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.

scholarly journals Empirically Derived Guidelines for Interpreting Effect Size in Social Psychology

2017 ◽  
Author(s):  
Andrey Lovakov ◽  
Elena Agadullina
Keyword(s):  
Social Psychology ◽  
Correlation Coefficient ◽  
Effect Size ◽  
Correlation Coefficients ◽  
Cohen’S D ◽  
Other Information ◽  
Mean Differences ◽  
D Values ◽  
Meta Analyses ◽  
Cohen's D

This study estimates empirically derived guidelines for effect size interpretation for research in social psychology overall and subdisciplines within social psychology, based on analysis of the true distributions of the two types of effect size measures widely used in social psychology (correlation coefficient and standardized mean differences). Analysis of empirically derived distributions of 12,170 correlation coefficients and 6,447 Cohen’s d statistics extracted from studies included in 134 published meta-analyses revealed that the 25th, 50th, and 75th percentiles corresponded to correlation coefficient values of 0.12, 0.24, and 0.41 and to Cohen’s d values of 0.15, 0.36, and 0.65 respectively. The analysis suggests that the widely used Cohen’s guidelines tend to overestimate medium and large effect sizes. Empirically derived effect size distributions in social psychology overall and subdisciplines can be used both for effect size interpretation and for sample size planning when other information about effect size is not available.

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 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.

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.

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.

scholarly journals Designing a Gait Enhancer and Determining its Effect on Standing Ability and Gait Speed of Children With Cerebral Palsy Spastic Diplegia

2021 ◽  
Vol 21 (4) ◽  
pp. 436-453
Author(s):  
Seyed Mehdi Hosseini ◽  
◽  
Saeid Fatorehchy ◽  
Seyed Ali Hosseini ◽  
Hojjat Allah Haghgoo ◽  
...  
Keyword(s):  
Cerebral Palsy ◽  
Occupational Therapy ◽  
Effect Size ◽  
Gait Speed ◽  
Walking Speed ◽  
Spastic Diplegia ◽  
Cohen’S D ◽  
Children With Cerebral Palsy ◽  
Cohen's D ◽  
Therapy Sessions

Objective: This study aimed to design a “gait enhancer” and investigate its effect on standing ability and gait speed of children with cerebral palsy spastic diplegia. Materials & Methods: A new gate trainer was designed based on Theo Johnson mechanism. Johnson's two separate movement chains were placed on either side of the gate trainer body and attached to the lower limbs by a foot plate. To investigate the effect of the designed device, a single-item experimental study with baseline design, treatment and maintenance (ABA) was performed on four children with available spastic diplegia cerebral palsy. These children received routine occupational therapy sessions. Results: The designed “gait enhancer” increased standing ability and gait speed scores in all subjects. Non-overlapping measures also indicated the improvement in both variables. Measured by Cohen’s d, the effect size for standing ability were 1.95, 2.29, 1.83, and 2.3 for the child No. 1, 2, 3, and 4, respectively. Regarding walking speed, the effect size for these children, No. 1 to 4, were 1.13, 3.37, 2.15, and 2.21, respectively. Cohen’s d values were greater than 0.8, indicating the considerable effect of the intervention. Hedges’ g was also calculated due to the small sample size, which was greater than 0.8 for all subjects in standing ability and gait speed. Conclusion: Following the use of Gait Enhancer along with conventional occupational therapy, we observed an increase in the ability to stand and walk at children with cerebral palsy. Findings showed that the change in standing ability and walking speed occurred more during the period of using the designed device than other stages, which could be a consequence of using Gait Enhancer along with routine occupational therapy sessions at this stage of the study. However, it should be noted that this study was only a single case study and to prove the effectiveness of this tool in children with cerebral palsy, it is necessary to conduct clinical trial studies.

scholarly journals Pre-medical students’ perceptions of educational environment and their subjective happiness: a comparative study before and after the COVID-19 pandemic

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yanyan Lin ◽  
Ye Ji Kang ◽  
Hyo jeong Lee ◽  
Do-Hwan Kim
Keyword(s):  
Medical Students ◽  
Effect Size ◽  
Educational Environment ◽  
Medium Effect ◽  
Subjective Happiness ◽  
Social Self ◽  
Self Perceptions ◽  
Cohen’S D ◽  
Before And After ◽  
Cohen's D

Abstract Background The COVID-19 pandemic necessarily changed pre-medical students’ educational environment into an online format—and students’ subjective happiness (SH) is highly impacted by their educational environment. This study investigates changes in pre-medical students’ perceptions of their educational environment and their SH before and after the pandemic, as well as explores the predictors related to their SH. Methods The Korean version of the Dundee Ready Educational Environment Measure (DREEM) questionnaire and single-item measures of SH and professional identity (PI) were used. The t-test was employed to analyze the differences of the SH, PI, and DREEM subscales scores before and after the onset of COVID-19. Cohen’s d was used as effect size and correlations between SH and different subscales of DREEM were analyzed using Pearson’s correlation. The multiple regression analysis was performed to reveal associations between predictors and SH. Results A total of 399 pre-medical students completed the survey both before and after the COVID-19 pandemic. The DREEM scores and all subscales scores significantly increased but each presents a different effect size. Students’ Perceptions of Learning (SPL: Cohen’s d = 0.97), Students’ Perceptions of Teaching (SPT: Cohen’s d = 1.13), and Students’ Perceptions of Atmosphere (SPA: Cohen’s d = 0.89) have large effect sizes. Students’ Academic Self-Perceptions (SASP: Cohen’s d = 0.66) have a medium effect size and Students’ Social Self-Perceptions (SSSP: Cohen’s d = 0.40) have a small effect size. In contrast, no significant change was noted in the SH and PI. Both PI and SSSP impacted SH before COVID-19, but after the pandemic, SH was impacted by SPL, SPA, and SSSP. Conclusions Students’ overall perception of their educational environment was more positive after the onset of COVID-19, but their social self-perceptions improved the least. Additionally, SSSP is the only predictor of SH both before and after the pandemic. The findings of this study suggest that educational institutions must pay attention to students’ social relationships when trying to improve their educational environment. Furthermore, so as to increase students’ SH, development of both educational environment and PI is essential.

scholarly journals Pengaruh Model Pembelajaran Kooperatif Tipe CO-OP CO-OP terhadap Kemampuan Pemecahan Masalah Statistika Peserta Didik Kelas VIII SMP Negeri 1 Minasate’ne Kab. Pangkep

Equals ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. 41-49
Author(s):  
Nurmahwati Nurmahwati ◽  
Rahmawati Rahmawati
Keyword(s):  
Effect Size ◽  
Purposive Sampling ◽  
Cohen’S D ◽  
Cohen's D

Penelitian ini merupakan penelitian eksperimen semu (quasi eksperiment) yang bertujuan untuk mengetahui pengaruh dari penerapan model pembelajaran kooperatif tipe co-op co-op terhadap kemampuan pemecahan masalah statistika peserta didik kelas VIII SMP Negeri 1 Minasate’ne Kab. Pangkep. Metode penelitian ini adalah penelitian kuantitatif dengan nonequivalent control grup design, dimana terdapat dua kelompok belajar. Kelompok eksperimen diajar menggunakan model pembelajaran kooperatif tipe co-op co-op, dan kelompok kontrol menggunakan model pembelajaran Konvensional. Populasi dalam penelitian ini adalah peserta didik kelas VIII SMP Negeri 1 Minasate’ne dan pengambilan sampel dilakukan dengan teknik purposive sampling. Sampel dalam penelitian ini adalah peserta didik kelas VIII B sebagai kelas eksperimen dan kelas VIII D sebagai kelas kontrol. Pengumpulan data menggunakan tes kemampuan pemecahan masalah yang telah divalidasi oleh ahli. Hasil penelitian ini dianalsis secara deskriptif dan inferensial dengan uji normalitas dan uji homogenitas sebagai uji prasyarat analisis dan uji t sebagai uji hipotesa. Hasil analisis deskriptif menunjukkan bahwa rata-rata posttest pada kelas kontrol adalah 73,59 dengan standar deviasi 7,243 sedangkan rata-rata posttest pada kelas eksperimen adalah 82,69 dengan standar deviasi 8,177. Berdasarkan uji t, diperoleh nilai sig (2-tailed) < ½ α (0,000 < 0,025) yang menunjukkan adanya pengaruh model pembelajaran kooperatif tipe co-op co-op terhadap kemampuan pemecahan masalah statistika peserta didik kelas VIII SMP Negeri 1 Minasate’ne, melalui perhitungan effect size cohen’s d diketahui bahwa besar pengaruhnya adalah 1,199 yang berada pada kategori tinggi dengan persentase 86% sehingga dapat disimpulkan bahwa Penerapan model pembelajaran kooperatif tipe co-op co-op memberikan pengaruh positif terhadap kemampuan pemecahan masalah statistika peserta didik SMP Negeri 1 Minasate’ne Kab. Pangkep.

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