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Females continue to be underrepresented in leadership despite research demonstrating that leadership effectiveness does not vary by leader gender (Paustian-Underdahl et al., 2014). The current study examines the gender bias in leadership through the lens of leadership perceptions and evaluations; in particular, how perceivers' ratings of a leader would change as a function of the leader's gender. Leadership judgments are based on the leader prototype activated in the perceiver and how consistent/inconsistent the leader is perceived to be with the activated prototype (Lord et al., 2001). Due to the mismatch between the communal-oriented female gender stereotype and agentic-oriented expectations of a successful leader (Eagly and Karau, 2002), it was expected that the female leader would be rated more negatively than the male leader. Furthermore, the perceiver's gender and prior engagement in emotion suppression are investigated as two additional factors that could bias information processing when evaluating leaders. Male perceivers, who tend to hold a stronger masculine understanding of leadership (Koenig et al., 2011), were expected to evaluate the female leader more harshly than the male leader. Additionally, those depleted of their finite self-regulatory resources due to prior emotion suppression (i.e. being in a state of ego depletion; Baumeister et al., 1998) were predicted to rely more heavily on their stereotypes when making subsequent judgments; hence, ego-depleted individuals would demonstrate more bias in their ratings of the female leader relative to the male leader. In the current study, participants were randomly assigned to an emotion suppression or no suppression condition as they watched funny clips from the comedy series "The Office''. Then, they watched four business videos featuring a leader and three business managers. Participants were also randomly assigned to one of the two versions of the business videos portraying either a male or female leader. Leadership perception and leader effectiveness ratings were collected after each of the four business videos, and leader competence and leader warmth ratings were measured once after all four videos. Additionally, behavior recognition accuracy of agentic and communal leadership behaviors that were displayed in the four business videos was assessed. Contrary to expectations, the study findings demonstrate a dominant female leader effect; the female leader was evaluated more favorably than the male leader on all four leader judgments. This was observed both within the repeated measures and overall leadership ratings. An ego depletion effect was also observed; ego-depleted individuals showed lower accuracy in behavior recognition ratings and more leniency in leader warmth ratings. Furthermore, ego-depleted individuals showed less discernment by giving higher leader effectiveness ratings over time compared to non-ego-depleted individuals. Perceiver gender did not meaningfully affect leadership judgments. The unexpected pattern of bias in favor of the female leader instead of against her suggests that the nature of gender and leader stereotypes may be changing; the incongruence between the female stereotype and leader expectations may be decreasing, leading to more favorable evaluations of the female leader by both male and female perceivers. Moreover, the ability to provide fair and accurate judgments of leader effectiveness is reduced when depleted. Limitations and future research directions are discussed. / Doctor of Philosophy / The gender gap persists in leadership; although leader effectiveness has not been found to vary by the leader's gender, female leaders tend to be perceived and evaluated more negatively than male leaders. One reason for this is the mismatch between societal expectations for how women are ideally expected to behave and the expectations associated with a successful leader. In this study, gender bias in leader judgments and behavior recognition accuracy is examined by a leader's gender. Additionally, the perceiver's gender and prior engagement in emotion suppression are studied as two additional factors that can influence bias in leader ratings. Study findings demonstrate an unexpected but dominant female leader effect, where the female leader was perceived as more leader-like and rated more effective, more competent, and warmer than the male leader by both male and female perceivers. The amount of self-regulatory resources available also affected subsequent processing capabilities; those who suppressed their emotions and were depleted of their self-regulatory resources were less accurate in their behavior recognition ratings and were more lenient in their leader warmth ratings. Future research should explore if and how the nature of gender and leader stereotypes are changing, as evaluations of female leaders may not be as negatively-biased as it was previously.

Os experimentos florestais que estudam os efeitos de espaçamento devem adotar delineamentos distintos daqueles utilizados convencionalmente, por conta da grande demanda de área experimental dos delineamentos convencionais, o delineamento sistemático tipo \"leque\" é a forma mais viável de se executar este tipo de ensaio. Neste delineamento, as árvores são dispostas em diversos círculos concêntricos, de modo que, vários espaçamentos são gerados, porém, sem que haja possibilidade para a casualização. Para este tipo de experimento, convencionalmente são realizadas análises geoestatísticas que modelam o comportamento espacial de dependência entre os elementos, utilizando além da variável observada, as coordenadas das observações. Assim, é modelada uma função denominada semivariograma que explica esta dependência espacial, possibilitando a criação de um mapa de tendências denominado krigagem. Neste trabalho, são tratadas as variáveis de altura, diâmetro do fuste, diâmetro da copa, área da copa e volume cilíndrico de árvores de Canafístula, aos seis meses para altura e aos 13, 25 e 37 meses para todas as variáveis, após o plantio de mudas de Canafístula (Peltophorum dubium) em um experimento conduzido em Mato Grosso do Sul. Além da análise geoestatística, também é realizada a análise multivariada objetivando relacionar as variáveis por meio de medidas de correlação, efetuar a análise de componentes principais, de agrupamentos e discriminante. Além disso, é realizada a análise de medidas repetidas, objetivando avaliar o comportamento dessas variáveis ao longo dos períodos. Por fim, algumas formas combinadas de avaliar e interpretar os resultados são apresentadas, de modo a relacionar as análises já realizadas, calculando novos componentes principais para as variáveis, por período, efetuando a análise geoestatística dos componentes principais e avaliando o comportamento desses componentes ao longo do tempo. / Forest experiments which study the spacing effects should adopt different delineations from those conventionally used, due to the great demand for experimental area of conventional delineations, the systematic fan design is the most viable way to perform this type of test. In this design, the trees are arranged in several concentric circles, so that various spacings are generated, however, with no possibility for randomization. For this type of experiment, statistical analyses modeling the spatial behavior of dependence between the elements are conventionally performed using, in addition to the variable observed, the coordinates of the observations. Thus, a function called semivariogram that explains the spatial dependence is modeled, enabling the creation of a map of trends called kriging. In this paper, the variables of height, bole diameter, treetop diameter, area and its cylindrical volume of trees Canafistula, are treated at six months for height and at 13, 25 and 37 months for all variables after planting canafístula seedlings (Peltophorum dubium) in an experiment carried out in Mato Grosso do Sul. In addition to the geostatistical analysis, a multivariate analysis is also performed, aiming to relate the variables by correlation measures and performing the analysis of the main, grouping and discriminating components. Furthermore, the repeated measures analysis is performed aiming to evaluate the behavior of these variables over the periods. Finally, some combined ways to assess and interpret the results are presented in order to relate the previous analyses, calculating new key components for the variables, by period, performing the geostatistical analysis of the main components and evaluating the behavior of these components over time.

Les études longitudinales sur une longue période permettent d’échantillonner plusieurs fois le phénomène étudié et ainsi, avec des mesures répétées, dégager une tendance confirmée. Mais, dès lors, elles produisent de très larges bases de données épidémiologiques accompagnées de plus de sources de bruit par rapport aux études à observation unique ; et souvent, contiennent de la corrélation dans les mesures. Ici, nous avons présenté à travers cette thèse une étude de long terme des facteurs épidémiologiques et génétiques du paludisme menée dans deux cohortes familiales du Sénégal, l’une dans le village de Dielmo suivi pendant 19 années consécutives (1990 – 2008) et l’autre dans le village de Ndiop suivi pendant 16 années consécutives (1993 – 2008). L’objectif de ce travail de thèse a été de développer des méthodes d’analyse statistique pour identifier des gênes de susceptibilité / résistance au paludisme prenant en compte les relations familiales, les mesures répétées et des potentielles interactions génotypes – environnement dans l’évaluation des phénotypes. Par la suite, de tels phénotypes corrigés des facteurs identifiés comme potentielles sources de confusion et/ou de bruit ont été alors utilisés pour les tests de liaison et d’association génétique. Le phénotype principal étudié chez chaque volontaire a été la survenue ou non d’accès palustre, attribué à une infection au parasite Plasmodium falciparum, durant chaque trimestre de présence (PFA). Les études ont été menées de manière indépendante dans chacun des deux villages, de même que les analyses descriptives, l’estimation de la contribution génétique humaine et des effets individuels. Les tests de liaison et d’association génétique ont été réalisés par des méthodes familiales basées sur l’analyse de la transmission d’allèles des parents aux enfants (Transmission Disequilibrium Test). Ces méthodes sont connues pour être robustes par rapport au problème de la stratification de population et donc nous permettent d’augmenter la taille de notre échantillon dans les études de liaison et d’association génétique en analysant les deux villages en même temps. / Long term longitudinal surveys have the advantage to enable several sampling of the studied phenomena and then, with the repeated measures obtained, find a confirmed tendency. However, these long term surveys generate large epidemiological datasets including more sources of noise than normal datasets (e.g. one single measure per observation unit) and potential correlation in the measured values. Here, we studied data from a long-term epidemiological and genetic survey of malaria disease in two family-based cohorts in Senegal, followed for 19 years (1990–2008) in Dielmo and for 16 years (1993–2008) in Ndiop. The main objectives of this work were to take into account familial relationships, repeated measures as well as effect of covariates to measure both environmental and host genetic (heritability) impacts on the outcome of infection with the malaria parasite Plasmodium falciparum, and then use findings from such analyses for linkage and association studies. The outcome of interest was the occurrence of a P. falciparum malaria attack during each trimester (PFA). The two villages were studied independently; epidemiological analyses, estimation of heritability and individual effects were then performed in each village separately. Linkage and association analyses used family-based methods (based on the original Transmission Disequilibrium Test) known to be immune from population stratification problems. Then to increase sample size for linkage and association analyses, data from the two villages were used together.

Os experimentos florestais que estudam os efeitos de espaçamento devem adotar delineamentos distintos daqueles utilizados convencionalmente, por conta da grande demanda de área experimental dos delineamentos convencionais, o delineamento sistemático tipo \"leque\" é a forma mais viável de se executar este tipo de ensaio. Neste delineamento, as árvores são dispostas em diversos círculos concêntricos, de modo que, vários espaçamentos são gerados, porém, sem que haja possibilidade para a casualização. Para este tipo de experimento, convencionalmente são realizadas análises geoestatísticas que modelam o comportamento espacial de dependência entre os elementos, utilizando além da variável observada, as coordenadas das observações. Assim, é modelada uma função denominada semivariograma que explica esta dependência espacial, possibilitando a criação de um mapa de tendências denominado krigagem. Neste trabalho, são tratadas as variáveis de altura, diâmetro do fuste, diâmetro da copa, área da copa e volume cilíndrico de árvores de Canafístula, aos seis meses para altura e aos 13, 25 e 37 meses para todas as variáveis, após o plantio de mudas de Canafístula (Peltophorum dubium) em um experimento conduzido em Mato Grosso do Sul. Além da análise geoestatística, também é realizada a análise multivariada objetivando relacionar as variáveis por meio de medidas de correlação, efetuar a análise de componentes principais, de agrupamentos e discriminante. Além disso, é realizada a análise de medidas repetidas, objetivando avaliar o comportamento dessas variáveis ao longo dos períodos. Por fim, algumas formas combinadas de avaliar e interpretar os resultados são apresentadas, de modo a relacionar as análises já realizadas, calculando novos componentes principais para as variáveis, por período, efetuando a análise geoestatística dos componentes principais e avaliando o comportamento desses componentes ao longo do tempo. / Forest experiments which study the spacing effects should adopt different delineations from those conventionally used, due to the great demand for experimental area of conventional delineations, the systematic fan design is the most viable way to perform this type of test. In this design, the trees are arranged in several concentric circles, so that various spacings are generated, however, with no possibility for randomization. For this type of experiment, statistical analyses modeling the spatial behavior of dependence between the elements are conventionally performed using, in addition to the variable observed, the coordinates of the observations. Thus, a function called semivariogram that explains the spatial dependence is modeled, enabling the creation of a map of trends called kriging. In this paper, the variables of height, bole diameter, treetop diameter, area and its cylindrical volume of trees Canafistula, are treated at six months for height and at 13, 25 and 37 months for all variables after planting canafístula seedlings (Peltophorum dubium) in an experiment carried out in Mato Grosso do Sul. In addition to the geostatistical analysis, a multivariate analysis is also performed, aiming to relate the variables by correlation measures and performing the analysis of the main, grouping and discriminating components. Furthermore, the repeated measures analysis is performed aiming to evaluate the behavior of these variables over the periods. Finally, some combined ways to assess and interpret the results are presented in order to relate the previous analyses, calculating new key components for the variables, by period, performing the geostatistical analysis of the main components and evaluating the behavior of these components over time.

Longitudinal data arise when individuals are measured several times during an ob- servation period and thus the data for each individual are not independent. There are several ways of analyzing longitudinal data when different treatments are com- pared. Multilevel models are used to analyze data that are clustered in some way. In this work, multilevel models are used to analyze longitudinal data from a case study. Results from other more commonly used methods are compared to multilevel models. Also, comparison in output between two software, SAS and R, is done. Finally a method consisting of fitting individual models for each individual and then doing ANOVA type analysis on the estimated parameters of the individual models is proposed and its power for different sample sizes and effect sizes is studied by simulation.

One of the most frequently encountered types of data is where the response variables are measured on an ordinal scale. Although there have been substantial developments in the statistical techniques for the analysis of ordinal data, methods appropriate for repeatedly assessed ordinal data collected from field experiments are limited. A series of biennial field screening trials for evaluating cultivar resistance of potato to the disease, late blight, caused by the fungus Phytophthora infestans (Mont.) de Bary has been conducted by the New Zealand Institute of Crop and Food Research since 1983. In each trial, the progression of late blight was visually assessed several times during the planting season using a nine-point ordinal scale based on the percentage of necrotic tissues. As for many other agricultural field experiments, spatial differences between the experimental units is one of the major concerns in the analysis of data from the potato late blight trial. The aim of this thesis is to construct a statistical model which can be used to analyse the data collected from the series of potato late blight trials. We review existing methodologies for analysing ordinal data with mixed effects particularly those methods in the Bayesian framework. Using data collected from the potato late blight trials we develop a Bayesian hierarchical model for the analyses of repeatedly assessed ordinal scores with spatial effects, in particular the time dependence of the scores assessed on the same experimental units was modelled by a sigmoid logistic curve. Data collected from the potato late blight trials demonstrated the importance of spatial effects in agricultural field trials. These effects cannot be neglected when analysing such data. Although statistical methods can be refined to account for the complexity of the data, appropriate trial design still plays a central role in field experiments. / Accompanying dataset is at http://hdl.handle.net/2292/5240

This study is intended to provide researchers with empirically derived guidelines for conducting factor analytic studies in research contexts that include dichotomous and continuous levels of measurement. This study is based on the hypotheses that ordinary least squares (OLS) factor analysis will yield more accurate parameter estimates than maximum likelihood (ML) and principal axis factor anlaysis (PAF); the level of improvement in estimates will be related to the proportion of observed variables that are dichotomized and the strength of communalities within the data sets. To achieve this study's objective, maximum likelihood, ordinary least squares, and principal axis factor extraction models were subjected to various research contexts. A Monte Carlo method was used to simulate data under 540 different conditions; specifically, this study is a four (sample size) by three (number of variables) by three (initial communality levels) by three (number of common factors) by five (ratios of categorical to continuous variables) design. Factor loading matrices derived through the tested factor extraction methods were evaluated through four measures of factor pattern agreement and three measures of congruence. To varying degrees, all of the design factors, as main effects, yielded significant differences in measures of factor loading sensitivity, agreement between sample and population, and congruence. However, in all cases, the main effects were components of interactions that yielded differences in values of these measures that were at least medium in effect size. The number of factors imbedded in the population was a component in six interactions that resulted in medium effect size differences in measures of agreement between population and sample factor loading matrices. of factor loading sensitivity, general pattern agreement, per element agreement, congruence, factor score correlations, and factor loading bias; in terms of the number of interactions that yielded at least medium effect size differences in measures of sensitivity, agreement, and congruence. The number of factors design factor exerted a larger influence than any of the other design factors. The level of communality interacted with the number of factors, number of observed variables, and sample size main effects to yield at least medium effect size differences in factor loading sensitivity, general pattern agreement, per element agreement, congruence, factor score correlations, factor loading bias, and RMSE; in terms of the number of factors that included communality as a component, this design factor exerted the second largest amount of influence on the measures of sensitivity, agreement, and congruence. The level of dichotomization, sample size, and number of observed variables were included in smaller numbers of interactions; however, these interactions yielded differences in all of the outcome variables that were at least medium in effect size. Across the majority of interactions among the manipulated research contexts, the ordinary least squares factor extraction method yielded factor loading matrices that were in better agreement with the population than either the maximum likelihood or the principal axis methods. In three of the four measures of congruence, the ordinary least squares method yielded factor loading matrices that exhibited less bias and error than the other two tested factor extraction methods. In general, the ordinary least squares method yielded factor loading matrices that correlated more strongly with the population than either of the other two tested methods. The suggested use of ordinary least squares factor analytic techniques represents the major, empirically derived recommendation derived from the results of this study. In all tested conditions, the ordinary least squares factor extraction method identified common factors with a high degree of efficacy. Suggested studies for future would incorporate the limiting constraints associated with this dissertation into methodological studies to extend the generalizability of conclusions and recommendations into areas that are beyond the scope of this dissertation.

One of the most frequently encountered types of data is where the response variables are measured on an ordinal scale. Although there have been substantial developments in the statistical techniques for the analysis of ordinal data, methods appropriate for repeatedly assessed ordinal data collected from field experiments are limited. A series of biennial field screening trials for evaluating cultivar resistance of potato to the disease, late blight, caused by the fungus Phytophthora infestans (Mont.) de Bary has been conducted by the New Zealand Institute of Crop and Food Research since 1983. In each trial, the progression of late blight was visually assessed several times during the planting season using a nine-point ordinal scale based on the percentage of necrotic tissues. As for many other agricultural field experiments, spatial differences between the experimental units is one of the major concerns in the analysis of data from the potato late blight trial. The aim of this thesis is to construct a statistical model which can be used to analyse the data collected from the series of potato late blight trials. We review existing methodologies for analysing ordinal data with mixed effects particularly those methods in the Bayesian framework. Using data collected from the potato late blight trials we develop a Bayesian hierarchical model for the analyses of repeatedly assessed ordinal scores with spatial effects, in particular the time dependence of the scores assessed on the same experimental units was modelled by a sigmoid logistic curve. Data collected from the potato late blight trials demonstrated the importance of spatial effects in agricultural field trials. These effects cannot be neglected when analysing such data. Although statistical methods can be refined to account for the complexity of the data, appropriate trial design still plays a central role in field experiments. / Accompanying dataset is at http://hdl.handle.net/2292/5240

One of the most frequently encountered types of data is where the response variables are measured on an ordinal scale. Although there have been substantial developments in the statistical techniques for the analysis of ordinal data, methods appropriate for repeatedly assessed ordinal data collected from field experiments are limited. A series of biennial field screening trials for evaluating cultivar resistance of potato to the disease, late blight, caused by the fungus Phytophthora infestans (Mont.) de Bary has been conducted by the New Zealand Institute of Crop and Food Research since 1983. In each trial, the progression of late blight was visually assessed several times during the planting season using a nine-point ordinal scale based on the percentage of necrotic tissues. As for many other agricultural field experiments, spatial differences between the experimental units is one of the major concerns in the analysis of data from the potato late blight trial. The aim of this thesis is to construct a statistical model which can be used to analyse the data collected from the series of potato late blight trials. We review existing methodologies for analysing ordinal data with mixed effects particularly those methods in the Bayesian framework. Using data collected from the potato late blight trials we develop a Bayesian hierarchical model for the analyses of repeatedly assessed ordinal scores with spatial effects, in particular the time dependence of the scores assessed on the same experimental units was modelled by a sigmoid logistic curve. Data collected from the potato late blight trials demonstrated the importance of spatial effects in agricultural field trials. These effects cannot be neglected when analysing such data. Although statistical methods can be refined to account for the complexity of the data, appropriate trial design still plays a central role in field experiments. / Accompanying dataset is at http://hdl.handle.net/2292/5240