Publication selection bias in minimum-wage research? A meta-regression analysis

Card and Krueger's meta-analysis of the employment effects of minimum wages challenged existing theory. Unfortunately, their meta-analysis confused publication selection with the absence of a genuine empirical effect. We apply recently developed meta-analysis methods to 64 US minimum-wage studies and corroborate that Card and Krueger's findings were nevertheless correct. The minimum-wage effects literature is contaminated by publication selection bias, which we estimate to be slightly larger than the average reported minimum-wage effect. Once this publication selection is corrected, little or no evidence of a negative association between minimum wages and employment remains.

Design-related bias in hospital fall risk screening tool predictive accuracy evaluations: systematic review and meta-analysis.

Introduction: Fall risk screening tools are frequently used as a part of falls prevention programs in hospitals. Design-related bias in evaluations of tool predictive accuracy could lead to overoptimistic results, which would then contribute to program failure in practice.

Methods: A systematic review was undertaken. Two blind reviewers assessed the methodology of relevant publications into a four-point classification system adapted from multiple sources. The association between study design classification and reported results was examined using linear regression with clustering based on screening tool and robust variance estimates with point estimates of Youden Index (= sensitivity + specificity - 1) as the dependent variable. Meta-analysis was then performed pooling data from prospective studies.

Results: Thirty-five publications met inclusion criteria, containing 51 evaluations of fall risk screening tools. Twenty evaluations were classified as retrospective validation evaluations, 11 as prospective (temporal) validation evaluations, and 20 as prospective (external) validation evaluations. Retrospective evaluations had significantly higher Youden Indices (point estimate [95% confidence interval]: 0.22 [0.11, 0.33]). Pooled Youden Indices from prospective evaluations demonstrated the STRATIFY, Morse Falls Scale, and nursing staff clinical judgment to have comparable accuracy.

Discussion: Practitioners should exercise caution in comparing validity of fall risk assessment tools where the evaluation has been limited to retrospective classifications of methodology. Heterogeneity between studies indicates that the Morse Falls Scale and STRATIFY may still be useful in particular settings, but that widespread adoption of either is unlikely to generate benefits significantly greater than that of nursing staff clinical judgment.

Subtle sexism : re-informing intergroup bias and regulating emotion in an Australian police organization

From a liberal feminist perspective, we argue that gender can both inform and should continue to be informed by social identity studies in efforts to understand and manage subtle sexism in contemporary workplaces. We investigated the presence of a form of subtle sexism, affective aversive sexism, in an Australian male-dominated organization: a police force. To do this we surveyed 159 policemen and examined relationships between individual emotional experience, emotional intensity and emotion regulation. Results indicated that, in a subtle display of intergroup bias, policemen experienced both higher positive and higher negative emotions in the presence of other policemen than of policewomen who, we argue, may be less central in the men's identities and relationships at work. Implications for research, training, and emotion management in the workplace are discussed and it is suggested that liberal feminist research can contribute much to understanding the dynamics that reproduce structural segregation in the workplace.

Learning from large data : bias, variance, sampling, and learning curves

One of the fundamental machine learning tasks is that of predictive classification. Given that organisations collect an ever increasing amount of data, predictive classification methods must be able to effectively and efficiently handle large amounts of data. However, it is understood that present requirements push existing algorithms to, and sometimes beyond, their limits since many classification prediction algorithms were designed when currently common data set sizes were beyond imagination. This has led to a significant amount of research into ways of making classification learning algorithms more effective and efficient. Although substantial progress has been made, a number of key questions have not been answered. This dissertation investigates two of these key questions. The first is whether different types of algorithms to those currently employed are required when using large data sets. This is answered by analysis of the way in which the bias plus variance decomposition of predictive classification error changes as training set size is increased. Experiments find that larger training sets require different types of algorithms to those currently used. Some insight into the characteristics of suitable algorithms is provided, and this may provide some direction for the development of future classification prediction algorithms which are specifically designed for use with large data sets. The second question investigated is that of the role of sampling in machine learning with large data sets. Sampling has long been used as a means of avoiding the need to scale up algorithms to suit the size of the data set by scaling down the size of the data sets to suit the algorithm. However, the costs of performing sampling have not been widely explored. Two popular sampling methods are compared with learning from all available data in terms of predictive accuracy, model complexity, and execution time. The comparison shows that sub-sampling generally products models with accuracy close to, and sometimes greater than, that obtainable from learning with all available data. This result suggests that it may be possible to develop algorithms that take advantage of the sub-sampling methodology to reduce the time required to infer a model while sacrificing little if any accuracy. Methods of improving effective and efficient learning via sampling are also investigated, and now sampling methodologies proposed. These methodologies include using a varying-proportion of instances to determine the next inference step and using a statistical calculation at each inference step to determine sufficient sample size. Experiments show that using a statistical calculation of sample size can not only substantially reduce execution time but can do so with only a small loss, and occasional gain, in accuracy. One of the common uses of sampling is in the construction of learning curves. Learning curves are often used to attempt to determine the optimal training size which will maximally reduce execution time while nut being detrimental to accuracy. An analysis of the performance of methods for detection of convergence of learning curves is performed, with the focus of the analysis on methods that calculate the gradient, of the tangent to the curve. Given that such methods can be susceptible to local accuracy plateaus, an investigation into the frequency of local plateaus is also performed. It is shown that local accuracy plateaus are a common occurrence, and that ensuring a small loss of accuracy often results in greater computational cost than learning from all available data. These results cast doubt over the applicability of gradient of tangent methods for detecting convergence, and of the viability of learning curves for reducing execution time in general.

Reverse ascertainment bias in microsatellite allelic diversity in owls (Aves, Strigiformes)

A rich source of markers may be overlooked by screening for polymorphism in the source species only. We screened 129 microsatellite loci isolated from the powerful owl (Ninox strenua) against two closely related species; Ninox  connivens and Ninox novaeseelandiae. From the screening effort 20 polymorphic markers were isolated, including six loci which were originally discarded as they were monomorphic in the source species. Further cross-species amplification of all 20 loci across species from two families, Strigidae and Tytonidae, revealed unusually high levels of polymorphism within closely related species, and limited success within phylogenetically distant species. Routine screening of multiple  species during the marker development phase can yield a wider range of  polymorphic markers which can subsequently enhance cross-species  amplification attempts.

Breeding ecology and bias in offspring sex ratio in little grassbirds (Megalurus gramineus)

Little grassbirds (Megalurus gramineus) are small, sexually monomorphic passerines that live in reed beds, lignum swamps and salt marshes in southern Australia. The breeding biology and patterns of sex allocation of the little grassbird were investigated over a single breeding season. Our observations of this species in the Edithvale Wetland Reserve revealed a highly male-biased population sex ratio, with some breeding territories containing several additional males. Nevertheless, there was little compelling evidence that little grassbirds breed cooperatively. The growth rates of male and female nestlings were similar and, as predicted by theory, there was no overall primary sex ratio bias. However, the primary sex ratio was female-biased early in the breeding season and became increasingly male-biased later in the breeding season.

Research indepedence matters for practitioners and researchers in the addictions

As governments, industry bodies, and other interest groups become more adept at influencing the conduct and dissemination of research, it is increasingly important that the alcohol and other drug (AOD) sector maintains and protects the integrity of its evidence base. This commentary discusses the level and type of influence being exerted on the research process by different interest groups within the field. It explores the impact and influence of funding bodies, other interest groups, and social systems on addiction and recovery using relevant examples to identify questions for practitioners and researchers to consider when encountering interested parties in their day-to-day practice. Ultimately, it is service users and clinicians at the "front line" of recovery who have the most to lose from research findings that have been unduly influenced. The best protection against bias in these forms is to practice critical self-reflection and to keep openly and honestly debating those things that we find most challenging.

Reducing performance bias by intrinsically insensitive learning for unbalanced text mining

This thesis proposes three effective strategies to solve the significant performance-bias problem in imbalance text mining: (1) creation of a novel inexact field learning algorithm to overcome the dual-imbalance problem; (2) introduction of the one-class classification-framework to optimize classifier-parameters, and (3) proposal of a maximal-frequent-item-set discovery approach to achieve higher accuracy and efficiency.

The effect of potential bias on the formation of ionic liquid generated surface films on Mg alloys

An electrochemical approach to the formation of a protective surface film on Mg alloys immersed in the ionic liquid (IL), trihexyl(tetradecyl)phosphonium–bis 2,4,4-trimethylpentylphosphinate, was investigated in this work. Initially, cyclic voltammetry was used with the Mg alloy being cycled from OCP to more anodic potentials. EIS data indicate that, under these circumstances, an optimum level of protection was achieved at intermediate potentials (e.g., 0 or 0.25 V versus Ag/AgCl). In the second part of this paper, a small constant bias was applied to the Mg alloy immersed in the IL for extended periods using a novel cell design. This electrochemical cell allowed us to monitor in situ surface film formation on the metal surface as well as the subsequent corrosion behaviour of the metal in a corrosive medium. This apparatus was used to investigate the evolution of the surface film on an AZ31 magnesium alloy under a potential bias (between ±100 mV versus open circuit) applied for over 24 h, and the film evolution was monitored using electrochemical impedance spectroscopy (EIS). A film resistance was determined from the EIS data and it was shown that this increased substantially during the first few hours (independent of the bias potential used) with a subsequent decrease upon longer exposure of the surface to the IL. Preliminary characterization of the film formed on the Mg alloy surface using ToF-SIMS indicates that a multilayer surface exists with a phosphorous rich outer layer and a native oxide/hydroxide film underlying this. The corrosion performance of a treated AZ31 specimen when exposed to 0.1 M NaCl aqueous solution showed considerable improvement, consistent with electrochemical data.

Extreme response bias in measuring susceptibility to smoking.

Pierce, Choi, Gilpin, Farkas, and Berry (1998) were the first to claim that they could provide causal evidence that tobacco industry advertising and promotion caused adolescent smoking. This claim continues to significantly influence the theory and conceptualization of how youth react to tobacco marketing. The Pierce et al. (1998) methodology has been used by many researchers to establish the influence of tobacco marketing on adolescent smoking (Goldberg, 2003; NCI, 2006; Sargent, Dalton, & Beach, 2000). Pierce et al. (1998) selected respondents for only the second of their two survey longitudinal study because they chose the extreme-negative response. This choice could be the result of the tendency of some significant number of sample members exhibiting extreme-response bias. The results from an analysis of several questions from the original data used by Pierce et al. (1998) has suggested that there is a significant extreme-response style pattern in the Pierce et al. data. This unaccounted for bias in the responses of their sample was due to the procedure used by Pierce et al. (1998) in the selection of their respondents. The Pierce et al. (1998) sample selection procedure requires more research before the causal link can be claimed.

Exploring negativity bias in brand beliefs and stated brand switching propensity

Negativity bias has been well studied by psychologists but limited research has been conducted on it in a marketing context. Given previous research, this exploratory study aims to examine whether there are any negativity bias effects in brand beliefs and whether there is any influence on stated brand switching propensity amongst current users of a brand. The results suggest that there is a negativity bias evident in brand image data.

The inclusion of ‘then-test’ questions in post-test questionnaires alters post-test responses: a randomized study of bias in health program evaluation

Objectives Program evaluations are frequently based on ‘then-test’ data, i.e., pre-test collected in retrospect. While the application of the then-test has practical advantages, little is known about the validity of then-test data. Because of the collection of then-test in close proximity to post-test questions, this study was aimed at exploring whether the presence of then-test questions in post-test questionnaires influenced subjects’ responses to post-test.
Patients and methods To test the influence of then-test questions, we designed a randomized three-group study in the context of chronic disease self-management programs. Interventions had comparable goals and philosophies, and all 949 study participants filled out identical Health Education Impact Questionnaires (heiQ) at pre-test. At post-test, participants were then randomized to one of the following three groups: Group A responded to post-test questions only (n = 331); Group B filled out transition questions in addition to post-test (n = 304); and Group C filled out then-test questions in addition to post-test (n = 314).
Results Significant post-test differences were found in six of eight heiQ scales, with respondents who filled out then test questions reporting significantly higher post-test scores than respondents of the other groups.
Conclusions This study provides evidence that the inclusion of then-test questions alters post-test responses,
suggesting that change scores based on then-test data be interpreted with care.

Not one of us is without bias : identifying and challenging racism and homophobia

This article reviews a teaching process that aimed to prepare final year social work students for critical practice with diverse and marginalized populations. Alongside lecture input, in small group discussions and in the two sequenced written assignments students were encouraged to personalize questions of bias and stigma by recalling both their experiences of being “other-ed” as well as their participation in practices that “other-ed”, such as racist and homophobic imaging and acting. Feedback to the unit’s first iteration in 2004 was generally positive yet a significant minority of students were clearly dissatisfied. Whilst retaining the same formal content in 2005, greater attention was devoted to generating a supportive group process and a positive environment for “negative” self-disclosure. This milieu acted to contain and normalize the students’ struggle with internalized stereotypes, a stage associated with their greater preparedness to identify and challenge their own personal, cultural and ideological locations. Within the context of the unit remaining explicit about its value stance, by adopting an approach to the teaching / learning process that neither collided nor colluded, as teachers we believe the 2005 revision better achieved the units aims. First, the unit received broader positive appraisal from students and, second, it appeared that the unit more firmly promoted the prospects for students carrying forward a capacity for critical self review post graduation.