ABC Cost Driver Framing and Altering the Balance of Power in Customer-Supplier Negotiations

This study examines how activity-based costing (ABC) cost driver framing affects suppliers’ ability to increase their bargaining power when facing powerful customers. Results of an experiment show that suppliers with high potential to contribute to increasing joint profits are able to increase their power and earn a higher share of joint profits than suppliers with low contribution potential. However, providing suppliers with externally framed cost drivers (cost drivers represented as customers’ activities) instead of internally framed cost drivers (cost drivers represented as suppliers’ own activities) reduces their ability to utilize contribution potential as a source of power. Analyses of negotiators’ behavior show that suppliers with high contribution potential and internally framed cost drivers use more integrative tactics to increase joint profits, allowing them to earn higher shares of joint profits. This study shows that the how firms frame cost drivers affects negotiators’ ability to improve joint profits and negotiation power.

Power to the People: A Beginner’s Tutorial to Power Analysis using jamovi

Authors have highlighted for decades that sample size justification through power analysis is the exception rather than the rule. Even when authors do report a power analysis, there is often no justification for the smallest effect size of interest, or they do not provide enough information for the analysis to be reproducible. We argue one potential reason for these omissions is the lack of a truly accessible introduction to the key concepts and decisions behind power analysis. In this tutorial, we demonstrate a priori and sensitivity power analysis using jamovi for two independent samples and two dependent samples. Respectively, these power analyses allow you to ask the questions: “How many participants do I need to detect a given effect size?”, and “What effect sizes can I detect with a given sample size?”. We emphasise how power analysis is most effective as a reflective process during the planning phase of research to balance your inferential goals with your available resources. By the end of the tutorial, you will be able to understand the fundamental concepts behind power analysis and extend them to more advanced statistical models.

Challenges for assessing replicability in preclinical cancer biology

We conducted the Reproducibility Project: Cancer Biology to investigate the replicability of preclinical research in cancer biology. The initial aim of the project was to repeat 193 experiments from 53 high-impact papers, using an approach in which the experimental protocols and plans for data analysis had to be peer reviewed and accepted for publication before experimental work could begin. However, the various barriers and challenges we encountered while designing and conducting the experiments meant that we were only able to repeat 50 experiments from 23 papers. Here we report these barriers and challenges. First, many original papers failed to report key descriptive and inferential statistics: the data needed to compute effect sizes and conduct power analyses was publicly accessible for just 4 of 193 experiments. Moreover, despite contacting the authors of the original papers, we were unable to obtain these data for 68% of the experiments. Second, none of the 193 experiments were described in sufficient detail in the original paper to enable us to design protocols to repeat the experiments, so we had to seek clarifications from the original authors. While authors were extremely or very helpful for 41% of experiments, they were minimally helpful for 9% of experiments, and not at all helpful (or did not respond to us) for 32% of experiments. Third, once experimental work started, 67% of the peer-reviewed protocols required modifications to complete the research and just 41% of those modifications could be implemented. Cumulatively, these three factors limited the number of experiments that could be repeated. This experience draws attention to a basic and fundamental concern about replication – it is hard to assess whether reported findings are credible.

STATISTICAL REPORT REFORM IN SECOND LANGUAGE RESEARCH: A CASE OF EXPERIMENTAL DESIGNS

This survey aims to review statisical report procedures in the experimental studies appearing in ten SLA and Applied Linguistic journals from 2011 to 2017. We specify our study on how the authors report and interprete their power analyses, effect sizes, and confidence intervals. Results reveal that of 217 articles, the authors reported effect sizes (70%), apriori power and posthoc power consecutively (1.8% and 6.9%), and confidence intervals (18.4%). Additionally, it shows that the authors interprete those statistical terms counted 5.5%, 27.2%, and 6%, respectively. The call for statistical report reform recommended and endorsed by scholars, researchers, and editors is inevitably echoed to shed more light on the trustworthiness and practicality of the data presented.

Altered electroencephalography resting state network coherence in remitted MDD

Individuals with remitted depression are at greater risk for subsequent depression and therefore may provide a unique opportunity to understand the neurophysiological correlates underlying the risk of depression. Research has identified abnormal resting-state electroencephalography (EEG) power metrics and functional connectivity patterns associated with major depression, however little is known about these neural signatures in individuals with remitted depression. We investigate the spectral dynamics of 64-channel EEG surface power and source-estimated network connectivity during resting states in 37 individuals with depression, 56 with remitted depression, and 49 healthy adults that did not differ on age, education, and cognitive ability across theta, alpha, and beta frequencies. Average reference spectral EEG surface power analyses identified greater left and midfrontal theta in remitted depression compared to healthy adults. Using Network Based Statistics, we also demonstrate within and between network alterations in LORETA transformed EEG source-space coherence across the default mode, fronto-parietal, and salience networks where individuals with remitted depression exhibited enhanced coherence compared to those with depression, and healthy adults. This work builds upon our currently limited understanding of resting EEG connectivity in depression, and helps bridge the gap between aberrant EEG power and brain network connectivity dynamics in this disorder. Further, our unique examination of remitted depression relative to both healthy and depressed adults may be key to identifying brain-based biomarkers for those at high risk for future, or subsequent depression.

Detection of internal and external faults of single-phase induction motor using current signature

<span>The main aim of this work is to analyze the input current waveform for a single-phase induction capacitor-run motor (SIMCR) to detect the faults. Internal and external faults were applied to the SIMCR and the current was measured. An armature (broken rotor bar) and bearing faults were applied to the SIMCR. A microcontroller was used to record the motor current signal and MATLAB software was used to analyze it with the different types of fault with varying load operations. Various values of the running capacitor were used to investigate the effect of these values on the wave-current shape. Total harmonic distortion (THD) for the voltage and current was measured. A deep demonstration of the experimental results was also provided for a better understanding of the mechanisms of bearing and armature faults (broken rotor bars) and the vibration was recorded. Spectral and power analyses revealed the difference in total harmonic distortion. The proposed method in this paper can be used in various industrial applications and this technique is quite cheap and helps most of the researchers and very effectual.</span>

Power analyses for moderator effects with (non)randomly varying slopes in cluster randomized trials

Researchers often apply moderation analyses to examine whether the effects of an intervention differ conditional on individual or cluster moderator variables such as gender, pretest, or school size. This study develops formulas for power analyses to detect moderator effects in two-level cluster randomized trials (CRTs) using hierarchical linear models. We derive the formulas for estimating statistical power, minimum detectable effect size difference and 95% confidence intervals for cluster- and individual-level moderators. Our framework accommodates binary or continuous moderators, designs with or without covariates, and effects of individual-level moderators that vary randomly or nonrandomly across clusters. A small Monte Carlo simulation confirms the accuracy of our formulas. We also compare power between main effect analysis and moderation analysis, discuss the effects of mis-specification of the moderator slope (randomly vs. non-randomly varying), and conclude with directions for future research. We provide software for conducting a power analysis of moderator effects in CRTs.

PowerLAPIM: An Application to Conduct Power Analysis for Longitudinal Actor-Partner Interdependence Models that Include Quadratic Effects

The longitudinal actor-partner interdependence modeling framework (L-APIM) is often used to study actor and partner effects in dyadic intensive longitudinal data. To capture curvilinear actor and partner patterns, the L-APIM can be extended to include quadratic actor and partner effects. A burning question is how to conduct power analyses for different L-APIM variants. In this paper, we introduce a power analysis application, called PowerLAPIM, and provide a hands-on tutorial for conducting simulation-based power analyses for 32 L-APIM variants, many of which include quadratic effects. With PowerLAPIM, we target the number of dyads needed, but not the number of repeated measurements for both partners, because this is usually fixed in many longitudinal dyadic studies. PowerLAPIM allows studying moderation of the linear and quadratic actor and partner effects by incorporating time-varying covariates or a categorical dyad-level predictor to test group differences. We also provide the functionality to account for serial dependency in the outcome variable by including autoregressive effects. We illustrate how to perform a power analysis for a longitudinal dyadic study using PowerLAPIM based on data from 94 heterosexual couples for which both partners simultaneously reported on their feelings and experiences several times a day for one week.