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Within neuroscience, psychology, and neuroimaging, the most frequently used statistical approach is null hypothesis significance testing (NHST) of the population mean. An alternative approach is to perform NHST within individual participants and then infer, from the proportion of participants showing an effect, the prevalence of that effect in the population. We propose a novel Bayesian method to estimate such population prevalence that offers several advantages over population mean NHST. This method provides a population-level inference that is currently missing from study designs with small participant numbers, such as in traditional psychophysics and in precision imaging. Bayesian prevalence delivers a quantitative population estimate with associated uncertainty instead of reducing an experiment to a binary inference. Bayesian prevalence is widely applicable to a broad range of studies in neuroscience, psychology, and neuroimaging. Its emphasis on detecting effects within individual participants can also help address replicability issues in these fields. Scientists use statistical tools to evaluate observations or measurements from carefully designed experiments. In psychology and neuroscience, these experiments involve studying a randomly selected group of people, looking for patterns in their behaviour or brain activity, to infer things about the population at large. The usual method for evaluating the results of these experiments is to carry out null hypothesis statistical testing (NHST) on the population mean – that is, the average effect in the population that the study participants were selected from. The test asks whether the observed results in the group studied differ from what might be expected if the average effect in the population was zero. However, in psychology and neuroscience studies, people’s brain activity and performance on cognitive tasks can differ a lot. This means important effects in individuals can be lost in the overall population average. Ince et al. propose that this shortcoming of NHST can be overcome by shifting the statistical analysis away from the population mean, and instead focusing on effects in individual participants. This led them to create a new statistical approach named Bayesian prevalence. The method looks at effects within each individual in the study and asks how likely it would be to see the same result if the experiment was repeated with a new person chosen from the wider population at random. Using this approach, it is possible to quantify how typical or uncommon an observed effect is in the population, and the uncertainty around this estimate. This differs from NHST which only provides a binary ‘yes or no’ answer to the question, ‘does this experiment provide sufficient evidence that the average effect in the population is not zero?’ Another benefit of Bayesian prevalence is that it can be applied to studies with small numbers of participants which cannot be analysed using other statistical methods. Ince et al. show that the Bayesian prevalence can be applied to a range of psychology and neuroimaging experiments, from brain imaging to electrophysiology studies. Using this alternative statistical method could help address issues of replication in these fields where NHST results are sometimes not the same when studies are repeated.

This study examined the nature and prevalence of bullying/victimization by peers and teachers reported by 1,284 students (mean age = 15.2 years) drawn from a representative sample of 25 South Australian government and private schools. Students completed a self-report survey containing questions relating to teacher and peer-related bullying, measures of psychosocial adjustment, and personality. The results showed that students could be clearly differentiated according to the type of victimization they had experienced. Students reporting peer victimization typically showed high levels of social alienation, poorer psychological functioning, and poorer self-esteem and self-image. By contrast, victims of teacher victimization were more likely to be rated as less able academically, had less intention to complete school and were more likely to be engaged in high-risk behaviours such as gambling, drug use and under-age drinking. Most bullying was found to occur at school rather than outside school and involved verbal aggression rather than physical harm. Boys were significantly more likely to be bullied than girls, with the highest rates being observed amongst boys attending single-sex government schools. Girls were more likely to be subject to bullying if they attended coeducational private schools. The implications of this work for enhancing school-retention rates and addressing psychological distress amongst adolescent students are discussed. [Author abstract]

Lack of pediatric advance care planning has been associated with poor communication, increased hospitalization, poor quality of life, and legal actions. Clinicians presume that families understand adolescents' treatment preferences for end-of-life care. To examine patient-reported end-of-life values and needs of adolescents with cancer and congruence with their families' understanding of these needs. This cross-sectional survey was conducted among adolescent-family dyads from July 16, 2016, to April 30, 2019, at 4 tertiary care pediatric US hospitals. Participants included 80 adolescent-family dyads (160 participants) within a larger study facilitating pediatric advance care planning. Adolescent eligibility criteria included being aged 14 to 21 years, English speaking, being diagnosed with cancer at any stage, and knowing their diagnosis. Family included legal guardians for minors or chosen surrogate decision-makers for those aged 18 years or older. Data analysis was performed from April 2019 to November 2019. Session 1 of the 3-session Family Centered Pediatric Advance Care Planning for Teens With Cancer intervention. The main outcome was congruence between adolescents with cancer and their families regarding adolescents' values, goals, and beliefs about end-of-life care. Prevalence-adjusted and bias-adjusted κ (PABAK) values were used to measure congruence on the Lyon Advance Care Planning Survey-Revised (Patient and Surrogate versions). A total of 80 adolescent-family dyads (160 participants) were randomized to the intervention group in the original trial. Among the adolescents, 44 (55.0%) were female and 60 (75.0%) were white, with a mean (SD) age of 16.9 (1.8) years. Among family members, 66 (82.5%) were female and 65 (81.3%) were white, with a mean (SD) age of 45.3 (8.3) years. Family members' understanding of their adolescent's beliefs about the best time bring up end-of-life decisions was poor: 86% of adolescents wanted early timing (before getting sick, while healthy, when first diagnosed, when first sick from a life-threatening illness, or all of the above), but only 39% of families knew this (PABAK, 0.18). Families' understanding of what was important to their adolescents when dealing with their own dying was excellent for wanting honest answers from their physician (PABAK, 0.95) and understanding treatment choices (PABAK, 0.95) but poor for dying a natural death (PABAK, 0.18) and being off machines that extend life, if dying (PABAK, 0). Many families had a poor understanding of their adolescent's values regarding their own end-of-life care, such as when to initiate end-of-life conversations and preference for being off machines that extend life. Pediatric advance care planning could minimize these misunderstandings with the potential for a substantial impact on quality of care.

Background/Objective The aim of this study was to systematically assess the association of insulin‐manipulation (intentional under‐ and/or overdosing of insulin), psychiatric comorbidity and diabetes complications. Methods Two diagnostic interviews (Diabetes‐Self‐Management‐Patient‐Interview and Children's‐Diagnostic‐Interview for Psychiatric Disorders) were conducted with 241 patients (age 10‐22) with type 1 diabetes (T1D) from 21 randomly selected Austrian diabetes care centers. Medical data was derived from medical records. Results Psychiatric comorbidity was found in nearly half of the patients with insulin‐manipulation (46.3%) compared to a rate of 17.5% in patients, adherent to the prescribed insulin therapy. Depression (18.3% vs 4.9%), specific phobia (21.1% vs 2.9%), social phobia (7.0% vs 0%), and eating disorders (12.7% vs 1.9%) were elevated in patients with insulin‐manipulation. Females (37.7%) were more often diagnosed (P = 0.001) with psychiatric disorders than males (18.4%). In females, the percentage of psychiatric comorbidity significantly increased with the level of non‐adherence to insulin therapy. Insulin‐manipulation had an effect of +0.89% in HbA1c (P = <0.001) compared to patients adherent to insulin therapy, while there was no association of psychiatric comorbidity with metabolic control (HbA1c 8.16% vs 8.12% [65.68 vs 65.25 mmol/mol]). Ketoacidosis, severe hypoglycemia, and frequency of outpatient visits in a diabetes center were highest in patients with insulin‐manipulation. Conclusions This is the first study using a systematic approach to assess the prevalence of psychiatric disorders in patients who do or do not manipulate insulin in terms of intentional under‐ and/or overdosing. Internalizing psychiatric disorders were associated with insulin‐manipulation, especially in female patients and insulin‐manipulation was associated with deteriorated metabolic control and diabetes complications.

Objectives: Despite extensive research on turnover among nursing personnel very little is known about the impact of physical workload and health on leaving. The aim of this study was to find predictors for leaving nursing care with special reference to physical working conditions and musculoskeletal problems. Methods: This study is based on longitudinal data from a survey of nursing personnel who were employed at various county hospitals in Sweden from 1992–95. A self administrated follow up questionnaire was used to identify their present position in the labour market. The response rate was 73% (n = 1095). Results: The results showed that nursing personnel reporting musculoskeletal problems of the neck/shoulder or knees and those who had limited use of transfer devices were more likely to leave nursing care. Conclusions: The study highlights the importance of taking musculoskeletal problems and use of transfer devices into consideration in order to retain nursing personnel.

In recent years, scientists have paid increasing attention to reproducibility. For example, the Reproducibility Project, a large-scale replication attempt of 100 studies published in top psychology journals found that only 39% could be unambiguously reproduced. There is a growing consensus among scientists that the lack of reproducibility in psychology and other fields stems from various methodological factors, including low statistical power, researcher’s degrees of freedom, and an emphasis on publishing surprising positive results. However, there is a contentious debate about the extent to which failures to reproduce certain results might also reflect contextual differences (often termed “hidden moderators”) between the original research and the replication attempt. Although psychologists have found extensive evidence that contextual factors alter behavior, some have argued that context is unlikely to influence the results of direct replications precisely because these studies use the same methods as those used in the original research. To help resolve this debate, we recoded the 100 original studies from the Reproducibility Project on the extent to which the research topic of each study was contextually sensitive. Results suggested that the contextual sensitivity of the research topic was associated with replication success, even after statistically adjusting for several methodological characteristics (e.g., statistical power, effect size). The association between contextual sensitivity and replication success did not differ across psychological subdisciplines. These results suggest that researchers, replicators, and consumers should be mindful of contextual factors that might influence a psychological process. We offer several guidelines for dealing with contextual sensitivity in reproducibility.

We have empirically assessed the distribution of published effect sizes and estimated power by analyzing 26,841 statistical records from 3,801 cognitive neuroscience and psychology papers published recently. The reported median effect size was D = 0.93 (interquartile range: 0.64-1.46) for nominally statistically significant results and D = 0.24 (0.11-0.42) for nonsignificant results. Median power to detect small, medium, and large effects was 0.12, 0.44, and 0.73, reflecting no improvement through the past half-century. This is so because sample sizes have remained small. Assuming similar true effect sizes in both disciplines, power was lower in cognitive neuroscience than in psychology. Journal impact factors negatively correlated with power. Assuming a realistic range of prior probabilities for null hypotheses, false report probability is likely to exceed 50% for the whole literature. In light of our findings, the recently reported low replication success in psychology is realistic, and worse performance may be expected for cognitive neuroscience.

One of the most successful methods for discovering the way mental processes are organized is to observe the effects in experiments of selectively influencing the processes. Selective influence is crucial in techniques such as Sternberg's additive factor method for reaction times and Jacoby's process dissociation procedure for accuracy. The successful uses of selective influence have encouraged application extensions to complex architectures, to dependent variables such as evoked potentials, and to complex interpretations. But the common themes have become lost in the details of separate uses and specialized terminology. The book gives an introductory and unified account of the many uses of the technique in cognitive psychology. Related models from operations research and human factors are covered. The applications include dual tasks, visual and memory search, timing, categorization, and recall. The book takes a self-contained approach starting with clear explanations of the elementary notions and a building to advanced techniques. The book is written with graduate students in mind, but has content of interest to all researchers in cognitive science and cognitive engineering.

In the field of psychology, the practice of p value null-hypothesis testing is as widespread as ever. Despite this popularity, or perhaps because of it, most psychologists are not aware of the statistical peculiarities of the p value procedure. In particular, p values are based on data that were never observed, and these hypothetical data are themselves influenced by subjective intentions. Moreover, p values do not quantify statistical evidence. This article reviews these p value problems and illustrates each problem with concrete examples. The three problems are familiar to statisticians but may be new to psychologists. A practical solution to these p value problems is to adopt a model selection perspective and use the Bayesian information criterion (BIC) for statistical inference (Raftery, 1995). The BIC provides an approximation to a Bayesian hypothesis test, does not require the specification of priors, and can be easily calculated from SPSS output.

Documents the sociodemographic diversity of clinical psychology programme enrolments in New Zealand universities from 1994 to 2017. Source: National Library of New Zealand Te Puna Matauranga o Aotearoa, licensed by the Department of Internal Affairs for re-use under the Creative Commons Attribution 3.0 New Zealand Licence.