Comparisons between Aquaponic and Conventional Hydroponic Crop Yields: A Meta-Analysis

Aquaponic is a relatively new system of farming, which has received much research attention due to its potential for sustainability. However, there is no consensus on comparability between crop yields obtained from aquaponics (AP) and conventional hydroponics (cHP). Meta-analysis was used to synthesize the literature on studies that compared crop yields of AP and cHP. Factors responsible for differences were also examined through subgroup analysis. A literature search was conducted in five databases with no time restriction in order to capture any publication on AP and cHP crop yield comparisons. The search was, however, limited to journal and conference articles published in English. Study characteristics and outcome measures of food crops were extracted. A natural log response ratio effect size measure was used to transform study outcomes. An unweighted meta-analysis was conducted through bootstrapping to calculate overall effect size and its confidence interval. Between-study heterogeneity (I2) was estimated using a random effects model. Subgroup and meta-regression were used to assess moderators, in an attempt to explain heterogeneity in the effect size. The results showed that although crop yield in AP was lower than conventional cHP, the difference was not statistically significant. However, drawing conclusions on the overall effect size must be done with caution due to the use of unweighted meta-analysis. There were statistically significant effects of aquatic organism, hydroponic system type, and nutrient supplementation used in the studies on crop yield comparisons. Nutrient supplementation, particularly, led to on average higher crop yield in AP relative to cHP. These findings are a vital information source for choosing factors to include in an AP study. These findings also synthesize the current trends in AP crop yields in comparison with cHP.

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Effect size: a statistical basis for clinical practice

Revista Odonto Ciência ◽

10.15448/1980-6523.2018.1.29437 ◽

2018 ◽

Vol 33 (1) ◽

pp. 84

Author(s):

José Valladares-Neto

Keyword(s):

Effect Size ◽

Risk Estimation ◽

Meta Analysis ◽

Scientific Evidence ◽

P Value ◽

Conceptual Basis ◽

Statistical Measure ◽

The Difference ◽

Strength Of Association ◽

Calculate Sample Size

OBJECTIVE: Effect size (ES) is the statistical measure which quantifies the strength of a phenomenon and is commonly applied to observational and interventional studies. The aim of this review was to describe the conceptual basis of this measure, including its application, calculation and interpretation.RESULTS: As well as being used to detect the magnitude of the difference between groups, to verify the strength of association between predictor and outcome variables, to calculate sample size and power, ES is also used in meta-analysis. ES formulas can be divided into these categories: I – Difference between groups, II – Strength of association, III – Risk estimation, and IV – Multivariate data. The d value was originally considered small (0.20 > d ≤ 0.49), medium (0.50 > d≤ 0.79) or large (d ≥ 0.80); however, these cut-off limits are not consensual and could be contextualized according to a specific field of knowledge. In general, a larger score implies that a larger difference was detected.CONCLUSION: The ES report, in conjunction with the confidence interval and P value, aims to strengthen interpretation and prevent the misinterpretation of data, and thus leads to clinical decisions being based on scientific evidence studies.

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Use of Effect Size Measures along with p-Value in Scientific Publications

Borneo Epidemiology Journal ◽

10.51200/bej.v2i2.3629 ◽

2021 ◽

Vol 2 (2) ◽

pp. 89-97

Author(s):

Sandheep Sugathan ◽

Lilli Jacob

Keyword(s):

Effect Size ◽

Meta Analysis ◽

Research Articles ◽

Outcome Variable ◽

P Value ◽

Continuous Variables ◽

Statistical Concept ◽

Scientific Publications ◽

Actual Difference ◽

The Difference

Background: To describe various measures for estimation of effect size, how it can be calculated and the scenarios in which each measures of effect size can be applied. Methods: The researchers can display the effect size measures in research articles which evaluate the difference between the means of continuous variables in different groups or the difference in proportions of outcomes in different groups of individuals. When p-value alone is displayed in a research article, without mentioning the effect size, reader may not get the correct pictures regarding the effect or role of independent variable on the outcome variable. Results: Effect size is a statistical concept that measures the actual difference between the groups or the strength of the relationship between two variables on a numeric scale. Conclusion: Effect size measures in scientific publications can communicate the actual difference between groups or the estimate of association between the variables, not just if the association or difference is statistically significant. The researchers can make their findings more interpretable, by displaying a suitable measure of effect size. Effect size measure can help the researchers to do meta-analysis by combining the data from multiple research articles.

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The Effectiveness of Dynamic Geometry Software Applications in Learning Mathematics: A Meta-Analysis Study

International Journal of Interactive Mobile Technologies (iJIM) ◽

10.3991/ijim.v15i02.18853 ◽

2021 ◽

Vol 15 (02) ◽

pp. 18

Author(s):

Dadang Juandi ◽

Yaya Sukjaya Kusumah ◽

Maximus Tamur ◽

Krisna Satrio Perbowo ◽

Muhammad Daut Siagian ◽

...

Keyword(s):

High Schools ◽

Effect Size ◽

Meta Analysis ◽

Random Effect Model ◽

Dynamic Geometry ◽

Dynamic Geometry Software ◽

Analysis Study ◽

Mathematical Abilities ◽

Study Characteristics ◽

The Impact

The purpose of this study was to (1) assess the impact of using Dynamic Geometry Software (DGS) on students’ mathematical abilities, (2) determine the differences in effectiveness based on study characteristics in order to help educators decide under what conditions the use of DGS would be suitable in improving students' mathematical abilities. This meta-analysis study investigates 57 effect sizes from 50 articles that have been published in journals, international and domestic proceedings from 2010 to 2020 using the Comprehensive Meta-Analysis (CMA) tool as a calculation tool. Meanwhile, the Hedges coefficient is applied to the calculation of the effect size at the 95% confidence level. Based on a random effect model with a standard error of 0.09, the analysis results have found an overall effect size of 1.07. This means that learning using DGS has a high positive effect on students' mathematical abilities. The effect size of 1.07 explains the average student who uses DGS exceeds 84% math ability of those in conventional classes that are initially equivalent. Analysis of the study characteristics found significant differences in terms of sample size, student to computer ratio, and education level. This research showed the DGS used was more effective under certain conditions. First, it is very effective in sample conditions less than or equal to 30. Second, it provides classrooms with a sufficient number of computers, allowing students to use them individually, which is required to achieve higher effectiveness levels. Third, DGS is effective in high schools and colleges than in junior high schools. These facts can help educators in deciding on the appropriate sample sizes, student to computer ratios, and future levels of education in using DGS.

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Do animated triangles reveal a marked difficulty among autistic people with reading minds?

Autism ◽

10.1177/1362361321989152 ◽

2021 ◽

pp. 136236132198915

Author(s):

Alexander C Wilson

Keyword(s):

Effect Size ◽

Meta Analysis ◽

General Information ◽

Medium Effect ◽

Group Differences ◽

Social Challenges ◽

Autistic People ◽

The Social ◽

Small Effect Size ◽

The Difference

This meta-analysis tested whether autistic people show a marked, isolated difficulty with mentalising when assessed using the Frith -Happé Animations, an advanced test of mentalising (or ‘theory of mind’). Effect sizes were aggregated in multivariate meta-analysis from 33 papers reporting data for over 3000 autistic and non-autistic people. Relative to non-autistic individuals, autistic people underperformed, with a small effect size on the non-mentalising control conditions and a medium effect size on the mentalising condition. This indicates that studies have reliably found mentalising to be an area of challenge for autistic people, although the group differences were not large. It remains to be seen how important mentalising difficulties are in accounting for the social difficulties diagnostic of autism. As autistic people underperformed on the control conditions as well as the mentalising condition, it is likely that group differences on the test are partly due to domain-general information processing differences. Finally, there was evidence of publication bias, suggesting that true effects on the Frith -Happé Animations may be somewhat smaller than reported in the literature. Lay abstract Autistic people are thought to have difficulty with mentalising (our drive to track and understand the minds of other people). Mentalising is often measured by the Frith -Happé Animations task, where individuals need to interpret the interactions of abstract shapes. This review article collated results from over 3000 people to assess how autistic people performed on the task. Analysis showed that autistic people tended to underperform compared to non-autistic people on the task, although the scale of the difference was moderate rather than large. Also, autistic people showed some difficulty with the non-mentalising as well as mentalising aspects of the task. These results raise questions about the scale and specificity of mentalising difficulties in autism. It also remains unclear how well mentalising difficulties account for the social challenges diagnostic of autism.

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Effects of stimulants and atomoxetine on emotional lability in adults: A systematic review and meta-analysis

European Psychiatry ◽

10.1016/j.eurpsy.2017.05.021 ◽

2017 ◽

Vol 44 ◽

pp. 198-207 ◽

Cited By ~ 17

Author(s):

T.R. Moukhtarian ◽

R.E. Cooper ◽

E. Vassos ◽

P. Moran ◽

P. Asherson

Keyword(s):

Systematic Review ◽

Effect Size ◽

Meta Analysis ◽

Emotional Lability ◽

Adhd Symptoms ◽

Double Blind ◽

Hyperactivity Disorder ◽

Ovid Medline ◽

Weighted Effect ◽

The Difference

AbstractBackground:Emotional lability (EL) is an associated feature of attention-deficit/hyperactivity disorder (ADHD) in adults, contributing to functional impairment. Yet the effect of pharmacological treatments for ADHD on EL symptoms is unknown. We conducted a systematic review and meta-analysis to examine the effects of stimulants and atomoxetine on symptoms of EL and compare these with the effects on core ADHD symptoms.Methods:A systematic search was conducted on the databases Embase, PsychInfo, and Ovid Medline®and the clinicaltrials.gov website. We included randomised, double-blind, placebo-controlled trials of stimulants and atomoxetine in adults aged 18–60 years, with any mental health diagnosis characterised by emotional or mood instability, with at least one outcome measure of EL. All identified trials were on adults with ADHD. A random-effects meta-analysis with standardised mean difference and 95% confidence intervals was used to investigate the effect size on EL and compare this to the effect on core ADHD symptoms.Results:Of the 3,864 publications identified, nine trials met the inclusion criteria for the meta-analysis. Stimulants and atomoxetine led to large mean weighted effect-sizes for on ADHD symptoms (n= 9, SMD = −0.8, 95% CI:−1.07 to −0.53). EL outcomes showed more moderate but definite effects (n= 9, SMD = −0.41, 95% CI:−0.57 to −0.25).Conclusions:In this meta-analysis, stimulants and atomoxetine were moderately effective for EL symptoms, while effect size on core ADHD symptoms was twice as large. Methodological issues may partially explain the difference in effect size. Reduced average effect size could also reflect heterogeneity of EL with ADHD pharmacotherapy responsive and non-responsive sub-types. Our findings indicate that EL may be less responsive than ADHD symptoms overall, perhaps indicating the need for adjunctive psychotherapy in some cases. To clarify these questions, our findings need replication in studies selecting subjects for high EL and targeting EL as the primary outcome.

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Revisiting and expanding the meta-analysis of variation: The log coefficient of variation ratio, lnCVR

10.1101/2020.01.06.896522 ◽

2020 ◽

Cited By ~ 1

Author(s):

Alistair M. Senior ◽

Wolfgang Viechtbauer ◽

Shinichi Nakagawa

Keyword(s):

Effect Size ◽

Meta Analysis ◽

Experimental Treatment ◽

Worked Examples ◽

Sampling Variance ◽

Post Test ◽

Total Variability ◽

The Difference ◽

Meta Analyses ◽

Analysis Of Variation

AbstractMeta-analyses are frequently used to quantify the difference in the average values of two groups (e.g., control and experimental treatment groups), but examine the difference in the variability (variance) of two groups. For such comparisons, the two relatively new effect size statistics, namely the log-transformed ‘variability ratio’ (the ratio of two standard deviations; lnVR) and the log-transformed ‘CV ratio’ (the ratio of two coefficients of variation; lnCVR) are useful. In practice, lnCVR may be of most use because a treatment may affect the mean and the variance simultaneously. We review current, and propose new, estimators for lnCVR and lnVR. We also present methods for use when the two groups are dependent (e.g., for cross-over and pre-test-post-test designs). A simulation study evaluated the performance of these estimators and we make recommendations about which estimators one should use to minimise bias. We also present two worked examples that illustrate the importance of accounting for the dependence of the two groups. We found that the degree to which dependence is accounted for in the sampling variance estimates can impact heterogeneity parameters such as τ2 (i.e., the between-study variance) and I2 (i.e., the proportion of the total variability due to between-study variance), and even the overall effect, and in turn qualitative interpretations. Meta-analytic comparison of the variability between two groups enables us to ask completely new questions and to gain fresh insights from existing datasets. We encourage researchers to take advantage of these convenient new effect size measures for the meta-analysis of variation.

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Autumn and winter application of nitrogen fertilizers on clay soils

Agricultural and Food Science ◽

10.23986/afsci.71786 ◽

1971 ◽

Vol 43 (2) ◽

pp. 76-85

Author(s):

Yrjö Pessi ◽

Mikko Ylänen ◽

Auvo Leskelä ◽

Jorma Syvälahti

Keyword(s):

Protein Content ◽

Crop Yield ◽

Crop Yields ◽

Early Summer ◽

Frozen Soil ◽

Nitrogen Fertilizers ◽

Clay Soils ◽

Spring Cereals ◽

The Difference ◽

Autumn And Winter

In order to examine the application time of nitrogen given to cereals, several tests have been arranged on the Kotkaniemi Experimental Farm at Vihti ever since 1965. The tests have been carried out on solid clay soils, where the leaching of nitrogen has been expected to be slow. In spring cereals the autumn application of nitrogen in November on frozen soil has given a good crop yield. The protein content of the crop in the plots where nitrogen was given in autumn was lower than in those where the spreading took place in spring. As for winter wheat, application in December has given the best average crop yields but the decline of the protein content is to be considered a disadvantage. In rye, spring fertilization has given the best average crop yield. There has, however, clearly been less lodging in autumn applications than in plots where the nitrogen was spread in the spring. Regarding nitrogen fertilization of autumn sown plants the usual custom in Finland is to give nitrogen in autumn for growth during the autumn and in the spring for the coming growing season. However, as low rainfall is typical ofthe Finnish spring, the effect of nitrogen given by broadcasting in early summer is slow, especially on solid soils like clay. As for spring cereals, the fertilizer placement at a depth of 8 to 10 cms has given distinctly better results than broadcasting and the usual mixing into the soil (Elonen 1967, Larpes 1966 and 1968, Nieminen 1967, Pessi 1970). The difference in the growth intensity has most clearly been evident in the early development of cereals. Simultaneously it has become clear that the placement of nitrogen has been of the greatest importance (Pessi 1970). As during winter in Finland the soil is usually frozen and covered with snow, no noteworthy leaching of nutrients takes place. On the basis of the results and observations mentioned above the question are as to what it would mean in practice in solid soils if the nitrogen was spread already before snowfall or on the snow, when the water from the melting snow would in spring cause the nitrogen to penetrate the soil. For this purpose tests were started on the Kotkaniemi Experimental Farm of Rikkihappo Oy in autumn 1965.

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Effect Size

Psychology ◽

10.1093/obo/9780199828340-0247 ◽

2019 ◽

Author(s):

David B. Flora

Keyword(s):

Effect Size ◽

Null Hypothesis ◽

Power Analysis ◽

Meta Analysis ◽

Significance Test ◽

Effect Sizes ◽

Research Reporting ◽

The Difference ◽

Strength Of Association ◽

True Population

Simply put, effect size (ES) is the magnitude or strength of association between or among variables. Effect sizes (ESs) are commonly represented numerically (i.e., as parameters for population ESs and statistics for sample estimates of population ESs) but also may be communicated graphically. Although the word “effect” may imply that an ES quantifies the strength of a causal association (“cause and effect”), ESs are used more broadly to represent any empirical association between variables. Effect sizes serve three general purposes: research results reporting, power analysis, and meta-analysis. Even under the same research design, an ES that is appropriate for one of these purposes may not be ideal for another. Effect size can be conveyed graphically or numerically using either unstandardized metrics, which are interpreted relative to the original scales of the variables involved (e.g., the difference between two means or an unstandardized regression slope), or standardized metrics, which are interpreted in relative terms (e.g., Cohen’s d or multiple R2). Whereas unstandardized ESs and graphs illustrating ES are typically most effective for research reporting, that is, communicating the original findings of an empirical study, many standardized ES measures have been developed for use in power analysis and especially meta-analysis. Although the concept of ES is clearly fundamental to data analysis, ES reporting has been advocated as an essential complement to null hypothesis significance testing (NHST), or even as a replacement for NHST. A null hypothesis significance test involves making a dichotomous judgment about whether to reject a hypothesis that a true population effect equals zero. Even in the context of a traditional NHST paradigm, ES is a critical concept because of its central role in power analysis.

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Strategy-oriented web-based English instruction — A meta-analysis

Australasian Journal of Educational Technology ◽

10.14742/ajet.67 ◽

2013 ◽

Vol 29 (2) ◽

Cited By ~ 8

Author(s):

Mei-Mei Chang ◽

Mei-Chen Lin

Keyword(s):

Effect Size ◽

Meta Analysis ◽

English Learning ◽

Web Based ◽

Text Annotation ◽

Web Based Instruction ◽

Study Characteristics ◽

Design Characteristics ◽

Based Instruction ◽

English Instruction

In this meta-analysis study, a total of thirty-one studies related to strategy use of students in Taiwan in a web-based English context were identified, collected, and analysed. Three criteria for selecting the appropriate studies are (a) they must have applied web-based instruction; (b) they must be related to English learning; and (c) they had to provide one of the following data in order to calculate effect size (ES), including means and standard deviation (SD), F values, or t values. The characteristics were divided into three groups; that is, study characteristics, methodological characteristics, and design characteristics. The findings reveal that predicting, summarizing, self-questioning and clarifying; text annotation with pictures, and glossing were strategies with higher effect size (ES > 0.920). Findings from the meta-analysis in this study could provide a basis for designing and developing a strategy-oriented web-based instruction.

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Different Spectral Analysis Methods for the Theta/Beta Ratio Calculate Different Ratios But Do Not Distinguish ADHD from Controls

Applied Psychophysiology and Biofeedback ◽

10.1007/s10484-020-09471-2 ◽

2020 ◽

Vol 45 (3) ◽

pp. 165-173

Author(s):

Hanneke van Dijk ◽

Roger deBeus ◽

Cynthia Kerson ◽

Michelle E. Roley-Roberts ◽

Vincent J. Monastra ◽

...

Keyword(s):

Signal Processing ◽

Effect Size ◽

Meta Analysis ◽

Healthy Controls ◽

Eeg Signal ◽

Ongoing Research ◽

Beta Power ◽

Signal Processing Algorithms ◽

The Difference ◽

Eeg Signal Processing

Abstract There has been ongoing research on the ratio of theta to beta power (Theta/Beta Ratio, TBR) as an EEG-based test in the diagnosis of ADHD. Earlier studies reported significant TBR differences between patients with ADHD and controls. However, a recent meta-analysis revealed a marked decline of effect size for the difference in TBR between ADHD and controls for studies published in the past decade. Here, we test if differences in EEG processing explain the heterogeneity of findings. We analyzed EEG data from two multi-center clinical studies. Five different EEG signal processing algorithms were applied to calculate the TBR. Differences between resulting TBRs were subsequently assessed for clinical usability in the iSPOT-A dataset. Although there were significant differences in the resulting TBRs, none distinguished between children with and without ADHD, and no consistent associations with ADHD symptoms arose. Different methods for EEG signal processing result in significantly different TBRs. However, none of the methods significantly distinguished between ADHD and healthy controls in our sample. The secular effect size decline for the TBR is most likely explained by factors other than differences in EEG signal processing, e.g. fewer hours of sleep in participants and differences in inclusion criteria for healthy controls.

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