Academic literature on the topic 'Doubly multivariate repeated measures model'

The purpose of this study was to examine the impact of internships on students’ career-related affect and intentions. Data were gathered from 138 upper-level undergraduate sport management students (71 interns, 67 noninterns). A doubly multivariate repeated measures model indicated that, although they did not differ at the beginning of the internship, interns had less positive attitudes toward the profession than did noninterns at the end of the internship. Structural equation modeling indicated that affective occupational commitment fully mediated the relationship between anticipated career satisfaction and intentions to enter the profession. The results contribute to the extant literature by demonstrating that internships can influence career-related affect and intentions.

Despite normative and legal injunctions against targeting civilians in war, as well as doubts regarding the effectiveness of such strategies, belligerents have frequently turned their guns on noncombatants. Two variables—desperation to win and to save lives on one's own side in protracted wars of attrition, and the intention to conquer and annex enemy territory—explain this repeated resort to civilian targeting. According to the desperation logic, costly and prolonged wars of attrition cause states to become increasingly anxious to prevail and to reduce their losses. Adopting a policy of civilian victimization permits states to continue the war while managing their losses and hopefully coercing the adversary to quit. In the appetite for conquest model, by contrast, belligerents specifically intend to seize and annex territory. Attackers in this model employ civilian victimization to eliminate enemy civilians, who can threaten the aggressor's immediate military position and present a future threat of rebellion. Multivariate analysis of interstate wars between 1816 and 2003 corroborates the importance of these factors,and a case study of the British starvation blockade of Germany in World War I supports the plausibility of the desperation mechanism.

Abstract Grasshoppers, Melanoplus sanguinipes (Orthoptera: Acrididae), develop larger head width (HW) and shorter leg length, relative to body size, when fed low nutrient, lignin-rich grasses compared to sibs fed a diet of high nutrient grasses. To elucidate how underlying genetic variation and plasticity of growth generate plasticity of this linear static allometry within coarse-grained environments, I measured head and leg size of three nymphal instars and adult grasshoppers raised on either a low or high nutrient diet within a half-sib quantitative genetic experiment. Doubly-multivariate repeated measures multiple analysis of variance (MANOVA) of head, mandible, and hind leg size and their rate of growth (mm/period) and growth period (days) through ontogeny were used to analyze how the ontogeny of diet-induced plasticity for these variables and additive genetic variation for plasticity (genotype × environment interaction [G×E]) contribute to plasticity in functional linear static allometry. Genetic variation for diet-induced plasticity (G×E) of head and leg size varied through ontogeny, as did genetic variation for plasticity of growth in third and fourth instar nymphs. Despite extensive genetic variation in plasticity of HW and leg length in fourth instar nymphs, the static allometry between head and leg was stable within each diet because the patterns of G×E were similar for HW, leg length and their coordinated growth. Nutrient sensitive plasticity in growth shifted the intercept but not the slope of static allometry, a result consistent with one outcome of a graphical model of the relationships between G× E and plasticity of within environment static allometry. In addition, G×E of fourth instar head and leg size was reduced in adults by negatively size-dependent, convergent growth in the last period of ontogeny. Consequently, the bivariate reaction norms of head and leg size for adults exhibited no G×E and, again, plasticity in the intercept but not in the slope of static allometry. The ontogeny of seemingly simple diet-induced linear static allometry between functional body parts in grasshoppers arises from a complex combination of differing patterns of nutrient-sensitive growth, duration of growth, convergent growth, and G×E, all relevant to understanding the development and evolution of functional allometry in hemimetabolous insects.

Repeated measures data occur in a wide variety of experimental situations and are often analyzed without full consideration of the statistical issues involved. In this paper, a discussion of model construction, univariate versus multivariate solutions, and statistical assumptions is motivated by examples from a tree physiology experiment. In addition, several examples from the forestry literature are reviewed. It is hoped that this discussion will help scientists with little statistical training to become aware of the different analyses available and perhaps to recognize the associated models in their own research. The examples range from a simple repeated measures design with one within-subject factor and no between-subjects factors to a more complex design involving multiple within-subject and between-subjects factors. The modelling approach used here permits a straightforward comparison between the univariate and multivariate solutions. Although no single approach is consistently best, the multivariate approach is always appropriate and provides the same interpretations as the univariate approach. However, when appropriate assumptions such as sphericity are met, power considerations tend to favor the more traditional univariate analysis.

Abstract In this paper we consider a set of T repeated measurements on p characteristics on each of n individuals. The n individuals themselves may be divided and randomly assigned to K groups. These data are analyzed using a mixed effect MANOVA model, assuming that the data on an individual have a covariance matrix which is a Kronecker product of two positive definite matrices. Results are illustrated on a data set obtained from experiments with varieties of winter rye.

The total score, which is calculated as the sum of scores in multiple items or questions, is repeatedly measured in longitudinal clinical studies. A mixed effects model for repeated measures method is often used to analyze these data; however, if one or more individual items are not measured, the method cannot be directly applied to the total score. We develop two simple and interpretable procedures that infer fixed effects for a longitudinal continuous composite variable. These procedures consider that the items that compose the total score are multivariate longitudinal continuous data and, simultaneously, handle subject-level and item-level missing data. One procedure is based on a multivariate marginalized random effects model with a multiple of Kronecker product covariance matrices for serial time dependence and correlation among items. The other procedure is based on a multiple imputation approach with a multivariate normal model. In terms of the type-1 error rate and the bias of treatment effect in total score, the marginalized random effects model and multiple imputation procedures performed better than the standard mixed effects model for repeated measures analysis with listwise deletion and single imputations for handling item-level missing data. In particular, the mixed effects model for repeated measures with listwise deletion resulted in substantial inflation of the type-1 error rate. The marginalized random effects model and multiple imputation methods provide for a more efficient analysis by fully utilizing the partially available data, compared to the mixed effects model for repeated measures method with listwise deletion.

This study assessed the effects of behavior-setting changes, partners' behavior toward an interloper, and importance of interloper characteristics for romantic jealousy. Participants were 162 individuals in dating relationships. Four jealousy dilemmas were presented that crossed two levels of behavior setting and two levels of partners' extradyadic behavior toward an interloper. Interloper characteristics were also embedded in the dilemmas. A 2 x 2 withinsubjects ANOVA revealed significant variation between behavior settings and extradyadic behaviors. A doubly-multivariate repeated measures MANOVA assessed the importance of interloper characteristics within behavior setting and extradyadic behavior, showing that interloper characteristics do play a role in reported jealousy. Findings are explained in terms of behavior-setting influences, coping appraisals, and self-maintenance evaluations.

Nesse trabalho foi abordada a situação em que observações de produtividade da cana-de-açúcar (TCH) foram tomadas na mesma unidade experimental em diferentes condições de avalições (anos). Foram avaliados os perfis médios de resposta de 48 genótipos de cana-de-açúcar em dois experimentos: Experimento 1 e Experimento 2, durante três e cinco anos respectivamente, ambos com o delineamento de blocos ao acaso. Esse tipo de planejamento produz uma forma de relação entre as observações tomadas na mesma unidade experimental, portanto requer outras suposições, além das usuais, para que análise seja correta e os testes produzam resultados válidos. Para que as inferências sobre as médias de produtividade sejam válidas e seguras é necessário que o modelo da matriz de covariância dos dados seja apropriado. Diante disso, foram avalidos três alterantivas de análise para dados longitudinais (medidas repetidas no tempo ), sendo utilizados portanto, o modelo univariado, conforme o planejamento do tipo \"split-plot on time\", que impõe forte restrição quanto a matriz de variâncias-covariâncias; o modelo multivariado, que utiliza uma matriz de variâncias-covariâncias não-estruturada e o modelo mistos, que possibilita a seleção de uma matriz que melhor representa os dados. Contudo, verificou-se que não houve diferença entre os resultados dos testes para as diferentes metodologias. Porém, é interessante a continuidade do estudo em relação ao modelo misto, pois devido a sua flexibilidade e precisão é possível obter estimativas mais seguras dos componentes de variância e predizer os valores genotípicos, que por fim poderá proporcionar a predição de produção de uma futura colheita para um determinado genótipo.
This work has been dealt with situation in which observations of productivity of sugar of cane (TCH) were taken in the same unit experimental in different condition of assessments (years). The response profiles average of 48 genotypes of sugar of cane were evaluated in two experiments: Experiment 1 and Experiment 2, for three and five years respectively, both with the randomized complete block design. This type of planning produces a form of relationship between the observations made in the same unit experimental therefore requires other assumptions, in addition to the usual, so that analysis is correct and the test results valid. To that inferences on the means of productivity are valid and safe it is necessary that the model of covariance matrix of the data is appropriate. Therefore, were evaluated three alternatives for analysis of longitudinal data (repeated measures over time), the univariate model as the planning of the split-plot on time which imposes strong restrictions on variances - covariances matrix, the multivariate model, which uses a non-structured variances - covariances matrix and mixed model, which they are enable the selection of a matrix that best represents the data. However, it was found that there was no difference between the results of tests for the different methodologies. But it is interesting the continuity of the study in relation to mixed model, because due to its flexibility and accuracy will be possible to obtain more reliable estimates of the variance components and predict the genotypic values, which ultimately could provide a prediction of production of a future harvest for a given genotype.