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General Linear Modeling of Magnetoencephalography Data
Published in Hualou Liang, Joseph D. Bronzino, Donald R. Peterson, Biosignal Processing, 2012
Dimitrios Pantazis, Juan Luis Poletti Soto, Richard M. Leahy
In the multivariate approach we use the multivariate analysis of variance (MANOVA) or multivariate analysis of covariance (MANCOVA) framework. In this case, the MEG observations are organized into vectors and stacked as rows in an observation matrix Y (Figure 4.3). Classical analysis of this model proceeds by computing sample covariance matrices of the data and the residuals, and then estimating test statistics such as Roy’s maximum root, Wilk’s lambda, Pillai’s trace, or Hotelling’s trace (Seber, 2004).
Multivariate Methods
Published in Shayne C. Gad, Carrol S. Weil, Statistics and Experimental Design for Toxicologists, 1988
Multivariate analysis of covariance (MANCOVA) is the multivariate analog of analysis of covariance. As with MANOVA, it is based on the assumption that the data being analyzed are from a multivariate normal population. The MANCOVA test utilizes the two residual matrices using the statistic, and is an extension of ANCOVA with two or more uncontrolled variables (or covariates). A detailed discussion can be found in Tatsuoka (1971).
Stimulating Suspense in Gamified Virtual Reality Sports: Effect on Flow, Fun, and Behavioral Intention
Published in International Journal of Human–Computer Interaction, 2022
Jun-Phil Uhm, Sanghoon Kim, Hyun-Woo Lee
Using SPSS v. 27.0 (IBM Corp, 2020), we performed four statistical analyses. Descriptive statistics of the study variables including mean, standard deviation, skewness and kurtosis are listed in Table 1. Given that skewness and kurtosis values were within the recommended range, it is assumed that all the variables were normally distributed. Also, internal consistency reliability was adequate for all measures as shown in Table 1. In order to check whether the participants were evenly assigned to the three groups, multivariate analysis of variance (MANOVA) was used to investigate whether there were differences in the level of challenge, involvement in table tennis, and VR familiarity between groups. In the manipulation check stage, the difference in the sense of suspense between groups was analyzed through an analysis of variance (ANOVA) to confirm whether gamification was effectively applied to VR sports. To test our research hypotheses, we employed a multivariate analysis of covariance (MANCOVA). Specifically, we examined the differences in the level of flow, fun, and usage intention between three groups while controlling for perceived presence and interactivity.
Physical activity assessment and vascular function in adults with cystic fibrosis and their non-CF peers
Published in Journal of Sports Sciences, 2022
James Shelley, Lynne Boddy, Zoe Knowles, Claire Stewart, Freddy Frost, Dilip Nazareth, Martin Walshaw, Ellen Dawson
Descriptive statistics are displayed as mean ± SD unless otherwise stated. Independent t-tests were used to compare baseline characteristics between groups (Table 1). Analysis of covariance (ANCOVA) and multivariate analysis of covariance (MANCOVA) were used to compare variables between groups and to control for covariates (age and sex). Pearson’s correlation analyses were performed to explore the relationship between variables, and Spearman’s correlation were performed where the assumptions of normal distribution were violated.
Evaluating the usability of a commercial cooling vest in the Hong Kong industries
Published in International Journal of Occupational Safety and Ergonomics, 2018
Albert P. Chan, Yang Yang, Wen-fang Song
A factorial multivariate analysis of covariance (MANCOVA) was performed to measure the relationship between two or more dependent variables (i.e., subjective perceptions) and two or more independent variables (i.e., occupations, gender) by removing the effects of uncontrolled variations (i.e., age, work experience). This exercise was conducted using SPSS version 19.