Confessions of a Subversive Student

I would characterize my lifelong relationship with formal education as a kind of dissonant harmony. As a kid, part of me loved school, yet I would sometimes feel like I was being assimilated by regimented institutions of homogenization. Living off the grid with hippie parents was a stark contrast to the school environment of bright fluorescent lights, equidistant rows of desks, and tightly managed schedules. Today, the dissonance continues as I find myself at times upholding and perpetuating systems of conformity in education, while simultaneously trying to disrupt and subvert those systems in order to reveal and dismantle anything that could be inhumane or obsolete. Reconciliation is slow-going and messy. The pendulum swings a wide arc before settling in its consonant center

Ideological Misalignment in the Discourse(s) of Higher Education: Comparing University Mission Statements with Texts from Commercial Learning Analytics Providers

This study analyzes, interprets, and compares texts from different educational discourses. Using the Critical Discourse Analysis method, I reveal how texts from university mission statements and from commercial learning analytics providers communicate and construct different ideologies. To support this analysis, I explore literature strands related to public higher education in America and the emerging field of study and practice called learning analytics. Learning analytics is the administrative, research, and instructional use of large sets of digital data that are associated with and generated by students. The data in question may be generated by incidental online activity, and it may be correlated with a host of other data related to student demographics or academic performance. The intention behind educational data systems is to find ways to use data to “optimize” instructional materials and practices by tailoring them to perceived student needs and behaviors, and to trigger “interventions” ranging from warning messages to prescribed courses of study. The use of data in this way raises questions about how such practices relate to the goals and ideals of higher education, especially as these data systems employ similar theories and techniques as those used by corporate juggernauts such as Facebook and Google. Questions not only related to privacy and ownership but also related to how learning, education, and the purpose of higher education are characterized, discussed, and defined in various discourses are explored in this study

Moniker Maladies When Names Sabotage Success

In five studies, we found that people like their names enough to unconsciously pursue consciously avoided outcomes that resemble their names. Baseball players avoid strikeouts, but players whose names begin with the strikeout-signifying letter K strike out more than others (Study 1). All students want As, but students whose names begin with letters associated with poorer performance (C and D) achieve lower grade point averages (GPAs) than do students whose names begin with A and B (Study 2), especially if they like their initials (Study 3). Because lower GPAs lead to lesser graduate schools, students whose names begin with the letters C and D attend lower-ranked law schools than students whose names begin with A and B (Study 4). Finally, in an experimental study, we manipulated congruence between participants\u27 initials and the labels of prizes and found that participants solve fewer anagrams when a consolation prize shares their first initial than when it does not (Study 5). These findings provide striking evidence that unconsciously desiring negative name-resembling performance outcomes can insidiously undermine the more conscious pursuit of positive outcomes

Better P-curves: Making P-Curve Analysis More Robust to Errors, Fraud, and Ambitious P-Hacking, a Reply to Ulrich and Miller

When studies examine true effects, they generate right-skewed p-curves, distributions of statistically significant results with more low (.01 s) than high (.04 s) p values. What else can cause a right-skewed p-curve? First, we consider the possibility that researchers report only the smallest significant pvalue (as conjectured by Ulrich & Miller, 2015), concluding that it is a very uncommon problem. We then consider more common problems, including (a) p-curvers selecting the wrong p values, (b) fake data, (c) honest errors, and (d) ambitiously p-hacked (beyond p \u3c .05) results. We evaluate the impact of these common problems on the validity of p-curve analysis, and provide practical solutions that substantially increase its robustness

P-curve: A Key to The File Drawer

Because scientists tend to report only studies (publication bias) or analyses (p-hacking) that “work,” readers must ask, “Are these effects true, or do they merely reflect selective reporting?” We introduce p-curve as a way to answer this question. P-curve is the distribution of statistically significant p values for a set of studies (ps .05). Because only true effects are expected to generate right-skewed p-curves— containing more low (.01s) than high (.04s) significant p values— only right-skewed p-curves are diagnostic of evidential value. By telling us whether we can rule out selective reporting as the sole explanation for a set of findings, p-curve offers a solution to the age-old inferential problems caused by file-drawers of failed studies and analyses

Specification Curve: Descriptive and Inferential Statistics on All Reasonable Specifications

Empirical results often hinge on data analytic decisions that are simultaneously defensible, arbitrary, and motivated. To mitigate this problem we introduce Specification-Curve Analysis, which consists of three steps: (i) identifying the set of theoretically justified, statistically valid, and non-redundant analytic specifications, (ii) displaying alternative results graphically, allowing the identification of decisions producing different results, and (iii) conducting statistical tests to determine whether as a whole results are inconsistent with the null hypothesis. We illustrate its use by applying it to three published findings. One proves robust, one weak, one not robust at all

p-Curve and Effect Size: Correcting for Publication Bias Using Only Significant Results

Journals tend to publish only statistically significant evidence, creating a scientific record that markedly overstates the size of effects. We provide a new tool that corrects for this bias without requiring access to nonsignificant results. It capitalizes on the fact that the distribution of significant p values, p-curve, is a function of the true underlying effect. Researchers armed only with sample sizes and test results of the published findings can correct for publication bias. We validate the technique with simulations and by reanalyzing data from the Many-Labs Replication project. We demonstrate that p-curve can arrive at conclusions opposite that of existing tools by reanalyzing the meta-analysis of the “choice overload” literature

p-Curve and Effect Size: Correcting for Publication Bias Using Only Significant Results

Journals tend to publish only statistically significant evidence, creating a scientific record that markedly overstates the size of effects. We provide a new tool that corrects for this bias without requiring access to nonsignificant results. It capitalizes on the fact that the distribution of significant p values, p-curve, is a function of the true underlying effect. Researchers armed only with sample sizes and test results of the published findings can correct for publication bias. We validate the technique with simulations and by reanalyzing data from the Many-Labs Replication project. We demonstrate that p-curve can arrive at conclusions opposite that of existing tools by reanalyzing the meta-analysis of the “choice overload” literature

Field Trial of Residual LNAPL Recovery Using CO2-Supersaturated Water Injection in the Borden Aquifer

The ability of supersaturated water injection (SWI) to recover non-aqueous phase liquids (NAPLs) was studied at the field scale as part of an ongoing program to evaluate its applicability to groundwater remediation. SWI uses Gas inFusionTM technology to efficiently dissolve gases into liquids at elevated pressures. SWI has been shown to both volatilize and mobilize residual NAPL ganglia (Li, 2004). During SWI pressurized water containing high concentrations of CO2 is injected into the subsurface below the zone of contamination. Once the injected water is in the aquifer the pressure drops substantially and the concentration of CO2 is no longer in equilibrium with the water and as a result CO2 bubbles nucleate. These bubbles then migrate upwards through the contaminated zone towards the water table. As they move they come into contact with residual NAPL ganglia and they either volatilize this NAPL, resulting in a bubble comprised of CO2 and gaseous NAPL, or mobilize this NAPL, resulting in a film of NAPL surrounding the bubble. In either case the bubbles continue to rise until they reach the water table at which point they are removed by a dual phase extraction system. In this work, a known amount of NAPL was emplaced below the water table at residual concentrations to represent a residual source of weathered gasoline. The source was created in a hydraulically isolated cell in an unconfined sand aquifer at CFB Borden, Ontario. After the source was emplaced SWI was used to remove as much of the contaminant mass as possible in 22.25 days of operation over three months. The goal of this project was to determine if SWI was capable of removing residual NAPL at a field site. It was successful in removing volatile NAPL but not non-volatile NAPL. 64% of the volatile compounds were removed but contaminant mass was still being removed when the system was shut down so with continued operation more mass would have been removed. There is no way of knowing how much more would have been removed had the project continued. These results indicate that continued development of the technology is warranted

The Effect of Accuracy Motivation on Anchoring and Adjustment: Do People Adjust from Provided Anchors?

Increasing accuracy motivation (e.g., by providing monetary incentives for accuracy) often fails to increase adjustment away from provided anchors, a result that has led researchers to conclude that people do not effortfully adjust away from such anchors. We challenge this conclusion. First, we show that people are typically uncertain about which way to adjust from provided anchors and that this uncertainty often causes people to believe that they have initially adjusted too far away from such anchors (Studies 1a and 1b). Then, we show that although accuracy motivation fails to increase the gap between anchors and final estimates when people are uncertain about the direction of adjustment, accuracy motivation does increase anchor–estimate gaps when people are certain about the direction of adjustment, and that this is true regardless of whether the anchors are provided or self-generated (Studies 2, 3a, 3b, and 5). These results suggest that people do effortfully adjust away from provided anchors but that uncertainty about the direction of adjustment makes that adjustment harder to detect than previously assumed. This conclusion has important theoretical implications, suggesting that currently emphasized distinctions between anchor types (self-generated vs. provided) are not fundamental and that ostensibly competing theories of anchoring (selective accessibility and anchoring-and-adjustment) are complementary