Bibliografías temáticas / ANOVA

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Literatura académica sobre el tema "ANOVA"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 11 de junio de 2025

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Artículos de revistas sobre el tema "ANOVA"

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Davison, Mark L., and Anu R. Sharma. "ANOVA and ANCOVA of pre- and post-test, ordinal data." Psychometrika 59, no. 4 (1994): 593–600. http://dx.doi.org/10.1007/bf02294394.

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Tabatabaei, Mohammad, Alberto Lovison, Matthias Tan, Markus Hartikainen, and Kaisa Miettinen. "ANOVA-MOP: ANOVA Decomposition for Multiobjective Optimization." SIAM Journal on Optimization 28, no. 4 (2018): 3260–89. http://dx.doi.org/10.1137/16m1096505.

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Algina, James, and Stephen Olejnik. "Conducting Power Analyses for Anova and Ancova in between-Subjects Designs." Evaluation & the Health Professions 26, no. 3 (2003): 288–314. http://dx.doi.org/10.1177/0163278703255248.

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Emerson, Robert Wall. "ANOVA Assumptions." Journal of Visual Impairment & Blindness 116, no. 4 (2022): 585–86. http://dx.doi.org/10.1177/0145482x221124187.

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Rayner, J. C. W. "Extended ANOVA." Journal of Statistical Theory and Practice 11, no. 1 (2016): 208–19. http://dx.doi.org/10.1080/15598608.2016.1268550.

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Last, Michael, Gheorghe Luta, Alex Orso, Adam Porter, and Stan Young. "Pooled ANOVA." Computational Statistics & Data Analysis 52, no. 12 (2008): 5215–28. http://dx.doi.org/10.1016/j.csda.2008.04.024.

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Rasmussen, Jeffrey Lee. "ANOVA MultiMedia: A Program for Teaching ANOVA Designs." Teaching of Psychology 23, no. 1 (1996): 55–56. http://dx.doi.org/10.1207/s15328023top2301_15.

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A multimedia program for teaching analysis of variance (ANOVA) designs is described. The program tests students' understanding of independent groups and repeated measures variables, as well as the source and degrees of freedom columns of the ANOVA source table. The program uses colorful images, brief animations, interactive tasks, and immediate feedback. Information about availability is provided.
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Johnson, Lester W., and Ellen R. Girden. "ANOVA: Repeated Measures." Journal of Marketing Research 32, no. 2 (1995): 243. http://dx.doi.org/10.2307/3152054.

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Ziegel, Eric R., and E. Girden. "ANOVA: Repeated Measures." Technometrics 35, no. 4 (1993): 464. http://dx.doi.org/10.2307/1270301.

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Anderson-Cook, Christine M. "Regression and ANOVA." American Statistician 58, no. 2 (2004): 172–73. http://dx.doi.org/10.1198/tas.2004.s237.

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Tesis sobre el tema "ANOVA"

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Ozbozkurt, Pelin. "Bayesian Inference In Anova Models." Phd thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/3/12611532/index.pdf.

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Estimation of location and scale parameters from a random sample of size n is of paramount importance in Statistics. An estimator is called fully efficient if it attains the Cramer-Rao minimum variance bound besides being unbiased. The method that yields such estimators, at any rate for large n, is the method of modified maximum likelihood estimation. Apparently, such estimators cannot be made more efficient by using sample based classical methods. That makes room for Bayesian method of estimation which engages prior distributions and likelihood functions. A formal combination of the prior knowledge and the sample information is called posterior distribution. The posterior distribution is maximized with respect to the unknown parameter(s). That gives HPD (highest probability density) estimator(s). Locating the maximum of the posterior distribution is, however, enormously difficult (computationally and analytically) in most situations. To alleviate these difficulties, we use modified likelihood function in the posterior distribution instead of the likelihood function. We derived the HPD estimators of location and scale parameters of distributions in the family of Generalized Logistic. We have extended the work to experimental design, one way ANOVA. We have obtained the HPD estimators of the block effects and the scale parameter (in the distribution of errors)<br>they have beautiful algebraic forms. We have shown that they are highly efficient. We have given real life examples to illustrate the usefulness of our results. Thus, the enormous computational and analytical difficulties with the traditional Bayesian method of estimation are circumvented at any rate in the context of experimental design.
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Halldestam, Markus. "ANOVA - The Effect of Outliers." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-295864.

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This bachelor’s thesis focuses on the effect of outliers on the one-way analysis of variance and examines whether the estimate in ANOVA is robust and whether the actual test itself is robust from influence of extreme outliers. The robustness of the estimates is examined using the breakdown point while the robustness of the test is examined by simulating the hypothesis test under some extreme situations. This study finds evidence that the estimates in ANOVA are sensitive to outliers, i.e. that the procedure is not robust. Samples with a larger portion of extreme outliers have a higher type-I error probability than the expected level.
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Liu, Hangcheng. "Comparing Welch's ANOVA, a Kruskal-Wallis test and traditional ANOVA in case of Heterogeneity of Variance." VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/3985.

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Analysis of variance (ANOVA) is a robust test against the normality assumption, but it may be inappropriate when the assumption of homogeneity of variance has been violated. Welch ANOVA and the Kruskal-Wallis test (a non-parametric method) can be applicable for this case. In this study we compare the three methods in empirical type I error rate and power, when heterogeneity of variance occurs and find out which method is the most suitable with which cases including balanced/unbalanced, small/large sample size, and/or with normal/non-normal distributions.
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Prosser, Robert James. "Robustness of multivariate mixed model ANOVA." Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/25511.

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In experimental or quasi-experimental studies in which a repeated measures design is used, it is common to obtain scores on several dependent variables on each measurement occasion. Multivariate mixed model (MMM) analysis of variance (Thomas, 1983) is a recently developed alternative to the MANOVA procedure (Bock, 1975; Timm, 1980) for testing multivariate hypotheses concerning effects of a repeated factor (called occasions in this study) and interaction between repeated and non-repeated factors (termed group-by-occasion interaction here). If a condition derived by Thomas (1983), multivariate multi-sample sphericity (MMS), regarding the equality and structure of orthonormalized population covariance matrices is satisfied (given multivariate normality and independence for distributions of subjects' scores), valid likelihood-ratio MMM tests of group-by-occasion interaction and occasions hypotheses are possible. To date, no information has been available concerning actual (empirical) levels of significance of such tests when the MMS condition is violated. This study was conducted to begin to provide such information. Departure from the MMS condition can be classified into three types— termed departures of types A, B, and C respectively: (A) the covariance matrix for population ℊ (ℊ = 1,...G), when orthonormalized, has an equal-diagonal-block form but the resulting matrix for population ℊ is unequal to the resulting matrix for population ℊ' (ℊ ≠ ℊ'); (B) the G populations' orthonormalized covariance matrices are equal, but the matrix common to the populations does not have equal-diagonal-block structure; or (C) one or more populations has an orthonormalized covariance matrix which does not have equal-diagonal-block structure and two or more populations have unequal orthonormalized matrices. In this study, Monte Carlo procedures were used to examine the effect of each type of violation in turn on the Type I error rates of multivariate mixed model tests of group-by-occasion interaction and occasions null hypotheses. For each form of violation, experiments modelling several levels of severity were simulated. In these experiments: (a) the number of measured variables was two; (b) the number of measurement occasions was three; (c) the number of populations sampled was two or three; (d) the ratio of average sample size to number of measured variables was six or 12; and (e) the sample size ratios were 1:1 and 1:2 when G was two, and 1:1:1 and 1:1:2 when G was three. In experiments modelling violations of types A and C, the effects of negative and positive sampling were studied. When type A violations were modelled and samples were equal in size, actual Type I error rates did not differ significantly from nominal levels for tests of either hypothesis except under the most severe level of violation. In type A experiments using unequal groups in which the largest sample was drawn from the population whose orthogonalized covariance matrix has the smallest determinant (negative sampling), actual Type I error rates were significantly higher than nominal rates for tests of both hypotheses and for all levels of violation. In contrast, empirical levels of significance were significantly lower than nominal rates in type A experiments in which the largest sample was drawn from the population whose orthonormalized covariance matrix had the largest determinant (positive sampling). Tests of both hypotheses tended to be liberal in experiments which modelled type B violations. No strong relationships were observed between actual Type I error rates and any of: severity of violation, number of groups, ratio of average sample size to number of variables, and relative sizes of samples. In equal-groups experiments modelling type C violations in which the orthonormalized pooled covariance matrix departed at the more severe level from equal-diagonal-block form, actual Type I error rates for tests of both hypotheses tended to be liberal. Findings were more complex under the less severe level of structural departure. Empirical significance levels did not vary with the degree of interpopulation heterogeneity of orthonormalized covariance matrices. In type C experiments modelling negative sampling, tests of both hypotheses tended to be liberal. Degree of structural departure did not appear to influence actual Type I error rates but degree of interpopulation heterogeneity did. Actual Type I error rates in type C experiments modelling positive sampling were apparently related to the number of groups. When two populations were sampled, both tests tended to be conservative, while for three groups, the results were more complex. In general, under all types of violation the ratio of average group size to number of variables did not greatly affect actual Type I error rates. The report concludes with suggestions for practitioners considering use of the MMM procedure based upon the findings and recommends four avenues for future research on Type I error robustness of MMM analysis of variance. The matrix pool and computer programs used in the simulations are included in appendices.<br>Education, Faculty of<br>Educational and Counselling Psychology, and Special Education (ECPS), Department of<br>Graduate
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Storm, Christine. "Permutation procedures for ANOVA, regression and PCA." Diss., University of Pretoria, 2012. http://hdl.handle.net/2263/24960.

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Parametric methods are effective and appropriate when data sets are obtained by well-defined random sampling procedures, the population distribution for responses is well-defined, the null sampling distributions of suitable test statistics do not depend on any unknown entity and well-defined likelihood models are provided for by nuisance parameters. Permutation testing methods, on the other hand, are appropriate and unavoidable when distribution models for responses are not well specified, nonparametric or depend on too many nuisance parameters; when ancillary statistics in well-specified distributional models have a strong influence on inferential results or are confounded with other nuisance entities; when the sample sizes are less than the number of parameters and when data sets are obtained by ill-specified selection-bias procedures. In addition, permutation tests are useful not only when parametric tests are not possible, but also when more importance needs to be given to the observed data set, than to the population model, as is typical for example in biostatistics. The different types of permutation methods for analysis of variance, multiple linear regression and principal component analysis are explored. More specifically, one-way, twoway and three-way ANOVA permutation strategies will be discussed. Approximate and exact permutation tests for the significance of one or more regression coefficients in a multiple linear regression model will be explained next, and lastly, the use of permutation tests used as a means to validate and confirm the results obtained from the exploratory PCA will be described.<br>Dissertation (MSc)--University of Pretoria, 2012.<br>Statistics<br>unrestricted
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Liu, Gang. "A New Approach to ANOVA Methods for Autocorrelated Data." University of Toledo / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1461226897.

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Lind, Ingela. "Regressor and Structure Selection : Uses of ANOVA in System Identification." Doctoral thesis, Linköping : Linköpings universitet, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7000.

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Hammi, Malik, and Ahmet Hakan Akdeve. "Poweranalys : bestämmelse av urvalsstorlek genom linjära mixade modeller och ANOVA." Thesis, Linköpings universitet, Statistik och maskininlärning, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-149026.

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In research where experiments on humans and animals is performed, it is in advance important to determine how many observations that is needed in a study to detect any effects in groups and to save time and costs. This could be examined by power analysis, in order to determine a sample size which is enough to detect any effects in a study, a so called “power”. Power is the probability to reject the null hypothesis when the null hypothesis is false. Mälardalen University and the Caroline Institute have in cooperation, formed a study (The Climate Friendly and Ecological Food on Microbiota) based on individual’s dietary intake. Every single individual have been assigned to a specific diet during 8 weeks, with the purpose to examine whether emissions of carbon dioxide, CO2, differs reliant to the specific diet each individuals follows. There are two groups, one treatment and one control group. Individuals assigned to the treatment group are supposed to follow a climatarian diet while the individuals in the control group follows a conventional diet. Each individual have been followed up during 8 weeks in total, with three different measurements occasions, 4 weeks apart. The different measurements are Baseline assessment, Midline assessment and End assessment. In the CLEAR-study there are a total of 18 individuals, with 9 individuals in each group. The amount of individuals are not enough to reach any statistical significance in a test and therefore the sample size shall be examined through power analysis. In terms of, data, every individual have three different measurements occasions that needs to be modeled through mixed-design ANOVA and linear mixed models. These two methods takes into account, each individual’s different measurements. The models which describes data are applied in the computations of sample sizes and power. All the analysis are done in the programming language R with means and standard deviations from the study and the models as a base. Sample sizes and power have been computed for two different linear mixed models and one ANOVA model. The linear mixed models required less individuals than ANOVA in terms of a desired power of 80 percent. 24 individuals in total were required by the linear mixed model that had the factors group, time, id and the covariate sex. 42 individuals were required by ANOVA that includes the variables id, group and time.<br>Inom forskning där försök, dels utförs på människor och djur, vill man försäkra sig om en lämplig urvalsstorlek för att spara tid och kostnad samtidigt som en önskad statistisk styrka uppnås. Mälardalens högskola och Karolinska institutet har gjort en pilotstudie (CLEAR) som undersöker människors koldioxidutsläpp i förhållande till kosthållning. Varje individ i studien har fått riktlinjer om att antingen följa en klimatvänlig- eller en konventionell kosthållning i totalt 8 veckor. Individerna följs upp med 4 veckors mellanrum, vilket har resulterat i tre mättillfällen, inklusive en baslinjemätning. I CLEAR-studien finns variabler om individernas kön, ålder, kosthållning samt intag av makro- och mikronäringsämnen. Nio individer i respektive grupp finns, där grupperna är klimat- och kontrollgruppen. Totala antalet individer i pilotstudien är för få för att erhålla statistisk signifikans vid statistiska tester och därför bör urvalsstorleken undersökas genom att göra styrkeberäkningar. Styrkan som beräknas är sannolikheten att förkasta nollhypotesen när den är falsk. För att kunna beräkna urvalsstorlekar måste modeller skapas utifrån strukturen på data, vilket kommer att göras med metoderna mixed-design ANOVA och linjära mixade modeller. Metoderna tar hänsyn till att varje individ har fler än en mätning. Modellerna som beskriver data tillämpas i beräkningarna av styrka. Urvalsstorlekarna och styrkan som beräknats är simuleringsbaserad och har analyserats i programspråket R med modellerna och värden från pilotstudien som grund. Styrka och urvalsstorlekar har beräknats för två linjära mixade modeller och en ANOVA. De linjära mixade modellerna kräver färre individer än ANOVA för en önskad styrka på 80 procent. Av de linjära mixade modellerna som krävde minst individer behövdes totalt 24 individer medan mixed design-ANOVA krävde 42 individer totalt.
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Adnan, Arisman. "Analysis of taste-panel data using ANOVA and ordinal logistic regression." Thesis, University of Newcastle Upon Tyne, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.402150.

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Liu, Yuan. "Mixed anova model analysis of microarray experiments with locally polled error /." Electronic version (PDF), 2004. http://dl.uncw.edu/etd/2004/liuy/yuanliu.pdf.

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Libros sobre el tema "ANOVA"

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Rutherford, Andrew. Anova and Ancova. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118491683.

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Rutherford, Andrew. ANOVA and ANCOVA: A GLM approach. 2nd ed. Wiley, 2011.

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Rutherford, Andrew, and Andrew Rutherford. ANOVA and ANCOVA: A GLM approach. 2nd ed. Wiley, 2011.

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Girden, Ellen. ANOVA. SAGE Publications, Inc., 1992. http://dx.doi.org/10.4135/9781412983419.

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Introducing ANOVA and ANCOVA: A GLM approach. SAGE, 2001.

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ANOVA: Repeated measures. Sage Publications, 1992.

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Jackson, Sally, and Dale Brashers. Random Factors in ANOVA. SAGE Publications, Inc., 1994. http://dx.doi.org/10.4135/9781412985567.

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S, Fidell Linda, ed. Experimental designs using ANOVA. Thomson/Brooks/Cole, 2007.

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Gu, Chong. Smoothing Spline ANOVA Models. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-5369-7.

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Gu, Chong. Smoothing Spline ANOVA Models. Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4757-3683-0.

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Capítulos de libros sobre el tema "ANOVA"

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Woodrow, Lindy. "ANOVA, ANCOVA and MANOVA." In Writing about Quantitative Research in Applied Linguistics. Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9780230369955_7.

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Herzog, Michael H., Gregory Francis, and Aaron Clarke. "ANOVA." In Understanding Statistics and Experimental Design. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-03499-3_6.

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DiMaggio, Charles. "ANOVA." In SAS for Epidemiologists. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4854-9_11.

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Baguley, Thom. "ANOVA and ANCOVA with independent measures." In Serious Stats. Macmillan Education UK, 2012. http://dx.doi.org/10.1007/978-0-230-36355-7_13.

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Janssen, Jürgen, and Wilfried Laatz. "EinfaktorielleVarianzanalyse (ANOVA)." In Statistische Datenanalyse mit SPSS für Windows. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-662-13046-9_14.

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Janssen, Jürgen, and Wilfried Laatz. "EinfaktorielleVarianzanalyse (ANOVA)." In Statistische Datenanalyse mit SPSS für Windows. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-10039-4_14.

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Ross, Amanda, and Victor L. Willson. "Factorial Anova." In Basic and Advanced Statistical Tests. SensePublishers, 2017. http://dx.doi.org/10.1007/978-94-6351-086-8_6.

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Lecoutre, Bruno, and Jacques Poitevineau. "ANOVA Procedures." In The Significance Test Controversy Revisited. Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-65705-8_11.

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Judd, Charles M., Gary H. McClelland, and Carey S. Ryan. "Factorial ANOVA." In Data Analysis. Routledge, 2017. http://dx.doi.org/10.4324/9781315744131-9.

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Morettin, Pedro A., Aluísio Pinheiro, and Brani Vidakovic. "Functional ANOVA." In SpringerBriefs in Mathematics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59623-5_5.

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Actas de conferencias sobre el tema "ANOVA"

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Campbell, Zachary, Andrew Bray, Anna Ritz, and Adam Groce. "Differentially Private ANOVA Testing." In 2018 1st International Conference on Data Intelligence and Security (ICDIS). IEEE, 2018. http://dx.doi.org/10.1109/icdis.2018.00052.

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Raz, Jonathan, and Bruce I. Turetsky. "Wavelet ANOVA and fMRI." In SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, edited by Michael A. Unser, Akram Aldroubi, and Andrew F. Laine. SPIE, 1999. http://dx.doi.org/10.1117/12.366814.

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Thomson, Norman. "Understanding ANOVA the APL way." In the international conference. ACM Press, 1993. http://dx.doi.org/10.1145/166197.166234.

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Chandrakantha, Leslie. "Learning ANOVA concepts using simulation." In 2014 Zone 1 Conference of the American Society for Engineering Education (ASEE Zone 1). IEEE, 2014. http://dx.doi.org/10.1109/aseezone1.2014.6820644.

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Khan, Asim ur Rehman, and Muhammad Junaid Rabbani. "An ANOVA based predictive parameter estimation." In 2017 International Conference on Innovations in Electrical Engineering and Computational Technologies (ICIEECT). IEEE, 2017. http://dx.doi.org/10.1109/icieect.2017.7916552.

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Sykes, Alan M. "Doing ANOVA calculations on the computer." In the international conference. ACM Press, 1995. http://dx.doi.org/10.1145/206913.207007.

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Behnad, Amin, Konstantinos N. Plataniotis, and Xiaolin Wu. "Directional image interpolation with ANOVA methodology." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5652904.

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Chan, Jasmine. "Analysis of variance and its applications in Macau educational resarches." In Statistics education for Progress: Youth and Official Statistics. International Association for Statistical Education, 2013. http://dx.doi.org/10.52041/srap.13302.

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In 2012, Macau government approved and passed the “System Framework for Private School Teaching Staff of Non-tertiary Education” in the legislative council. It is aimed at improving non-tertiary teachers’ professionalism in terms of setting hierarchies and provides training continuously. Teachers having a high level of research capacity can improve and solve education issues in Macau. Education Statistics are one of the important methods in research capacity. To develop this idea, therefore, my topic is “Analysis of variance and Its Applications in Macao Educational Researches”.In this paper a series of statistical methods will be studied, namely, ANOVA, ANCOVA and MANCOVA. They can be used to analyze multivariate data. Knowledge of these techniques will enable researchers to investigate and answer a vast array of research questions. This paper will apply also a real example in educational field in order to explain the manipulation of ANOVA, ANCOVA and MANOVA for data analysis.
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Zhan, Shuang-Huan, and Hong-Bin Zhang. "Blind Steganalysis using Wavelet Statistics and ANOVA." In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370570.

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ANDRIANAJAINA, T., D. T. RAZAFIMAHEFA, N. HERAUD, and E. J. R. SAMBATRA. "ANOVA fault detection method in photovoltaic system." In 2020 7th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2020. http://dx.doi.org/10.1109/codit49905.2020.9263843.

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Informes sobre el tema "ANOVA"

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Stephens, M. A. On Watson's ANOVA for Directions. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada253576.

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Federer, W. T., Z. D. Feng, and N. J. Miles-McDermott. Annotated Computer Output for Split Plot Design: GENSTAT ANOVA. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada187120.

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Verrill, Steve, and David E. Kretschmann. Reminder about potentially serious problems with a type of blocked ANOVA analysis. U.S. Department of Agriculture, Forest Service, Forest Products Laboratory, 2017. http://dx.doi.org/10.2737/fpl-rp-683.

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Hu, Xin, Guang Lin, Thomas Y. Hou, and Pengchong Yan. An Adaptive ANOVA-Based Data-Driven Stochastic Method for Elliptic PDE with Random Coefficients. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada560090.

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5

Becker, Sarah, Heather Sussman, S. Blundell, Vern Vanderbilt, and Igor Semyonov. Analysis of spectropolarimetric responses in the visible and infrared for differentiation between similar materials. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45422.

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Spectropolarimetric research has focused on target detections of materials that have a high degree of contrast from background materials, such as identification of a manmade object embedded in a vegetative background. This study presents an approach using spectropolarimetric imagery in visible, shortwave infrared, and longwave infrared bands to differentiate between similar natural and manmade materials. The method employs Michelson contrast and Kruskal-Wallis one-way analysis of variance (ANOVA) H-test to determine if a distinction can be found in pairwise comparisons of similar and different materials using the Stokes parameters in the visible, shortwave infrared, and longwave infrared bands. Results showed that similar natural and manmade materials were differentiable in spectropolarimetric imagery using the Michelson contrast and ANOVA. This approach provides a way to use spectropolarimetric imagery to distinguish between materials that are similar to each other.
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Idakwo, Gabriel, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang, and Ping Gong. Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41302.

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Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented. To further explore the advantages of DL over shallow learning, we conducted this case study using two cell-based androgen receptor (AR) activity datasets with 10K chemicals generated from the Tox21 program. A nested double-loop cross-validation approach was adopted along with a stratified sampling strategy for partitioning chemicals of multiple AR activity classes (i.e., agonist, antagonist, inactive, and inconclusive) at the same distribution rates amongst the training, validation and test subsets. Deep neural networks (DNN) and random forest (RF), representing deep and shallow learning algorithms, respectively, were chosen to carry out structure-activity relationship-based chemical toxicity prediction. Results suggest that DNN significantly outperformed RF (p &lt; 0.001, ANOVA) by 22–27% for four metrics (precision, recall, F-measure, and AUPRC) and by 11% for another (AUROC). Further in-depth analyses of chemical scaffolding shed insights on structural alerts for AR agonists/antagonists and inactive/inconclusive compounds, which may aid in future drug discovery and improvement of toxicity prediction modeling.
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Kumar, Anil R., and Hannah Bowman. Understanding the Safety and Usability of Personal Vehicles for Non-Driving Individuals with Disabilities and their Families/Care Providers. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2110.

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The connections between shared personal vehicles of individuals with disabilities (IWDs) and their household family members play an important role in the mobility, overall health, and well-being of all involved actors, yet this topic remains mostly overlooked within publicly available research. Families that include a non-driving IWD are more likely to be low-income, and often struggle with the costs of operating a family car but, due to insufficient public transportation options, they own vehicles despite their prohibitive cost. This exploratory study utilized the Systems Engineering Initiative for Patient Safety (SEIPS) model, a framework focused on assessing the interplaying sociotechnical factors that contribute toward work-systems to gain a holistic understanding of the factors that influence household vehicles, safety, and a sense of well-being for non-driving IWDs and their household family members. A combined effort of surveys, interviews, qualitative coding, and statistical analysis (including one-way ANOVA) revealed a series of influential factors, including: (1) slow bureaucratic processes for vehicle funding; (2) error-prone modifications including lift and tie-downs; (3) miscommunications between IWDs and family members; and (4) residential area development and subsequent social support. Findings highlight the need for improved access to government funding, more reliable modification equipment, and interior vehicle designs that consider better social integration for IWDs.
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Kim, Joseph J., Samuel Dominguez, and Luis Diaz. Freight Demand Model for Southern California Freeways with Owner–Operator Truck Drivers. Mineta Transportation Institute, 2020. http://dx.doi.org/10.31979/mti.2020.1931.

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This study evaluates the demand for truck-only toll lanes on Southern California freeways with owner–operator truck drivers. The study implemented the stated preference survey method to estimate the value placed by drivers on time, reliability, and safety measures using various scenarios geared towards assessing those values. The project team met face-to-face with owner- operator truck drivers near the Ports of Los Angeles and Long Beach to understand the drivers’ perspectives regarding truck-only toll lanes on Southern California freeways. A data set containing 31 survey responses is obtained and used for statistical data analysis using analysis of variable (ANOVA) and two sample t-tests. The analysis results showed that 75.27% of the owner– operator truck drivers responded are willing to pay toll fees when they choose routes. The tolerated average toll fees are $13.77/ hr and $12.82/hr for weekdays and weekends, respectively. The analysis results also showed that owner–operator truck drivers will take truck-only toll lanes when they take the routes used in four comparisons out of six comparisons according to the three measures such as values of time, reliability, and safety, despite sharing a common origin and destination. The highest toll fee per mile on any day that drivers are willing to pay when the main factor being compared is value of time is $0.31/mile or $18.35/hr. The toll fees associated with reliability and safety measures are $0.30/mile or $8.94/hr and $0.22/mile or $11.01/hr, respectively. These results are meaningful for legislators and transportation agencies as the behaviors and route choice characteristics of owner–operator truck drivers help them better understand the utility and demand for truck-only toll lanes.
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Tucker-Blackmon, Angelicque. Engagement in Engineering Pathways “E-PATH” An Initiative to Retain Non-Traditional Students in Engineering Year Three Summative External Evaluation Report. Innovative Learning Center, LLC, 2020. http://dx.doi.org/10.52012/tyob9090.

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The summative external evaluation report described the program's impact on faculty and students participating in recitation sessions and active teaching professional development sessions over two years. Student persistence and retention in engineering courses continue to be a challenge in undergraduate education, especially for students underrepresented in engineering disciplines. The program's goal was to use peer-facilitated instruction in core engineering courses known to have high attrition rates to retain underrepresented students, especially women, in engineering to diversify and broaden engineering participation. Knowledge generated around using peer-facilitated instruction at two-year colleges can improve underrepresented students' success and participation in engineering across a broad range of institutions. Students in the program participated in peer-facilitated recitation sessions linked to fundamental engineering courses, such as engineering analysis, statics, and dynamics. These courses have the highest failure rate among women and underrepresented minority students. As a mixed-methods evaluation study, student engagement was measured as students' comfort with asking questions, collaboration with peers, and applying mathematics concepts. SPSS was used to analyze pre-and post-surveys for statistical significance. Qualitative data were collected through classroom observations and focus group sessions with recitation leaders. Semi-structured interviews were conducted with faculty members and students to understand their experiences in the program. Findings revealed that women students had marginalization and intimidation perceptions primarily from courses with significantly more men than women. However, they shared numerous strategies that could support them towards success through the engineering pathway. Women and underrepresented students perceived that they did not have a network of peers and faculty as role models to identify within engineering disciplines. The recitation sessions had a positive social impact on Hispanic women. As opportunities to collaborate increased, Hispanic womens' social engagement was expected to increase. This social engagement level has already been predicted to increase women students' persistence and retention in engineering and result in them not leaving the engineering pathway. An analysis of quantitative survey data from students in the three engineering courses revealed a significant effect of race and ethnicity for comfort in asking questions in class, collaborating with peers outside the classroom, and applying mathematical concepts. Further examination of this effect for comfort with asking questions in class revealed that comfort asking questions was driven by one or two extreme post-test scores of Asian students. A follow-up ANOVA for this item revealed that Asian women reported feeling excluded in the classroom. However, it was difficult to determine whether these differences are stable given the small sample size for students identifying as Asian. Furthermore, gender differences were significant for comfort in communicating with professors and peers. Overall, women reported less comfort communicating with their professors than men. Results from student metrics will inform faculty professional development efforts to increase faculty support and maximize student engagement, persistence, and retention in engineering courses at community colleges. Summative results from this project could inform the national STEM community about recitation support to further improve undergraduate engineering learning and educational research.
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Patston, L. L. M., A. N. Henry, M. McEwen, J. Mannion, and L. A. Ewens-Volynkina. Thinking While Standing: An exploratory study on the effect of standing on cognitive performance. Unitec ePress, 2017. http://dx.doi.org/10.34074/ocds.32017.

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Sedentary behaviour is extremely prevalent in Western societies and is significantly associated with an elevated risk of all-cause mortality that cannot be mitigated by physical activity. The introduction of standing desks into the workplace offers a solution to this inactivity, but there is limited investigation regarding the effects of standing on cognition, which is a major consideration in much office-based work. In this study we aimed to provide an exploratory investigation on the effect on cognitive performance of standing while working. We tested 30 office-based adults on a battery of 19 cognitive tasks (tapping five cognitive domains) in a randomised, repeatedmeasures crossover design study. Two conditions (standing versus sitting) were investigated over two 7.5-hour work days including morning, midday and afternoon sessions (Time of Day). Effects were analysed using multivariate two-way repeated-measures ANOVAs (Condition by Time of Day) for five cognitive domains. Overall, after correcting for multiple comparisons, there were no differences in performance between sitting and standing. At an uncorrected level, however, significant effects of Condition were found in three of the 19 tasks, with all demonstrating better performance while standing. Importantly, these results suggest that there is no detriment to cognitive performance through standing. They also provide an initial indication that there may be cognitive benefits of standing in the attention and working memory domains, which may be a promising avenue for future inquiry.
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