paul Mazerolle, Alex R PiqueroAbstract:Criminologists are often interested in examining interactive effects within a regression context. For example, “holding other relevant factors constant, is the effect of delinquent peers on one's own delinquent conduct the same for males and females?” or “is the effect of a given treatment program comparable between first-time and repeat offenders?” A frequent strategy in examining such interactive effects is to Test for the difference between two regression coefficients across independent samples. That is, does b1= b2? Traditionally, criminologists have employed a t or z Test for the difference between slopes in making these coefficient comparisons. While there is considerable consensus as to the appropriateness of this strategy, there has been some confusion in the criminological literature as to the correct estimator of the standard error of the difference, the standard deviation of the sampling distribution of coefficient differences, in the t or z formula. Criminologists have employed two different estimators of this standard deviation in their empirical work. In this note, we point out that one of these estimators is correct while the other is incorrect. The incorrect estimator biases one's hypothesis Test in favor of rejecting the null hypothesis that b1= b2. Unfortunately, the use of this incorrect estimator of the standard error of the difference has been fairly widespread in criminology. We provide the formula for the correct Statistical Test and illustrate with two examples from the literature how the biased estimator can lead to incorrect conclusions.
Niko Beerenwinkel - One of the best experts on this subject based on the ideXlab platform.
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accurate single nucleotide variant detection in viral populations by combining probabilistic clustering with a Statistical Test of strand bias
BMC Genomics, 2013Co-Authors: Kerensa Mcelroy, Osvaldo Zagordi, Rowena A Bull, Fabio Luciani, Niko BeerenwinkelAbstract:Deep sequencing is a powerful tool for assessing viral genetic diversity. Such experiments harness the high coverage afforded by next generation sequencing protocols by treating sequencing reads as a population sample. Distinguishing true single nucleotide variants (SNVs) from sequencing errors remains challenging, however. Current protocols are characterised by high false positive rates, with results requiring time consuming manual checking. By Statistical modelling, we show that if multiple variant sites are considered at once, SNVs can be called reliably from high coverage viral deep sequencing data at frequencies lower than the error rate of the sequencing technology, and that SNV calling accuracy increases as true sequence diversity within a read length increases. We demonstrate these findings on two control data sets, showing that SNV detection is more reliable on a high diversity human immunodeficiency virus sample as compared to a moderate diversity sample of hepatitis C virus. Finally, we show that in situations where probabilistic clustering retains false positive SNVs (for instance due to insufficient sample diversity or systematic errors), applying a strand bias Test based on a beta-binomial model of forward read distribution can improve precision, with negligible cost to true positive recall. By combining probabilistic clustering (implemented in the program ShoRAH) with a Statistical Test of strand bias, SNVs may be called from deeply sequenced viral populations with high accuracy.
