Miracle-dz/UniFAR-FTUs huggingface dataset:
How Confident are Students in their Misconceptions about Hypothesis Tests?

Both researchers and teachers of statistics have made considerable efforts during the last decades to re-conceptualize statistics courses in accordance with the general reform movement in mathematics education. However, students still hold misconceptions about statistical inference even after following a reformed course. The study presented in this paper addresses the need to further investigate misconceptions about hypothesis tests by (1) documenting which misconceptions are the most common among university students of introductory courses of statistics, and (2) concentrating on an aspect of research about misconceptions that has not yet received much attention thus far, namely the confidence that students have in their misconceptions. Data from 144 college students were collected by means of a questionnaire addressing the most common misconceptions found in the literature about the definitions of hypothesis test, p-value, and significance level. In this questionnaire, students were asked to select a level of confidence in their responses (from 0 to 10) for each item. A considerable number of participants seemed to hold misconceptions and lower levels of concept-specific self-perceived efficacy were found to be related to misconceptions more than to the correct answers. On average, students selected significantly lower levels of confidence for the question addressing the definition of the significance level than for the other two items. Suggestions for further research and practice that emerge from this study are proposed.
Bias correction in a dynamic panel data model of economic growth: The African dummy re-examined

The discrepancy between the observed and expected growth rates of African economies in cross-country or panel growth regressions is often summarised in a significant African dummy. However, the existence of this dummy may be an artifact of the panel data techniques used. The standard LSDV (least squares dummy variable) method produces a large bias in the estimate of the coefficient on the lagged dependent variable, which could generate the observed African dummy. The lagged dependent variable in a growth model is used to calculate the cross-country rate of convergence. If, however, the convergence rate is overestimated, then the Africa dummy would result due to the clustering of African economies at the lower end of the world cross-country income distribution. Correcting for the bias - using Kiviet’s (1995) algorithm - allows a fresh look at the apparent systematic underperformance of African countries relative to their growth predictions. Little evidence remains of such underperformance by African economies once the relevant bias in the dynamic panel has been accounted for.
Compensation‐based incentives, ERP and delivery performance: Analysis from production and improvement perspectives

Purpose – The purpose of this paper is to investigate the role of compensation‐based incentives in relationships between enterprise resource planning (ERP) usage and delivery performance in manufacturing.Design/methodology/approach – The authors carry out two studies exploring links between ERP, incentives, and performance from alternative perspectives: first, of incentives tied to regular production activities, and their relationship with delivery performance advantage over competitors; second, of incentives tied to improvement activities and their relationship with delivery performance improvements. Statistical analysis is carried out on data from 698 metal‐working manufacturers from 22 countries, giving a broad cross‐sectional view of a global industry.Findings – The studies indicate that ERP usage relates positively with both delivery advantage and delivery improvements. Furthermore, incentives tied to improvement initiatives may explain delivery improvements, both directly and as moderators in the re.
Exploring counter-theoretical instances of graduate learners’ self-regulatory processes when using an online repository

Academic self-regulation theories have proposed that learning involves a complex set of cognitive and metacognitive mechanisms that are enacted in phases. These phases include task understanding, strategy adoption, monitoring, and reflection. Whereas classical approaches to self-regulation contend that these phases work together to influence academic performance, the empirical research reported herein reveals that, for essay writing in an online learning environment, improved self-regulation is not necessarily associated with improved learning outcomes. We begin by reviewing frameworks for academic self-regulation, specifically in the context of learners’ experiences in online repositories equipped with Topic Maps (ISO 13250) indexes. We then offer explanations for counter-theoretical interactions found between task understanding (a frontline phase of self-regulation) and academic performance in 38 graduate learners who used Topic Maps to tackle ill-structured essay tasks. Our investigation sheds light not only on how learners’ perceptions of feedback facilitate task understanding, but also on the complex relationship between task understanding and monitoring proficiencies.
Monetary Cooperation In The CFA Zone

The CFA or franc zone in Africa is the largest and most enduring of currency blocs. This paper is designed as a guide to understanding how the zone works as a mechanism for monetary cooperation. Unfortunately, the experience described in this paper is not encouraging. Despite fixed exchange rates and an elaborate set of rules for avoiding excessive credit expansion, the CFA zone has almost foundered in widespread bank insolvency. The paper is organized as follows : a) section 2 describes member countries of the zone, highlighting their diversity not only in economic structure but in the state of their financial system; b) section 3 shows that there is no effective regional money market and considers how this might be remedied; c) section 4 discusses how credit has been distributed between the member countries and highlights asymmetries which have arisen; d) section 5 explains how seignorage is divided in the unions and shows how the losses may fall disproportionately on non favored countries; and e) section 6 concludes by considering how the recent banking crisis has revealed deficiencies in the zone's arrangements.
Agenda Setting and Evidence in Maternal Health: Connecting Research and Policy in Timor-Leste

The evidence-based policy (EBP) movement has received significant attention in the scientific literature; however, there is still very little empirical research to provide insight into how policy decisions are made and how evidence is used. The lack of research on this topic in low- and middle-income countries is of particular note. We examine the maternity waiting home policy in Timor-Leste to understand the role of context, policy characteristics, individual actors, and how evidence is used to influence the policy agenda. The research tracked the maternity waiting home policy from 2005 to 2009 and is based on in-depth interviews with 31 senior policy-makers, department managers, non-government organization representatives, and United Nations advisors. It is also informed by direct observation, attendance at meetings and workshops, and analysis of policy documents. The findings from this ethnographic case study demonstrate that although the post-conflict context opened up space for new policy ideas senior Ministry of Health officials rather than donors had the most power in setting the policy agenda. Maternity waiting homes were appealing because they were a visible, non-controversial, and logical solution to the problem of accessing maternal health services. Evidence was used in a variety of ways, from supporting pre-determined agendas to informing new policy directions. In the pursuit of EBP, we conclude that the power of research to inform policy lies in its timeliness and relevance, and is facilitated by the connection between researchers and policy-makers.
Impact of authentic leadership on performance: Role of followers' positive psychological capital and relational processes

Summary Authentic leadership has received considerable attention and research support over the past decade. Now the time has come to refine and better understand how it impacts performance. This study investigates the moderating role followers’ positive psychological capital (PsyCap) and the mediating role that leader– member exchange (LMX) may play in influencing the relationship between authentic leadership and followers’ performance. Specifically, we tested this mediated moderation model with matched data from 794 followers and their immediate leaders. We found that authentic leadership is positively related to LMX and consequently followers’ performance, and to a larger degree, among followers who have low rather than high levels of PsyCap. Our discussion highlights the benefits of understanding the roles of relational processes and followers’ positive psychological resources involved in the effectiveness of authentic leadership and how they can be practically implemented. Copyright © 2012 John Wiley & Sons, Ltd.
Intermediary Leverage Cycles and Financial Stability

We develop a theory of financial intermediary leverage cycles in the context of a dynamic model of the macroeconomy. The interaction between a production sector, a financial intermediation sector, and a household sector gives rise to amplification of fundamental shocks that affect real economic activity. The model features two state variables that represent the dynamics of the economy: the net worth and the leverage of financial intermediaries. The leverage of the intermediaries is procyclical owing to risk-sensitive funding constraints. Relative to an economy with constant leverage, financial intermediaries generate higher output and consumption growth and lower consumption volatility in normal times, but at the cost of systemic solvency and liquidity risks. We show that tightening intermediariesâ€™ risk constraints affects the systemic risk-return trade-off, by lowering the likelihood of systemic crises at the cost of higher pricing of risk. Our model thus represents a conceptual framework for cyclical macroprudential policies within a dynamic stochastic general equilibrium model.
Multiple Organ Dysfunction Syndrome in Humans and Animals

Multiple organ dysfunction syndrome (MODS), defined as the presence of altered organ function in an acutely ill patient such that homeostasis cannot be maintained without intervention, is a cause of high morbidity and mortality in humans and animals. Many advances have been made in understanding the pathophysiology and treatment of this syndrome in human medicine, but much still is unknown. This comparative review will provide information regarding the history and pathophysiology of MODS in humans and discuss how MODS affects each major organ system in animals.
What Do Systems Users Have to Fear? Using Fear Appeals to Engender Threats and Fear that Motivate Protective Security Behaviors

Because violations of information security (ISec) and privacy have become ubiquitous in both personal and work environments, academic attention to ISec and privacy has taken on paramount importance. Consequently, a key focus of ISec research has been discovering ways to motivate individuals to engage in more secure behaviors. Over time, the protection motivation theory (PMT) has become a leading theoretical foundation used in ISec research to help motivate individuals to change their security-related behaviors to protect themselves and their organizations. Our careful review of the foundation for PMT identified four opportunities for improving ISec PMT research. First, extant ISec studies do not use the full nomology of PMT constructs. Second, only one study uses fear-appeal manipulations, even though these are a core element of PMT. Third, virtually no ISec study models or measures fear. Fourth, whereas these studies have made excellent progress in predicting security intentions, none of them have addressed actual security behaviors. This article describes the theoretical foundation of these four opportunities for improvement. We tested the nomology of PMT, including manipulated fear appeals, in two different ISec contexts that model the modern theoretical treatment of PMT more closely than do extant ISec studies. The first data collection was a longitudinal study in the context of data backups. The second study was a short-term cross-sectional study in the context of anti-malware software. Our new model demonstrated better results and stronger fit than the existing models and confirms the efficacy of the four potential improvements we identified.
The Impact of Federal Tax Credits for Higher Education Expenses

The 1997 creation of the Hope and Lifetime Learning Tax Credits marked a dramatic shift in the way in which federal support for college expenses is distributed to students and their families. Unlike other aid programs, the tax credits have exceptionally broad eligibility requirements, and there is a significant delay between when a recipient enrolls in college and when they receive the benefit. When introduced, the projected benefits of the tax credits were $9.7 billion, over fifty percent greater than the total amount spent at the time on the Pell Grant, the primary Federal grant program. This study examines the impact of the tax credits on students, families, colleges, and states. Using several data sources, I analyze the distribution of the benefits and the effect on enrollment decisions and college pricing. Analysis of tax return data suggests that what was intended to be a transfer to the middle class did benefit families with incomes between $30,000 and $75,000 the most. Insufficient tax liability due to low income levels and the interaction of the credits with other aid programs prevents many low-income individuals from qualifying for a benefit. Additionally, many eligible students did not claim a credit, particularly those from minority groups. Further analysis finds no evidence of increased postsecondary enrollment among eligible students in spite of the stated goal to increase access to higher education. On the other hand, some states and public institutions appear to have responded to incentives to increase the prices of colleges at which students face a low marginal cost. However, the results of this analysis are mixed and less conclusive.
School Desegregation, "Tipping," and Private School Enrollment

This paper examines the role of school desegregation in the demand for private school enrollment by whites. A model is presented that emphasizes the interdependence of whites' private enrollment decisions and introduces the possibility of "tipping points" in white flight from public schools. In order to assess the importance of racial composition on white private school enrollment, data from two samples are analyzed. This empirical analysis supports the hypothesis that desegregation has a significant effect on private school enrollment and that this effect is greater in districts with high proportions of nonwhites. In northern as well as southern cities, there is a growing recognition that the problem of "white flight" from desegregating schools threatens the long-run efficacy of desegregation policies. There are fears that this white flight-to predominantly white private schools or suburban systems-may bring about the virtual resegregation of schools in this country. In addition, these trends may be fostering the separation of low income students from higher income students, especially where private school enrollment is involved. Whether these fears are in fact justified is open to question, however. This paper examines one aspect of the white flight problem-the alternative to the public schools that is provided by enrollment in private schools. The central aim is to assess the impact of public school desegregation on private enrollment among white households. Special attention is given to two questions of particular interest in the evaluation of school policy: (1) whether there is a "tipping point" in white flight from public to private schools, and (2) how family income modifies the effect of The author is Assistant Professor of Economics, Bureau of Business and Economic Research, University of Maryland. * I would like to thank Martin Feldstein, Richard Freeman, John Kain, Thomas Schelling, members of the Harvard labor seminar, and the editor and a referee of this Journal for their comments on earlier versions of this paper. Support was provided by the Ford Foundation, the National Science Foundation, and the University of Maryland Computer Science Center. [Manuscript received September 1974; accepted April 1975. ] The Journal of Human Resources XI * 1 This content downloaded from 207.46. 13.115 on Sat, 08 Oct 2016 05:16:13 UTC All use subject to http://about.jstor. org/terms
The absence of H i in the Boötes dwarf spheroidal galaxy

Neutral hydrogen (H I) observations towards the Bootes dwarf spheroidal (dSph) galaxy (here- after Bootes), a very low-luminosity metal-poor Galactic satellite, have been obtained using the Parkes Radio Telescope. We do not detect any H I in or around Bootes to a 3σ upper limit of 180 Mwithin the optical half-light radius and 8000 Mwithin 1.6 kpc. Its H I mass-to-light ratio is less than 0.002 M� /L� , making Bootes one of the most gas-poor galaxies known. Either reionization severely inhibited gas infall on to the proto-Bootes, or large amounts of gas have been removed by ram pressure and/or tidal stripping. Since Bootes lies on the mass- metallicity fundamental line, this relation and the inefficiency of star formation at the faintest end of the galaxy luminosity function must be partly driven, or at least not disrupted, by ex- treme gas loss in such low-luminosity galaxies. We also do not detect any H I associated with the leading tidal tail of the Sagittarius dSph galaxy, which fortuitously passes through the observed field, to a 3σ column density limit of 2 × 10 17 cm −2 . This suggests that either the leading gaseous tail is ionized, or the gas in the trailing tail was removed before the current tidal disruption of the parent dSph began.
An estimated DSGE model of the Hungarian economy

This paper presents and estimates a dynamic stochastic general equilibrium (DSGE) small-open-economy model for the Hungarian economy. The model features different types of frictions, real and nominal rigidities which are necessary to replicate the empirical persistence of Hungarian data. Bayesian methods are applied, and the structural break due to changing monetary regime over the studied period is explicitly taken into account in the estimation procedure. A real-time adaptive learning mechanism describes agents’ perception on underlying inflation. This creates an additional inertia in inflation. We describe the properties of the estimated model by impulse-response analysis, variance decomposition and the analysis of identified structural shocks. Our results are compared with that of estimated euro-area DSGE models, and estimated non-DSGE models of the Hungarian economy. As a robustness check, a model without real time adaptive learning is also estimated and it’s results are also compared to those of the original model.
Portfolio Optimization Using Forward-Looking Information

We develop a new family of estimators of the covariance matrix that relies solely on forwardlooking information. It uses only current prices of plain-vanilla options. In an out-of-sample study we show that a minimum-variance strategy based on these fully-implied estimators outperforms several benchmark strategies, including various strategies based on historical estimates, index investing, and 1/N investing. The outperformance originates in crisis periods when information ow and information asymmetry are high. Although the historical benchmark strategies improve when more recent data is used, they never outperform fully-implied strategies. Thus, our results suggest that investors are better off relying on forward-looking information.
Big Data’s End Run around Anonymity and Consent

Introduction Big data promises to deliver analytic insights that will add to the stock of scientific and social scientific knowledge, significantly improve decision making in both the public and private sector, and greatly enhance individual self-knowledge and understanding. They have already led to entirely new classes of goods and services, many of which have been embraced enthusiastically by institutions and individuals alike. And yet, where these data commit to record details about human behavior, they have been perceived as a threat to fundamental values, including everything from autonomy, to fairness, justice, due process, property, solidarity, and, perhaps most of all, privacy. Given this apparent conflict, some have taken to calling for outright prohibitions on various big data practices, while others have found good reason to finally throw caution (and privacy) to the wind in the belief that big data will more than compensate for its potential costs. Still others, of course, are searching for a principled stance on privacy that offers the flexibility necessary for these promises to be realized while respecting the important values that privacy promotes. This is a familiar situation because it rehearses many of the long-standing tensions that have characterized each successive wave of technological innovation over the past half-century and their inevitable disruption of constraints on information flows through which privacy had been assured. It should come as no surprise that attempts to deal with new threats draw from the toolbox assembled to address earlier upheavals. Ready-to-hand, anonymity and informed consent remain the most popular tools for relieving these tensions – tensions that we accept, from the outset, as genuine and, in many cases, acute. Taking as a given that big data implicates important ethical and political values, we direct our focus instead on attempts to avoid or mitigate the conflicts that may arise. We do so because the familiar pair of anonymity and informed consent continues to strike many as the best and perhaps only way to escape the need to actually resolve these conflicts one way or the other.
Innovation and firm growth in "complex technology" sectors: a quantile regression approach

Innovation is commonly seen as the principal engine of economic development. In this paper, we investigate the microfoundations of economic growth by relating innovation to sales growth at the firm-level, for incumbent firms in four «complex technology» sectors. The average firm, which experiences only modest growth, may grow for a number of reasons that may or may not be related to «innovativeness». However, given that firms are heterogeneous and that growth rates distributions are typically heavy-tailed, it may be misleading to use regression techniques that focus on the average firm. Using a quantile regression approach, we observe that innovativeness is of crucial importance for a handful of «superstar» fast-growth firms.