Statistical practices of educational researchers: An analysis of their ANOVA, MANOVA, and ANCOVA analyses

Articles published in several prominent educational journals were examined to investigate the use of data-analysis tools by researchers in four research paradigms: between-subjects univariate designs, between-subjects multivariate designs, repeated measures designs, and covariance designs. In addition to examining specific details pertaining to the research design (e.g., sample size, group size equality/inequality) and methods employed for data analysis, we also catalogued whether: (a) validity assumptions were examined, (b) effect size indices were reported, (c) sample sizes were selected based on power considerations, and (d) appropriate textbooks and/or articles were cited to communicate the nature of the analyses that were performed. Our analyses imply that researchers rarely verify that validity assumptions are satisfied and accordingly typically use analyses that are nonrobust to assumption violations. In addition, researchers rarely report effect size statistics, nor do they routinely perform power analyses to determine sample size requirements. We offer many recommendations to rectify these shortcomings.Social Sciences and Humanities Research Counci

Intelligent Bayes Classifier (IBC) for ENT infection classification in hospital environment

Electronic Nose based ENT bacteria identification in hospital environment is a classical and challenging problem of classification. In this paper an electronic nose (e-nose), comprising a hybrid array of 12 tin oxide sensors (SnO(2)) and 6 conducting polymer sensors has been used to identify three species of bacteria, Escherichia coli (E. coli), Staphylococcus aureus (S. aureus), and Pseudomonas aeruginosa (P. aeruginosa) responsible for ear nose and throat (ENT) infections when collected as swab sample from infected patients and kept in ISO agar solution in the hospital environment. In the next stage a sub-classification technique has been developed for the classification of two different species of S. aureus, namely Methicillin-Resistant S. aureus (MRSA) and Methicillin Susceptible S. aureus (MSSA). An innovative Intelligent Bayes Classifier (IBC) based on "Baye's theorem" and "maximum probability rule" was developed and investigated for these three main groups of ENT bacteria. Along with the IBC three other supervised classifiers (namely, Multilayer Perceptron (MLP), Probabilistic neural network (PNN), and Radial Basis Function Network (RBFN)) were used to classify the three main bacteria classes. A comparative evaluation of the classifiers was conducted for this application. IBC outperformed MLP, PNN and RBFN. The best results suggest that we are able to identify and classify three bacteria main classes with up to 100% accuracy rate using IBC. We have also achieved 100% classification accuracy for the classification of MRSA and MSSA samples with IBC. We can conclude that this study proves that IBC based e-nose can provide very strong and rapid solution for the identification of ENT infections in hospital environment

Setting an Optimal α That Minimizes Errors in Null Hypothesis Significance Tests

Null hypothesis significance testing has been under attack in recent years, partly owing to the arbitrary nature of setting α (the decision-making threshold and probability of Type I error) at a constant value, usually 0.05. If the goal of null hypothesis testing is to present conclusions in which we have the highest possible confidence, then the only logical decision-making threshold is the value that minimizes the probability (or occasionally, cost) of making errors. Setting α to minimize the combination of Type I and Type II error at a critical effect size can easily be accomplished for traditional statistical tests by calculating the α associated with the minimum average of α and β at the critical effect size. This technique also has the flexibility to incorporate prior probabilities of null and alternate hypotheses and/or relative costs of Type I and Type II errors, if known. Using an optimal α results in stronger scientific inferences because it estimates and minimizes both Type I errors and relevant Type II errors for a test. It also results in greater transparency concerning assumptions about relevant effect size(s) and the relative costs of Type I and II errors. By contrast, the use of α = 0.05 results in arbitrary decisions about what effect sizes will likely be considered significant, if real, and results in arbitrary amounts of Type II error for meaningful potential effect sizes. We cannot identify a rationale for continuing to arbitrarily use α = 0.05 for null hypothesis significance tests in any field, when it is possible to determine an optimal α

Expression profiling of blood samples from an SU5416 Phase III metastatic colorectal cancer clinical trial: a novel strategy for biomarker identification

BACKGROUND: Microarray-based gene expression profiling is a powerful approach for the identification of molecular biomarkers of disease, particularly in human cancers. Utility of this approach to measure responses to therapy is less well established, in part due to challenges in obtaining serial biopsies. Identification of suitable surrogate tissues will help minimize limitations imposed by those challenges. This study describes an approach used to identify gene expression changes that might serve as surrogate biomarkers of drug activity. METHODS: Expression profiling using microarrays was applied to peripheral blood mononuclear cell (PBMC) samples obtained from patients with advanced colorectal cancer participating in a Phase III clinical trial. The PBMC samples were harvested pre-treatment and at the end of the first 6-week cycle from patients receiving standard of care chemotherapy or standard of care plus SU5416, a vascular endothelial growth factor (VEGF) receptor tyrosine kinase (RTK) inhibitor. Results from matched pairs of PBMC samples from 23 patients were queried for expression changes that consistently correlated with SU5416 administration. RESULTS: Thirteen transcripts met this selection criterion; six were further tested by quantitative RT-PCR analysis of 62 additional samples from this trial and a second SU5416 Phase III trial of similar design. This method confirmed four of these transcripts (CD24, lactoferrin, lipocalin 2, and MMP-9) as potential biomarkers of drug treatment. Discriminant analysis showed that expression profiles of these 4 transcripts could be used to classify patients by treatment arm in a predictive fashion. CONCLUSIONS: These results establish a foundation for the further exploration of peripheral blood cells as a surrogate system for biomarker analyses in clinical oncology studies

Analysis of parcel-based image classification methods for monitoring the activities of the Land Bank of Galicia (Spain)

[EN] The abandonment of agricultural plots entails a low economic productivity of the land and a higher vulnerability to wildfires and degradation of affected areas. In this sense, the local government of Galicia is promoting new methodologies based on high-resolution images in order to classify the territory in basic and generic land uses. This procedure will be used to control the sustainable management of plots belonging to the Land Bank. This paper presents an application study for maintaining and updating land use/land cover geospatial databases using parcel-oriented classification. The test is performed over two geographic areas of Galicia, in the northwest of Spain. In this region, forest and shrublands in mountain environments are very heterogeneous with many private unproductive plots, some of which are in a high state of abandonment. The dataset is made of high spatial resolution multispectral imagery, cadastral cartography employed to define the image objects (plots), and field samples used to define evaluation and training samples. A set of descriptive features is computed quantifying different properties of the objects, i.e. spectral, texture, structural, and geometrical. Additionally, the effect on the classification and updating processes of the historical land use as a descriptive feature is tested. Three different classification methodologies are analyzed: linear discriminant analysis, decision trees, and support vector machine. The overall accuracies of the classifications obtained are always above 90 % and support vector machine method is proved to provide the best performance. Forest and shrublands areas are especially undefined, so the discrimination between these two classes is low. The results enable to conclude that the use of automatic parcel-oriented classification techniques for updating tasks of land use/land cover geospatial databases, is effective in the areas tested, particularly when broad and well defined classes are required.The authors appreciate the collaboration and support provided by Xunta de Galicia, Sociedade para o Desenvolvemento Comarcal de Galícia, and Banco de Terras de Galicia. The financial support provided by the Spanish Ministerio de Ciencia e Innovación in the framework of the projects CGL2010-19591/BTE and CGL2009-14220 is also acknowledged.Hermosilla, T.; Díaz Manso, J.; Ruiz Fernández, LÁ.; Recio Recio, JA.; Fernández-Sarría, A.; Ferradáns Nogueira, P. (2012). Analysis of parcel-based image classification methods for monitoring the activities of the Land Bank of Galicia (Spain). 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Int Arch Photogramm Remote Sens Spat Inf Sci 38:217–222Perveen F, Nagasawa R, Ali S, Husnain (2008) Evaluation of ASTER spectral bands for agricultural land cover mapping using pixel-based and object-based classification approaches. Int Arch Photogramm Remote Sens Spat Inf Sci 37(4-C1)Petit CC, Lambin EF (2002) Impact of data integration technique on historical land-use/land-cover change: comparing historical maps with remote sensing data in the Belgian Ardennes. Landsc Ecol 17:117–132Quinlan JR (1993) C4.5: Programs for machine learning. Kaufmann, San FranciscoRabe A, van der Linden S, Hostert P (2010) imageSVM, Version 2.1. www.hu-geomatics.deRecio JA, Hermosilla T, Ruiz LA, Fernández-Sarría A (2011) Historical land use as a feature for image classification. Photogramm Eng Remote Sens 77(4):377–387Ruiz LA, Fernández-Sarría A, Recio JA (2004) Texture feature extraction for classification of remote sensing data using wavelet decomposition: a comparative study. 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Randomised feasibility trial of a teaching assistant led extracurricular physical activity intervention for 9 to 11 year olds: Action 3:30

Background: Extracurricular programmes could provide a mechanism to increase the physical activity (PA) of primary-school-aged children. The aim of this feasibility study was to examine whether the Action 3:30 intervention, which is delivered by teaching assistants, holds promise as a means of increasing the PA of Year 5 and 6 children. Methods: A cluster randomised feasibility trial was conducted in 20 primary schools. Ten schools received the Action 3:30 intervention and 10 schools were allocated to the control arm. The intervention was 40 one-hour sessions, delivered twice a week by teaching assistants. The proportion of participants recruited per school was calculated. Session delivery and session attendance was calculated for intervention schools. Weekday and after-school (3.30 to 8.30 pm) moderate to vigorous intensity physical (MVPA) was assessed by accelerometer at baseline (T0), during the last few weeks of the intervention (T1) and four months after the intervention had ended (T2). The costs of delivering the intervention were estimated. Results: Five intervention schools ran all 40 of the intended sessions. Of the remaining five, three ran 39, one ran 38 and one ran 29 sessions. Mean attendance was 53%. The adjusted difference in weekday MVPA at T1 was 4.3 minutes (95% CI −2.6 to 11.3). Sex-stratified analyses indicated that boys obtained 8.6 more minutes of weekday MVPA than the control group (95% CI 2.8 to 14.5) at T1 with no effect for girls (0.15 minutes, 95% CI −9.7 to 10.0). There was no evidence that participation in the programme increased MVPA once the club sessions ceased (T2). The indicative average cost of this intervention was £2,425 per school or £81 per participating child during its first year and £1,461 per school or £49 per participating child thereafter. Conclusions: The effect of the Action 3:30 intervention was comparable to previous physical activity interventions but further analysis indicated that there was a marked sex difference with a positive impact on boys and no evidence of an effect on girls. The Action 3:30 intervention holds considerable promise but more work is needed to enhance the effectiveness of the intervention, particularly for girls

The Effect of Plant Inbreeding and Stoichiometry on Interactions with Herbivores in Nature: Echinacea angustifolia and Its Specialist Aphid

Fragmentation of once widespread communities may alter interspecific interactions by changing genetic composition of interacting populations as well as their abundances and spatial distributions. In a long-term study of a fragmented population of Echinacea angustifolia, a perennial plant native to the North American prairie, we investigated influences on its interaction with a specialist aphid and tending ants. We grew plant progeny of sib-matings (I), and of random pairings within (W) and between (B) seven remnants in a common field within 8 km of the source remnants. During the fifth growing season, we determined each plant's burden of aphids and ants, as well as its size and foliar elemental composition (C, N, P). We also assayed composition (C, N) of aphids and ants. Early in the season, progeny from genotypic classes B and I were twice as likely to harbor aphids, and in greater abundance, than genotypic class W; aphid loads were inversely related to foliar concentration of P and positively related to leaf N and plant size. At the end of the season, aphid loads were indistinguishable among genotypic classes. Ant abundance tracked aphid abundance throughout the season but showed no direct relationship with plant traits. Through its potential to alter the genotypic composition of remnant populations of Echinacea, fragmentation can increase Echinacea's susceptibility to herbivory by its specialist aphid and, in turn, perturb the abundance and distribution of aphids

The effect of a curriculum-based physical activity intervention on accelerometer-assessed physical activity in schoolchildren: a non-randomised mixed methods controlled before-and-after study

Classroom-based physical activity (PA) interventions offer the opportunity to increase PA without disrupting the curriculum. We aimed to explore the feasibility and potential effectiveness of a classroom-based intervention on moderate to vigorous PA (MVPA) and total PA. The secondary aim was to assess the acceptability and sustainability of the intervention. In a mixed-methods, non-randomised, exploratory controlled before-and-after study, 152 children (10 ± 0.7 years) were recruited from five schools; two intervention (n = 72) and three control (n = 80) schools. School teachers delivered an 8-week classroom-based intervention, comprising of 10 minutes daily MVPA integrated into the curriculum. The control schools maintained their usual school routine. Mean daily MVPA (min), total PA (mean cpm), physical fitness, and health-related quality of life measurements were taken at baseline, end of intervention, and 4-weeks post-intervention (follow-up). Data were analysed using a constrained baseline longitudinal analysis model accounting for the hierarchical data structure. For the primary outcomes (MVPA and total PA) the posterior mean difference and 95% compatibility interval were derived using a semi-Bayesian approach with an explicit prior. The acceptability and sustainability of the intervention was explored via thematic content analysis of focus group discussions with teachers (n = 5) and children (n = 50). The difference in mean daily MVPA (intervention-control) was 2.8 (-12.5 to 18.0) min/day at 8 weeks and 7.0 (-8.8 to 22.8) min/day at follow-up. For total PA, the differences were -2 (-127 to 124) cpm at 8-weeks and 11 (-121 to 143) cpm at follow-up. The interval estimates indicate that meaningful mean effects (both positive and negative) as well as trivial effects are reasonably compatible with the data and design. The intervention was received positively with continuation reported by the teachers and children. Classroom-based PA could hold promise for increasing average daily MVPA, but a large cluster randomised controlled trial is required

Characterization of Italian honeys (Marche Region) on the basis of their mineral content and some typical quality parameters

<p>Abstract</p> <p>Background</p> <p>The characterization of three types of Marche (Italy) honeys (Acacia, Multifloral, Honeydew) was carried out on the basis of the their quality parameters (pH, sugar content, humidity) and mineral content (Na, K, Ca, Mg, Cu, Fe, and Mn). Pattern recognition methods such as principal components analysis (PCA) and linear discriminant analysis (LDA) were performed in order to classify honey samples whose botanical origins were different, and identify the most discriminant parameters. Lastly, using ANOVA and correlations for all parameters, significant differences between diverse types of honey were examined.</p> <p>Results</p> <p>Most of the samples' water content showed good maturity (98%) whilst pH values were in the range 3.50 – 4.21 confirming the good quality of the honeys analysed. Potassium was quantitatively the most relevant mineral (mean = 643 ppm), accounting for 79% of the total mineral content. The Ca, Na and Mg contents account for 14, 3 and 3% of the total mineral content respectively, while other minerals (Cu, Mn, Fe) were present at very low levels. PCA explained 75% or more of the variance with the first two PC variables. The variables with higher discrimination power according to the multivariate statistical procedure were Mg and pH. On the other hand, all samples of acacia and honeydew, and more than 90% of samples of multifloral type have been correctly classified using the LDA. ANOVA shows significant differences between diverse floral origins for all variables except sugar, moisture and Fe.</p> <p>Conclusion</p> <p>In general, the analytical results obtained for the Marche honeys indicate the products' high quality. The determination of physicochemical parameters and mineral content in combination with modern statistical techniques can be a useful tool for honey classification.</p