Viele systematische Überblicksarbeiten und Meta-Analysen haben sich mit den einzelnen Faktoren befasst, die zu Schulerfolg beitragen. Diese Doktorarbeit erweitert den bisherigen Forschungsstand. Es wird untersucht, wie Gewissenhaftigkeit, Neurotizismus und Selbstwirksamkeit zu Schulerfolg in der Schule beitragen. In Anlehnung an das Big Five Narrow Trait Modell (B5NT) wird angenommen, dass Selbstwirksamkeit den Zusammenhang zwischen Persönlichkeit und Schulerfolg mediiert. Außerdem wird der Einfluss verschiedener Moderatoren untersucht. Bivariate Meta-Analysen werden genutzt, um Forschungsergebnisse bezüglich der bivariaten Zusammenhänge zwischen den Variablen zu aggregieren. MASEM werden genutzt, um die Mediationshypothese zu testen. Um die Abhängigkeitsstruktur der Effektstärken zu berücksichtigen, werden Drei-Ebenen-Modelle und robuste Varianzschätzmethoden (RVE) angewandt. Insgesamt konnte eine der Hauptannahmen des Big Five Narrow Trait Modells bestätigt werden. Der Einfluss von Persönlichkeit auf Schulerfolg wird (partiell) durch Selbstwirksamkeit mediiert. Während Selbstwirksamkeit den gesamten Einfluss von Neurotizismus auf Schulerfolg mediiert, wirkt Selbstwirksamkeit als partieller Mediator in dem Zusammenhang von Gewissenhaftigkeit und Schulerfolg. Diese Ergebnisse haben praktische Relevanz für den Bildungssektor. Leistungsschwache Schüler sollten demnach vor allem Selbstwirksamkeitstrainings erhalten, um ihren Schulerfolg zu verbessern. Dies hätte den Größten Einfluss auf Schulerfolg, mit einer mittleren bis großen Effektstärke. Persönlichkeitsinventare hingegen könnten diagnostisch eingesetzt werden. So könnten Schüler identifiziert werden, die eine Unterstützung durch Selbstwirksamkeitstrainings am meisten benötigen. Gewissenhaftigkeit hängt am stärksten mit Selbstwirksamkeit zusammen. Demnach würden Schüler mit geringen Werten in Gewissenhaftigkeit am meisten von den Trainings profitieren. / The factors that individually contribute to academic achievement have been the topic of many systematic reviews. This doctoral thesis contributes to the current state of research by meta-analytically testing a comprehensive theory regarding the underlying processes through which conscientiousness, neuroticism, and self-efficacy are influencing achievement in primary and secondary school. Following the Big Five Narrow Trait (B5NT) model, it is examined whether self-efficacy mediates the relationship between personality and achievement. Also, the influence of different potential moderator variables is analyzed. Methodologically, this doctoral thesis is applying a combination of separate bivariate meta-analyses and meta-analytical structural equation modeling (MASEM). Bivariate meta-analyses are used to aggregate research on the bivariate relationships between all variables. MASEM techniques are used to test the mediation hypothesis. Three-level random-effects models and robust variance estimation methods are used to account for the dependency among effect sizes. Overall, the main idea of the B5NT model was supported by showing that self-efficacy (partially) mediates the relationship between personality and scholastic achievement. Self-efficacy fully mediated the relationship between neuroticism and achievement. Whereas the relationship between conscientiousness and achievement was only partially mediated. These findings have practical implications for the educational sector. Most important, self-efficacy trainings are recommended for low-achieving students. This would lead to the biggest improvement in scholastic achievement – with a medium-to-large effect size. Personality inventories could be used for identifying students that need support through self-efficacy trainings. Conscientiousness was having the strongest effect on self-efficacy beliefs. Thus, students with low levels of conscientiousness should be primarily targeted.

Submitted by Paulo Gustavo Grahl (pgrahl@fgvmail.br) on 2013-10-18T05:32:44Z No. of bitstreams: 1 DoutoradoPG_final.pdf: 23501670 bytes, checksum: 55b15051b9acc69ac74e639efe776fae (MD5) / Approved for entry into archive by ÁUREA CORRÊA DA FONSECA CORRÊA DA FONSECA (aurea.fonseca@fgv.br) on 2013-10-28T18:22:53Z (GMT) No. of bitstreams: 1 DoutoradoPG_final.pdf: 23501670 bytes, checksum: 55b15051b9acc69ac74e639efe776fae (MD5) / Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2013-10-29T18:24:15Z (GMT) No. of bitstreams: 1 DoutoradoPG_final.pdf: 23501670 bytes, checksum: 55b15051b9acc69ac74e639efe776fae (MD5) / Made available in DSpace on 2013-10-29T18:25:35Z (GMT). No. of bitstreams: 1 DoutoradoPG_final.pdf: 23501670 bytes, checksum: 55b15051b9acc69ac74e639efe776fae (MD5) Previous issue date: 2012-12-22 / Esta dissertação concentra-se nos processos estocásticos espaciais definidos em um reticulado, os chamados modelos do tipo Cliff & Ord. Minha contribuição nesta tese consiste em utilizar aproximações de Edgeworth e saddlepoint para investigar as propriedades em amostras finitas do teste para detectar a presença de dependência espacial em modelos SAR (autoregressivo espacial), e propor uma nova classe de modelos econométricos espaciais na qual os parâmetros que afetam a estrutura da média são distintos dos parâmetros presentes na estrutura da variância do processo. Isto permite uma interpretação mais clara dos parâmetros do modelo, além de generalizar uma proposta de taxonomia feita por Anselin (2003). Eu proponho um estimador para os parâmetros do modelo e derivo a distribuição assintótica do estimador. O modelo sugerido na dissertação fornece uma interpretação interessante ao modelo SARAR, bastante comum na literatura. A investigação das propriedades em amostras finitas dos testes expande com relação a literatura permitindo que a matriz de vizinhança do processo espacial seja uma função não-linear do parâmetro de dependência espacial. A utilização de aproximações ao invés de simulações (mais comum na literatura), permite uma maneira fácil de comparar as propriedades dos testes com diferentes matrizes de vizinhança e corrigir o tamanho ao comparar a potência dos testes. Eu obtenho teste invariante ótimo que é também localmente uniformemente mais potente (LUMPI). Construo o envelope de potência para o teste LUMPI e mostro que ele é virtualmente UMP, pois a potência do teste está muito próxima ao envelope (considerando as estruturas espaciais definidas na dissertação). Eu sugiro um procedimento prático para construir um teste que tem boa potência em uma gama de situações onde talvez o teste LUMPI não tenha boas propriedades. Eu concluo que a potência do teste aumenta com o tamanho da amostra e com o parâmetro de dependência espacial (o que está de acordo com a literatura). Entretanto, disputo a visão consensual que a potência do teste diminui a medida que a matriz de vizinhança fica mais densa. Isto reflete um erro de medida comum na literatura, pois a distância estatística entre a hipótese nula e a alternativa varia muito com a estrutura da matriz. Fazendo a correção, concluo que a potência do teste aumenta com a distância da alternativa à nula, como esperado. / This dissertation focus on spatial stochastic process on a lattice (Cliff & Ord--type of models). My contribution consists of using Edgeworth and saddlepoint series to investigate small sample size and power properties of tests for detecting spatial dependence in spatial autoregressive (SAR) stochastic processes, and proposing a new class of spatial econometric models where the spatial dependence parameters that enter the mean structure are different from the ones in the covariance structure. This allows a clearer interpretation of models' parameters and generalizes the set of local and global models suggested by Anselin (2003) as an alternative to the traditional Cliff & Ord models. I propose an estimation procedure for the model's parameters and derive the asymptotic distribution of the parameters' estimators. The suggested model provides some insights on the structure of the commonly used mixed regressive, spatial autoregressive model with spatial autoregressive disturbances (SARAR). The study of the small sample properties of tests to detect spatial dependence expands on the existing literature by allowing the neighborhood structure to be a nonlinear function of the spatial dependence parameter. The use of series approximations instead of the often used Monte Carlo simulation allows a simple way to compare test properties across different neighborhood structures and to correct for size when comparing power. I obtain the power envelope for testing the presence of spatial dependence in the SAR process using the optimal invariant test statistic, which is also locally uniformly most powerful invariant (LUMPI). I have found that the LUMPI test is virtually UMP since its power is very close to the power envelope. I suggest a practical procedure to build a test that, while not UMP, retain good power properties in a wider range for the spatial parameter when compared to the LUMPI test. I find that power increases with sample size and with the spatial dependence parameter -- which agrees with the literature. However, I call into question the consensus view that power decreases as the spatial weight matrix becomes more densely connected. This finding in the literature reflects an error of measure because the hypothesis being compared are at very different statistical distance from the null. After adjusting for this, the power is larger for alternative hypothesis further away from the null -- as one would expect.