Bridges and duckweed have large effects on aquatic communities: better data interpretation using standardized effect size

In this study a standardized effect size was created for use with the SIBTEST procedure. Using this standardized effect size, a single set of heuristics was developed that are appropriate for data fitting different item response models (e.g., 2-parameter logistic, 3-parameter logistic). The standardized effect size rescales the raw beta-uni value using a pooled variation that incorporates the beta-uni inclusion factor. Although the heuristics for the standardized and unstandardized effect sizes provide similar true-positive and false-positive rates in most conditions, the standardized effect size provides higher true-positive rates for conditions where item response variability is smaller in proportion to raw score differences. Inflated false-positive rates were solely impacted by smaller sample sizes, whereas larger sample sizes improved true-positive rates. An empirical application is provided to demonstrate how the standardized effect size provides for a more consistent comparison across items with varying response distributions. This study lays the foundation for the utilization of a standardized effect size for both dichotomous and polytomous item response models using the suite of SIBTEST procedures.

To assess whether perspective-taking, which researchers in other fields have shown to induce empathy, improves patient satisfaction in encounters between student-clinicians and standardized patients (SPs). In three studies, randomly assigned students (N = 608) received either a perspective-taking instruction or a neutral instruction prior to a clinical skills examination in 2006-2007. SP satisfaction was the main outcome in all three studies. Study 1 involved 245 third-year medical students from two universities. Studies 2 and 3 extended Study 1 to examine generalizability across student and SP subpopulations. Study 2 (105 physician assistant students, one university) explored the effect of perspective-taking on African American SPs' satisfaction. Study 3 (258 third-year medical students, two universities) examined the intervention's effect on students with high and low baseline perspective-taking tendencies. Intervention students outscored controls in patient satisfaction in all studies: Study 1: P = .01, standardized effect size = 0.16; Study 2: P = .001, standardized effect size = 0.31; Study 3: P = .009, standardized effect size = 0.13. In Study 2, perspective-taking improved African American SPs' satisfaction. In Study 3, intervention students with high baseline perspective-taking tendencies outscored controls (P = .0004, standardized effect size = 0.25), whereas those with low perspective-taking tendencies did not (P = .72, standardized effect size = 0.00). Perspective-taking increased patient satisfaction in all three studies, across medical schools, clinical disciplines, and racially diverse students and SPs. Perspective-taking as a means for improving patient satisfaction deserves further exploration in clinical training and practice.

BackgroundNeuromyelitis optica spectrum disorder (NMOSD) is a frequently disabling neuroinflammatory syndrome with a relapsing course. Blood-based disease severity and prognostic biomarkers for NMOSD are a yet unmet clinical need. Here, we evaluated serum glial fibrillary acidic protein (sGFAP) and neurofilament light (sNfL) as disease severity and prognostic biomarkers in patients with aquaporin-4 immunoglobulin (Ig)G positive (AQP4-IgG+) NMOSD.MethodssGFAP and sNfL were determined by single-molecule array technology in a prospective cohort of 33 AQP4-IgG+ patients with NMOSD, 32 of which were in clinical remission at study baseline. Sixteen myelin oligodendrocyte glycoprotein IgG-positive (MOG-IgG+) patients and 38 healthy persons were included as controls. Attacks were recorded in all AQP4-IgG+ patients over a median observation period of 4.25 years.ResultsIn patients with AQP4-IgG+ NMOSD, median sGFAP (109.2 pg/ml) was non-significantly higher than in MOG-IgG+ patients (81.1 pg/ml; p = 0.83) and healthy controls (67.7 pg/ml; p = 0.07); sNfL did not substantially differ between groups. Yet, in AQP4-IgG+, but not MOG-IgG+ patients, higher sGFAP was associated with worse clinical disability scores, including the Expanded Disability Status Scale (EDSS, standardized effect size = 1.30, p = 0.007) and Multiple Sclerosis Functional Composite (MSFC, standardized effect size = − 1.28, p = 0.01). While in AQP4-IgG+, but not MOG-IgG+ patients, baseline sGFAP and sNfL were positively associated (standardized effect size = 2.24, p = 0.001), higher sNfL was only non-significantly associated with worse EDSS (standardized effect size = 1.09, p = 0.15) and MSFC (standardized effect size = − 1.75, p = 0.06) in patients with AQP4-IgG+ NMOSD. Patients with AQP4-IgG+ NMOSD with sGFAP > 90 pg/ml at baseline had a shorter time to a future attack than those with sGFAP ≤ 90 pg/ml (adjusted hazard ratio [95% confidence interval] = 11.6 [1.3–105.6], p = 0.03). In contrast, baseline sNfL levels above the 75th age adjusted percentile were not associated with a shorter time to a future attack in patients with AQP4-IgG+ NMOSD.ConclusionThese findings suggest a potential role for sGFAP as biomarker for disease severity and future disease activity in patients with AQP4-IgG+ NMOSD in phases of clinical remission.

Longitudinal clinical and neuropsychological outcomes in limbic predominant age related TDP_43 encephalopathy vs. Alzheimer’s disease from <i>The 90+ Study</i>

Response to Commentary by Spielmans

It has been suggested that self-reported, common, non-clinical psychotic experiences may represent the transitory developmental expression of distributed genetic risk for psychosis. In a sample of female MZ (176 pairs) and DZ twins (113 pairs), cross-twin, cross-trait analyses were conducted to investigate the association between repeated continuous measures of self-reported psychotic experiences (PE-three measures over 18 months), assessed with the CAPE, in one twin and clinical interview categorical measures of psychotic symptoms (PS), assessed with SCID-I, in the other twin. The results showed that in MZ but not DZ pairs (interaction: chi(2) = 7.9, df = 1, P = 0.005), the cross-twin association between PE and PS was large and significant (standardized effect size: 0.26, 95% CI: 0.10-0.42) and of similar magnitude as the within-twin PE-PS association (standardized effect size: 0.28, 95% CI: 0.10-0.45), demonstrating both PE validity and genetic effects. In addition, the cross-twin association between PE and PS was significantly larger (interaction: chi(2) = 20.3, df = 1, P < 0.0001) for younger MZ twins (standardized effect size: 0.67, 95% CI: 0.44-0.90) than older MZ twins (standardized effect size: -0.05, 95% CI: -0.26 to 0.16), demonstrating developmental effects. This study indicates that self-reported psychotic experiences in the general population may represent the developmental expression of population genetic risk for psychosis.

Brain-wide association studies (BWAS) are a fundamental tool in discovering brain–behaviour associations1,2. Several recent studies have shown that thousands of study participants are required for good replicability of BWAS1, 2–3. Here we performed analyses and meta-analyses of a robust effect size index using 63 longitudinal and cross-sectional MRI studies from the Lifespan Brain Chart Consortium4 (77,695 total scans) to demonstrate that optimizing study design is critical for increasing standardized effect sizes and replicability in BWAS. A meta-analysis of brain volume associations with age indicates that BWAS with larger variability of the covariate and longitudinal studies have larger reported standardized effect size. Analysing age effects on global and regional brain measures from the UK Biobank and the Alzheimer’s Disease Neuroimaging Initiative, we showed that modifying study design through sampling schemes improves standardized effect sizes and replicability. To ensure that our results are generalizable, we further evaluated the longitudinal sampling schemes on cognitive, psychopathology and demographic associations with structural and functional brain outcome measures in the Adolescent Brain and Cognitive Development dataset. We demonstrated that commonly used longitudinal models, which assume equal between-subject and within-subject changes can, counterintuitively, reduce standardized effect sizes and replicability. Explicitly modelling the between-subject and within-subject effects avoids conflating them and enables optimizing the standardized effect sizes for each separately. Together, these results provide guidance for study designs that improve the replicability of BWAS.

BackgroundBiventricular pacing (CRT) shows clear benefits in heart failure with wide QRS, but results in narrow QRS have appeared conflicting. We tested the hypothesis that study design might have influenced findings.Method and ResultsWe identified all reports of CRT‐P/D therapy in subjects with narrow QRS reporting effects on continuous physiological variables. Twelve studies (2074 patients) met these criteria. Studies were stratified by presence of bias‐resistance steps: the presence of a randomized control arm over a single arm, and blinded outcome measurement. Change in each endpoint was quantified using a standardized effect size (Cohen's d). We conducted separate meta‐analyses for each variable in turn, stratified by trial quality. In non‐randomized, non‐blinded studies, the majority of variables (10 of 12, 83%) showed significant improvement, ranging from a standardized mean effect size of +1.57 (95%CI +0.43 to +2.7) for ejection fraction to +2.87 (+1.78 to +3.95) for NYHA class. In the randomized, non‐blinded study, only 3 out of 6 variables (50%) showed improvement. For the randomized blinded studies, 0 out of 9 variables (0%) showed benefit, ranging from −0.04 (−0.31 to +0.22) for ejection fraction to −0.1 (−0.73 to +0.53) for 6‐minute walk test.ConclusionsDifferences in degrees of resistance to bias, rather than choice of endpoint, explain the variation between studies of CRT in narrow‐QRS heart failure addressing physiological variables. When bias‐resistance features are implemented, it becomes clear that these patients do not improve in any tested physiological variable. Guidance from studies without careful planning to resist bias may be far less useful than commonly perceived.

It is regarded as best practice for psychologists to report effect size when disseminating quantitative research findings. Reporting of effect size in the psychological literature is patchy - though this may be changing - and when reported it is far from clear that appropriate effect size statistics are employed. This paper considers the practice of reporting point estimates of standardized effect size and explores factors such as reliability, range restriction and differences in design that distort standardized effect size unless suitable corrections are employed. For most purposes simple (unstandardized) effect size is more robust and versatile than standardized effect size. Guidelines for deciding what effect size metric to use and how to report it are outlined. Foremost among these are: (i) a preference for simple effect size over standardized effect size, and (ii) the use of confidence intervals to indicate a plausible range of values the effect might take. Deciding on the appropriate effect size statistic to report always requires careful thought and should be influenced by the goals of the researcher, the context of the research and the potential needs of readers.

A study was conducted to implement the use of a standardized effect size and corresponding classification guidelines for polytomous data with the POLYSIBTEST procedure and compare those guidelines with prior recommendations. Two simulation studies were included. The first identifies new unstandardized test heuristics for classifying moderate and large differential item functioning (DIF) for polytomous response data with three to seven response options. These are provided for researchers studying polytomous data using POLYSIBTEST software that has been published previously. The second simulation study provides one pair of standardized effect size heuristics that can be employed with items having any number of response options and compares true-positive and false-positive rates for the standardized effect size proposed by Weese with one proposed by Zwick et al. and two unstandardized classification procedures (Gierl; Golia). All four procedures retained false-positive rates generally below the level of significance at both moderate and large DIF levels. However, Weese's standardized effect size was not affected by sample size and provided slightly higher true-positive rates than the Zwick et al. and Golia's recommendations, while flagging substantially fewer items that might be characterized as having negligible DIF when compared with Gierl's suggested criterion. The proposed effect size allows for easier use and interpretation by practitioners as it can be applied to items with any number of response options and is interpreted as a difference in standard deviation units.

Vegas et al. IEEE Trans Softw Eng 42(2):120:135 (2016) raised concerns about the use of AB/BA crossover designs in empirical software engineering studies. This paper addresses issues related to calculating standardized effect sizes and their variances that were not addressed by the Vegas et al.’s paper. In a repeated measures design such as an AB/BA crossover design each participant uses each method. There are two major implication of this that have not been discussed in the software engineering literature. Firstly, there are potentially two different standardized mean difference effect sizes that can be calculated, depending on whether the mean difference is standardized by the pooled within groups variance or the within-participants variance. Secondly, as for any estimated parameters and also for the purposes of undertaking meta-analysis, it is necessary to calculate the variance of the standardized mean difference effect sizes (which is not the same as the variance of the study). We present the model underlying the AB/BA crossover design and provide two examples to demonstrate how to construct the two standardized mean difference effect sizes and their variances, both from standard descriptive statistics and from the outputs of statistical software. Finally, we discuss the implication of these issues for reporting and planning software engineering experiments. In particular we consider how researchers should choose between a crossover design or a between groups design.

In community ecology, randomization tests with problem specific test statistics (e.g., nestedness, functional diversity, etc.) are often applied. Researchers in such studies may want not only to detect the significant departure from randomness, but also to measure the effect size (i.e., the magnitude of this departure). Measuring the effect size is necessary, for instance, when the roles of different assembly forces (e.g., environmental filtering, competition) are compared among sites. The standard method is to calculate standardized effect size (SES), i.e., to compute the departure from the mean of random communities divided by their standard deviations. Standardized effect size is a useful measure if the test statistic (e.g., nestedness index, phylogenetic or functional diversity) in the random communities follows a symmetric distribution. In this paper, I would like to call attention to the fact that SES may give us misleading information if the distribution is asymmetric (skewed). For symmetric distribution median and mean values are equal (i.e., SES = 0 indicates p = 0.5). However, this condition does not hold for skewed distributions. For symmetric distributions departure from the mean shows the extremity of the value, regardless of the sign of departure, while in asymmetric distributions the same deviation can be highly probable and extremely improbable, depending on its sign. To avoid these problems, I recommend checking symmetry of null-distribution before calculating the SES value. If the distribution is skewed, I recommend either log-transformation of the test statistic, or using probit-transformed p-value as effect size measure.

The differential effectiveness of parent training has led researchers to examine a variety of child, parent, and familial variables that may predict treatment response. Studies have identified a diverse set of child, parent psychological/behavioral and demographic variables that are associated with treatment outcome and dropout. The parent training literature was examined to isolate child, parent, and family variables that predict response to parent training for child externalizing behavior problems. A literature review was conducted spanning articles published from 1980 to 2004 of indicated prevention (children with symptoms) and treatment (children with diagnosis) studies. Meta-analyses were conducted to determine standardized effect sizes associated with the identified predictors. Many of the predictors of treatment response examined in this meta-analysis resulted in moderate standardized effect sizes when study results were subjected to meta-analytic procedures (i.e., low education/occupation, more severe child behavior problems pretreatment, maternal psychopathology). Only low family income resulted in a large standardized effect size. Predictors of drop-out resulted in standardized effect sizes in the small or insubstantial range. Response to parent training is often influenced by variables not directly involving the child, with socioeconomic status and maternal mental health being particularly salient factors.

With a focus on innovation, the current study aims to provide a modeling pattern of the effective factors in the development of mountain sports tourism. The population included sports management specialists and all the state university professors of Sports Management in Tehran, the directors of mountaineering boards and tourism specialists of Tehran province. Given the limitations for choosing the sample, all the population was chosen as the sample from whom 100 questionnaires were collected. Kasker’s standard questionnaire (2012) was the instrument. After translation and localization, the validity of the questionnaire was verified by 10 specialized professors in the sports management field; using Cronbach’s alpha the reliability for the environment, development and innovation variables were 0.84, 0.91 and, 0.93 respectfully which show the high reliability of the questionnaire. The analysis of the data took place at two. The descriptive were used to describe the demographic features and in inferential statistics the structural equation modeling test was used through the PLS. The results of the modeling showed that the development variable on innovation has the most effect among all factors with 0.58 standard effect size. The environment on development was second with a 0.53 standard effect size and the environment on innovation with 0.41 standard effect size was the least effective among all factors. The findings of this study can be a platform for sports and tourism managers, tour guides and other organizations which work in relation to mountains to plan for an increase in creating jobs and revenues in this area.

Objective: To determine whether aerobic exercise improves aerobic capacity in individuals with stroke. Design: A systematic review of randomized controlled trials. Databases searched: MEDLINE, CINAHL, EMBASE, Cochrane Database of Systematic Reviews and Physiotherapy Evidence Database were searched. Inclusion criteria: Design: randomized controlled trials (RCTs). Participants: individuals with stroke. Interventions: aerobic exercise training aimed at improving aerobic capacity. Outcomes: Primary outcomes: aerobic capacity (peak oxygen consumption (VO2), peak workload). Secondary outcomes: walking velocity, walking endurance. Data analysis: The methodological quality was assessed by the PEDro scale. Meta-analyses were performed for all primary and secondary outcomes. Results: Nine articles (seven RCTs) were identified. The exercise intensity ranged from 50% to 80% heart rate reserve. Exercise duration was 20–40 min for 3–5 days a week. The total number of subjects included in the studies was 480. All studies reported positive effects on aerobic capacity, regardless of the stage of stroke recovery. Meta-analysis revealed a significant homogeneous standardized effect size (SES) in favour of aerobic exercise to improve peak VO2 (SES 0.42; 95% confidence interval (CI) 0.15–0.69; P=0.001) and peak workload (SES 0.50; 95% CI 0.26–0.73; P<0.001). There was also a significant homogeneous SES in favour of aerobic training to improve walking velocity (SES 0.26; 95% CI 0.05–0.48; P=0.008) and walking endurance (SES 0.30; 95% CI 0.06–0.55; P=0.008). Conclusions: There is good evidence that aerobic exercise is beneficial for improving aerobic capacity in people with mild and moderate stroke. Aerobic exercise should be an important component of stroke rehabilitation.