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As more courses move to online testing, it is important to understand how it can be used to enhance student learning. Adopting online testing strategies which have been documented to be effective (including increasing the frequency of testing, allowing the students to take the test “open book” and allowing the students two opportunities to take each test) may enhance student learning. This study assesses whether adopting these strategies, facilitated by online testing, leads to greater student learning.

I gathered data from eight sections of an undergraduate auditing course in which students in four sections of the class were tested using six online tests taken by the students outside of class. These six online tests were “open book” and allowed the students two opportunities to take each test. Scores from a common final exam are then compared to those from four sections of the same course where three in-class, traditional paper tests were administered. I also surveyed the online test group to gather information regarding their perceptions of online testing.

Students in the online group scored significantly higher on the tests and the final exam. Additionally, the online group reported a positive perception about their experience with online testing.

Online testing did not impair students’ learning, and if the testing environment is designed correctly, online testing may increase student learning.

Instructors considering introducing online testing should consider introducing some of the specific strategies and practical implications described in the chapter to increase student learning.

Turvey (2007, Physica A) introduced a scaled variance ratio procedure for testing the random walk hypothesis (RWH) for financial time series by estimating Hurst coefficients for a fractional Brownian motion model of asset prices. The purpose of this paper is to extend his work by making the estimation procedure robust to heteroskedasticity and by addressing the multiple hypothesis testing problem.

Unbiased, heteroskedasticity consistent, variance ratio estimates are calculated for end of day price data for eight time lags over 12 agricultural commodity futures (front month) and 40 US equities from 2000-2014. A bootstrapped stepdown procedure is used to obtain appropriate statistical confidence for the multiplicity of hypothesis tests. The variance ratio approach is compared against regression-based testing for fractionality.

Failing to account for bias, heteroskedasticity, and multiplicity of testing can lead to large numbers of erroneous rejections of the null hypothesis of efficient markets following an independent random walk. Even with these adjustments, a few futures contracts significantly violate independence for short lags at the 99 percent level, and a number of equities/lags violate independence at the 95 percent level. When testing at the asset level, futures prices are found not to contain fractional properties, while some equities do.

Only a subsample of futures and equities, and only a limited number of lags, are evaluated. It is possible that multiplicity adjustments for larger numbers of tests would result in fewer rejections of independence.

This paper provides empirical evidence that violations of the RWH for financial time series are likely to exist, but are perhaps less common than previously thought.

In the field of public relations and communication management, message evaluation has been one of the starting points for evaluation and measurement research at least since the 1970s. Reliable and valid message evaluation has a central role in message effects research and campaign design in other disciplines as well as communication science. The purpose of this paper is to offer a message testing protocol to efficiently acquire valid and reliable message evaluation data.

A message testing protocol is described in terms of how to conceptualize and evaluate the content and format of messages, in terms of procedures for acquiring and testing messages and in terms of using efficient, reliable and valid measures of perceived message effectiveness (PME) and perceived argument strength (PAS). The evidence supporting the reliability and validity of PME and PAS measures is reviewed.

The message testing protocol developed and reported is an efficient, reliable and valid approach for testing large numbers of messages.

Researchers’ ability to select candidate messages for subsequent deeper testing, for various types of communication campaigns, and for research in theory testing contexts is facilitated. Avoiding the limitations of using a single instance of a message to represent a category (also known as the case-category confound) is reduced.

Communication campaign designers are armed with tools to assess messages and campaign concepts quickly and efficiently, reducing pre-testing time and resources while identifying “best-in-show” examples and prototypes.

Message structures are conceptualized in terms of content and format features using theoretically driven constructs. Measures of PAS and PME are reviewed for their reliability, construct and predictive validity, finding that the measures are acceptable surrogates for actual effectiveness for a wide variety of messages and applications. Coupled with procedures that reduce confounding by randomly nesting messages within respondents and respondents to messages, the measures used and protocol deployed offer an efficient and utilitarian approach to message testing and modeling.

The No Child Left Behind Act (2002) and Race to the Top (2009) led to the highest rate of standardized-state testing in the history of the United States of America. As a result, the Every Student Succeeds Act (2015) aims to reevaluate standardized-state testing. Previous research has assessed its impact on schools, educators, and students; yet, youth’s voices are almost absent. Therefore, this qualitative analysis examines how youth of color perceive and experience standardized-state testing.

Seventy-three youth participated in a semistructured interview during the summer of 2015. The sample consists of 34 girls and 39 boys, 13–18 years of age, of African American, Latino/a, Jamaican American, multiracial/ethnic, and other descent. It includes 6–12th graders who attended 61 inter-district and intra-district schools during the 2014–2015 academic year in a Northeastern metropolitan area in the United States that is undergoing a racial/ethnic integration reform.

Youth experienced testing overload under conflicting adult authorities and within an academically stratified peer culture on an ever-shifting policy terrain. While the parent-adult authority remained in the periphery, the state-adult authority intrusively interrupted the teacher-student power dynamics and the disempowered teacher-adult authority held youth accountable through the “attentiveness” rhetoric. However, youth’s perspectives and lived experiences varied across grade levels, school modalities, and school-geographical locations.

In this adult-dominated society, the market approach to education reform ultimately placed the burden of teacher and school evaluation on youth. Most importantly, youth received variegated messages from their conflicting adult authorities that threatened their academic journeys.

Introduction Employers have often hired graduates of vocational training programmes based on their diplomas and certificates rather than on their capabilities. As a result, these employers do not frequently hold vocational education/training in the highest regard. Additionally, the profession itself is concerned about the discouraging outcomes of some vocational programmes. Employers, governing bodies and taxpayers are all insisting that vocational programmes become more accountable.

Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.

The purpose of this paper is to consider the concepts of individual and complete statistical power used for multiple testing and shows their relevance for determining the number of statistical tests to perform when assessing non-response bias.

A statistical power analysis of 55 survey-based research papers published in three prestigious logistics journals (International Journal of Physical Distribution and Logistics Management, Journal of Business Logistics, Transportation Journal) over the last decade was conducted.

Results show that some of the low complete power levels encountered could have been avoided if fewer tests had been used in the assessment of non-response bias.

The research offers important recommendations to scholars engaged in survey research as they assess the effects of non-respondents on research findings. By following the recommended strategies for testing non-response bias, researchers can improve the statistical power of their findings.

Educational testing launched under “No Child Left Behind” (NCLB) brings unprecedented levels of surveillance to public education in the U.S. The testing regime is moving American pedagogy away from types of teaching which are either politically disfavored or not easily tested. The impact of NCLB will be strongest in lower-income schools which fare poorly on such tests; these schools can expect to see sanctions, shaming, and a concomitant departure of committed families and teachers. The reshaping of American education wrought by NCLB compels us to reimagine mass surveillance as not primarily a means of watching the world, but as expressions of power capable of effecting significant changes in institutions and behaviors.

Mediating effects are often tested using hierarchical multiple regression (HMR) procedures. Typical of the HMR-based strategies is the very frequently cited and widely used procedure described by Baron and Kenny (1986). Unfortunately, there are several important problems with it. More specifically, as we demonstrate below, it: (a) is of virtually no value for buttressing claims of mediating effects for data from non-experimental research; (b) produces erroneous inferences about the existence of mediating effects for misspecified mediation models; and (c) is incapable of providing credible evidence of such effects in a large proportion of cases, even for properly specified mediation models. We detail a number of important implications of our analyses.

In this article we design an econometric test for monotone comparative statics (MCS) often found in models with multiple equilibria. Our test exploits the observable implications of the MCS prediction: that the extreme (high and low) conditiona l quantiles of the dependent variable increase monotonically with the explanatory variable. The main contribution of the article is to derive a likelihood-ratio test, which to the best of our knowledge is the first econometric test of MCS proposed in the literature. The test is an asymptotic “chi-bar squared” test for order restrictions on intermediate conditional quantiles. The key features of our approach are: (1) we do not need to estimate the underlying nonparametric model relating the dependent and explanatory variables to the latent disturbances; (2) we make few assumptions on the cardinality, location, or probabilities over equilibria. In particular, one can implement our test without assuming an equilibrium selection rule.