The order of questions on a test affects how well students believe they performed

<p>We know that students are more optimistic about their performance after they take a test that progresses from the easiest to hardest questions than after taking one that progresses in the opposite order¹. In fact, these “Easy-Hard” students are more optimistic than “Hard-Easy” students even when the two groups perform equally. The literature explains this question order bias as a result of students’ failing to sufficiently adjust, in the face of new information, their extreme initial impressions about the test. In the first two of six studies, we investigated the possibility that a biased memory for individual questions on the test is an alternative mechanism driving the question order bias. The pattern of results was inconsistent with this mechanism, but fit with the established impression-based mechanism. In the next four studies, we addressed the role that the number of test questions plays in determining the size of the question order bias, discovered that warning students is only a partially effective method for reducing the bias, and established a more precise estimate of the bias’ size. Taken together, this work provides evidence that the question order bias is a robust phenomenon, likely driven by insufficient adjustment from extreme initial impressions. ¹ Although the research in this thesis is my own, I conducted it in a lab and supervised a team comprised of research assistants and honours students. I also received advice and direction from my supervisors. Therefore, I often use the word “we” in this thesis to reflect these facts.</p>

The order of questions on a test affects how well students believe they performed

<p>We know that students are more optimistic about their performance after they take a test that progresses from the easiest to hardest questions than after taking one that progresses in the opposite order¹. In fact, these “Easy-Hard” students are more optimistic than “Hard-Easy” students even when the two groups perform equally. The literature explains this question order bias as a result of students’ failing to sufficiently adjust, in the face of new information, their extreme initial impressions about the test. In the first two of six studies, we investigated the possibility that a biased memory for individual questions on the test is an alternative mechanism driving the question order bias. The pattern of results was inconsistent with this mechanism, but fit with the established impression-based mechanism. In the next four studies, we addressed the role that the number of test questions plays in determining the size of the question order bias, discovered that warning students is only a partially effective method for reducing the bias, and established a more precise estimate of the bias’ size. Taken together, this work provides evidence that the question order bias is a robust phenomenon, likely driven by insufficient adjustment from extreme initial impressions. ¹ Although the research in this thesis is my own, I conducted it in a lab and supervised a team comprised of research assistants and honours students. I also received advice and direction from my supervisors. Therefore, I often use the word “we” in this thesis to reflect these facts.</p>

Estimating Population Size of Criminals: A New Horvitz–Thompson Estimator under One-Inflated Positive Poisson–Lindley Model

Many crime datasets often display an excess of “1” counts, arises when arrested criminals have the desire and ability to avoid subsequent arrests. In this study, a new Horvitz–Thompson (HT) estimator under one-inflated positive Poisson–Lindley (OIPPL) distribution which allow for one-inflation and the existence of heterogeneity in the data is developed to estimate the hidden population size of criminals. From the simulation study and applications to real crime datasets, the OIPPL is capable to provide an adequate fit to the datasets considered and the proposed HT estimator is found to produce a more precise estimate of the population size with a narrower 95% confidence interval as compared to several other contending estimators considered in this study.

Handedness and depression: a meta-analysis across 87 studies

Alterations in functional brain lateralization, often indicated by an increased prevalence of left- and/or mixed-handedness, have been demonstrated in several psychiatric and neurodevelopmental disorders like schizophrenia or autism spectrum disorder. For depression, however, this relationship is largely unclear. While a few studies found evidence that handedness and depression are associated, both the effect size and the direction of this association remain elusive. Here, we collected data from 87 studies totaling 35,501 individuals diagnosed with depression disorders to provide a precise estimate of differences in left-, mixed- and non-right-handedness between depressed and healthy samples. We found no differences in left- (OR = 1.04, p = .384), mixed- (OR = 1.64, p = .060) or non-right-handedness (OR = 1.05, p = .309) between the two groups. We could thus find no link between handedness and depression on the meta-analytical level.

Estimating the energy expenditure of endotherms at the species level

The ability to account with precision for the quantitative variation in the basal rate of metabolism (BMR) at the species level is explored in four groups of endotherms: arvicoline rodents, ducks, melaphagid honeyeaters, and phyllostomid bats. An effective analysis requires the inclusion of the factors that distinguish species and their responses to the conditions they encounter in the environment. These factors are implemented by changes in body composition and are responsible for the non-conformity of species to a scaling curve. Two concerns may limit an analysis. The factors correlated with energy expenditure often correlate with each other, which usually prevents them from being included together in an analysis, thereby preventing a complete analysis, implying the presence of factors other than mass. Many of the relevant factors, such as food habits and an island residence, are qualitative, which complicates their inclusion in a quantitative analysis, a difficulty that is solved by ANCOVA. The precision of an analysis, based on an inclusive equation, can be determined by comparing its estimates with measurements of the performance of species. Without this comparison, the effectiveness of an analysis cannot be determined, which then simply becomes a suggestion. A proposed standard for a precise estimate is for it to be within 10% of the measured rate.

On a Counting Theorem for Weakly Admissible Lattices

We give a precise estimate for the number of lattice points in certain bounded subsets of $\mathbb{R}^{n}$ that involve “hyperbolic spikes” and occur naturally in multiplicative Diophantine approximation. We use Wilkie’s o-minimal structure $\mathbb{R}_{\exp }$ and expansions thereof to formulate our counting result in a general setting. We give two different applications of our counting result. The 1st one establishes nearly sharp upper bounds for sums of reciprocals of fractional parts and thereby sheds light on a question raised by Lê and Vaaler, extending previous work of Widmer and of the author. The 2nd application establishes new examples of linear subspaces of Khintchine type thereby refining a theorem by Huang and Liu. For the proof of our counting result, we develop a sophisticated partition method that is crucial for further upcoming work on sums of reciprocals of fractional parts over distorted boxes.

Partitioning uncertainty components of an incomplete ensemble of climate projections using data augmentation

&lt;p&gt;The quantification of internal variability and model uncertainty sources in Multi-scenario Multi-model Ensembles of climate experiments (MMEs) is a key issue. It is expected to both help decision makers to identify robust adaptation measures and scientists to identify where their efforts are needed to narrow uncertainty. The setup of available MMEs makes however uncertainty analyses difficult. In the popular single-time ANOVA approach for instance, a precise estimate of internal variability requires multiple members for each simulation chain (e.g. each emission scenario/climate model combination) but multiple members are typically available for a few chains only (Hingray et al. 2019). In almost all ensembles also, the matrix of available scenario/models combinations is incomplete making a precise estimate of the main effects of each model difficult (e.g. projections are typically missing for some GCM/RCM combinations) (Evin et al. 2019).&lt;/p&gt;&lt;p&gt;We present QUALYPSO, a Bayesian approach developed to assess the different sources of uncertainty in incomplete MMEs (Evin et al. submitted). It is based on the quasi-ergodic assumption for transient climate projections and uses data augmentation (Hingray and Said, 2014). The climate response of each available simulation chain is first estimated with a trend model fitted to raw climate projections. Residuals from the climate change response are used to estimate the internal variability of the chain. Scenario uncertainty and the different components of model uncertainty (e.g. GCM uncertainty, RCM uncertainty) are then estimated with a Bayesian ANOVA model applied to the climate change responses of all available chains. The different parameters of the ANOVA model and the missing quantities associated to the missing chains (e.g. missing scenario/GCM/RCM combinations) are jointly estimated using data augmentation techniques.&lt;/p&gt;&lt;p&gt;QUALYPSO presents many advantages over classical estimation approaches. It first exploits all available experiments, avoiding a dramatic loss of information (the classical case when standard approaches are applied; where the typical solution is to select a complete subset of climate experiments). Along with the estimation of missing data, it also provides an assessment of the estimation uncertainty and adequately propagates the uncertainty due to missing chains. With the explicit treatment of missing experiments, it is then expected to produce unbiased estimates of all parameters, in contrast to direct empirical estimates.&lt;/p&gt;&lt;p&gt;QUALYPSO can be applied to any kind of climate variable and any kind of MMEs. We present examples of application for different hydroclimatic variables from different ensembles of projections including EUROCORDEX and CORDEX-Africa.&lt;/p&gt;&lt;p&gt;Hingray, B., Sa&amp;#239;d, M., 2014. Partitioning internal variability and model uncertainty components in a multimodel multireplicate ensemble of climate projections. J.Climate.&lt;/p&gt;&lt;p&gt;Hingray, B., Blanchet, J., Evin, G. Vidal, J.P. 2019. Uncertainty components estimates in transient climate projections. Precision of estimators in the single time and time series approaches. Clim.Dyn.&lt;/p&gt;&lt;p&gt;Evin, G., Hingray, B., Blanchet, J., Eckert, N., Morin, S., Verfaillie, D. 2019. Partitioning uncertainty components of an incomplete ensemble of climate projections using data augmentation. J.Climate.&lt;/p&gt;&lt;p&gt;Evin, G., Hingray, B. Blanchet, J., Eckert, N., Menegoz, M. Morin, S. (revision). Partitioning uncertainty components of an incomplete ensemble of climate projections using smoothing splines. J.Climate.&lt;/p&gt;