Contrasts and Correlations in Effect-size Estimation

This article describes procedures for presenting standardized measures of effect size when contrasts are used to ask focused questions of data. The simplest contrasts consist of comparisons of two samples (e.g., based on the independent t statistic). Useful effect-size indices in this situation are members of the g family (e.g., Hedges's g and Cohen's d) and the Pearson r. We review expressions for calculating these measures and for transforming them back and forth, and describe how to adjust formulas for obtaining g or d from t, or r from g, when the sample sizes are unequal. The real-life implications of d or g calculated from t become problematic when there are more than two groups, but the correlational approach is adaptable and interpretable, although more complex than in the case of two groups. We describe a family of four conceptually related correlation indices: the alerting correlation, the contrast correlation, the effect-size con-elation, and the BESD (binomial effect-size display) correlation. These last three correlations are identical in the simple setting of only two groups, but differ when there are move than two groups.Psycholog

Does modifying the thick texture and creamy flavour of a drink change portion size selection and intake?

Previous research indicates that a drink's sensory characteristics can influence appetite regulation. Enhancing the thick and creamy sensory characteristics of a drink generated expectations of satiety and improved its actual satiating effects. Expectations about food also play an important role in decisions about intake, in which case enhancing the thick and creamy characteristics of a drink might also result in smaller portion size selection. In the current study forty-eight participants (24 female) completed four test days where they came into the laboratory for a fixed-portion breakfast, returning two hours later for a mid-morning drink, which they could serve themselves and consume as much as they liked. Over the test days, participants consumed an iso-energetic drink in four sensory contexts: thin and low-creamy; thin and high-creamy; thick and low-creamy; thick and high-creamy. Results indicated that participants consumed less of the thick drinks, but that this was only true of the female participants; male participants consumed the same amount of the four drinks regardless of sensory context. The addition of creamy flavour did not affect intake but the thicker drinks were associated with an increase in perceived creaminess. Despite differences in intake, hunger and fullness ratings did not differ across male and female participants and were not affected by the drinks sensory characteristics. The vast majority of participants consumed all of the drink they served themselves indicating that differences in intake reflected portion size decisions. These findings suggest women will select smaller portions of a drink when its sensory characteristics indicate that it will be satiating

The validity of the Spelling and Grammar Waiver as a reasonable accommodation in Leaving Certificate examinations in Ireland

In Ireland, dyslexic students can apply for reasonable accommodations in Leaving Certificate examinations. One such accommodation is the Spelling and Grammar Waiver (SGW). Questions have been raised regarding its validity, and it has been suggested that it gives an unfair advantage. Mock Leaving Certificate English paper scripts were collected from 31 dyslexic students who had been granted an SGW and 31 nondyslexic students who had not been granted any accommodations. All scripts were marked twice, eight weeks apart, by the same marker, once in the standard fashion and once with an SGW. Dyslexic students’ scripts marked with an SGW had a significantly higher mean score than when marked in the standard way, and it was similar to the mean unaccommodated score of nondyslexic students. However, nondyslexic students also received a similar boost in scores when accommodated. Two-way repeated measures ANOVA showed no “differential boost” for the dyslexic group, but a significant boost for both groups when accommodated. Results suggest that the SGW is not a valid accommodation but confers an advantage to those that have it. This study needs replication using larger numbers, with real Leaving Certificate scripts and examiners, and the reasons for the increase in scores also need investigation

Network segregation in a model of misinformation and fact checking

Misinformation under the form of rumor, hoaxes, and conspiracy theories spreads on social media at alarming rates. One hypothesis is that, since social media are shaped by homophily, belief in misinformation may be more likely to thrive on those social circles that are segregated from the rest of the network. One possible antidote is fact checking which, in some cases, is known to stop rumors from spreading further. However, fact checking may also backfire and reinforce the belief in a hoax. Here we take into account the combination of network segregation, finite memory and attention, and fact-checking efforts. We consider a compartmental model of two interacting epidemic processes over a network that is segregated between gullible and skeptic users. Extensive simulation and mean-field analysis show that a more segregated network facilitates the spread of a hoax only at low forgetting rates, but has no effect when agents forget at faster rates. This finding may inform the development of mitigation techniques and overall inform on the risks of uncontrolled misinformation online

Caveats for using statistical significance tests in research assessments

This paper raises concerns about the advantages of using statistical significance tests in research assessments as has recently been suggested in the debate about proper normalization procedures for citation indicators. Statistical significance tests are highly controversial and numerous criticisms have been leveled against their use. Based on examples from articles by proponents of the use statistical significance tests in research assessments, we address some of the numerous problems with such tests. The issues specifically discussed are the ritual practice of such tests, their dichotomous application in decision making, the difference between statistical and substantive significance, the implausibility of most null hypotheses, the crucial assumption of randomness, as well as the utility of standard errors and confidence intervals for inferential purposes. We argue that applying statistical significance tests and mechanically adhering to their results is highly problematic and detrimental to critical thinking. We claim that the use of such tests do not provide any advantages in relation to citation indicators, interpretations of them, or the decision making processes based upon them. On the contrary their use may be harmful. Like many other critics, we generally believe that statistical significance tests are over- and misused in the social sciences including scientometrics and we encourage a reform on these matters.Comment: Accepted version for Journal of Informetric

Gossip in organisations: Contexts, consequences and controversies

This article examines the key themes surrounding gossip including its contexts, the various outcomes (positive and negative) of gossip as well as a selection of challenges and controversies. The challenges which are highlighted revolve around definitional issues, methodological approaches, and ethical considerations. Our analysis suggests that the characteristics and features of gossip lend itself to a process-oriented approach whereby the beginning and, particularly, end points of gossip are not always easily identified. Gossip about a subject or person can temporarily disappear only for it to re-surface at some later stage. In addition, questions pertaining to the effects of gossip and ethical-based arguments depend on the nature of the relationships within the gossip triad (gossiper, listener/respondent and target)

Exploring Differences in Pain Beliefs Within and Between a Large Nonclinical (Workplace) Population and a Clinical (Chronic Low Back Pain) Population Using the Pain Beliefs Questionnaire

BACKGROUND: Beliefs, cognitions, and behaviors relating to pain can be associated with a range of negative outcomes. In patients, certain beliefs are associated with increased levels of pain and related disability. There are few data, however, showing the extent to which beliefs of patients differ from those of the general population. OBJECTIVE: This study explored pain beliefs in a large nonclinical population and a chronic low back pain (CLBP) sample using the Pain Beliefs Questionnaire (PBQ) to identify differences in scores and factor structures between and within the samples. DESIGN: This was a cross-sectional study. METHODS: The samples comprised patients attending a rehabilitation program and respondents to a workplace survey. Pain beliefs were assessed using the PBQ, which incorporates 2 scales: organic and psychological. Exploratory factor analysis was used to explore variations in factor structure within and between samples. The relationship between the 2 scales also was examined. RESULTS: Patients reported higher organic scores and lower psychological scores than the nonclinical sample. Within the nonclinical sample, those who reported frequent pain scored higher on the organic scale than those who did not. Factor analysis showed variations in relation to the presence of pain. The relationship between scales was stronger in those not reporting frequent pain. LIMITATIONS: This was a cross-sectional study; therefore, no causal inferences can be made. CONCLUSIONS: Patients experiencing CLBP adopt a more biomedical perspective on pain than nonpatients. The presence of pain is also associated with increased biomedical thinking in a nonclinical sample. However, the impact is not only on the strength of beliefs, but also on the relationship between elements of belief and the underlying belief structur

Are all ‘research fields’ equal? Rethinking practice for the use of data from crowd-sourcing market places

New technologies like large-scale social media sides (e.g., Facebook and Twitter) and crowdsourcing services (e.g., Amazon Mechanical Turk, Crowdflower, Clickworker) impact social science research and provide many new and interesting avenues for research. The use of these new technologies for research has not been without challenges and a recently published psychological study on Facebook led to a widespread discussion on the ethics of conducting large-scale experiments online. Surprisingly little has been said about the ethics of conducting research using commercial crowdsourcing market places. In this paper, I want to focus on the question of which ethical questions are raised by data collection with crowdsourcing tools. I briefly draw on implications of internet research more generally and then focus on the specific challenges that research with crowdsourcing tools faces. I identify fair-pay and related issues of respect for autonomy as well as problems with power dynamics between researcher and participant, which has implications for ‘withdrawal-withoutprejudice’, as the major ethical challenges with crowdsourced data. Further, I will to draw attention on how we can develop a ‘best practice’ for researchers using crowdsourcing tools

Worry is associated with robust reductions in heart rate variability: a transdiagnostic study of anxiety psychopathology

Background Individuals with anxiety disorders display reduced resting-state heart rate variability (HRV), although findings have been contradictory and the role of specific symptoms has been less clear. It is possible that HRV reductions may transcend diagnostic categories, consistent with dimensional-trait models of psychopathology. Here we investigated whether anxiety disorders or symptoms of anxiety, stress, worry and depression are more strongly associated with resting-state HRV. Methods Resting-state HRV was calculated in participants with clinical anxiety (n = 25) and healthy controls (n = 58). Symptom severity measures of worry, anxiety, stress, and depression were also collected from participants, regardless of diagnosis. Results Participants who fulfilled DSM-IV criteria for an anxiety disorder displayed diminished HRV, a difference at trend level significance (p = .1, Hedges’ g = -.37, BF10 = .84). High worriers (Total n = 41; n = 22 diagnosed with an anxiety disorder and n = 19 not meeting criteria for any psychopathology) displayed a robust reduction in resting state HRV relative to low worriers (p = .001, Hedges’ g = -.75, BF10 = 28.16). Conclusions The specific symptom of worry – not the diagnosis of an anxiety disorder – was associated with the most robust reductions in HRV, indicating that HRV may provide a transdiagnostic biomarker of worry. These results enhance understanding of the relationship between the cardiac autonomic nervous system and anxiety psychopathology, providing support for dimensional-trait models consistent with the Research Domain Criteria framework

The earth is flat (p < 0.05): significance thresholds and the crisis of unreplicable research

The widespread use of ‘statistical significance’ as a license for making a claim of a scientific finding leads to considerable distortion of the scientific process (according to the American Statistical Association). We review why degrading p -values into ‘significant’ and ‘nonsignificant’ contributes to making studies irreproducible, or to making them seem irreproducible. A major problem is that we tend to take small p -values at face value, but mistrust results with larger p -values. In either case, p -values tell little about reliability of research, because they are hardly replicable even if an alternative hypothesis is true. Also significance ( p ≤ 0.05) is hardly replicable: at a good statistical power of 80%, two studies will be ‘conflicting’, meaning that one is significant and the other is not, in one third of the cases if there is a true effect. A replication can therefore not be interpreted as having failed only because it is nonsignificant. Many apparent replication failures may thus reflect faulty judgment based on significance thresholds rather than a crisis of unreplicable research. Reliable conclusions on replicability and practical importance of a finding can only be drawn using cumulative evidence from multiple independent studies. However, applying significance thresholds makes cumulative knowledge unreliable. One reason is that with anything but ideal statistical power, significant effect sizes will be biased upwards. Interpreting inflated significant results while ignoring nonsignificant results will thus lead to wrong conclusions. But current incentives to hunt for significance lead to selective reporting and to publication bias against nonsignificant findings. Data dredging, p -hacking, and publication bias should be addressed by removing fixed significance thresholds. Consistent with the recommendations of the late Ronald Fisher, p -values should be interpreted as graded measures of the strength of evidence against the null hypothesis. Also larger p -values offer some evidence against the null hypothesis, and they cannot be interpreted as supporting the null hypothesis, falsely concluding that ‘there is no effect’. Information on possible true effect sizes that are compatible with the data must be obtained from the point estimate, e.g., from a sample average, and from the interval estimate, such as a confidence interval. We review how confusion about interpretation of larger p -values can be traced back to historical disputes among the founders of modern statistics. We further discuss potential arguments against removing significance thresholds, for example that decision rules should rather be more stringent, that sample sizes could decrease, or that p -values should better be completely abandoned. We conclude that whatever method of statistical inference we use, dichotomous threshold thinking must give way to non-automated informed judgment