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G*Power 3: A flexible statistical power analysis program for the social, behavior, and biomedical sciences
Abstract
G*Power (Erdfelder, Faul, & Buchner, 1996) was designed as a general stand-alone power analysis program for statistical tests commonly used in social and behavioral research. G*Power 3 is a major extension of, and improvement over, the previous versions. It runs on widely used computer platforms (i.e., Windows XP, Windows Vista, and Mac OS X 10.4) and covers many different statistical tests of the t, F, and chi2 test families. In addition, it includes power analyses for z tests and some exact tests. G*Power 3 provides improved effect size calculators and graphic options, supports both distribution-based and design-based input modes, and offers all types of power analyses in which users might be interested. Like its predecessors, G*Power 3 is free.
... The study involved 111 male motorcycle speedway riders of different nationalities from several teams of the highest speedway league in Poland, PGE Speedway Ekstraliga. The athletes were characterized by the following parameters: age: 25 . All participants were considered professional athletes, including one multiple Senior World Champion, several Senior World Championship medalists, multiple Junior World Champions, Speedway Grand Prix participants, national champions, and national representatives. ...
... The quantitative investigation involved a 4-dimensional approach (alpha, power, sample size, and effect size) [24], Due to the retrospective character of this study, it was established post-hoc using G*Power 3.1.9.6 software (Franz Faul, Kiel, Germany) [25]. The obtained average value of effect size for comparison differences between means (F-test) was set at (eta-square η 2 ) 0.05 (small). ...
The main objective of this research was to analyze the physical profile and experience of elite male motorcycle speedway riders, in order to develop a mathematical model to predict their level of sport performance. One hundred and eleven male professional motorcycle speedway riders in the top Polish motorcycle speedway league participated in this study (age: 25.04 ± 6.03 years, height: 172.28 ± 4.76 cm, weight: 65.60 ± 4.66 kg). The riders were divided by the k-means clustering method into three groups of sport level: high (n1 = 31), medium (n2 = 30), low (n3 = 50). To define sport level, several parameters were normalized and aggregated into a new conceptual metric of sport level called ‘Indicator of Sport Level’ (ISL). Body composition assessment, Wingate Anaerobic Test on a cycloergometer, and analysis of acute cardiorespiratory and biochemical responses were performed. One-way ANOVA tests were performed for independent samples, and Pearson’s r linear correlation was calculated between clusters of riders. Multiple linear regression (MLR) was performed to predict the ISL. The elite male motorcycle speedway athletes were characterized by average age (25.10 ± 5.80 years) and years of experience (9.16 ± 5.62 years), which made them different from other clusters (p < 0.05). Also, they were 1.99% taller (173.02 ± 4.91 cm) than low sport level riders (p = 0.003). The strongest predictive variable of sport level in the regression model (R² = 0.28) was body height (p = 0.043) and sport experience (p < 0.001). The results of this study present the optimal range of physical attributes, body height, sports experience, age, and body surface area of the best motorcycle speedway riders and indicate that body height and experience are the most predictive parameters in the model to estimate the sport level of riders.
... Therefore, the sample size for these analyses was determined based on the number of datasets available from the 2 RCTs and was fixed and could not be altered. To ensure appropriate interpretation of our results, we conducted a post hoc power analysis using G*Power (version 3.1) [90,91]. As the Spearman rank correlation coefficient is computationally identical to the Pearson product-moment coefficient, power analyses were conducted using the same methodology as for estimating the power of a Pearson correlation for nonparametric analyses. ...
... First and most importantly, the sample size was small, and we did not conduct an a priori sample size calculation as we analyzed existing datasets from studies conducted as part of the Brain-IT project. To ensure appropriate interpretation of our results, we conducted a post hoc power analysis using G*Power (version 3.1) [90,91], which revealed that most of our analyses were underpowered. This might have resulted in potential false-negative or false-positive results, affects the robustness and generalizability of our findings, and warrants confirmatory studies with a priori sample size calculations to ensure more reliable and robust conclusions. ...
BACKGROUND:
Exergame-based training enhances physical and cognitive performance in older adults, including those with mild neurocognitive disorder (mNCD). In-game metrics generated from user interactions with exergames enable individualized adjustments. However, there is a need to systematically investigate how well such game metrics capture true cognitive and motor-cognitive performance to provide a more robust basis for personalized training.
OBJECTIVE:
The primary objective was to identify valid game metrics as indicators for in-game domain-specific cognitive performance during exergaming in individuals with mNCD. We also aimed to explore game metric performance changes over time during exergame-based training.
METHODS:
Data were analyzed from individuals with mNCD who completed a 12-week home-based, exergame-based intervention following the Brain-IT training concept. A cross-sectional analysis was conducted by correlating game metrics with standardized neurocognitive reference assessments. To confirm the alternative hypothesis, we predetermined the following criteria: (1) statistically significant correlation (P≤.05; uncorrected; 1-sided) with (2) a correlation coefficient (Pearson r or Spearman ρ) of ≥0.4. Visual and curve-fitting longitudinal analyses were conducted to explore game performance changes over time.
RESULTS:
Data were available from 31 participants (mean age 76.4, SD 7.5 y; n=9, 29% female). In total, 33% (6/18) of the game metrics were identified as valid indicators for in-game cognitive performance during exergaming. In the neurocognitive domain of learning and memory, these metrics included the mean reaction time (ρ=–0.747), the number of collected items (ρ=0.691), and the precision score (r=–0.607) for the game Shopping Tour (P<.001 in all cases), as well as the point rate (P=.008; r=0.471) for the game Simon. In addition, point rate was a valid indicator for executive function (P=.006; r=0.455) and visuospatial skills (P=.02; r=0.474) for the games Targets and Gears, respectively. The exploratory longitudinal analysis revealed high interindividual variability, with a general trend of the expected typical curvilinear curves of rapid initial improvements followed by a plateau in performance.
CONCLUSIONS:
This study demonstrated that metrics reflecting the precision of responses generally performed better than metrics reflecting the speed of responses. These observations highlight the importance of selecting valid game metrics for implementation in exergame designs. Further research is needed to explore the potential of game metrics and identify factors contributing to individual variability in in-game performance and performance progression, as well as identifying and adopting strategies that facilitate individual learning success and thus promote effectiveness in improving health outcomes.
... In this study, we calculated the effect size using a logistic mixed-effects model, particularly when comparing full and reduced models. We employed G*Power software [40] with a likelihood ratio Chi-square test to determine the difference in Chi-squares between the models. This test indicates the contribution of the excluded variable to explain the variability in the outcome [36]. ...
While Artificial Intelligence (AI) has demonstrated human-level capabilities in many prediction tasks, collaboration between humans and machines is crucial in mission-critical applications, especially in the healthcare sector. An important factor that enables successful human-AI collaboration is the user interface (UI). This paper evaluated the UI of BiTNet, an intelligent assisting tool for human biliary tract diagnosis via ultrasound images. We evaluated the UI of the assisting tool with 11 healthcare professionals through two main research questions: 1) did the assisting tool help improve the diagnosis performance of the healthcare professionals who use the tool? and 2) how did different UI elements of the assisting tool influence the users’ decisions? To analyze the impacts of different UI elements without multiple rounds of experiments, we propose the novel AISIM strategy. We demonstrated that our proposed strategy, AISIM, can be used to analyze the influence of different elements in the user interface in one go. Our main findings show that the assisting tool improved the diagnostic performance of healthcare professionals from different levels of experience (OR = 3.326, p-value <10⁻¹⁵). In addition, high AI prediction confidence and correct AI attention area provided higher than twice the odds that the users would follow the AI suggestion. Finally, the interview results agreed with the experimental result that BiTNet boosted the users’ confidence when they were assigned to diagnose abnormality in the biliary tract from the ultrasound images.
... The determination of the sample size needed was based on adopting a significance level of 5% with 90% power using Gpower software, version 3.1.9.4. 25 The sample size calculation was also based on a previous prospective clinical trial that evaluated wound healing after wisdom tooth extraction and reported similar effect sizes. 24 A representative sample size of 50 participants were required to detect a mean difference of 1 and standard deviation of 0.8, and a significance level of 0.05 in cumulative scores of early wound healing (measured using the IPR wound healing scale), between the groups. ...
... α = .05; Faul et al., 2007) indicated that small effects with a size of f = 0.11/ η 2 = 0.01 could be reliably detected. The study was approved by the University of Konstanz ethics board. ...
Background: Selective processing of attitude-consistent information is a substantial obstacle in convincing vaccine-skeptical people of the benefits of vaccinations. Aims: This study tests (i) which types of information are particularly prone to such selective information processing, and (ii) whether a deliberative (vs. implemental) mindset focusing on potential benefits and harms may diminish its effects. Method: 612 Mturk participants were randomized into an implemental or deliberative mindset and received a flu vaccine-skeptical narrative, a flu vaccination facts box transparently summarizing risks and benefits, and a message by the Center for Disease Prevention and Control (CDC) in favor of the flu vaccine either referring to COVID-19 or not. We tested how these variations affected the acceptance of and the willingness to share each message. Furthermore, we evaluated their impact on flu vaccination attitudes and intentions. Results: The mindset manipulation failed to diminish generally prevalent selective information processing. While vaccine-skeptics did not accept and like the CDC message referring to COVID-19 (particularly in a deliberative mindset), they generally accepted the vaccination facts box more readily compared to both CDC messages. Limitations: Future studies should particularly focus on vaccine-skeptics and experimentally test the effects of facts boxes also on vaccination attitudes and intentions. Conclusion: While mindsets were ineffective, more general and transparent information may be more likely to reach an anti-vaccine audience.
... A post hoc power analysis was conducted to assess whether the sample size was sufficient to detect significant differences between the two groups. Using Cohen's d values derived from the data, the achieved statistical power (1 − β) was computed for each variable using GPower (Faul et al., 2007). The analysis was conducted with an alpha level of 0.05. ...
... In this study, we conducted experiments based on the assumption that changes in facial expression recognition due to behavior are not moderated by gender differences (Di Tella et al., 2020). The sample size was determined by performing a statistical power analysis using G*Power (version 3.1.9.7) (Faul et al., 2007(Faul et al., , 2009) for a within-participants two-factor analysis of variance interaction with the direction condition (approach or avoidance) and the agency condition (active or passive). The parameters were as follows: effect size (f) of 0.25, α of .05, ...
Facial expression recognition influences approach-avoidance behaviors, but can these behaviors affect facial expression recognition? We conducted psychophysical experiments using virtual reality to investigate this reverse causal relationship. Participants responded to static 3D face stimuli generated by morphing expressions between happy and angry in Experiments 1 and 3. For Experiment 2, happy-fearful morphed stimuli were employed. Participants either approached, avoided, or were approached or avoided by the face. The results showed that participants recognized the face as angrier when they avoided it rather than when it avoided them (Experiment 1); as happy when approaching and fearful when avoiding, irrespective of who acted (Experiment 2); and as angrier when the face approached them rather than when they approached it if both parties were physically close (Experiment 3). These findings suggest that approach-avoidance behavior influences facial expression recognition. We posit that unconscious learning rooted in biological instincts creates this connection.
... To determine the number of participants required for the study, including the number of participants in the control and experimental groups, a G*Power analysis (Faul et al., 2007(Faul et al., , 2009 was conducted. The sample size calculation was conducted using a t-test to compare the means of two independent groups, with the aim of detecting a difference between experimental group participants and control group participants in their ability to identify synthetic faces. ...
Fake identities created using highly realistic synthetic face images have become increasingly prevalent in recent years, driven by advancements in generative neural networks that are readily accessible online and easy to use. These fake identities can be exploited for malicious purposes, such as spreading misinformation or committing fraud. Given the widespread availability of online content and the ease of generating fake online identities, it is desirable that users are able to distinguish real face images from synthetic ones. Additionally, it is important to explore whether specialized training can enhance the ability of individuals to detect synthetically generated face images. In this work, we address these challenges by designing an online experiment to evaluate human detection capabilities and the impact of training on detecting synthetic face images. As part of the experiments, we recruited 184 participants divided into an experimental group and a control group, where the experimental group underwent a tailored training session halfway through the experiment. The study shows that training may moderately enhance human capabilities to detect synthetic face images. Specifically, it was found that the experimental group generally outperformed the control group after training, primarily due to improved abilities in detecting synthetic face images. However, after training, the experimental group showed increased sensitivity and misclassified also more authentic face images, as compared to the control group.
... Participants Experiment 1 utilized a 2 (Participant Group: compound retrieval, control) × 2 (Trial Transition: switch, repeat) × 2 (Congruency: incongruent, congruent) mixed design, with participant group as the only between-subjects variable. G*Power (Faul et al., 2007) calculations indicated that 82 participants were required to detect a medium effect size (f = 0.25) with 80% power at α = 0.05. Although 90 participants from the authors' university were initially recruited, eight were excluded due to excessive EEG noise, resulting in a final sample of 82 (41 per group; males = 34, mean age = 20.52, ...
Introduction
In task-switching paradigms, Switch Positivity, the N2 difference wave, and the P3 difference wave are typically observed in the cue-target interval (CTI) design, where the cue precedes the target. The ERP components are indicative of task-set control processes (i.e., task-set reconfiguration and task-set inertia). However, in the composite design, where the cue and target appear simultaneously, these components are absent. Previous research has hypothesized that in the composite design task-switching experiments, participants may employ compound retrieval strategies based on associative learning to complete the tasks. This strategy circumvents task rules, thereby eliminating ERP components related to task-set control.
Methods
This study aims to examine whether the use of compound retrieval strategies affects the task-set related ERP components. In Experiment 1, we manipulated participants’ semantic understanding of the target stimuli to control their strategies. Participants in the compound retrieval group exclusively used the compound retrieval strategy, while those in the control group could employ both the compound retrieval strategy and task rules. In Experiment 2, we varied the number of target stimuli to influence participants’ strategies, with participants in the task rule group utilizing task rules, and those in the control group permitted to use both task rules and the compound retrieval strategy.
Results
The results revealed that Switch Positivity, the N2 difference wave, and the P3 difference wave were absent across all group conditions, regardless of the strategies employed.
Discussion
These findings suggest that the disappearance of these ERP components in the composite design is not attributable to the use of compound retrieval strategies.
... An a priori power analysis conducted in G*Power found that to detect a medium effect size (d = .42) with a repeated measures ttest (our initial planned analysis), power of .80 and an alpha of .05, a minimum sample of 47 participants was needed (Faul et al., 2007). To achieve this, assuming approximately 60% retention rates at 5-month follow-up (as per Haslam et al., 2016), we aimed to recruit a total sample of 80 participants for the G4H groups. ...
Objectives
Current best‐practice treatments for social anxiety disorder do not directly address loneliness, despite its role in the maintenance of the condition. The current study targets this issue directly, using mixed methods to provide an initial test of the efficacy of an established loneliness intervention, groups 4 health (G4H), among 33 people with clinically elevated social anxiety symptoms.
Design
A single‐arm design was used and outcomes were assessed at baseline, programme completion and 5‐month follow‐up (3 months after programme completion).
Methods
Loneliness, social anxiety symptoms, depression symptoms and well‐being were assessed at each time point. Semi‐structured follow‐up interviews were also conducted to explore the feasibility and acceptability of G4H in this population.
Results
Results from intention‐to‐treat analyses provide initial evidence of the programme's efficacy: participants' loneliness (d = −1.08), social anxiety symptoms (d = −.45), and depression symptoms (d = −.60) reduced significantly from baseline to 5‐month follow‐up while their well‐being (d = 1.00) increased. Four themes emerged from reflexive thematic analysis: (1) the importance of challenging initial anxiety about attending group therapy, (2) the value of being vulnerable with fellow group members, (3) the role of G4H in increasing participants' social confidence, and (4) processes which both helped and hindered participants' ability to engage with their group.
Conclusions
Together, results suggest that G4H is a promising and innovative treatment option for people with social anxiety, and further controlled evaluation is warranted.
... According to the G*power3.1 analysis (Faul et al. 2007), achieving a medium effect size (0.3) with an acceptable power of 95% (at a 1⁄4 0.05) requires a sample size of 111 participants. To control data quality of behavioral data, participants were excluded if they either: (1) showed > ¥24 increases (20% of maximum delayed reward) between adjacent delays, or (2) exhibited < 10% value decay over 90 days (final indifference point > ¥108). ...
Delay discounting is the tendency for people to devalue future rewards as the time required to obtain them increases over time. Self-control is the ability to regulate behavior, emotions and cognition to achieve goals or adhere to social norms despite temptations, impulses or distractions. Previous studies have found that self-control was negatively correlated with delay discounting. However, the neural mechanism underlying the relationship between self-control and delay discounting remains unclear. To address this question, we examined the neural basis of the relationship between self-control and delay discounting using voxel-based morphometry(VBM) and resting-state functional connectivity analysis(RSFC). The behavioral results demonstrated a negative correlation between delay discounting and self-control. Furthermore, the voxel-based morphometry results showed a positive correlation between self-control and gray matter volume in the dorsolateral prefrontal cortex(dlPFC). Moreover, self-control was positively correlated with functional connectivity between the medial prefrontal cortex(mPFC) and the dorsolateral prefrontal cortex. More importantly, the association between self-control and delay discounting was shown to be partially mediated by the functional connectivity between the dlPFC and mPFC. These findings suggested that dlPFC-mPFC functional connectivity could be the neural basis underlying the association between self-control and delay discounting, which provided novel insights into how self-control affected delay discounting and offered new explanations from a neural perspective.
... The G*Power Windows 3.1.9.4 software determines the sample size required for a study assuming a multiple linear regression with a moderate effect size, an alpha of 0.05, and a power of 0.95. Considering 25 variablesincluding eight sociodemographic and 17 scale-related dimensions-the minimum sample size was calculated to be 495 nursing students [43]. It was upgraded to 510 to compensate for possible non-response. ...
Background
Digital competence is increasingly crucial for academic success and lifelong learning, especially in health education fields such as nursing. However, limited research examines the relationship between digital competence, academic motivation, and lifelong learning among nursing students.
Aim
To assess the relationship between digital competence, academic motivation, and lifelong learning among undergraduate nursing students and explore the mediating role of academic motivation in this relationship.
Methods
A descriptive comparative cross-sectional study was conducted, guided by the STROBE guidelines. Using systematic random sampling, 500 undergraduate nursing students were selected from Mansoura University, Egypt. Data were collected from July to August 2024 using three validated scales: the Students’ Digital Competence Scale, the Lifelong Learning Scale, and the Academic Motivation Scale. Descriptive statistics, Pearson correlation, and multiple regression analysis were used to analyze the data.
Results
The results showed a strong positive correlation between digital competence and academic motivation (r = 0.53, p < 0.001), as well as between digital competence and lifelong learning (r = 0.61, p < 0.001). Students with higher digital competence scores also had significantly higher academic motivation (4.21 ± 0.45) and lifelong learning tendencies (4.37 ± 0.48). Multiple regression analysis confirmed that digital competence significantly predicted both academic motivation (β = 0.38, p < 0.001) and lifelong learning (β = 0.44, p < 0.001).
Conclusion
Digital competence significantly enhances academic motivation and promotes lifelong learning among nursing students. The findings emphasize the need for nursing curricula to integrate digital competence training to improve educational outcomes and prepare students for future challenges in healthcare.
Clinical trial number
Not applicable.
... Following confirmation of normality using Shapiro-Wilk tests and testing for the presence of extreme outliers (beyond 3 * inter quartile range from the first and third quartile (Kassambara, 2023)) within each gravity condition, one-way repeated measures ANOVA was used to compare mean-normalized area between the gravity conditions with Post-hoc Bonferroni-corrected pairwise t-tests. Post hoc power analysis was performed for the post hoc comparisons using G * Power (Faul et al., 2007) Version 3.1.9.7. ...
Introduction
The effect of micro-gravity on the lumbar and in particular thoracic regions is poorly understood. The aim of this study was to evaluate spinal curvature across the lumbar and thoracic region, and extensor muscle activity during acute micro-gravity and hyper-gravity induced by parabolic flight. In addition, the association between our proxy measure of spinal curvature, and extensor muscle activity in micro-gravity was investigated.
Methods
During two ESA parabolic flight campaigns, 18 participants (8 female; 33 ± 11 years) were measured under earth-gravity, micro-gravity and hyper-gravity conditions. Spinal curvature was assessed using “spinal curvature backpacks” equipped with 15 laser distance sensors to measure the distance between the backpack and the subject’s back. Change in the area enclosed between the back and the backpack was used to measure change in spinal curvature. Muscle activity of the erector spinae (in 4 locations) and multifidus muscles (1 location) was assessed using surface electromyography transmitters. In addition, the spearmen correlation between muscle activity and spinal curvature in micro-gravity was investigated.
Results
Spinal flattening was observed during micro-gravity exposure, with changes most pronounced in the upper lumbar and lower thoracic spine. Mean-normalized area between the back and backpack decreased significantly in micro-gravity compared to earth-gravity (p = 0.001), but not during hyper-gravity (p = 1.00). The erector spinae responded heterogeneously to different gravity conditions across different assessment sites. Multifidus activity at L5 and erector spinae activity at L4 significantly decreased in micro-gravity compared to earth-gravity and hyper-gravity (p’s < = 0.01) and correlated with spinal flattening ( ρ = 0.69, p = 0.004; ρ = 0.67, p = 0.030).
Discussion/Conclusion
Parabolic flight-induced gravity changes caused upper lumbar and lower thoracic spine flattening in micro-g, while spinal curvature remained unchanged in hyper-g. In micro-g, Multifidus (L5) and Erector Spinae (L4) activity decreased, while in hyper-g, increased ES activity was observed at the upper middle transmitter. The maintained curvature and targeted muscle activation in hyper-g demonstrate protective mechanisms against increased axial loading, crucial for posture and injury prevention in both terrestrial and space environments. The spinal and muscular changes in micro-g indicate the need for targeted countermeasures during spaceflight, warranting comprehensive assessment in future research.
Ethics
French “EST-III” (Nr-ID-RCB: 2022-A01696-37).
... This study was not preregistered. (Boudewyn et al. 2017), an alpha level of 0.05, and a power of 0.95 (Faul et al. 2007). In addition to the a priori power analysis, we conducted sensitivity analyses to clarify the minimum effect sizes the current study was adequately powered to detect. ...
Emotion regulation is pivotal in human interactions and well‐being. Modulating one's emotional state is intricately linked with psychological, behavioral, and physiological responses. Extensive research has explored how individuals with varying attachment orientations manage emotions, predominantly through self‐report measures and behavioral assessments. However, the influence of attachment orientations on temporal electrophysiological dynamics during emotion regulation tasks remains underexplored. Here, 90 adults' EEG brain activity was recorded while they engaged in tasks of attending to, reappraising, or suppressing emotions elicited by unpleasant images. Their attachment orientations were assessed using the Experiences in Close Relationships‐12 (ECR‐12) questionnaire to explore the association between Late Positive Potential (LPP) and attachment anxiety and avoidance amidst the deployment of emotion regulation strategies. Using Linear Mixed‐Effects Model analysis, our results revealed a lower amplitude of the LPP during cognitive reappraisal, suggesting the efficacy of this strategy in diminishing emotional intensity. Moreover, higher attachment anxiety exhibited increased LPP amplitude during both Reappraisal and Suppression, as well as during the negative natural condition, indicating heightened emotional responses. This study provides novel insights into the relationship between attachment orientations and emotion regulation, as evidenced by EEG‐based measurements of the LPP. The findings indicate that individuals with higher attachment anxiety display distinct electrophysiological responses, particularly in emotional scenarios.
... This suggested descriptively stronger feature binding effects for the no effect condition. 5 A post-hoc power analysis was conducted (using G*Power; Faul et al., 2007) to assess the statistical power of the critical t-test comparison. Based on an observed effect size of d = 0.23, a significance level of α = 0.05, and a sample size of n = 61, the power was calculated to be 1-β = 0.42, indicating that there was a 42% chance of detecting an effect of this size. ...
Event files that bind features of stimuli, responses, and action effects figure prominently in contemporary views of action control. When a feature repeats, the previous event file including this feature is retrieved and can then influence current performance. It is still unclear, however, what terminates an event file. A tacit assumption is that registering the distal (e.g., visual, or auditory) sensory consequences of an action (i.e., the “action effect”) terminates the event file, thereby making it available for retrieval. Yet recently Frings et al. (2023) tested different distal action-effect conditions in standard stimulus–response (S-R) binding tasks and observed no modulation of S-R binding effects. It is conceivable though that the impact of distal action effects on feature binding hinges on the action mode of agents with intention-based mode (as compared to a stimulus-based mode) of action particularly emphasizing the possible impact of a meaningful distal action effect. Thus, we analyzed semantically meaningful distal action effects (participants switched simulated light bulbs on and off) in three experiments (Ntotal = 181). We found no clear impact of meaningful additional action effect presence or contingency on feature binding effects. The present study thus corroborates the notion that especially S-R binding tasks induce a stimulus-based action mode, in which proximal action effects are used for event file termination.
People with flatfoot encounter challenges while walking. This study investigated whether arch support insoles (ASIs) can reduce fatigue of the lower extremity muscles during a walking task in people with flatfoot compared with flat insoles (FIs). A total of fifteen female college students with flatfoot were recruited and considered eligible for the study, with each subject fulfilling the inclusion criteria of Chippaux-Smirak index > 45%. A paired sample t test was used to compare the difference in power spectral area (PSA) of the subdivided frequency band interval of 20–140 Hz between ASIs and FIs. A Wilcoxon test was used for assessing nonnormal data. Effect size (ES) was calculated as a measure of the practical relevance of the significance by using Cohen’s d. The results showed that the PSA of the RF muscles (P < 0.05; ES = 0.90) during uphill walking and of the RF (P < 0.05; ES = 1.21) and TA muscles (P < 0.05; ES = 0.61) during downhill walking was significantly smaller for ASIs than for FIs. The current evidence suggests that ASIs alleviates RF muscle fatigue during uphill walking and relieves RF and TA muscle fatigue during downhill walking.
Aging is the major risk factor for neurodegeneration and is associated with structural and functional alterations in white matter. Myelin is particularly vulnerable to aging, resulting in white matter-associated microglia activation. Here we used pharmacological and genetic approaches to investigate microglial functions related to aging-associated changes in myelinated axons of mice. Our results reveal that maladaptive microglia activation promotes the accumulation of harmful CD8⁺ T cells, leading to the degeneration of myelinated axons and subsequent impairment of brain function and behavior. We characterize glial heterogeneity and aging-related changes in white matter by single-cell and spatial transcriptomics and reveal elaborate glial–immune interactions. Mechanistically, we show that the CXCL10–CXCR3 axis is crucial for the recruitment and retention of CD8⁺ T cells in aged white matter, where they exert pathogenic effects. Our results indicate that myelin-related microglia dysfunction promotes adaptive immune reactions in aging and identify putative targets to mitigate their detrimental impact.
Digital voice platforms provide a new channel for employees to engage in online voice behavior. Voice visibility as one fundamental characteristic of digital voice platforms garners the attention of scholars in voice research. Existing research has only focused on the effect of voice visibility on employee voice intention. However, no research has empirically investigated the relationship between voice visibility and online voice behavior. By combining conservation of resources theory with proactive motivation model, our research reveals the processes linking voice visibility to online voice behavior. 257 full-time employees from a large Chinese enterprise are adopted to examine the theoretical model. According to our results, voice visibility had a positive indirect effect on online voice behavior through voice efficacy and a negative indirect effect on online voice behavior through positive mood. Further, employees’ ability for thinking out solutions to voice moderated the relationship between voice visibility and online voice behavior through voice efficacy. Our research contributes a holistic perspective of the bright side and dark side effects of voice visibility on online voice behavior, which provides valuable management implications for effectively managing employee online voice behavior while introducing digital voice platforms in the enterprises.
This study tested double standards in prejudice towards asylum seekers as a function of their religion. A sample of 334 Australian adults completed measures of explicit and implicit attitudes towards asylum seekers; however, in each measure the targets were experimentally attributed to either a Christian or Muslim religious affiliation. Analysis of these data revealed religious affiliation effects for the targets (Muslim asylum seekers were the target of more explicit and implicit prejudice than Christian asylum seekers, regardless of the participants’ religious affiliation), but no participant religious affiliation effects were identified. Finally, the analysis revealed an interaction effect: Christian participants (but not non-religious participants) demonstrated stronger explicit and implicit prejudice towards Muslim targets than Christian targets. Taken together, this paper presents evidence that religion is an important factor in shaping perceptions of asylum seekers, and in contributing to double standards in migration — even in a relatively secular country such as Australia.
The issue of environmental and ecosystem protection has emerged as one of the most significant challenges facing today’s socio-economic context to ensure ecological sustainability. Prosociality is encompassed within the broader spectrum of behaviours that prioritise environmental well-being. Similar to pro-social behaviour, pro-environmental behaviour may involve certain positive risks (related, for instance, to public exposure), of which individuals must be mindful when choosing to act sustainably. Opting for pro-environmental actions that carry a positive risk can be facilitated through subjective risk intelligence, a capacity that enables individuals to view risk as an opportunity to achieve a goal rather than as a threat. This cross-sectional study, involving 464 Italian adults, aimed to examine the moderating effect of subjective risk intelligence on the relationship between prosociality and the intention to engage in pro-environmental behaviour incorporating a risk component. The moderation model confirmed the hypotheses, indicating that prosociality precedes the intention to engage in sustainable risk taking, and that subjective risk intelligence can moderate and strengthen this relationship. Practical implications of the findings are discussed, along with recommendations for further research.
This study aimed to compare the effectiveness
of a submaximal isometric conditioning
activity (CA) on bench press throw (BPT)
performance between strong and weakmen.
The study sample included 52 men, aged 19–
25, randomly divided into the experimental
stronger (EX-S [bench press >135% body
mass];n= 14), weaker (EX-W;n= 13), control
stronger (CON-S;n= 13) weaker (CON-W;n=
12) groups. The BPT was performed in three
repetitions per set with a 30% one-repetition
max (1-RM) load and measured at baseline
and at the 1st, 4th, 7th, and 10th minutes
after the protocol which consisted of four
three-second sets of isometric bench press.
The load was at 70%-1 RM, and the rest
interval was 60 s. Set mean velocity (SMV) and
set mean power (SMP) were measured. The
three-way (intervention× strength level×
time) repeatedmeasures ANCOVA (covariate:
the baselinemeasures) was performed and
showed a statistically significant betweensubjects
interaction of intervention×strength
level for SMV (F= 6.27;ηp
2= 0.11;p= 0.02)
and for SMP (F= 9.42; ηp
2= 0.16;p< 0.01).
Bonferroni post hoc tests revealed that EX-S
achieved better results in SMV and SMP than
CON-S (p< 0.01), CON-W (p< 0.01) and EX-W
(p< 0.01). EX-Wachieved better results in SMV
than CON-W (p= 0.02). The within-subject
analysis revealed a statistically significant
interaction between intervention×time
for SMV (F= 4.24, ηp2= 0.08, p< 0.01) and
SMP (F= 4.51, ηp2= 0.09, p< 0.01). The EXgroups
achieved better results in SMV at the
7th and 10th minutes than the CON-groups
(p< 0.01) as well in SMP at 4th, 7th and
10th minutes than the CON-groups (p< 0.01)
The introduced CA effectively improved
barbell kinematics during the BPT. Strong
participants exhibited greater performance
enhancements than weak ones.
Our perceptual system is highly sensitive to statistical regularities in our environment. In particular, we respond faster to targets that appear in frequently attended locations—a phenomenon known as target-location probability learning (LPL). Is attention proactively aligned with the high-probability target location, or reactively allocated to that location when the learning context is detected? The studies that addressed this question tested whether a spatial attentional bias learned in one task transfers to another. However, they yielded conflicting findings and were open to alternative accounts. We reexamined whether LPL-guided attention is proactively allocated to the high-probability target location while addressing these previous studies’ potential caveats, in two experiments. During learning, the search target appeared more often at one location than elsewhere, and during extinction, all search target locations were equiprobable. In both learning and extinction, letter-probe trials were interspersed among the search trials. We found that LPL acquired during search transferred to the letter-probe task during both learning and extinction. Importantly, during extinction, participants continued to prioritize the previously high-probability location on both search and letter-probe trials, even when they were informed after the learning phase that the bias would be discontinued and were asked to start their search at the location indicated by an arrow precue. We conclude that LPL guides attention proactively and inflexibly.
People vary in their capacity for phenomenological control, which enables them to align their perceptual experiences with their intentions and goals. The Phenomenological Control Scale was developed to measure this trait, and we developed and validated a Japanese version of this scale (PCS-J) based on preregistered online surveys (n = 261; retest n = 152). The PCS-J demonstrated sufficient internal consistency and test–retest reliability. Given the known association between hypnotic susceptibility and positive schizotypy, the convergent validity of the PCS-J was supported by a weak positive correlation with positive schizotypy. The discriminant validity of the PCS-J was demonstrated by the absence of a correlation with negative schizotypy. The PCS-J would be useful for research on perception, phenomenological control, and their individual differences in Japanese samples, as well as for intercultural studies.
Background/Objectives: The outcomes of diabetes therapy depend largely on how well patients can implement medical advice in their lives. The main aim of the DiabPeerS study was to evaluate a peer support instant messaging service (IMS) approach to diabetes self-management education and support (DSMES) for people with type 2 diabetes mellitus (T2DM). Methods: Participants with T2DM took part in a randomized controlled trial. Both the intervention group (IG) and the control group (CG) received standard therapy, but the IG additionally participated in the peer support IMS intervention. The duration of the intervention was 7 months, succeeded by a follow-up 7 months later. Eleven biochemical, six behavioral, and six psychosocial parameters were measured at four times. Results: The targeted sample size could not be reached, and 68 participants took part. The following results have been found for the main hypotheses: No influence on HbA1c was detected (IG: −0.27, CG: +0.06, p > 0.05). Diabetes self-management behaviors were unaffected (IGdiet: +0.02, CGdiet: +0.46, p > 0.05; IGexercise: −0.72, CGexercise: +0.44, p > 0.05; IGbloodsugar: −0.21, CGbloodsugar: +0.65, p > 0.05; IGfootcare: +0.37, CGfootcare: +1.13, p > 0.05). Quality of life increased during the intervention in both the IG (KSK: +8.92, PSK: +7.41, p < 0.001) and the CG (KSK: +8.73, PSK: +7.48, p < 0.001). Medication adherence increased in the IG (+3.31, p < 0.01), although these participants were still classified as non-adherent. Conclusions: A peer support IMS intervention is a promising approach, but we recommend combining the online setting with an initial face-to-face situation.
Background
Early treatment is critical for improving eating disorder prognosis. Single-session interventions (SSIs) can provide short-term support to people on waitlists for eating disorder treatment; however, it is not always possible to access SSIs. We co-designed and developed a rule-based chatbot called ED ESSI (Eating Disorder Electronic Single-Session Intervention), which delivered an SSI and demonstrated its acceptability and feasibility. However, the effectiveness of ED ESSI is yet to be investigated.
Objective
This study aimed to investigate the effectiveness of an SSI delivered by ED ESSI. We examined the chatbot’s impact on eating disorder pathology, psychosocial impairment, depression, anxiety, stress, and motivation for change or treatment in individuals aged ≥16 years on waitlists for treatment for all types of eating disorders.
Methods
This multicenter 2-armed randomized controlled trial included 60 people on waitlists for eating disorder treatment in the chatbot intervention group (n=30, 50%) or the control group (n=30, 50%). The ED ESSI chatbot guided participants through a 30-minute SSI of assessment and psychoeducation, while the control group received web-based information on the same core topics covered in the SSI. There were 4 time points: before intervention or baseline (time point 1 [T1]), after intervention within 72 hours of baseline (T2), 1 month after baseline (T3), and 3 months after baseline (T4). Eating disorder pathology (primary outcome) and psychosocial impairment, depression, anxiety, and stress (secondary outcomes) were measured at T1, T3, and T4, and motivation for change or treatment (secondary outcome) was measured at all 4 time points. Furthermore, the System Usability Scale was implemented at T2 for the chatbot intervention group only.
Results
ED ESSI induced significantly greater reductions compared to the control group in the primary outcome of eating disorder pathology (P=.003) and secondary outcomes of psychosocial impairment (P=.008), depression (P=.002), and anxiety (P=.040) over the 1- and 3-month time points, with small to moderate effects (Cohen d=0.15-0.53). Chatbot use also induced an increase in participants’ confidence in their ability to change (secondary outcome) immediately after use (T2), with a moderate effect (P<.001; Cohen d=0.74). The chatbot was rated as “excellent” in terms of usability. A significantly higher proportion of participants in the chatbot group (28/30, 93%) entered treatment by 3 months upon the opportunity being offered to them, compared to the control group, with a moderate effect (21/30, 70%; P=.042; ϕ=0.30).
Conclusions
ED ESSI promptly induced improvements in eating disorder pathology, psychosocial impairment, depression, and anxiety, which were detectable at 1 month and maintained to 3 months. ED ESSI potentially represents an effective, accessible, and scalable form of early intervention for people aged ≥16 years waiting for eating disorder treatment. Further research is needed to determine the longer-term effectiveness of ED ESSI.
Trial Registration
Australian New Zealand Clinical Trial Registry ACTRN12623000680662; https://tinyurl.com/2h9v7hh7
Large Language Models (LLMs) demonstrate expertise across diverse domains, yet their capacity for emotional intelligence remains uncertain. This research examined whether LLMs can solve and generate performance-based emotional intelligence tests. Results showed that ChatGPT-4, ChatGPT-o1, Gemini 1.5 flash, Copilot 365, Claude 3.5 Haiku, and DeepSeek V3 outperformed humans on five standard emotional intelligence tests, achieving an average accuracy of 81%, compared to the 56% human average reported in the original validation studies. In a second step, ChatGPT-4 generated new test items for each emotional intelligence test. These new versions and the original tests were administered to human participants across five studies (total N = 467). Overall, original and ChatGPT-generated tests demonstrated statistically equivalent test difficulty. Perceived item clarity and realism, item content diversity, internal consistency, correlations with a vocabulary test, and correlations with an external ability emotional intelligence test were not statistically equivalent between original and ChatGPT-generated tests. However, all differences were smaller than Cohen’s d ± 0.25, and none of the 95% confidence interval boundaries exceeded a medium effect size (d ± 0.50). Additionally, original and ChatGPT-generated tests were strongly correlated (r = 0.46). These findings suggest that LLMs can generate responses that are consistent with accurate knowledge about human emotions and their regulation.
This study examined the mediating role of prenatal cortisol on the relationship between gestational weight or adiposity and postpartum depression (PPD), while considering the moderating roles of breastfeeding (BF) or socioeconomic resources. We hypothesized that women with a higher pre-pregnancy body mass index (PPBMI) or a larger abdominal circumference would have elevated diurnal cortisol levels in late pregnancy, which would predict more PPD symptoms. Additionally, we hypothesized that BF and access to more socioeconomic resources would buffer the positive relationship between prenatal diurnal cortisol and PPD symptoms. We used longitudinal data from the Infant Development and Health Outcomes in Mothers Study, in which women self-reported PPBMI, BF frequency at 6 months, familial education, occupation, and income and completed the Edinburgh Postnatal Depression Scale. The abdominal circumference, cortisol area under the curve with respect to ground (AUCG), and cortisol awakening response (CAR) were measured. Higher breastfeeding frequency and greater socioeconomic resources were associated with fewer PPD symptoms. There were main and interactive associations of AUCG with BF frequency with PPD. Notably, higher cortisol levels were linked to more PPD symptoms among women with less frequent BF. Thus, BF may mitigate the relationship between prenatal stress and PPD, highlighting the importance of BF support in PPD prevention.
Objective
To compare the safety and efficacy of nonopioid versus opioid pain management following adenotonsillectomy (AT) among pediatric patients.
Study Design
An open‐label randomized controlled trial.
Setting
Tertiary care children's hospital.
Methods
Patients aged 3 to 17 years undergoing AT were eligible. Participants were randomly assigned to receive either acetaminophen and ibuprofen (nonopioid group) or acetaminophen, ibuprofen, and oxycodone (opioid group). Pain scores and prevalence of emergency department (ED) visits, hospital readmission, and posttonsillectomy hemorrhage (PTH) were compared between groups.
Results
From January 2019 to March 2020, 267 patients were enrolled and randomly assigned; 144 completed a postoperative pain diary. Of the 144, 69 (48%) patients received an opioid prescription, and 75 (52%) did not. Mean pain scores before (opioid: 5.78, 95% CI: 5.29‐6.27 vs nonopioid: 5.66, 95% CI: 5.20‐6.12) and after (opioid: 2.33, 95% CI: 1.89‐2.78 vs nonopioid: 2.24, 95% CI: 1.82‐2.66) analgesics were not significantly different between opioid and nonopioid groups. Although 7/75 (9%) from the nonopioid group crossed over and requested opioids, only 43/69 (62%) randomly assigned to receive opioid prescription consumed opioids. The rate of opioid consumption increased with increasing age: 18/71 (25%) patients aged 3 to 7 years, 22/57 (39%) 8 to 12 years, and 10/16 (63%) 13 to 17 years, P = .015. Differences in ED visits, hospital readmissions, and PTH between opioid and nonopioid groups were not significant.
Conclusion
Many children do not require opioid analgesics following AT, particularly children less than 8 years of age. Postoperative pain scores and outcomes were similar in opioid versus nonopioid groups. Opioid prescriptions should be limited or avoided altogether after pediatric AT.
Trial Registration
Title: Nonopioids for analgesia after adenotonsillectomy in children; ID: NCT03618823, Clinicaltrials.gov .
The inferior olive provides powerful inputs to the cerebellum hypothesized to support cerebellar learning and error detection. Given cerebellar involvement in verbal working memory and the close interplay with the inferior olive, the inferior olive is likely also involved in verbal working memory. In order to elucidate the inferior olive’s role in verbal working memory, we utilized an MRI-based Sternberg verbal working memory task which involved learning novel vs repeated sequences. As hypothesized, inferior olive activation was stronger during encoding and retrieval compared to maintenance, especially for novel compared to repeated sequences, indicative of diminished inferior olive activity with stimulus repetition. Results also revealed differential inferior olive activation during retrieval, with increased activation on matching probes for novel and on non-matching probes for repeated sequences. This underlines the crucial role of the inferior olive in novel information encoding and error feedback, and that conditions triggering strong inferior olive responses can change as a function of novelty.
Purpose
Collaborative robots are key enabling technologies within the framework of Industry 5.0. Inevitable robot errors may lead to negative attitudes toward robots and cause deviations from task goals. However, collaborative robots with high ability and performance can gain informal status in the human–robot collaborative team. Since status is a significant factor influencing interactions, it is crucial to explore how variations in the status of erring robots influence team members’ attitudes.
Design/methodology/approach
This study employs affective events theory (AET) with the experimental vignette methodology (EVM), engaging a total of 388 participants (N student = 216, N non-student = 172) in the experiment.
Findings
First, erring robot status positively influences error tolerance (lower punishment tendency and higher future collaboration tendency). Second, negative attitudes toward robots mediate the relationship between erring robot status and error tolerance. Third, task goal deviation moderates the relationship between erring robot status and negative attitudes toward robots. Fourth, the protective effect of erring robot status differs by prior robot experience.
Research limitations/implications
This study enriches theoretical research on robot status and expands the application of AET to the industrial human–robot interaction (iHRI) domain. Additionally, it offers critical guidance to enhance the effectiveness and error tolerance of human–robot collaboration in practical contexts.
Originality/value
These findings offer a mechanistic explanation for employee attitudes and reactions following robot errors and propose practical measures to mitigate the negative impacts of robot errors on human–robot collaboration.
Background
Healthcare workers risk stress, burnout and fatigue from time pressures, insufficient workload control and ineffective support. Unaddressed, these issues can lead to the attrition of the workforce. Retention of speech and language therapists (SLTs) is an ongoing concern, but little is known about the relationship between SLT wellbeing and their workplaces.
Aims
The aims of the present study were therefore to: (1) Explore the levels of job satisfaction and general wellbeing of SLTs working clinically. (2) Describe the occupational environments of SLTs working clinically. (3) Investigate the relationship between job design and wellbeing outcomes. (4) Establish whether job satisfaction and general psychosocial wellbeing levels were consistent over time. (5) Explore whether personal changes/changes to work made by participants over a 3‐month period impacted their job satisfaction and/or general wellbeing.
Method
All SLTs practising clinically in the UK were eligible to participate. Participants were recruited via selective sampling (advertising in Royal College of Speech and Language Therapists publications) and snowballing (using social media). A longitudinal study, using an online survey at two time points (P1 and P2), approximately 3 months apart, measured work stressors of SLTs, job satisfaction and general wellbeing, using the Speech‐language Pathologist Stress Inventory, Generic Job Satisfaction Survey and General Health Questionnaire‐28 (GHQ‐28). Relationships between job types, wellbeing and subsequent changes were analysed using multivariate analysis of variance.
Results
Of the 632 participants at P1, 98% were women, 95% were white and the average age was 38.7 years. Ninety‐one percent were working in England, and 85% were organisationally employed. At P1, 53.3% of participants who completed the GHQ‐28 reported anxiety, depression, somatic symptoms of stress and social dysfunction. Those in high‐demand, low‐control, and low‐support (‘Isostrain’) jobs (n = 105) had the lowest job satisfaction (p < 0.001, partial η² = 0.383) and poorest wellbeing (p < 0.001, partial η² = 0.210). Wellbeing scores were stable from P1 to P2 (n = 295). Making ‘positive personal changes’ had no impact on job satisfaction, although changing jobs did (t (16) = −3.225, p < 0.01).
Conclusion and Implications
Over half the participants in this study reported psychosocial ill health associated with the demands, control and support in their jobs. Psychosocial risks are embedded within work organisation, suggesting that employers and managers of SLTs consider the overall psychosocial design of jobs, with a view to improving retention. The use of a step‐by‐step risk assessment and intervention approach is recommended. Further research may corroborate the results and ensure better psychosocial risk management.
WHAT THIS PAPER ADDS
What is already known on the subject The extant literature on speech and language therapist (SLT) wellbeing reports various levels of job satisfaction and burnout. While this has been associated with several job factors, there is yet to be a comprehensive analysis of how job design impacts wellbeing. This study aimed to investigate this and provide a starting point for improving wellbeing through the consideration of job design.
What this paper adds to the existing knowledge Results of this study indicate that, before the onset of COVID‐19, just over one in two SLTs working clinically in the UK were at risk of being psychologically vulnerable to anxiety, depression, experiencing somatic symptoms of stress, and social dysfunction. Those who experienced high demand, low control and low support at work were more likely to experience psychological distress and low job satisfaction.
What are the potential or actual clinical implications of this work? This study has revealed a combination of potential workplace features that are associated with SLT wellbeing, suggesting employers consider the overall psychosocial design of SLT jobs, instead of viewing occupational factors individually. The JDCS model proved a suitable framework for describing SLT jobs; it could be used in the future to gain detailed knowledge about different elements that constitute particular SLT jobs. It is essential that the voices of SLTs who are primarily clinical are heard and used to inform this appraisal; collaboration between managers and clinicians is necessary for success in this endeavour.
Quantitative ultrasound (QUS) methods were applied to detect changes in placental microstructure in the reduced uterine perfusion pressure (RUPP) model of preeclampsia in rats. Preeclampsia is a life-threatening pregnancy disorder associated with abnormal placental development that is inadequately treated and managed. Nine timed-pregnant Sprague–Dawley rats were used in this study. On gestational day 14 (GD 14), five rats received RUPP surgery to induce preeclampsia. US radiofrequency data were acquired for QUS analysis on GD18. On GD19, animals were sacrificed and dissected to acquire placental tissue samples. The cell nuclear diameter in each anatomical layer of the placenta was measured to compare with regional effective scatterer diameter (ESD) values. ESD measurements obtained using in vivo QUS imaging correlated well (R2 = 0.58, p = 3.8e–6) with cell nucleus diameter measurements from microscopy images. RUPP placentas had significantly smaller junctional zones compared to control placentas (p = 0.013). The average ESD in RUPP placentas was 1.0 µm smaller compared to control placentas (p = 0.040). This decrease in ESD in RUPP placentas is consistent with the decreased size of the junctional zone, which, in comparison to the labyrinth zone and chorionic plate, has larger cell nuclei (p = 3.3e–21 and p = 9.5e–27, respectively) and larger ESD (p = 5.6e–4 and p = 4.5e–8, respectively). These results demonstrate the potential of QUS as a non-invasive tool for detecting critical changes in placental microstructure, improving maternal and fetal outcomes by enabling earlier diagnosis and more timely therapeutic interventions in preeclampsia.
Background and Objectives
Synthetic cannabinoids (SCs) cause adverse physiological and psychological effects. There is little information about current SC use in the general United States (U.S.) population (i.e., nonclinical samples). The current study investigated the frequency and characteristics of SC use in a nonclinical U.S. sample and investigated potential motivations for use via the Acquired Preparedness Model (APM) of addiction (i.e., impulsivity, positive outcome expectancies (POEs)).
Methods
In this cross‐sectional study, adults who endorsed past‐year SC use ( n = 232) completed an online survey. Descriptive statistics were performed to examine characteristics of the sample. Mediation analyses were performed to examine the relationship between facets of impulsivity (Sensation Seeking, Positive Urgency, Negative Urgency, Total Impulsivity) and frequency of SC use with POEs as a mediating variable.
Results
The mean age of the sample was 41 years old (SD = 13.36) and 62.9% identified as male. The mean frequency of past‐year SC use was 83 days (SD = 93.1). Greater Sensation Seeking was the only facet of impulsivity significantly associated with a higher frequency of SC use. POEs were not significantly associated with the frequency of SC use and did not mediate the relationship between impulsivity and frequency of SC use.
Discussion and Conclusions
U.S. adults with past‐year SC use reported using SC to manage stress, anxiety, and pain. Greater Sensation Seeking was associated with greater frequency of past‐year SC use.
Scientific Significance
The present study is novel for its examination of SC in a nonclinical, adult sample and the use of the APM to examine motivations for SC use.
Approximately 13% of children and adolescents struggle with a mental health disorder. Adolescents must often disclose information about their mental health (MH) distress to their parents to receive social support or professional care, but parents’ anticipated responses to these disclosures have also been identified as a barrier to disclosure. This study explored factors that influence parents’ negative emotional response to MH disclosure. Parents ( N = 322) of adolescents were recruited to take an online survey that explored their depression literacy, stigmatizing beliefs about depression, and perceptions of a positive parent/child relationship. Results suggest that parents’ depression literacy is indirectly related to parents’ negative emotional response to disclosure through influencing parents’ depression stigma, and this indirect relationship was higher among parents who perceived a more warm parent/child relationship. The proposed model shows that parents’ depression literacy was first negatively associated with parents’ stigmatizing beliefs and that this was indirectly related to parents’ negative emotional response to disclosure. Physicians, MH professionals, and parents can use these findings to better understand factors that influence parents’ negative responses to MH disclosures.
Green Human Resource Management (GHRM) has emerged as a key strategic approach for aligning organizational practices with environmental sustainability goals. While prior studies have primarily examined the direct influence of GHRM on sustainable development (SD), limited attention has been given to the mediating role of green creativity (GC), particularly in emerging economies like Saudi Arabia. This study aims to investigate the relationship between GHRM practices and SD, focusing on the mediating effect of GC. Data were collected from 250 employees in diverse private-sector organizations and analyzed using Structural Equation Modeling via Mplus. The findings reveal that GHRM practices positively influence both GC and SD. More importantly, GC significantly mediates the relationship between GHRM and SD. This study highlights the critical role of fostering employee creativity through GHRM practices, such as training, performance management, and employee involvement, to achieve sustainability outcomes. Theoretically, the study extends the application of the theory of planned behavior by positioning GC as a key behavioral mechanism linking HR practices to sustainability outcomes. It also offers practical insights for organizations seeking to enhance their sustainability performance by integrating environmentally focused HRM strategies and cultivating a culture of innovation, in line with the sustainability objectives outlined in Vision 2030. Clinical trial number: “Not applicable.”
The aim of this study is to develop a self-help intervention based on acceptance and commitment therapy; to examine the effect of the self-help intervention on the depression, anxiety, stress, and psychological flexibility levels of university students; and to examine their experiences. In this study, we used a mixed explanatory basic design. In the quantitative part of the study, we used a 2 (experimental and control group) x 3 (pretest-posttest and follow-up test) experimental design. The participants consisted of 47 university students. The experimental group received a 7-week guided self-help intervention based on acceptance and commitment therapy, whereas the control group received no intervention. Participants completed a Personal Information Form, Depression Anxiety and Stress Scales-42, and Acceptance and Action Questionnaire-II. We conducted semi structured interviews to examine the experiences of participants in the experimental group. We analyzed the data using an independent samples t test, repeated measures ANOVA, one-way ANCOVA, and content analysis. According to the study’s results, we found guided acceptance and commitment therapy-based self-help intervention decreased students’ depression, anxiety, and stress levels and increased their psychological flexibility levels. The qualitative findings of the study revealed four general concepts: “difficulties experienced while implementing the intervention program,” “prominent parts of the intervention program,” “benefits of the intervention program,” and “components of the intervention program.” We discussed self-help experiences in light of the relevant academic literature.
Purpose
To evaluate the morphological and radiographic features of hip instability in hip dysplasia.
Methods
Eighty-four patients who had ultrasonography for the assessment of hip instability and computed tomography scan for the assessment of bony morphology were included. The lateral center–edge angle (LCEA), vertical-center-anterior angle (VCA), acetabular roof obliquity (ARO), acetabular head index (AHI), and acetabular version angle (AVA) were calculated as pelvic parameters. Neck shaft angle (NSA), α-angle, femoral offset (FO), and femoral anteversion (FA) were obtained as femoral parameters. The combined anteversion angle (CAA) was defined as the sum of AVA and FA.
Results
Pelvic morphology analysis revealed that LCEA, VCA, and AHI were significantly lower, whereas ARO and AVA were significantly higher in the hip instability group. Furthermore, NSA, FO, and CAA were significantly higher in the hip instability group. The cutoff values for LCEA, VCA, and AHI were 17.6°, 34.7°, and 73.6%, respectively. Multivariate analysis revealed that LCEA and VCA were significantly associated with hip instability, with odds ratios of 1.57 and 1.56, respectively. Hip instabilities were associated with lateral/anterior/superior coverage deficiencies in the pelvis and with the NSA, FO, and CAA in the femur. Furthermore, a correlation between pelvic and femoral morphological parameters suggests that hip instability evaluations should include the evaluation of the anterior/lateral coverage of the acetabulum and femoral parameters.
Conclusion
Our findings suggest that the LCEA, VCA, and AHI could serve as diagnostic markers for hip instability.
Concerns. Prevailing analytic practices do not reflect the inherent relation between estimation and hypothesis testing (test-based estimation; see, e.g., Cox and Hinkley 1974). Rather unrelated types of analysis are being used for the three types of comparative parameter -- rate difference, rate ratio and odds ratio. Remedies are suggested in both areas.
Summary. Chi Square functions for the evaluation of three comparative parameters for two rates, employing the same principles for all three are proposed. Limited evaluation suggests good performance in extreme small-sample situations
Power analysis after study completion has been suggested to interpret study results. We present 3 methods of estimating power and discuss their limitations. We use simulation studies to show that estimated power can be biased, extremely variable, and severely bounded. We endorse the practice of computing power to detect a biologically meaningful difference as a tool for study planning but suggest that calculation of confidence intervals on the parameter of interest is the appropriate way to gauge the strength and biological meaning of study results.
Recently developed methods for power analysis expand the options available for study design. We demonstrate how easily the methods can be applied by (1) reviewing their formulation and (2) describing their application in the preparation of a particular grant proposal. The focus is a complex but ubiquitous setting: repeated measures in a longitudinal study. Describing the development of the research proposal allows demonstrating the steps needed to conduct an effective power analysis. Discussion of the example also highlights issues that typically must be considered in designing a study. First, we discuss the motivation for using detailed power calculations, focusing on multivariate methods in particular. Second, we survey available methods for the general linear multivariate model (GLMM) with Gaussian errors and recommend those based on F approximations. The treatment includes coverage of the multivariate and univariate approaches to repeated measures, MANOVA, ANOVA, multivariate regression, and univariate regression. Third, we describe the design of the power analysis for the example, a longitudinal study of a child's intellectual performance as a function of mother's estimated verbal intelligence. Fourth, we present the results of the power calculations. Fifth, we evaluate the tradeoffs in using reduced designs and tests to simplify power calculations. Finally, we discuss the benefits and costs of power analysis in the practice of statistics. We make three recommendations: Align the design and hypothesis of the power analysis with the planned data analysis, as best as practical.Embed any power analysis in a defensible sensitivity analysis.Have the extent of the power analysis reflect the ethical, scientific, and monetary costs. We conclude that power analysis catalyzes the interaction of statisticians and subject matter specialists. Using the recent advances for power analysis in linear models can further invigorate the interaction.
No scientifi c worker has a fi xed level of signifi cance at which from year to year, and in all circumstances, he rejects hypotheses; he rather gives his mind to each particular case in the light of his evidence and his ideas. (Ronald A. Fisher, 1956, p. 42) It is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail. One of us once had a student who ran an experiment for his thesis. Let us call him Pogo. Pogo had an experimental group and a control group and found that the means of both groups were exactly the same. He believed it would be unscientifi c to simply state this result; he was anxious to do a signifi cance test. Th e result of the test was that the two means did not diff er signifi cantly, which Pogo reported in his thesis. In 1962, Jacob Cohen reported that the experiments published in a major psychology journal had, on average, only a 50 : 50 chance of detecting a medium-sized eff ect if there was one. Th at is, the statistical power was as low as 50%. Th is result was widely cited, but did it change researchers' practice? Sedlmeier and Gigerenzer (1989) checked the studies in the same journal, 24 years later, a time period that should allow for change. Yet only 2 out of 64 researchers mentioned power, and it was never estimated. Unnoticed, the average power had decreased (researchers now used alpha adjustment, which shrinks power). Th us, if there had been an eff ect of a medium size, the researchers would have had a better chance of fi nding it by throwing a coin rather than conducting their experiments. When we checked the years 2000 to 2002, with some 220 empirical articles, we fi nally found 9 researchers who computed the power of their tests. Forty years after Cohen, there is a fi rst sign of change. Editors of major journals such as A. W. Melton (1962) made null hypothesis testing a neces-sary condition for the acceptance of papers and made small p-values the hallmark of excellent experimentation. Th e Skinnerians found themselves forced to start a new journal, the Journal of the Experimental Analysis of Behavior, to publish their kind of experiments (Skinner, 1984, p. 138). Similarly, one reason for launching the Journal of Mathematical Psychology was to escape the edi-tors' pressure to routinely perform null hypothesis testing. One of its founders, R. D. Luce (1988), called this practice a "wrongheaded view about what constituted scientifi c progress" and "mind-less hypothesis testing in lieu of doing good research: measuring eff ects, constructing substantive theories of some depth, and developing probability models and statistical procedures suited to these theories" (p. 582).
In contrast to prospective uses of power analysis, retrospective power analysis provides an estimate of the statistical power of a hypothesis test after an investigation has been conducted. The purpose of this research was to empirically investigate the bias and sampling errors of three point estimators of retrospective power and the confidence band coverage of an interval estimate approach. Monte Carlo methods were used to investigate a broad range of research designs and population effect sizes that may be encountered in field research. The results suggest that none of the retrospective power estimation techniques were effective across all of the conditions examined. For point estimates, the "unbiased" and "median unbiased" estimators showed improved performance relative to the plug-in estimator, but these procedures were not completely free from bias except under large sample sizes and large effect sizes (as the statistical power approaches unity). Further the RMSE of these estimates suggests large amounts of sampling error for all three of the point estimators. The interval estimates showed good confidence band coverage under most conditions examined, but the width of the bands suggests that they are relatively uninformative except for large sample and large effect size conditions. tatistical power analysis is useful from both prospective and retrospective viewpoints. Prospectively, power analysis is used in the planning of inquiry, typically to provide an estimate of the sample size required to obtain a desired level of statistical power under an assumed population effect size, experimental design and nominal alpha level. In contrast, retrospective uses of power analysis involve a consideration of statistical power after inquiry has been completed. This important application of power analysis is somewhat more complicated than the prospective uses.
Although ecologists have become increasingly sophisticated in applying tests for statistical significance, few are aware of the power of these tests. Statistical power is the probability of getting a statistically significant result given that there is a biologically real effect in the population being studied. If a particular test is not statistically significant, is it because there is no effect or because the study design makes it unlikely that a biologically real effect would be detected? Power analysis can distinguish between these alternatives, and is therefore a critical component of designing experiments and testing results (Toft and Shea 1983
In this paper, we examine comparative analysis of rates with a view to each of the usual comparative parameters-rate difference (RD), rate ratio (RR) and odds ratio (OR)-and with particular reference to first principles. For RD and RR we show the prevailing statistical practices to be rather poor. We stress the need for restricted estimation of variance in the chi-square function underlying interval estimation (and also point estimation and hypothesis testing). For RR analysis we propose a chi-square formulation analogous to that for RD and, thus, one which obviates the present practice of log transformation and its associated use of Taylor series approximation of the variance. As for OR analysis, we emphasize that the chi-square function, introduced by Cornfield for unstratified data, and extended by Gart to the case of stratified analysis, is based on the efficient score and thus embodies its optimality properties. We provide simulation results to evince the better performance of the proposed (parameter-constrained) procedures over the traditional ones.
GPOWER is a completely interactive, menu-driven program for IBM-compatible and Apple Macintosh personal computers. It performs
high-precision statistical power analyses for the most common statistical tests in behavioral research, that is,t tests,F tests, andχ
2 tests. GPOWER computes (1) power values for given sample sizes, effect sizes andα levels (post hoc power analyses); (2) sample sizes for given effect sizes,α levels, and power values (a priori power analyses); and (3)α andβ values for given sample sizes, effect sizes, andβ/α ratios (compromise power analyses). The program may be used to display graphically the relation between any two of the relevant
variables, and it offers the opportunity to compute the effect size measures from basic parameters defining the alternative
hypothesis. This article delineates reasons for the development of GPOWER and describes the program’s capabilities and handling.
A general rationale and specific procedures for examining the statistical power characteristics of psychology-of-aging empirical studies are provided. First, 4 basic ingredients of statistical hypothesis testing are reviewed. Then, 2 measures of effect size are introduced (standardized mean differences and the proportion of variation accounted for by the effect of interest), and methods are given for estimating these measures from already-completed studies. Power and sample size formulas, examples, and discussion are provided for common comparison-of-means designs, including independent samples I-factor and factorial analysis of variance (ANOVA) designs, analysis of covariance designs, repeated measures (correlated samples) ANOVA designs, and split-plot (combined between- and within-subjects) ANOVA designs. Because of past conceptual differences, special attention is given to the power associated with statistical interactions, and cautions about applying the various procedures are indicated. Illustrative power estimations also are applied to a published study from the literature. It is argued that psychology-of-aging researchers will be both better informed consumers of what they read and more ''empowered'' with respect to what they research by understanding the important roles played by power and sample size in statistical hypothesis testing.
It is well known that statistical power calculations can be valuable in planning an experiment. There is also a large literature advocating that power calculations be made whenever one performs a statistical test of a hypothesis and one obtains a statistically nonsignificant result. Advocates of such post-experiment power calculations claim the calculations should be used to aid in the interpretation of the experimental results. This approach, which appears in various forms, is fundamentally flawed. We document that the problem is extensive and present arguments to demonstrate the flaw in the logic.
Several MS/PC—DOS programs are now available to help with statistical power analysis and sample-size choice. This article compares these programs with respect to the statistical methods they cover, their user interface, their ease of use, their graphics capabilities, and their computational accuracy.
Gail and Gart (1973) present tables showing the required sample size, n, for the Fisher-Irwin exact conditional test for 2 x 2 tables to achieve a power of at least 0.50, 0.80 and 0.90 against one-sided alternatives at the nominal .05 and .01 significance levels. However, calculations for n > 35 were based on the Arc sine approximation, which was found to underestimate the actual required sample size by as much as 34%. The purpose of this note is to revise the Gail-Gart tables, giving exact sample sizes in all cases. The problem of nominal versus actual significance levels when the underlying probability of response is low is also briefly discussed.
This chapter presents analytic methods for matched studies with multiple risk factors of interest. We consider matched sample designs of two types, prospective (cohort or randomized) and retrospective (case-control) studies. We discuss direct and indirect parametric modeling of matched sample data and then focus on conditional logistic regression in matched case-control studies. Next, we describe the general case for matched samples including polytomous outcomes. An illustration of matched sample case-control analysis is presented. A problem solving section appears at the end of the chapter.
Exact attained significance level and sample size methods are developed for use with the unconditional Z statistic in the 2 × 2 contingency table from two independent binomial samples of equal size. The resulting sample sizes tend to be smaller than those required by tables for the exact conditional test. Moreover, the proposed exact Z test is uniformly more powerful than the exact conditional test for α = 0.01, 0.025 and 0.05, and common sample size n = 10(1)150, the only combinations of α and n considered herein. These methods therefore provide an exact approach to unconditional tests which, in contrast to the conditional ones, are easily explained to and understood by non-statisticians. This paper is the first for which sample size calculations are made based on exact unconditional tests for any situation in which no ancillary statistic exists for the nuisance parameter.
This paper considers twenty-two alternative tests which have been suggested for testing the null hypothesis p1 = p2 in a 2 × 2 comparative trial. Attention is confined to the case where the null hypothesis is true. The dependence of the tests on m and n, the sample sizes, and on p, the common binomial parameter, is investigated. Amongst other results it is shown that the exact test of Fisher, and the corresponding Yates correction to Pearson's X2 test, give tests which are both extremely conservative and inappropriate. The uncorrected X2 test performs well. On both empirical and theoretical grounds, the preferred test is the scaled version (N - 1)/N X2.
Clinical predictions made by mental health practitioners are compared with those using statistical approaches. Sixty-seven studies were identified from a comprehensive search of 56 years of research; 92 effect sizes were derived from these studies. The overall effect of clinical versus statistical prediction showed a somewhat greater accuracy for statistical methods. The most stringent sample of studies, from which 48 effect sizes were extracted, indicated a 13% increase in accuracy using statistical versus clinical methods. Several variables influenced this overall effect. Clinical and statistical prediction accuracy varied by type of prediction, the setting in which predictor data were gathered, the type of statistical formula used, and the amount of information available to the clinicians and the formulas. Recommendations are provided about when and under what conditions counseling psychologists might use statistical formulas as well as when they can rely on clinical methods. Implications for clinical judgment research and training are discussed.
Violation of sphericity of covariance across repeated measures inflates Type I error rates in univariate repeated-measures analysis of variance. Hence the use of Geisser—Greenhouse or Huynh—Feldt is now common (to provide improved Type I error rate). With nonsphericity, no method has been available to compute power. A convenient method is suggested for approximating power and test size under nonsphericity. New approximations are suggested for (a) a weighted sum of independent noncentral χ,s, (b) the trace of a noncentral Wishart (or pseudo-Wishart) matrix, (c) the expected values of and , and (d) the noncentral test statistic, whether corrected or uncorrected. The new approximations are extensions of the work of Box (1954a,b) and Satterthwaite (1941). The method performed well when evaluated against published and new simulations.
Investigation is made of the character of the covariance matrix which will result in exact F-distributions for the treatments and interaction variance ratios in repeated measurements designs. It is shown, assuming multivariate normality, that the matrix may exhibit a more general character than is typically implied to be essential. Equality of variances and equality of covariances, with identical matrices for all levels of a second treatment factor, are sufficient but not necessary conditions. The necessary and sufficient condition is the equality of variances of differences for all pairs of treatment measures assumed to be correlated. An alternative statement is that the Box-Geisser-Greenhouse parameter ε = 1.0. A test is described which bears on the tenability of this condition.
This article provides a simple explanation of how power analysis can help us determine how large a sample should be. It covers basic examples of three commonly used tests: t-test, χ2 test and analysis of variance. It presents several graphs which can be used to obtain important critical values, such as required sample size for a particular task, and the power (or ability) of a chosen statistical test to actually determine the task required of it.
Repeated measures and longitudinal studies arise often in social and behavioral science research. During the planning stage of such studies, the calculations of sample size are of particular interest to the investigators and should be an integral part of the research projects. In this article, we consider the power and sample size calculations for normal outcomes within the framework of multivariate general linear models that represent the most fundamental method for the analysis of repeated measures and longitiudinal data. Direct extensions of the existing generalized estimating equation and likelihood-based approaches are presented. The major feature of the proposed modificaiion is the accommodation of both fixed and random models. A child development example is provided to illustrate the usefulness of the methods. The adequacies of the sample size formulas are evaluated through Monte Carlo simulation study.
Zusammenfassung. Empirisch-psychologische Daten sind durchgangig fallibel, also fehlerbehaftet, und zudem Theoriegetrankt. Aus diesem Grund ist es logisch nicht moglich, im Popperschen Sinne psychologische Hypothesen zu falsifizieren, und Verifikationen im Sinne Carnaps sind logisch ebenfalls nicht moglich. Ein Ausweg aus diesem Dilemma besteht darin, Beurteilungen psychologischer Hypothesen nicht mittels logischer Schlussfiguren herbeizufuhren, sondern mittels Entscheidungen. Fur diese sind jedoch Regeln festzulegen, die festlegen, ab wann man eine festgestellte Variation als systematisch ansehen will und nicht mehr als unsystematisch. Die von Psycholog/inn/en mehrheitlich eingesetzten statistischen Tests konnen als derartige Entscheidungsregeln fungieren, da sie die Trennung von systematischer und unsystematischer Variation nach allgemein akzeptierten probabilistischen Kriterien ermoglichen. Ublicherweise werden in der psychologischen Forschungspraxis die Signifikanztests nach R. A. Fisher eingesetzt, m...
Two F approximations are proposed for the distribution of Hotelling's T2o. The first approximation fits the first three moments, the second specifies the value of the numerator degrees of freedom and fits the first two moments. The second approximation is slightly better than the first, and improves on F approximations proposed by Pillai and Sampson (1959) and Hughes and Saw (1972).
For testing the equality of two independent binomial populations the Fisher exact test and the chi-squared test with Yates's continuity correction are often suggested for small and intermediate size samples. The use of these tests is inappropriate in that they are extremely conservative. In this article we demonstrate that, even for small samples, the uncorrected chi-squared test (i.e., the Pearson chi-squared test) and the two-independent-sample t test are robust in that their actual significance levels are usually close to or smaller than the nominal levels. We encourage the use of these latter two tests.
The accuracy of the two-moment, three-moment, square root, and cube root approximations to the noncentralF distribution was assessed using 7,920 entries from Tiku’s (1967) power tables. Tiku’s tables list exact values of β for α=
.005−.05, ν1 = 1−12, ν2 = 2−120, and ϕ= 0.5−3.0. Analysis of the errors showed generally satisfactory performance for all four approximations. The
three-moment approximation was most accurate, registering a maximum error of only .009. The other three approximations had
maximum errors of ±.02, except for the square root approximation at ν2 = 2, where maximum errors of .05 occur. Approximation error increased with decreases in ν1 and, less consistently, with increases in ν2. Error was nonmonotonically related to ϕ. A second investigation explored the
accuracy of the approximations at values of αranging from .10 to .90. All four approximations degraded substantially in this
situation, with maximum errors ranging from −.09 to .05. If the analysis is restricted to cases where ν1 τ; 1 and ν2 τ; 2, maximum errors drop to roughly ±.03. We conclude that the approximations perform reasonably well for small αand moderately
well for larger values, if certain restrictions are imposed. From a computational standpoint, however, there is little advantage
to using approximate as opposed to exact methods unless exact values ofF
α are known in advance.
The impacts associated with streamside clear-cut logging (e.g., increased temperatures and sedimentation, loss of habitat complexity) are potentially stressful to stream-dwelling fish. We examined stream habitat and rainbow trout physiological stress responses to clear-cut logging in north-central British Columbia using 15 streams divided into three categories: old growth (reference), recently logged (clear-cut to both banks 19 years prior to the study), and second growth (clear-cut 2528 years prior to the study). We used plasma cortisol and chloride concentrations as indicators of acute stress, and interrenal nuclear diameters, impairment of the plasma cortisol response, and trout condition and length-at-age estimates as indicators of chronic stress. No statistically significant acute or chronic stress responses to streamside logging were found, despite increases in summertime stream temperatures (daily maxima and diurnal fluctuations) and a reduction in the average overall availability of pool habitat. Our observed stress responses were approximately an order of magnitude lower than what has previously been reported in the literature for a variety of different stressors, and trout interrenal nuclear diameters responses to the onset of winter were approximately five times greater than those to logging. The overall consistency of our results suggests that the impacts of streamside clear-cut logging are not acutely or chronically stressful to rainbow trout in our study area.
The well-known problem of cumulating error probabilities is reconsidered from a general epistemological perspective, namely, the concepts of severity (Popper) and of fairness of tests. Applying these concepts to hypothesis-testing research leads to a reevaluation of the relative importance of the probabilities of Type 1 and Type 2 errors connected with those statistical hypotheses that have been derived from the substantive ones. It is shown that not only Type 1 but also Type 2 errors can cumulate. This cumulation is discussed for various basic types of empirical situations in which substantive hypotheses are examined by means of statistical ones. A new adjustment strategy based on the Dunn-Bonferroni inequality for planned tests is proposed and applied to some empirical examples.
Adults of the reddish green symbiotic crab Allopetrolisthes spinifrons obtain different degrees of camouflage from the red, green, reddish green, and blue sea anemones Phymactis clematis, and the orange Phymanthea pluvia on which they are commonly found. Previous studies have indicated that the distribution of the crab on sea anemones may be influenced by the host's coloration. During the present study, we tested whether adult crabs dwell more commonly on those sea anemones that offer a higher degree of camouflage, and whether ''behavioral preferences'' can explain crab distribution on sea anemone species and color morphs in the field. We examined the frequency of occurrence of adult crabs on 200 sea anemones of each color morph, and crab distribution was compared to that expected by the relative abundance of different anemone hosts in the field. Adult crabs occurred more frequently than expected by chance on sea anemones most similar to their own color (reddish green), and occurred less frequently than expected by chance on sea anemones where homochromy was reduced (e.g. blue). Multiple host selection experiments conducted under laboratory conditions demonstrated that adult crabs avoid blue sea anemones compared to other host color morphs offered for colonization, but no preference was observed among red, green, reddish green, and orange sea anemones. These results suggest that ''behavioral preference'' play a role in determining the field distribution of A. spinifrons adult crabs in the field. The lack of preference for reddish green sea anemones in the laboratory, but its high frequency of occupancy by adult crabs in the intertidal, further suggests that factors other than homochromy-related preferences (e.g. sea anemones habitat) might affect crab distribution in the field. Alternative host selection experiments revealed that the specific host from which crabs were 0022-0981/02/$ -see front matter D 2002 Elsevier Science B.V. All rights reserved. PII: S 0 0 2 2 -0 9 8 1 (0 2) 0 0 4 7 6 -8
Discusses a relatively unknown test statistic for multivariate analysis of variance (ANOVA)—K. Pillai and Barlett's V-criterion. The computation and testing of V are discussed in the framework of multiple regression and multiple correlation, considering global multivariate null hypotheses and special multivariate null hypotheses. An approximation method is described, which allows control of Type II error probability (beta) of multivariate tests based on the V statistic. (English & French abstracts) (24 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Contends that when analyzing qualitative data by means of log-linear or logit-linear models, the acceptance of a given model cannot be justified statistically if only the so-called alpha error is controlled. A method for controlling the beta error in the case of chi-square goodness of fit tests is given. The problem of cumulating error probabilities in multiple tests of hierarchical log-linear models is discussed, and strategies for adjusting for probabilities of error are recommended. (English abstract) (36 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
By using the exact expression of the correlation coefficient distribution function under the alternative hypothesis in the case of a random sample from a normal population, the distribution functions of the Student - and Snedecor -distributions are provided in closed form. The distribution functions obtained are implemented as routines in the symbolic programming language Mathematica.
This article shows how to assess and control the power of chi-square tests for categorical data. It discusses both the case of simple null hypotheses (i.e., the category or cell probabilities are completely specified a priori) and the case of composite null hypotheses for parameterized multinomial models requiring parameter estimation (e.g., log-linear models). Power calculations are illustrated for chi-square tests of association and chi-square goodness-of-fit tests using standard examples from behavioral research.
Statistical power analysis can be used to increase the efficiency of research efforts and to clarify research results. Power analysis is most valuable in the design or planning phases of research efforts. Such prospective (a priori) power analyses can be used to guide research design and to estimate the number of samples necessary to achieve a high probability of detecting biologically significant effects. Retrospective (a posteriori) power analysis has been advocated as a method to increase information about hypothesis tests that were not rejected. However, estimating power for tests of null hypotheses that were not rejected with the effect size observed in the study is incorrect; these power estimates will always be ≤0.50 when bias adjusted and have no relation to true power. Therefore, retrospective power estimates based on the observed effect size for hypothesis tests that were not rejected are misleading; retrospective power estimates are only meaningful when based on effect sizes other than the observed effect size, such as those effect size hypothesized to be biologically significant. Retrospective power analysis can be used effectively to estimate the number of samples or effect size that would have been necessary for a completed study to have rejected a specific null hypothesis. Simply presenting confidence intervals can provide additional information about null hypotheses that were not rejected, including information about the size of the true effect and whether or not there is adequate evidence to 'accept' a null hypothesis as true. We suggest that (1) statistical power analyses be routinely incorporated into research planning efforts to increase their efficiency. (2) confidence intervals be used in lieu of retrospective power analyses for null hypotheses that were not rejected to assess the likely size of the true effect, (3) minimum biologically significant effect sizes be used for all power analyses, and (4) if retrospective power estimates are to be reported, then the β-level, effect sizes, and sample sizes used in calculation must also be reported.
8.1 INTRODUCTION Determining adequate and efficient sample sizes is often critical in designing worthy studies. Yet too many studies have sample sizes that are too small to ensure enough statistical power to confirm meaningful effects. Freiman, Chalmers, Smith, and Kuebler (1979) concluded this about clinical trials in medicine. Sedlmeier and Gigerenzer (1989) reached a similar judgment about studies in psychology. The message in both articles is cogent to all fields that rely on statistical inference. Perhaps such articles are having positive effects, for we see signs that researchers are now paying more attention to power. For example, reviewers of research proposals now often require that sound power analyses be done before they will recommend funding or access to facilities and subject populations. Going through the process of determining and justifying the sample size also has an important ancillary effect: it catalyzes the synergism between science and statistics at the study's conception. The statistician who performs a thorough power analysis is more likely to scrutinize the proposed design, assess issues regarding data management, and develop a sound plan for the data analysis. Such involvement can improve the proposal in a number of ways, thus increasing its chance for approval, funding, scientific success, and publication. In this chapter, we present a strategy for performing power analyses that is applicable to the broad range of methods subsumed by the classical normal-theory univariate or multivariate general linear models. First, we introduce the requisite concepts of statistical power using concepts from the familiar t-test to compare two independent group means. Second, we proceed to the comparison of two correlated means (matched-pairs problem) and on to the one-way analysis of variance (ANOVA) with contrasts for a completely randomized design. Third, we develop power analysis for the univariate general linear model, thus providing a broad range of applications. We illustrate this with an analysis of covariance (ANCOVA) problem that has unequal distributions of the covariate's values 1 Supported in part by grants from the US National Institutes of Health (GCRC: RR00082) and the University of Florida Division of Sponsored Research, which funded Zhanying Bai and Yonghwan Um in their wriitng of one portion of the OneWyPow.sas freeware module. Dan Bowling helped in many ways.
This paper addresses some misconceptions concerning prospective and retrospective power when journal editors, reviewers and readers want to know the observed power of statistical tests within a completed study. In addition, a practical solution to both the problem of computing the power after a study has been conducted and the critical evaluation of null findings is suggested