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Journal articles on the topic 'Analysis of Variance (ANOVA)'

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Published: 4 June 2021

Last updated: 25 July 2025

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1

St»hle, Lars, and Svante Wold. "Analysis of variance (ANOVA)." Chemometrics and Intelligent Laboratory Systems 6, no. 4 (1989): 259–72. http://dx.doi.org/10.1016/0169-7439(89)80095-4.

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2

Thompson, Hilary W., Robertino Mera, and Chandan Prasad. "The Analysis of Variance (ANOVA)." Nutritional Neuroscience 2, no. 1 (1999): 43–55. http://dx.doi.org/10.1080/1028415x.1999.11747262.

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3

Shahid, Iqbal, Leghari Imdad, Sethar D.V.M. M. Sc Abdullah, Memon Azizullah, and Bhatti M.Ali. "Analysis Of Variance (ANOVA) Cholesterol of Count of Broilers." International Journal of Case Studies 5, no. 7 (2016): 76–79. https://doi.org/10.5281/zenodo.3531378.

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This research investigates that Analysis of variance (ANOVA) Cholesterol ofcount of broilers. It was revealed that reported significant (P>0.05) difference in cholesterol count of broilers among the groups. All-pair-wise (LSD) test indicated that there was significant variation in total protein count between treatment groups B, C, D with control group-A, respectively. The result (Figure-14) indicated that average cholesterol count in groups A, B, C and D was 117.67, 121.67, 118.33 and 124 g/dl, respectively. Maximum cholesterol count of 124 g/dl were noted in group D where the bird fed on H

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4

Connelly, Lynne M. "Introduction to Analysis of Variance (ANOVA)." MEDSURG Nursing 30, no. 3 (2021): 218. https://doi.org/10.62116/msj.2021.30.3.218.

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5

Researcher. "BIOLOGICAL DATA INTERPRETATION – ANOVA." International Journal of Biological Sciences (IJBS) 2, no. 1 (2025): 1–7. https://doi.org/10.5281/zenodo.15575762.

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Analysis of Variance (ANOVA) is a pivotal statistical tool used to interpret biological data where comparisons among multiple groups are necessary. This paper discusses the application of ANOVA in biological research, emphasizing its utility in distinguishing differences among treatment groups, species, or experimental conditions. By analyzing variance within and between groups, ANOVA aids in determining the significance of biological effects. The paper further explores the assumptions, methodologies, and interpretation of ANOVA outcomes in the context of biological data. Visual representation

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6

Maxwell, Scott E., Harold D. Delaney, and Jerry M. Manheimer. "Anova of Residuals and Ancova: Correcting an Illusion by Using Model Comparisons and Graphs." Journal of Educational Statistics 10, no. 3 (1985): 197–209. http://dx.doi.org/10.3102/10769986010003197.

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Analysis of covariance is often conceptualized as an analysis of variance of a single set of residual scores that are obtained by regressing the dependent variable on the covariate. Although this conceptualization of an equivalence between the two procedures may be intuitively appealing, it is mathematically incorrect. If residuals are obtained from the pooled within-groups regression coefficient ( bw), an analysis of variance on the residuals results in an inflated α-level. If the regression coefficient for the total sample combined into one group ( bT) is used, ANOVA on the residuals yields

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Shahid, Iqbal, Leghari Imdad, Sethar D.V.M. M. Sc Abdullah, Memon Azizullah, and Bhatti .M.Ali. "Analysis Of Variance (ANOVA) In Total Protein Count of Broiler." International Journal of Case Studies 5, no. 7 (2016): 80–83. https://doi.org/10.5281/zenodo.3531380.

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This research investigates that Analysis of variance (ANOVA) in total protein count of broiler. Analysis of variance (ANOVA) reported significant (P>0.05) difference in total protein count of broilers among the groups. All-pair-wise (LSD) test indicated that there was significant variation in total protein count between treatment groups B, C, D with control group-A, respectively. The result (Figure-13) indicated that average total protein count in groups A, B, C and D was 3.33, 3.46, 3.53 and 4.03 g/dl, respectively. Maximum total protein count of 4.03 g/dl were noted in group D where the b

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8

Shelton, Heather K. "BASIC PREMISES OF FACTORIAL ANALYSIS OF VARIANCE (ANOVA)." Experimental Techniques 27, no. 6 (2003): 64–66. http://dx.doi.org/10.1111/j.1747-1567.2003.tb00142.x.

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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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10

Shehu, A., N. S. Dauran, and G. A. Usman. "Analysis of Covariance of Models of Sudoku Squares with no Treatment Effects on Concomitant Variable." Journal of Applied Sciences and Environmental Management 27, no. 11 (2023): 2661–67. http://dx.doi.org/10.4314/jasem.v27i11.39.

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Inclusion of concomitant variable(s) in an analysis of variance (ANOVA) model is an indication that the model is of ANCOVA model provided that there is correlation between the concomitant and response variable. This study employ numerical illustrations of the analysis of covariance of models of Sudoku square with no treatment effects on concomitant variable- Result of the illustration, showed that error variance from the ANCOVA Sudoku square models reduced 12.7761 to 5.5152 for model I; 13.6898 to 6.4690 for model II; 15.7926 to 4.8160 for model III and 16.5152 to 4.9226 for model IV respectiv

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11

Shawn M. Fitzgerald, Phillip Rumrill, and Raymond C. Hart. "Using analysis of variance (ANOVA) in rehabilitation research investigations." WORK: A Journal of Prevention, Assessment & Rehabilitation 15, no. 1 (2000): 61–65. https://doi.org/10.3233/wor-2000-00104.

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The article examines the underlying assumptions, applications, and interpretations of Analysis of Variance (ANOVA) in rehabilitation research. ANOVA is presented as a widely used and highly versatile statistical tool for assessing the performance of two or more groups on a broad range of dependent variables. Examples from the contemporary rehabilitation literature are used to demonstrate how ANOVA can be applied and interpreted in a number of scientific contexts.

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12

Emerson, Robert Wall. "MANOVA (Multivariate Analysis of Variance): An Expanded Form of the ANOVA (Analysis of Variance)." Journal of Visual Impairment & Blindness 112, no. 1 (2018): 125–26. http://dx.doi.org/10.1177/0145482x1811200113.

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13

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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Reed, James F. "Analysis of Variance (ANOVA) Models in Lower Extremity Wounds." International Journal of Lower Extremity Wounds 2, no. 2 (2003): 87–95. http://dx.doi.org/10.1177/1534734603256075.

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15

Kumar, Nand Kishor. "F-Test and Analysis of Variance (ANOVA) in Economics." Mikailalsys Journal of Mathematics and Statistics 2, no. 3 (2024): 102–13. http://dx.doi.org/10.58578/mjms.v2i3.3449.

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ANOVA remains a cornerstone of empirical economic research, providing economists with a robust framework to analyze differences between groups, eval_uate policy interventions, and draw meaningful conclusions from data. Its versatility and applicability across diverse economic contexts underscore its significance in advancing economic theory and informing evidence-based policymaking. As data availability and computational capabilities continue to expand, ANOVA's role in economic analysis is expected to evolve, supporting increasingly sophisticated studies of economic phenomena and policy impact

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Fitzgerald, Shawn M., and Sharon Flinn. "Evaluating Research Studies Using the Analysis of Variance (ANOVA)." Journal of Hand Therapy 13, no. 1 (2000): 56–60. http://dx.doi.org/10.1016/s0894-1130(00)80054-x.

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Aarts, Sil, and Eveline Wouters. "De t-toets en de analysis of variance, ANOVA." Podosophia 26, no. 1 (2018): 28–33. http://dx.doi.org/10.1007/s12481-018-0187-8.

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18

Lakens, Daniël, and Aaron R. Caldwell. "Simulation-Based Power Analysis for Factorial Analysis of Variance Designs." Advances in Methods and Practices in Psychological Science 4, no. 1 (2021): 251524592095150. http://dx.doi.org/10.1177/2515245920951503.

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Researchers often rely on analysis of variance (ANOVA) when they report results of experiments. To ensure that a study is adequately powered to yield informative results with an ANOVA, researchers can perform an a priori power analysis. However, power analysis for factorial ANOVA designs is often a challenge. Current software solutions do not allow power analyses for complex designs with several within-participants factors. Moreover, power analyses often need [Formula: see text] or Cohen’s f as input, but these effect sizes are not intuitive and do not generalize to different experimental desi

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19

Gardini, Aldo, Carlo Trivisano, and Enrico Fabrizi. "Bayesian Analysis of ANOVA and Mixed Models on the Log-Transformed Response Variable." Psychometrika 86, no. 2 (2021): 619–41. http://dx.doi.org/10.1007/s11336-021-09769-y.

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AbstractThe analysis of variance, and mixed models in general, are popular tools for analyzing experimental data in psychology. Bayesian inference for these models is gaining popularity as it allows to easily handle complex experimental designs and data dependence structures. When working on the log of the response variable, the use of standard priors for the variance parameters can create inferential problems and namely the non-existence of posterior moments of parameters and predictive distributions in the original scale of the data. The use of the generalized inverse Gaussian distributions

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20

Verma, Sourav. "Role of analysis of variance (One way-anova) in music." International Journal of Applied Research 6, no. 8 (2020): 12–15. http://dx.doi.org/10.22271/allresearch.2020.v6.i8a.6968.

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21

Davidson, Lisa. "Smoothing spline analysis of variance (ANOVA) for tongue curve comparison." Journal of the Acoustical Society of America 118, no. 3 (2005): 2023–24. http://dx.doi.org/10.1121/1.4785761.

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22

Zhu, Chengxuan, Zihan Fang, and Jiaying Zheng. "Second-hand sailboat price assessment based on ANOVA." Highlights in Science, Engineering and Technology 72 (December 15, 2023): 1195–202. http://dx.doi.org/10.54097/sdpxh789.

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As a luxury, the price of second-hand sailing will vary with the changes of years and markets, and its price may be affected by many factors, such as regional differences, various characteristics of sailing and the year of production of sailing. In order to determine the dominant factors affecting sailing prices and explain the changes of sailing prices, Spearman correlation analysis is used. Multivariate linear regression model is established to analyze and determine the weight of the indicators affecting the sailing, and finally get a formula to explain the sailing price. Based on the price

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23

Glenn, D. Michael. "Statistical Analysis of Root Count Data." HortScience 30, no. 4 (1995): 907A—907. http://dx.doi.org/10.21273/hortsci.30.4.907a.

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The minirhizotron approach for studying the dynamics of root systems is gaining acceptance; however, problems have arisen in the analysis of data. The purposes of this study were to determine if analysis of variance (ANOVA) was appropriate for root count data, and to evaluate transformation procedures to utilize ANOVA. In peach, apple, and strawberry root count data, the variance of treatment means was positively correlated with the mean, violating assumptions of ANOVA. A transformation based on Taylor's power law as a first approximation, followed by a trial and error approach, developed tran

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24

Adeniran, A. T., J. O. Olilima, and R. O. Akano. "ANALYSIS OF VARIANCE: THE FUNDAMENTAL CONCEPTS AND APPLICATION WITH R." ANALYSIS OF VARIANCE: THE FUNDAMENTAL CONCEPTS AND APPLICATION WITH R 9, no. 10 (2021): 2408–22. https://doi.org/10.47191/ijmcr/v9i10.04.

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The Analysis of Variance (ANOVA) test has long been an essential tool for researchers conducting studies on multiple experimental groups with or without one or more control groups. This article encapsulates the fundamentals of ANOVA for an intended benefit of the reader of scientific literature who does not possess expertise in statistics. The emphasis is on conceptually-based perspectives regarding the use and interpretation of ANOVA results, with minimal coverage of the mathematical foundations. Data entry, checking basic parametric assumptions of ANOVA, descriptive statistics of the data by

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25

Araújo, Adelson Paulo. "Analysis of variance of primary data on plant growth analysis." Pesquisa Agropecuária Brasileira 38, no. 1 (2003): 1–10. http://dx.doi.org/10.1590/s0100-204x2003000100001.

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Plant growth analysis presents difficulties related to statistical comparison of growth rates, and the analysis of variance of primary data could guide the interpretation of results. The objective of this work was to evaluate the analysis of variance of data from distinct harvests of an experiment, focusing especially on the homogeneity of variances and the choice of an adequate ANOVA model. Data from five experiments covering different crops and growth conditions were used. From the total number of variables, 19% were originally homoscedastic, 60% became homoscedastic after logarithmic transf

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Kim, Hae-Young. "Analysis of variance (ANOVA) comparing means of more than two groups." Restorative Dentistry & Endodontics 39, no. 1 (2014): 74. http://dx.doi.org/10.5395/rde.2014.39.1.74.

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27

Swanberg, Marika, Ira Globus-Harris, Iris Griffith, Anna Ritz, Adam Groce, and Andrew Bray. "Improved Differentially Private Analysis of Variance." Proceedings on Privacy Enhancing Technologies 2019, no. 3 (2019): 310–30. http://dx.doi.org/10.2478/popets-2019-0049.

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Abstract Hypothesis testing is one of the most common types of data analysis and forms the backbone of scientific research in many disciplines. Analysis of variance (ANOVA) in particular is used to detect dependence between a categorical and a numerical variable. Here we show how one can carry out this hypothesis test under the restrictions of differential privacy. We show that the F -statistic, the optimal test statistic in the public setting, is no longer optimal in the private setting, and we develop a new test statistic F1 with much higher statistical power. We show how to rigorously compu

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Bondarenko, Artem, and Sergiy Lapach. "Advantages of multivariate regression analysis over ANOVA." International Scientific and Technical conference "The Progressive Technics, Technology and Engineering Education", no. XXIII (June 9, 2023): 153–56. http://dx.doi.org/10.20535/2409-7160.2023.xxiii.278756.

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When modeling technological processes nowadays, it is necessary to take into account a large number of factors. Until now, analysis of variance (ANOVA) is often used in educational courses and in practical activities, although the possibility of using regression analysis instead, especially with a large number of factors, has been shown for quite some time. The paper examines in detail, using an example, the advantages of regression analysis over ANOVA in the modeling of technological processes. As an example, modeling of the manufacture of fiberglass bolts taking into account three factors is

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Xiao, Li. "ANOVA on College Students' Physical Fitness." MATEC Web of Conferences 365 (2022): 01034. http://dx.doi.org/10.1051/matecconf/202236501034.

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Through a sampling survey of the results of the physical fitness test of the students of Guilin Institute of Aerospace Industry in 2018, using the analysis of variance research method, the five variable indicators of the physical fitness of the surveyed students were analyzed, and the changes in the physical fitness of the students during the three years of university were studied. The test results show that during college, the physical fitness of students has been increasing year by year, but the growth rate has been declining, and the difference in physical fitness is mainly due to the chang

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Hobbs, Jonathan, Matthias Katzfuss, Hai Nguyen, Vineet Yadav, and Junjie Liu. "Functional analysis of variance (ANOVA) for carbon flux estimates from remote sensing data." Geoscientific Model Development 17, no. 3 (2024): 1133–51. http://dx.doi.org/10.5194/gmd-17-1133-2024.

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Abstract. The constellation of Earth-observing satellites has now produced atmospheric greenhouse gas concentration estimates covering a period of several years. Their global coverage is providing additional information on the global carbon cycle. These products can be combined with complex inversion systems to infer the magnitude of carbon sources and sinks around the globe. Multiple factors, including the atmospheric transport model and satellite product aggregation method, can impact such flux estimates. Analysis of variance (ANOVA) is a well-established statistical framework for estimating

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de los Campos, Gustavo, Torsten Pook, Agustin Gonzalez-Reymundez, Henner Simianer, George Mias, and Ana I. Vazquez. "ANOVA-HD: Analysis of variance when both input and output layers are high-dimensional." PLOS ONE 15, no. 12 (2020): e0243251. http://dx.doi.org/10.1371/journal.pone.0243251.

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Modern genomic data sets often involve multiple data-layers (e.g., DNA-sequence, gene expression), each of which itself can be high-dimensional. The biological processes underlying these data-layers can lead to intricate multivariate association patterns. We propose and evaluate two methods to determine the proportion of variance of an output data set that can be explained by an input data set when both data panels are high dimensional. Our approach uses random-effects models to estimate the proportion of variance of vectors in the linear span of the output set that can be explained by regress

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Abidoye A. O and Egburonu O. D. "On The Use of Analysis of Variance Under Unequal Group Variances." Indonesian Journal of Contemporary Multidisciplinary Research 2, no. 6 (2023): 1177–84. http://dx.doi.org/10.55927/modern.v2i6.3409.

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In this study, we imposed Analysis of variances test (ANOVA) which use when we have more than two treatments or different levels of a single factors that we wish to compare then we assume homogeneity of variances across the groups being compared although most of the earlier works that have addressed the problem of testing equality of mean variance overestimates the appropriate variance and the test statistic becomes conservative. This is the well known Behrens – Fisher problem. Then we are interested in comparing several treatment means in this work , we made use the analysis of variance under

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33

Zhou, Shi Lei, Ya Lin Guan, and Xin Kun Tang. "Analysis of PCB via for Signal Integrity Using ANOVA." Applied Mechanics and Materials 446-447 (November 2013): 956–60. http://dx.doi.org/10.4028/www.scientific.net/amm.446-447.956.

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This paper based on ANOVA (ANalysis Of VAriance) presents an investigation in the design of signal via in multilayered printed circuit boards (PCB) technology from a signal integrity point of view. Using the concept of the orthogonal array (OA), different vias physical aspect ratios have been set in the analysis. The impacts of these parameters are investigated with the help for a full-wave electromagnetic simulation soft HFSS. This study demonstrates the factors which is the most influence on the signal integrity.

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Abidoye A.O and Egburonu O. D. "On The Use of Analysis of Variance Under Unequal Group variances." Formosa Journal of Multidisciplinary Research 2, no. 3 (2023): 617–24. http://dx.doi.org/10.55927/fjmr.v2i3.3377.

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In this study, we imposed Analysis of variances test (ANOVA) which use when we have more than two treatments or different levels of a single factors that we wish to compare then we assume homogeneity of variances across the groups being compared although most of the earlier works that have addressed the problem of testing equality of mean variance overestimates the appropriate variance and the test statistic becomes conservative. This is the well-known Behrens – Fisher problem. Then we are interested in comparing several treatments means in this work, we made use the analysis of variance under

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35

Vercruyssen, Max, and James C. Edwaeds. "ANOVA/TT: Analysis of variance teaching template for lotus 1-2-3." Behavior Research Methods, Instruments, & Computers 20, no. 3 (1988): 349–54. http://dx.doi.org/10.3758/bf03203857.

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36

Ziegel, Eric. "Annotated Computer Output for Analysis of Variance of Unbalanced Data: SPSSX ANOVA." Technometrics 31, no. 3 (1989): 397–98. http://dx.doi.org/10.1080/00401706.1989.10488585.

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37

Donald, L. Buresh Ph.D. Esq. "Comments and Observations on The Analysis of Variance." International Journal of Social Science and Human Research 08, no. 06 (2025): 3971–76. https://doi.org/10.5281/zenodo.15590266.

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What is the purpose of conducting an analysis of variance? This is an interesting question that demands an answer. From a prima facia perspective, an analysis of variance helps a researcher evaluate how data is distributed. Does it bunch together, or are there wide gaps between data points? An analysis of variance can stand by itself, but it is particularly beneficial to appreciate before conducting a regression analysis. Thus, given that a researcher has previously determined the independent or explanatory variables and the dependent variable, this paper aims to help researchers understand th

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38

Harrison, S. Kent, and Emilie E. Regnier. "Assessing Herbicide Phytotoxicity with Covariance Analysis." Weed Technology 4, no. 4 (1990): 828–32. http://dx.doi.org/10.1017/s0890037x00026488.

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Greenhouse experiments were conducted to determine the statistical precision of estimating herbicide dose-response treatment effects by covariance analysis (ANOCOVA) relative to standard analysis of variance (ANOVA). Analyses of corn seedling response to the translocated herbicides fluazifop-P, sethoxydim, and quizalofop at 10 to 60 g ai ha-1 indicated that treatment effects were estimated with 26 to 116% greater precision by ANOCOVA than ANOVA. Covariance analyses of treatment effects for corn response to the contact herbicides paraquat, acifluorfen, and lactofen at 50 to 300 g ai ha-1 gave 8

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Wilda Roeska Simatupang, Nos Sutrisno, Haniza Haniza, Ninny Siregar, Budhi Santri Kesuma, and Reakha Zulvatricia. "Application of SQC and ANOVA in Quality Control of Palm Kernel Oil." JOURNAL OF INDUSTRIAL AND MANUFACTURE ENGINEERING 8, no. 2 (2024): 263–74. http://dx.doi.org/10.31289/jime.v8i2.13169.

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The measurement and control of free fatty acids (FFA), moisture content (MC), and impurity content (IC) using Statistical Quality Control (SQC) in the oil production process were crucial to ensure optimal quality of palm kernel oil (PKO). This study aimed to determine the variance influencing the quality of PKO using Anova analysis. Anova (Analysis of Variance) is a statistical test procedure used to test mean differences among more than two groups. The results of the study indicated that the analysis of variance Fcount values for free fatty acids, moisture content, and impurity content were s

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Júlio, A. Mabuie. "Alternatives Test in ANOVA with Unequal Variance and Unequal Sample Size." Alternatives Test in ANOVA with Unequal Variance and Unequal Sample Size 8, no. 11 (2023): 12. https://doi.org/10.5281/zenodo.10250330.

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This article presents a discussion about the methods used in experimental statistics, which considers a case study in which we have experimental units with unequal sample sizes. For this type of experimentation, non-observation of certain assumptions underlying the carrying out of the associated tests is observable, forcing the researcher to opt for alternative methods. With the aim of comparing the average pedagogical performance of students from different groups of students, a database was considered, made available in three groups of students according to the distances travelled to school.

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41

Govindasami, S., D. Sivakumar, and P. B Sakthivel. "A Study on Adsorption Capacity of Activated Carbons through Analysis of Variance (Anova)." International Journal of Engineering & Technology 7, no. 3.34 (2018): 449. http://dx.doi.org/10.14419/ijet.v7i3.34.19357.

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Recycling and reusing the waste is one of the agenda of sustainable development. Hence this investigation was carried out on utilizing the industrial sludge as activated carbon. The sludge was collected from treatment unit of sugar mill industry, paper mill industry and tannery industry and activated carbon was prepared and named as sugar mill sludge activated carbon (SSAC), paper mill sludge activated carbon (PSAC) and tannery industry sludge activated carbon (TSAC). Batch studies were performed between dye solution and activated carbon to determine adsorption capacity of adsorbent and optimu

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Abidoye, Adekunle Omotayo, and Egburonu O. D. "On The Use of Analysis of Variance under Unequal group variances." East Asian Journal of Multidisciplinary Research 2, no. 3 (2023): 1079–84. http://dx.doi.org/10.55927/eajmr.v2i3.3374.

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In this study, we imposed Analysis of variances test (ANOVA) which use when we have more than two treatments or different levels of a single factors that we wish to compare then we assume homogeneity of variances across the groups being compared although most of the earlier works that have addressed the problem of testing equality of mean variance overestimates the appropriate variance and the test statistic becomes conservative. This is the well-known Behrens – Fisher problem. Then we are interested in comparing several treatments means in this work, we made use the analysis of variance under

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43

LEE, JUNSEO, SEHYEON OH, SEUNGHYO AN, EUNHEE KIM, and BYUNGCHOL MA. "Analyzing Effective Factors on Hydrogen Release Based on Response Surface Method and Analysis of Variance." Transactions of the Korean Hydrogen and New Energy Society 34, no. 6 (2023): 712–21. http://dx.doi.org/10.7316/jhne.2023.34.6.712.

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44

Wang, Faye. "Analysis of Influencing Factors of Human Resource Salary Based on Anova Analysis." Journal of Business and Marketing 2, no. 1 (2025): 11–19. https://doi.org/10.62517/jbm.202509102.

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The main purpose of this report is to explore the key factors affecting the salary level of human resources. Through the analysis of variance (ANOVA) and linear regression analysis, this paper analyzes the influence of enterprise nature, enterprise scale, experience interval, academic requirements and city on the salary level. The results show that the nature of the enterprise, the size of the enterprise, experience, educational requirements and regional factors have a significant impact on wage. Based on this result, enterprises can optimize the salary structure according to these factors and

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45

Rayner, J. C. W., and G. C. Livingston. "Ordinal Cochran-Mantel-Haenszel Testing and Nonparametric Analysis of Variance: Competing Methodologies." Stats 5, no. 4 (2022): 970–76. http://dx.doi.org/10.3390/stats5040056.

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The Cochran-Mantel-Haenszel (CMH) and nonparametric analysis of variance (NP ANOVA) methodologies are both sets of tests for categorical response data. The latter are competitor tests for the ordinal CMH tests in which the response variable is necessarily ordinal; the treatment variable may be either ordinal or nominal. The CMH mean score test seeks to detect mean treatment differences, while the CMH correlation test assesses ordinary or (1, 1) generalized correlation. Since the corresponding nonparametric ANOVA tests assess arbitrary univariate and bivariate moments, the ordinal CMH tests hav

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Vispoel, Walter P., Hyeryung Lee, Tingting Chen, and Hyeri Hong. "Using Structural Equation Modeling to Reproduce and Extend ANOVA-Based Generalizability Theory Analyses for Psychological Assessments." Psych 5, no. 2 (2023): 249–73. http://dx.doi.org/10.3390/psych5020019.

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Generalizability theory provides a comprehensive framework for determining how multiple sources of measurement error affect scores from psychological assessments and using that information to improve those assessments. Although generalizability theory designs have traditionally been analyzed using analyses of variance (ANOVA) procedures, the same analyses can be replicated and extended using structural equation models. We collected multi-occasion data from inventories measuring numerous dimensions of personality, self-concept, and socially desirable responding to compare variance components, g

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Walsh, John F. "Using Summary Statistics as Data in ANOVA: A SYSTAT Macro." Teaching of Psychology 18, no. 4 (1991): 249–51. http://dx.doi.org/10.1207/s15328023top1804_17.

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In laboratory and classroom assignments, students are asked to review research findings. Often the data available include only the means, standard deviations, and number of subjects. A SYSTAT macro is given that generates sufficient information from the data available to compute an analysis of variance (ANOVA) and post hoc analyses.

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Sutrisno, Sutrisno, and Dewi Wulandari. "Multivariate Analysis of Variance (MANOVA) untuk Memperkaya Hasil Penelitian Pendidikan." AKSIOMA : Jurnal Matematika dan Pendidikan Matematika 9, no. 1 (2018): 37. http://dx.doi.org/10.26877/aks.v9i1.2472.

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MANOVA merupakan solusi teknik analisis data kuantitatif bagi peneliti di dunia pendidikan yang ingin mengamati hasil belajar peserta didik dalam rangka menerapkan prinsip kebulatan dalam Kurikulum 2013 (prinsip evaluasi hasil belajar meliputi aspek kognitif, afektif, dan psikomotor). MANOVA mampu mengungkapkan perbedaan yang tidak ditampilkan ANOVA secara terpisah, sehingga dapat meningkatkan kesempatan untuk menemukan perubahan sebagai akibat dari perlakuan yang berbeda dan interaksinya. Dengan demikian, temuan hasil penelitian akan semakin kaya dan sangat berguna bagi perkembangan ilmu peng

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邵, 梦瑶. "Construction of Four Factor Analysis of Variance (ANOVA) Model and Cancer Risk Assessment." Advances in Applied Mathematics 10, no. 06 (2021): 2155–65. http://dx.doi.org/10.12677/aam.2021.106225.

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Armstrong, R. A., F. Eperjesi, and B. Gilmartin. "The application of analysis of variance (ANOVA) to different experimental designs in optometry." Ophthalmic and Physiological Optics 22, no. 3 (2002): 248–56. http://dx.doi.org/10.1046/j.1475-1313.2002.00020.x.

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