A pilot study of the Austin Playing Card Assessment: A tool to detect and find the degree of visual perceptual difficulties related to clutter

2020 ◽  
Vol 38 (2) ◽  
pp. 118-136
Author(s):  
Nicola McDowell
Keyword(s):  
Pilot Study ◽  
Experimental Design ◽  
Effect Size ◽  
Assessment Process ◽  
Research Subjects ◽  
Large Effect Size ◽  
Playing Card ◽  
Control Subjects ◽  
Cohen’S D ◽  
Cohen's D

The Austin Playing Card Assessment was developed to help identify visual perceptual difficulties related to clutter and to determine the nature of each child’s difficulties. The aim of this pilot study was to find out whether a task of progressively increasing difficulty, for pairing playing cards, is effective in identifying these kinds of visual difficulties. Parents of 11 research and 11 control subjects completed an inventory to ascertain whether their child’s visual behaviours were suggestive of visual perceptual difficulties. All participants completed the Austin Playing Card Assessment in two separate locations, with an ABA experimental design. The time taken to complete each level of the test was recorded. Structured observations were made of the participants’ visual behaviours when completing the assessment. Inventory scores from the research subjects indicated a higher likelihood of visual perceptual difficulties, whereas the control subjects’ scores did not. Research subjects were also slower at completing the Austin Playing Card Assessment. Independent T-tests comparing the time taken across all levels of difficulty ranged between p = .011 to p < .001. Cohen’s d calculations demonstrated a large effect size. Overall, research subjects demonstrated more indicators of visual perceptual difficulties. The Austin Playing Card Assessment has the potential to be effective for detecting and identifying visual perceptual difficulties in children. Information gathered through the assessment process could also be used to inform intervention. As this was a pilot study, further research is needed to support these findings.

scholarly journals The Unforgettables: a chorus for people with dementia with their family members and friends

2018 ◽  
Vol 30 (6) ◽  
pp. 779-789 ◽  
Author(s):  
Mary Sherman Mittelman ◽  
Panayiota Maria Papayannopoulou
Keyword(s):  
Quality Of Life ◽  
Social Support ◽  
Pilot Study ◽  
Effect Size ◽  
Family Members ◽  
Positive Effects ◽  
Cohen’S D ◽  
People With Dementia ◽  
Cohen's D

Summary/AbstractOur experience evaluating a museum program for people with dementia together with their family members demonstrated benefits for all participants. We hypothesized that participation in a chorus would also have positive effects, giving them an opportunity to share a stimulating and social activity that could improve their quality of life. We inaugurated a chorus for people with dementia and their family caregivers in 2011, which rehearses and performs regularly. Each person with dementia must be accompanied by a friend or family member and must commit to attending all rehearsals and the concert that ensues. A pilot study included a structured assessment, take home questionnaires and focus groups. Analyses of pre-post scores were conducted; effect size was quantified using Cohen's d. Results showed that quality of life and communication with the other member of the dyad improved (Effect size: Cohen's d between 0.32 and 0.72) for people with dementia; quality of life, social support, communication and self-esteem improved (d between 0.29 and 0.68) for caregivers. Most participants stated that benefits included belonging to a group, having a normal activity together and learning new skills. Participants attended rehearsals in spite of harsh weather conditions. The chorus has been rehearsing and performing together for more than 6 years and contributing to its costs. Results of this pilot study suggest that people in the early to middle stage of dementia and their family members and friends can enjoy and learn from rehearsing and performing in concerts that also engage the wider community. It is essential to conduct additional larger studies of the benefits of participating in a chorus, which may include improved quality of life and social support for all, and reduced cognitive decline among people with dementia.

Improving Analytic Reasoning via Crowdsourcing and Structured Analytic Techniques

2020 ◽  
Vol 14 (3) ◽  
pp. 195-217
Author(s):  
Tim van Gelder ◽  
Ariel Kruger ◽  
Sujai Thomman ◽  
Richard de Rozario ◽  
Elizabeth Silver ◽  
...  
Keyword(s):  
Effect Size ◽  
General Public ◽  
Large Study ◽  
Collaborative Reasoning ◽  
Large Effect Size ◽  
Future Directions ◽  
Cohen’S D ◽  
General Conjecture ◽  
Cohen's D ◽  
Analytic Reasoning

How might analytic reasoning in intelligence reports be substantially improved? One conjecture is that this can be achieved through a combination of crowdsourcing and structured analytic techniques (SATs). To explore this conjecture, we developed a new crowdsourcing platform supporting groups in collaborative reasoning and intelligence report drafting using a novel SAT we call “Contending Analyses.” In this paper we present findings from a large study designed to assess whether groups of professional analysts working on the platform produce better-reasoned reports than those analysts produce when using methods and tools normally used in their organizations. Secondary questions were whether professional analysts working on the platform produce better reasoning than the general public working on the platform; and how usable the platform is. Our main finding is a large effect size (Cohen’s d = 1.37) in favor of working on platform. This provides early support for the general conjecture. We discuss limitations of our study, implications for intelligence organizations, and future directions for the work as a whole.

The effect of midodrine on lactate clearance in patients with septic shock: a pilot study

2021 ◽  
Author(s):  
Effat Davoudi-Monfared ◽  
Mostafa Mohammadi ◽  
Meysam Khoshavi ◽  
Hossein Khalili
Keyword(s):  
Septic Shock ◽  
Pilot Study ◽  
Effect Size ◽  
Critically Ill Patients ◽  
Lactate Clearance ◽  
Control Group ◽  
Large Effect Size ◽  
Group 13 ◽  
Registration Number ◽  
To Receive

Background: The effect of midodrine on lactate clearance has not been assessed in critically ill patients yet. Objective: The goal of this study was to assess the effect of adjunctive midodrine therapy on lactate clearance in patients with septic shock. Materials & methods: Patients with septic shock were assigned to receive either adjunctive midodrine 10 mg three-times a day for 5 days (midodrine group = 15 patients) or not (control group = 13 patients). Results: The lactate clearance was significantly faster in the midodrine group than the control group (p = 0.049) with a large effect size ([Formula: see text] = 0.141). Conclusion: When midodrine was added to intravenous vasopressors, it significantly accelerated lactate clearance in patients with septic shock. Trial registration number: IRCT20100228003449N25 (Clinicaltrials.gov).

A non-parametric effect size measure capturing changes in central tendency and shape of data distributions more flexibly than Cohen’s d

2020 ◽  
Author(s):  
Jörn Lötsch ◽  
Alfred Ultsch
Keyword(s):  
Effect Size ◽  
Data Science ◽  
Heterogeneous Data ◽  
Machine Learning Algorithms ◽  
Central Tendency ◽  
Effect Size Measure ◽  
Specific Data ◽  
Cohen’S D ◽  
Size Measure ◽  
Cohen's D

Abstract Calculating the magnitude of treatment effects or of differences between two groups is a common task in quantitative science. Standard effect size measures based on differences, such as the commonly used Cohen's, fail to capture the treatment-related effects on the data if the effects were not reflected by the central tendency. "Impact” is a novel nonparametric measure of effect size obtained as the sum of two separate components and includes (i) the change in the central tendency of the group-specific data, normalized to the overall variability, and (ii) the difference in the probability density of the group-specific data. Results obtained on artificial data and empirical biomedical data showed that impact outperforms Cohen's d by this additional component. It is shown that in a multivariate setting, while standard statistical analyses and Cohen’s d are not able to identify effects that lead to changes in the form of data distribution, “Impact” correctly captures them. The proposed effect size measure shares the ability to observe such an effect with machine learning algorithms. It is numerically stable even for degenerate distributions consisting of singular values. Therefore, the proposed effect size measure is particularly well suited for data science and artificial intelligence-based knowledge discovery from (big) and heterogeneous data.

scholarly journals 0636 Using AI To Predict Future CPAP Adherence and the Impact of Behavioral and Technical Interventions

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A243-A243
Author(s):  
W Hevener ◽  
B Beine ◽  
J Woodruff ◽  
D Munafo ◽  
C Fernandez ◽  
...  
Keyword(s):  
Effect Size ◽  
Statistical Significance ◽  
Post Intervention ◽  
Cpap Adherence ◽  
Cohen’S D ◽  
Usage Patterns ◽  
Patient Outreach ◽  
Cohen's D ◽  
The Impact ◽  
Usage Data

Abstract Introduction Clinical management of CPAP adherence remains an ongoing challenge. Behavioral and technical interventions such as patient outreach, coaching, troubleshooting, and resupply may be deployed to positively impact adherence. Previous authors have described adherence phenotypes that retrospectively categorize patients by discrete usage patterns. We design an AI model that predictively categorizes patients into previously studied adherence phenotypes and analyzes the statistical significance and effect size of several types of interventions on subsequent CPAP adherence. Methods We collected a cross-sectional cohort of subjects (N = 13,917) with 455 days of daily CPAP usage data acquired. Patient outreach notes and resupply data were temporally synchronized with daily CPAP usage. Each 30-days of usage was categorized into one of four adherence phenotypes as defined by Aloia et al. (2008) including Good Users, Variable Users, Occasional Attempters, and Non-Users. Cross-validation was used to train and evaluate a Recurrent Neural Network model for predicting future adherence phenotypes based on the dynamics of prior usage patterns. Two-sided 95% bootstrap confidence intervals and Cohen’s d statistic were used to analyze the significance and effect size of changes in usage behavior 30-days before and after administration of several resupply interventions. Results The AI model predicted the next 30-day adherence phenotype with an average of 90% sensitivity, 96% specificity, 95% accuracy, and 0.83 Cohen’s Kappa. The AI model predicted the number of days of CPAP non-use, use under 4-hours, and use over 4-hours for the next 30-days with OLS Regression R-squared values of 0.94, 0.88, and 0.95 compared to ground truth. Ten resupply interventions were associated with statistically significant increases in adherence, and ranked by adherence effect size using Cohen’s d. The most impactful were new cushions or masks, with a mean post-intervention CPAP adherence increase of 7-14% observed in Variable User, Occasional Attempter, and Non-User groups. Conclusion The AI model applied past CPAP usage data to predict future adherence phenotypes and usage with high sensitivity and specificity. We identified resupply interventions that were associated with significant increases in adherence for struggling patients. This work demonstrates a novel application for AI to aid clinicians in maintaining CPAP adherence. Support  

scholarly journals A Dietary Mobile App for Patients Undergoing Hemodialysis: Prospective Pilot Study to Improve Dietary Intakes

10.2196/17817 ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. e17817
Author(s):  
Cosette Fakih El Khoury ◽  
Rik Crutzen ◽  
Jos M G A Schols ◽  
Ruud J G Halfens ◽  
Mirey Karavetian
Keyword(s):  
Health Care ◽  
Pilot Study ◽  
Effect Size ◽  
Mobile App ◽  
Dietary Guidelines ◽  
Medium Effect ◽  
Dietary Intakes ◽  
Large Effect Size ◽  
Prospective Pilot Study ◽  
Medium Effect Size

Background Mobile technology has an impact on the health care sector, also within dietetics. Mobile health (mHealth) apps may be used for dietary assessment and self-monitoring, allowing for real-time reporting of food intakes. Changing eating behaviors is quite challenging, and patients undergoing hemodialysis, particularly, struggle to meet the target intakes set by dietary guidelines. Usage of mobile apps that are developed in a person-centered approach and in line with recommendations may support both patients and health care practitioners. Objective This study is a pilot that aims at estimating the potential efficacy of a dietary intervention using a theory-based, person-centered smartphone app. Results will be used to improve both the app and a planned large-scale trial intended to assess app efficacy thoroughly. Methods A prospective pilot study was performed at the hemodialysis unit of Al Qassimi Hospital (The Emirate of Sharjah). All patients that fulfilled the study inclusion criteria were considered eligible to be enrolled in the pilot study. Upon successful installation of the app, users met with a dietitian once a week. Outcomes were measured at baseline (T0) and 2 weeks post app usage (T1). This pilot is reported as per guidelines for nonrandomized pilot and feasibility studies and in line with the CONSORT 2010 checklist for reporting pilot or feasibility trials. Results A total of 23 patients completed the pilot intervention. Mean energy intakes increased from 24.4 kcal/kg/day (SD 8.0) to 29.1 kcal/kg/day (SD 7.8) with a medium effect size (d=0.6, 95% CI 0.0-1.2). Mean protein intakes increased from 0.9 g/kg/day (SD 0.3) to 1.3 g/kg/day (SD 0.5) with a large effect size (d=1.0, 95% CI 0.4-1.6); mean intake of high biological value (%HBV) proteins also increased from 58.6% (SD 10.1) to 70.1% (SD 10.7) with a large effect size (d=1.1, 95% CI 0.5-1.7). Dietary intakes of minerals did not change, apart from sodium which decreased from a mean intake of 2218.8 mg/day (SD 631.6) to 1895.3 mg/day (SD 581.0) with a medium effect size (d=0.5, 95% CI 0.1-1.1). Mean serum phosphorus, potassium, and albumin levels did not change relevantly. Mean serum iron increased from 7.9 mg/dL (SD 2.8) to 11.5 mg/dL (SD 7.9) postintervention with a medium effect size (d=0.6, 95% CI 0.0-1.2). Conclusions This pilot study showed that the KELA.AE app has the potential to improve dietary intakes. Processes related to procedure, resources, tools, and app improvement for a future trial were assessed. A more extended intervention using a randomized controlled trial is required to estimate parameters concerning app efficacy accurately.

scholarly journals Effect Size Guidelines, Sample Size Calculations, and Statistical Power in Gerontology

2019 ◽  
Vol 3 (4) ◽  
Author(s):  
Christopher R Brydges
Keyword(s):  
Individual Differences ◽  
Sample Size ◽  
Effect Size ◽  
Effect Sizes ◽  
Group Differences ◽  
Cohen’S D ◽  
Pearson's R ◽  
Sample Size Calculations ◽  
Cohen's D ◽  
Pearson’S R

Abstract Background and Objectives Researchers typically use Cohen’s guidelines of Pearson’s r = .10, .30, and .50, and Cohen’s d = 0.20, 0.50, and 0.80 to interpret observed effect sizes as small, medium, or large, respectively. However, these guidelines were not based on quantitative estimates and are only recommended if field-specific estimates are unknown. This study investigated the distribution of effect sizes in both individual differences research and group differences research in gerontology to provide estimates of effect sizes in the field. Research Design and Methods Effect sizes (Pearson’s r, Cohen’s d, and Hedges’ g) were extracted from meta-analyses published in 10 top-ranked gerontology journals. The 25th, 50th, and 75th percentile ranks were calculated for Pearson’s r (individual differences) and Cohen’s d or Hedges’ g (group differences) values as indicators of small, medium, and large effects. A priori power analyses were conducted for sample size calculations given the observed effect size estimates. Results Effect sizes of Pearson’s r = .12, .20, and .32 for individual differences research and Hedges’ g = 0.16, 0.38, and 0.76 for group differences research were interpreted as small, medium, and large effects in gerontology. Discussion and Implications Cohen’s guidelines appear to overestimate effect sizes in gerontology. Researchers are encouraged to use Pearson’s r = .10, .20, and .30, and Cohen’s d or Hedges’ g = 0.15, 0.40, and 0.75 to interpret small, medium, and large effects in gerontology, and recruit larger samples.

Comparing Normalized Gain g and Effect Size Cohen’s d to Measure the Improvement of Student’s Scientific Literacy

2019 ◽  
Author(s):  
Adib Rifqi Setiawan
Keyword(s):  
Education Research ◽  
Student Learning ◽  
Effect Size ◽  
Physics Education ◽  
Scientific Literacy ◽  
Biology Education ◽  
Physics Education Research ◽  
Cohen’S D ◽  
Cohen's D ◽  
Normalized Gain

In this work I investigate about my curiousity. My investigation focused on the implications on claims about student learning that result from choosing between one of two metrics. The metrics are normalized gain g, which is the most common method used in Physics Education Research (PER), and effect size Cohen’s d, which is broadly used in Discipline-Based Education Research (DBER) including Biology Education Research (BER). Data for the analyses came from the research about scientific literacy on Physics and Biology Education from courses at institutions across Indonesia. This work reveals that the bias in normalized gaing can harm efforts to improve student’s scientific literacy by misrepresenting the efficacy of teaching practices across populations of students and across institutions. This work, also, recommends use effect size Cohen’s d for measuring student learning, based on reliability statistical method for calculating student learning.

Cohen’s d and physicians’ opinion on effect size: a questionnaire on anemia treatment

2021 ◽  
pp. jim-2021-002031
Author(s):  
Kemal Hakan Gülkesen ◽  
Feyza Bora ◽  
Nevruz Ilhanli ◽  
Esin Avsar ◽  
Nese Zayim
Keyword(s):  
Effect Size ◽  
Clinical Importance ◽  
Deficiency Anemia ◽  
Duration Of Treatment ◽  
Cohen’S D ◽  
D Values ◽  
A Minor ◽  
Cohen's D ◽  
Anemia Treatment

A well-known effect size (ES) indicator is Cohen’s d. Cohen defined d measures of small, medium, and large ES as 0.2, 0.5, and 0.8, respectively. This approach has been criticized because practical and clinical importance depends on the context of research. The aim of the study was to examine physicians’ perception of ES using iron deficiency anemia treatment as an example and observing the effects of pretreatment level and duration of treatment on the magnitude of ES. We prepared a questionnaire describing four different clinical studies: (1) 1 month of treatment of anemia in a group of patients with a mean hemoglobin (Hb) of 10 g/dL; (2) 3 months of treatment at an Hb level of 10 g/dL; (3) 1 month of treatment at an Hb level of 8 g/dL; and (4) 3 months of treatment at an Hb level of 8 g/dL. In each scenario, respondents were required to evaluate six various levels of Hb improvement as being very small, small, medium, large, or very large effect: 0.1 g/dL, 0.3 g/dL, 0.7 g/dL, 1.1 g/dL, 1.7 g/dL, and 2.8 g/dL. The responses of 35 physicians were evaluated. For 10 mg/dL, the Cohen's d for small, medium, and large ES was 0.5, 0.8, and 1.2 respectively, for 1 month of treatment. In terms of 3 months of treatment, the Cohen's d was 0.8, 1.2, and 2, respectively. Two separate pretreatment Hb levels (8 g/dL and 10 g/dL) demonstrated a minor difference. Determination of ES during the planning phase of studies requires thorough evaluation of specific clinical cases. Our results are divergent from the classic Cohen’s d values. Additionally, duration of treatment affects ES perception.

Statistical inference and residual analysis for the evaluation of datum transformation models developed on 3D coordinate data

2020 ◽  
Vol 14 (1) ◽  
pp. 65-75
Author(s):  
Abubakr Hassan ◽  
Dingfa Huang ◽  
Elhadi K. Mustafa ◽  
Yahaya Mahama ◽  
Mohamed A. Damos ◽  
...  
Keyword(s):  
Effect Size ◽  
T Test ◽  
Rank Test ◽  
Transformation Models ◽  
Cohen’S D ◽  
Signed Rank ◽  
Wilcoxon Signed Rank Test ◽  
Signed Rank Test ◽  
Cohen's D ◽  
Paired T Test

AbstractThe evaluation of geoscience data is a far-reaching topic which cannot be systematically covered. The purpose of inferential statistics is to harness useful information from data for making decisions. This paper conducts in-depth statistical study for the Bursa-Wolf and Molodensky Badekas models of the three-dimensional transformation parameters. We also considered the combined and observation equations scenarios of these methods for the comparative study. Four key indicators are conducted to evaluate the performance of the two transformation models according to the residual results. These include root mean square error (RMSE), paired t-test, Wilcoxon signed-rank test and the Cohen’s d effect size measure. RMSE evaluation is based on the mean difference between model estimates and observed values. The correlations in the model results is investigated based on paired t-test. Wilcoxon signed-rank test assesses the statistical significance of the model’s paired differences. To estimate the effect size of the performance differences, Cohen’s d measures are computed. Further, the residuals of the estimated parameters are plotted according to their respective control points. The inference results of these tests generally show that Badekas transformation approach is more precise than Bursa-Wolf. Specifically, Badekas combined case is the most precise, followed by its observation case, then Bursa-Wolf combined and finally its observation case is the least performing model. The application of various data analysis and statistical verifications make the task of data interpretation and best model selection easier.

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