Relevant bibliographies by topics / Two-factor analysis of variance

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Two-factor analysis of variance'

Author: Grafiati
Published: 28 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "Two-factor analysis of variance"

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Even-Tzur, Gilad. "Variance Factor Estimation for Two-Step Analysis of Deformation Networks." Journal of Surveying Engineering 130, no. 3 (2004): 113–18. http://dx.doi.org/10.1061/(asce)0733-9453(2004)130:3(113).

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Ding, Peng. "Two seemingly paradoxical results in linear models: the variance inflation factor and the analysis of covariance." Journal of Causal Inference 9, no. 1 (2021): 1–8. http://dx.doi.org/10.1515/jci-2019-0023.

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Abstract A result from a standard linear model course is that the variance of the ordinary least squares (OLS) coefficient of a variable will never decrease when including additional covariates into the regression. The variance inflation factor (VIF) measures the increase of the variance. Another result from a standard linear model or experimental design course is that including additional covariates in a linear model of the outcome on the treatment indicator will never increase the variance of the OLS coefficient of the treatment at least asymptotically. This technique is called the analysis
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Tsorin, I. B. "Application of variance analysis in experimental pharmacology." Pharmacokinetics and Pharmacodynamics, no. 1 (May 10, 2023): 3–23. http://dx.doi.org/10.37489/2587-7836-2023-1-3-23.

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The article discusses various types of variance analysis: one-factor, two-factor, repeated measurements, including detailed analysis of variance categorical data. The criteria of multiple comparisons are described: according to Bonferroni, according to Newman-Kales, according to Dunnet, according to Scheffe. All the methods considered are accompanied by examples of analysis of pharmacological data.
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Ahmed, Abdalla, and Walla Maruod. "Comparative analysis of varimax and Promax rotation methods in exploratory factor analysis." Edelweiss Applied Science and Technology 9, no. 5 (2025): 501–13. https://doi.org/10.55214/25768484.v9i5.6929.

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This study compares two widely used rotation techniques in exploratory factor analysis (EFA): Varimax, an orthogonal method, and Promax, an oblique method. Sample data from 394 students were analyzed using JASP software to evaluate the two methods. Both rotations identified latent constructs influencing academic achievement after factor extraction via principal axis factoring. Although both methods retained the same number of factors, the pattern and magnitude of variable loadings differed. The Kaiser-Meyer-Olkin (KMO) test indicated superior reliability for Promax, which achieved significantl
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Rein, Judith A. "Analyzing a Two-Factor within-Subjects Design Using Mystat." Perceptual and Motor Skills 77, no. 2 (1993): 440–42. http://dx.doi.org/10.2466/pms.1993.77.2.440.

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MYSTAT is used to analyze a two-way analysis of variance with repeated measures on both factors without running out of memory. The simple solution is executing the analysis in successive batches versus a simultaneous block.
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Kaya, Fahrettin. "Applying Permanova for Multivariate Analysis of Variance in Health Studies." Black Sea Journal of Engineering and Science 8, no. 2 (2025): 9–22. https://doi.org/10.34248/bsengineering.1611775.

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Abstract: Health data often do not meet the normality assumption, which limits the applicability of traditional analysis of variance methods. The aim of this study is to propose a methodological framework for analyzing such data by examining PERMANOVA (Permutational Multivariate Analysis of Variance), a method that does not require the normality assumption and is particularly suitable for complex datasets, within the context of maternal health data. In the context of maternal healthcare in Bangladesh, the effects of two independent variables—risk and age factors—on multivariate response variab
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Hsiung, Tung-Hsing, and Stephen Olejnik. "Contrast analysis for additive non-orthogonal two-factor designs in unequal variance cases." British Journal of Mathematical and Statistical Psychology 47, no. 2 (1994): 337–54. http://dx.doi.org/10.1111/j.2044-8317.1994.tb01041.x.

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Harrar, Solomon W., and Arne C. Bathke. "A modified two-factor multivariate analysis of variance: asymptotics and small sample approximations." Annals of the Institute of Statistical Mathematics 64, no. 1 (2010): 135–65. http://dx.doi.org/10.1007/s10463-010-0299-0.

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Amador-Campos, Juan Antonio, Maria Forns-Santacana, Bernardí Martorell-Balanzó, Joan Guàrdia-Olmos, and Maribel Peró-Cebollero. "Confirmatory Factor Analysis of Parents' and Teachers' Ratings of DSM-IV Symptoms of Attention Deficit Hyperactivity Disorder in a Spanish Sample." Psychological Reports 97, no. 3 (2005): 847–60. http://dx.doi.org/10.2466/pr0.97.3.847-860.

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Confirmatory factor analysis examined the factor structure of DSM-IV Attention Deficit Hyperactivity Disorder symptoms and analyzed the differences in informants' ratings of ADHD symptoms by children's age and sex. A sample of 1,018 Spanish school children, 574 girls and 444 boys, ages 4 to 12 years, was rated by teachers and parents. Confirmatory factor analysis showed a similar fit for the three-factor model of Inattention, Hyperactivity, and Impulsivity (teachers: CFI = .976, RMR = .041, % of total variance = 83.2; parents: CFI = .969, RMR = .037, % of total variance = 82.7) and for the two
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Milanes-Baños, Norma A. "Step-by-step one-way ANOVA analysis with the Jamovi program." Mexican Journal of Medical Research ICSA 12, no. 23 (2024): 22–26. http://dx.doi.org/10.29057/mjmr.v12i23.10664.

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ANOVA is the acronym for Analysis of Variance, which stands for Analysis of Variance, and is used to establish statistically significant differences between the means of two groups. Currently there are several programs that synthesize this work allowing us to analyse statistical data in a simpler way. In this manuscript we will address a one-way or one-factor analysis of variance, which means that we must have an explanatory variable, also known as an independent variable, which must have two or more categories, we must also have a dependent or response variable of the type quantitatively disc
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Dissertations / Theses on the topic "Two-factor analysis of variance"

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Wiedemann, Eric A. "Reducing variance between two systems by inducing correlation." Thesis, Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/23345.

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Brien, Christopher J. "Factorial linear model analysis." Title page, table of contents and summary only, 1992. http://thesis.library.adelaide.edu.au/public/adt-SUA20010530.175833.

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"February 1992" Bibliography: leaf 323-344. Electronic publication; Full text available in PDF format; abstract in HTML format. Develops a general strategy for factorial linear model analysis for experimental and observational studies, an iterative, four-stage, model comparison procedure. The approach is applicable to studies characterized as being structure-balanced, multitiered and based on Tjur structures unless the structure involves variation factors when it must be a regular Tjur structure. It covers a wide range of experiments including multiple-error, change-over, two-phase, superimpos
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Przybyla, Craig P. "Methodology for Determining the Variance of the Taylor Factor: Application in Fe-3%Si." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd1047.pdf.

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Balogun, Tolulope Victoria. "An intergrated model of the role of authentic leadership, psychological capital, psychological climate and intention to quit on employee work engagement: A comparative analysis." University of the Western Cape, 2017. http://hdl.handle.net/11394/6327.

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Philosophiae Doctor - PhD (Industrial Psychology)<br>Organizations exist for the primary aim of meeting particular objectives: innovation and advancement, customer satisfaction, profit making and delivery of quality goods and services. These goals are mostly channelled with the intent of demonstrating high performance crucial for the continued existence of the organization especially in these rapidly changing global economies. This target, however, cannot be achieved without the aid of employees in the organization. A plethora of previous studies have proven that efficiency, productivity
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Чумак, О. О. "Аналіз сили впливу контрольованих факторів на вміст заліза у біосередовищах новонароджених". Thesis, Сумський державний університет, 2014. http://essuir.sumdu.edu.ua/handle/123456789/36169.

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Дисперсійний двофакторний аналіз (ДДА) застосовується в тих випадках, коли досліджується одночасна дія двох факторів на різні вибірки об’єктів, тобто коли різні вибірки опиняються під впливом різних поєднань двох факторів. Може статися, що одна змінна значуще діє на досліджувану ознаку тільки при певних значеннях іншої змінної. Таким чином, ДДА дозволяє оцінити не лише вплив кожного з факторів, але й їхню взаємодію. ДДА пред’являє особливі вимоги до кожного комплексу. Для кожного фактору має бути не менше двох градацій; у кожному осередку комплексу повинно бути не менше двох спостережуваних зн
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Alexander, Erika D. "Comparisons of Improvement-Over-Chance Effect Sizes for Two Groups Under Variance Heterogeneity and Prior Probabilities." Thesis, University of North Texas, 2003. https://digital.library.unt.edu/ark:/67531/metadc4242/.

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The distributional properties of improvement-over-chance, I, effect sizes derived from linear and quadratic predictive discriminant analysis (PDA) and from logistic regression analysis (LRA) for the two-group univariate classification were examined. Data were generated under varying levels of four data conditions: population separation, variance pattern, sample size, and prior probabilities. None of the indices provided acceptable estimates of effect for all the conditions examined. There were only a small number of conditions under which both accuracy and precision were acceptable. The result
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Whitmore, Marjorie Lee Threet. "A Comparison of Two Differential Item Functioning Detection Methods: Logistic Regression and an Analysis of Variance Approach Using Rasch Estimation." Thesis, University of North Texas, 1995. https://digital.library.unt.edu/ark:/67531/metadc278366/.

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Differential item functioning (DIF) detection rates were examined for the logistic regression and analysis of variance (ANOVA) DIF detection methods. The methods were applied to simulated data sets of varying test length (20, 40, and 60 items) and sample size (200, 400, and 600 examinees) for both equal and unequal underlying ability between groups as well as for both fixed and varying item discrimination parameters. Each test contained 5% uniform DIF items, 5% non-uniform DIF items, and 5% combination DIF (simultaneous uniform and non-uniform DIF) items. The factors were completely crossed, a
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Kuzmak, Barbara R. "An examination of outliers and interaction in a nonreplicated two-way table." Diss., Virginia Tech, 1990. http://hdl.handle.net/10919/37747.

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The additive-plus-multiplicative model, Y<sub>ij</sub> = μ + α<sub>i</sub> + β<sub>j</sub> + ∑<sub>p=1</sub><sup>k</sup>λ<sub>p</sub>τ<sub>pi</sub>γ<sub>pj</sub>, has been used to describe multiplicative interaction in an unreplicated experiment. Outlier effects often appear as interaction in a two-way analysis of variance with one observation per cell. I use this model in the same setting to study outliers. In data sets with significant interaction, one may be interested in determining whether the cause of the interaction is due to a true interaction, outliers or both. I develop a new techniq
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Safari, Katesari Hadi. "BAYESIAN DYNAMIC FACTOR ANALYSIS AND COPULA-BASED MODELS FOR MIXED DATA." OpenSIUC, 2021. https://opensiuc.lib.siu.edu/dissertations/1948.

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Available statistical methodologies focus more on accommodating continuous variables, however recently dealing with count data has received high interest in the statistical literature. In this dissertation, we propose some statistical approaches to investigate linear and nonlinear dependencies between two discrete random variables, or between a discrete and continuous random variables. Copula functions are powerful tools for modeling dependencies between random variables. We derive copula-based population version of Spearman’s rho when at least one of the marginal distribution is discrete. In
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Mays, Darcy P. "Design and analysis for a two level factorial experiment in the presence of dispersion effects." Diss., Virginia Tech, 1993. http://hdl.handle.net/10919/39723.

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Books on the topic "Two-factor analysis of variance"

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MacFarland, Thomas W. Two-Way Analysis of Variance. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-2134-4.

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American Physical Therapy Association (1921- ). Division of Research and Education., ed. Design of research and analysis of data in the clinic: Introduction to factorial designs and analysis of variance. American Physical Therapy Association, Division of Research and Education, 1985.

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Jacques, Herman. Analyse de données qualitatives. Masson, 1986.

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Herman, Jacques. Analyse de données qualitatives. Masson, 1986.

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service), SpringerLink (Online, ed. Two-Way Analysis of Variance: Statistical Tests and Graphics Using R. Thomas W. MacFarland, 2012.

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Byrne, Barbara M. A primer of LISREL: Basic applications and programming for confirmatory factor analytic models. Springer-Verlag, 1989.

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Morrison, Donald F. Multivariate statistical methods. 3rd ed. McGraw-Hill, 1990.

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Bartholomew, David J. Latent variable models and factor analysis. 2nd ed. Arnold, 1999.

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Bartholomew, David J. Latent variable models and factor analysis. C. Griffin, 1987.

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Ganesalingam, S. Two group discrimination analysis using factor scores: Statistics. College of Sciences, Massey University, 1999.

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Book chapters on the topic "Two-factor analysis of variance"

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Armstrong, Richard A., and Anthony C. Hilton. "Two-Factor Analysis of Variance." In Statistical Analysis in Microbiology: Statnotes. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9780470905173.ch12.

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Hinton, Perry R., Isabella McMurray, Charlotte Brownlow, and Peter C. Terry. "Two-factor analysis of variance." In SPSS Explained, 3rd ed. Routledge, 2023. http://dx.doi.org/10.4324/9780429350863-9.

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Williams, R. B. G. "Two-factor and Multi-factor Analysis of Variance." In Intermediate Statistics for Geographers and Earth Scientists. Macmillan Education UK, 1986. http://dx.doi.org/10.1007/978-1-349-06813-5_2.

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Quicke, Donald, Buntika A. Butcher, and Rachel Kruft Welton. "Analysis of variance (ANOVA)." In Practical R for biologists: an introduction. CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0013a.

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Abstract Analysis of variance is used to analyze the differences between group means in a sample, when the response variable is numeric (real numbers) and the explanatory variable(s) are all categorical. Each explanatory variable may have two or more factor levels, but if there is only one explanatory variable and it has only two factor levels, one should use Student's t-test and the result will be identical. Basically an ANOVA fits an intercept and slopes for one or more of the categorical explanatory variables. ANOVA is usually performed using the linear model function lm, or the more specif
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Quicke, Donald, Buntika A. Butcher, and Rachel Kruft Welton. "Analysis of variance (ANOVA)." In Practical R for biologists: an introduction. CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0155.

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Abstract Analysis of variance is used to analyze the differences between group means in a sample, when the response variable is numeric (real numbers) and the explanatory variable(s) are all categorical. Each explanatory variable may have two or more factor levels, but if there is only one explanatory variable and it has only two factor levels, one should use Student's t-test and the result will be identical. Basically an ANOVA fits an intercept and slopes for one or more of the categorical explanatory variables. ANOVA is usually performed using the linear model function lm, or the more specif
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Williams, R. B. G. "Single Factor Analysis of Variance." In Intermediate Statistics for Geographers and Earth Scientists. Macmillan Education UK, 1986. http://dx.doi.org/10.1007/978-1-349-06813-5_1.

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Hinton, Perry R., Isabella McMurray, Charlotte Brownlow, and Peter C. Terry. "One-factor analysis of variance." In SPSS Explained, 3rd ed. Routledge, 2023. http://dx.doi.org/10.4324/9780429350863-8.

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Sahai, Hardeo, and Mohammed I. Ageel. "Two-Way Nested (Hierarchical) Classification." In The Analysis of Variance. Birkhäuser Boston, 2000. http://dx.doi.org/10.1007/978-1-4612-1344-4_6.

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Sahai, Hardeo, and Mohammed I. Ageel. "Two-Way Crossed Classification Without Interaction." In The Analysis of Variance. Birkhäuser Boston, 2000. http://dx.doi.org/10.1007/978-1-4612-1344-4_3.

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Sahai, Hardeo, and Mohammed I. Ageel. "Two-Way Crossed Classification with Interaction." In The Analysis of Variance. Birkhäuser Boston, 2000. http://dx.doi.org/10.1007/978-1-4612-1344-4_4.

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Conference papers on the topic "Two-factor analysis of variance"

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Chang, Chaur-Yang, and Ding-Xue Wu. "A Study of Impact of Environment Factor on Power System Equipment Partial Discharge Using Multi-Groups Analysis of Variance and Correlation Analysis." In 2025 15th International Conference on Power, Energy, and Electrical Engineering (CPEEE). IEEE, 2025. https://doi.org/10.1109/cpeee64598.2025.10987263.

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Söding, A., and F. M. Neubauer. "Minimum variance analysis for 2D structures using two satellites." In The solar wind nine conference. AIP, 1999. http://dx.doi.org/10.1063/1.58679.

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Bittner, Alvah. "Criterion Referenced Factor Analysis (CRFA): Method and Illustration." In 33rd Annual International Occupational Ergonomics and Safety Conference. International Society for Occupational Ergonomics and Safety, 2021. http://dx.doi.org/10.47461/isoes.2021_041.

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CRFA is a valid method for evaluating relationships between criteria and “factors'' initially identified from both the predictor and the criterion variables. Among others, it may be applied to classical problems involving: 1) Changes in complex task ability requirements as a function of practice, 2) Impacts of environmental stressors on personality or performance factors overtime, or 3) Residential Lifestyle Factor Impacts on Energy-Use (as herein). CRFA differs from traditional battery developments in its: (a) Initial inclusion of both criterion and predictor variables for factor identificati
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Wang, Yue, and Yiqian Zhang. "Design of Composite Factor Variance and Component Analysis System Based on Data Analysis Algorithm." In 2024 IEEE 13th International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2024. http://dx.doi.org/10.1109/csnt60213.2024.10545721.

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Lei, Ming. "One-factor analysis of variance for medical statistical data with binomial distribution." In 2011 International Conference on Human Health and Biomedical Engineering (HHBE). IEEE, 2011. http://dx.doi.org/10.1109/hhbe.2011.6027909.

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Zhengfeng Cao and ZhiHui Zhang. "Parameter Settings of Genetic Algorithm Based on Multi-Factor Analysis of Variance." In 2010 Fourth International Conference on Genetic and Evolutionary Computing (ICGEC 2010). IEEE, 2010. http://dx.doi.org/10.1109/icgec.2010.82.

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Milev, Marian, Krastena Nikolova, Neli Keranova, et al. "Evaluation of aqueous extracts of arthrospira platensis by variance and factor analysis." In THE NATIONAL UNIVERSITY OF SCIENCE AND TECHNOLOGY INTERNATIONAL CONFERENCE FOR ENGINEERING SCIENCES. AIP Publishing, 2025. https://doi.org/10.1063/5.0246018.

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Cali`, M., G. Orsello, M. Santarelli, and P. Leone. "Experimental Activity on the Tubular SOFC CHP100 kWe Field Unit in Italy: Factor Significance, Effects and Regression Model Analysis." In ASME 8th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2006. http://dx.doi.org/10.1115/esda2006-95802.

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The CHP 100 kWe tubular SOFC plant built by Siemens is operating at the Gas Turbine Technologies (GTT) in Turin. The generator started up on the June 2005 and produces electric and thermal power used in the factory. A first period of the activity was needed to lead the operation of the SOFC plant at the nominal condition and to reach the complete conditioning of the SOFC tubular fuel cells. Then, in order to characterize the operation of the generator and of the cogenerative system a first experimental campaign was designed by using the factorial analysis. With these methods, the effect of som
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Schlossberger, Noah, and David Howe. "Analysis of powers-of-two calculations of the Allan variance and their relation to the standard variance." In 2019 Joint Conference of the IEEE International Frequency Control Symposium anEuropean Frequency and Time Forum (EFTF/IFC). IEEE, 2019. http://dx.doi.org/10.1109/fcs.2019.8856078.

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Esmaeeli, Roja, Haniph Aliniagerdroudbari, Seyed Reza Hashemi, Hammad Al-Shammari, Muapper Alhadri, and Siamak Farhad. "Univariate and Multivariate Gauge Repeatability and Reproducibility Analysis on the High Frequency Dynamic Mechanical Analysis (DMA) Measurement System." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-10986.

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Abstract The quality of the collected data from a measurement system affects eventual decision making process. Therefore, the reliability of any measurement system is an important factor to be studied. Gauge repeatability and reproducibility (Gauge R&amp;R) is the standard method to evaluate the measurement system and assess the adequacy of variation in the measurement data. Gauge R&amp;R is a statistical tool which evaluates two main characteristics of the measurement system: repeatability and reproducibility. The Dynamic Mechanical Analysis (DMA) is a common measurement system for studying t
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Reports on the topic "Two-factor analysis of variance"

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Zhang, Zhen. Multilevel SEM in Mplus. Instats Inc., 2022. http://dx.doi.org/10.61700/p80oftrbgz4z3469.

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This seminar introduces the Mplus multilevel latent variable modeling framework and describes topics including multilevel variance and effect decomposition, and random and fixed effects (including random slopes), and then proceeds to explore multilevel path analysis, multilevel CFA including multilevel bi-factor models, and multilevel SEM, including approaches for handling strong correlations at the between-group level, indirect effects (multilevel mediation), interaction effects (multilevel moderation), and conditional indirect effects (multilevel moderated mediation). An official Instats cer
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Zyphur, Michael. Multilevel Modeling and Multilevel SEM in Mplus. Instats Inc., 2023. http://dx.doi.org/10.61700/k7ptxsg8judu9469.

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This seminar introduces the Mplus multilevel latent variable modeling framework and describes multilevel variance/effect decomposition, random/fixed effects (including random slopes), and then proceeds to explore multilevel path analysis, multilevel CFA including multilevel bi-factor models, and multilevel SEM. Topics include ways to handle strong correlations at the between-group level, indirect effects (multilevel mediation), interaction effects (multilevel moderation), and conditional indirect effects (multilevel moderated mediation). An official Instats certificate of completion and 2 ECTS
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Zhang, Zhen. Multilevel SEM with Random Slopes and Moderated Mediation in Mplus. Instats Inc., 2022. http://dx.doi.org/10.61700/5a63kcb9bqqqq469.

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This seminar introduces the Mplus multilevel latent variable modeling framework and describes topics including multilevel variance and effect decomposition, and random and fixed effects (including random slopes), and then proceeds to explore multilevel path analysis, multilevel CFA including multilevel bi-factor models, and multilevel SEM, including approaches for handling strong correlations at the between-group level, indirect effects (multilevel mediation), interaction effects (multilevel moderation), and conditional indirect effects (multilevel moderated mediation). An official Instats cer
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Zyphur, Michael. Multilevel SEM with Moderated Mediation in Mplus. Instats Inc., 2022. http://dx.doi.org/10.61700/khz0yfni0ofmb469.

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This seminar introduces the Mplus multilevel latent variable modeling framework and describes topics including multilevel variance and effect decomposition, and random and fixed effects (including random slopes), and then proceeds to explore multilevel path analysis, multilevel CFA including multilevel bi-factor models, and multilevel SEM, including approaches for handling strong correlations at the between-group level, indirect effects (multilevel mediation), interaction effects (multilevel moderation), and conditional indirect effects (multilevel moderated mediation). An official Instats cer
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Zhang, Zhen. Multilevel SEM in Mplus (Free with Course Purchase). Instats Inc., 2023. http://dx.doi.org/10.61700/aicphe1kyt4hf469.

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This seminar introduces the Mplus multilevel latent variable modeling framework and describes topics including multilevel variance and effect decomposition, and random and fixed effects (including random slopes), and then proceeds to explore multilevel path analysis, multilevel CFA including multilevel bi-factor models, and multilevel SEM, including approaches for handling strong correlations at the between-group level, indirect effects (multilevel mediation), interaction effects (multilevel moderation), and conditional indirect effects (multilevel moderated mediation). An official Instats cer
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Valenzuela, Patricio, and Eduardo A. Cavallo. The Determinants of Corporate Risk in Emerging Markets: An Option-Adjusted Spread Analysis. Inter-American Development Bank, 2007. http://dx.doi.org/10.18235/0010975.

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This study explores the determinants of corporate bond spreads in emerging market economies. Using a largely unexploited dataset, the paper finds that corporate bond spreads are determined by firm-specific variables, bond characteristics, macroeconomic conditions, sovereign risk, and global factors. A variance decomposition analysis shows that firm-level characteristics account for the larger share of the variance. In addition, the paper finds two asymmetries. The first is in line the sovereign ceiling lite hypothesis which states that the transfer of risk from the sovereign to the private sec
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Kam, Chester. Mixture Modeling for Measurement Scale Assessment. Instats Inc., 2023. http://dx.doi.org/10.61700/8ll0tq1hym0nq469.

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This seminar will introduce the use of mixture models for measurement scale assessment, covering topics such as factor analysis and careless response detection. As a set of worked examples, mixture models will be applied to multitrait-multimethod (MTMM) and bifactor models. By attending this seminar, you will learn how to understand and statistically handle different types of method or source variance using mixture models, which will improve the quality and rigor of your research. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD stude
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Vecherin, Sergey, Stephen Ketcham, Aaron Meyer, Kyle Dunn, Jacob Desmond, and Michael Parker. Short-range near-surface seismic ensemble predictions and uncertainty quantification for layered medium. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45300.

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To make a prediction for seismic signal propagation, one needs to specify physical properties and subsurface ground structure of the site. This information is frequently unknown or estimated with significant uncertainty. This paper describes a methodology for probabilistic seismic ensemble prediction for vertically stratified soils and short ranges with no in situ site characterization. Instead of specifying viscoelastic site properties, the methodology operates with probability distribution functions of these properties taking into account analytical and empirical relationships among viscoela
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Kryzhanivs'kyi, Evstakhii, Liliana Horal, Iryna Perevozova, Vira Shyiko, Nataliia Mykytiuk, and Maria Berlous. Fuzzy cluster analysis of indicators for assessing the potential of recreational forest use. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/4470.

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Cluster analysis of the efficiency of the recreational forest use of the region by separate components of the recreational forest use potential is provided in the article. The main stages of the cluster analysis of the recreational forest use level based on the predetermined components were determined. Among the agglomerative methods of cluster analysis, intended for grouping and combining the objects of study, it is common to distinguish the three most common types: the hierarchical method or the method of tree clustering; the K-means Clustering Method and the two-step aggregation method. For
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Chan, Melvin Chee Yeen, and Jennifer Pei-Ling Tan. Secondary quantitative analysis of core research data (2004-2010): A multilevel study of academic achievement and 21st century competencies. National Institute of Education, Nanyang Technological University, Singapore, 2020. https://doi.org/10.32658/10497/22604.

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The Core Research Programme is a large-scale representative study of teaching, learning and cognitive assessment practices and student outcomes. Within this major project, survey and assessment data were collected across three subsidiary projects. Core 1 Panel 2 (2004) and Core 2 Panel 2 (2010) are two unique datasets that focus on how school, classroom and student level factors contribute to individual variation in student achievement and other key 21st century (21C) learning outcomes. Core 1 Panel 6 (2008), on the other hand, is another study that captures a broader range of affective, educa
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