The sampling precision of research in five major areas of psychology

Absolute precision confidence intervals for unstandardized mean differences using sequential stopping rules

Societies and journals in psychology encourage use of confidence intervals (CIs) on effect sizes. Gaining a maximum of precision of the CI at a minimum cost is desirable. Methods are available to calculate a sample size to provide some percent "assurance" that the final CI will be no wider than a desired value under the fixed-sample rule (FSR), in which a sample size must be decided a priori. Such assurance is expensive, and still subject to failure. The desired width can be specified either in standardized or unstandardized units, and this article focuses on unstandardized widths. A sequential stopping rule (SSR) can generate a CI for a mean difference that is always the desired width, and the average use of subjects is about the same as the FSR sample size without assurance. Improper use of sequential sampling can lead to a CI that has degraded coverage - the interval may contain the population value only 90% of the time for a nominal 95% CI. SSR methods are available to deliver CIs that are within a certain tolerance of the nominal coverage or that are at least the nominal coverage on average. These methods can be assessed by simulations, and they involve a manipulation of the minimum sample size, the desired width, or the practical confidence coefficient used to calculate CIs during the SSR experiment. As with FSR, more exact CIs are generated when the population standard deviation can be estimated accurately.

Statistical Assumptions and Reproducibility in Psychology: Data Mining Based on Open Science

The failures of reproducibility in psychology (or other social sciences) can be investigated by tracing their logical chains, from statistical hypothesis to their conclusion. This research starts with the normality hypothesis, the homoscedasticity hypothesis, and the robust hypothesis and uses the R language to simulate and analyze the original data of 100 studies in Estimating the Reproducibility of Psychological Science to explore the influence of the premise hypothesis on statistical methods on the reproducibility of psychological research. The results indicated the following: (1) the answer to the question about psychological studies being repeatable or not relates to the fields to which the subjects belonged, (2) not all the psychological variables meet the normal distribution hypothesis, (3) the t-test is a more robust tool for psychological research than the Analysis of Variance (ANOVA), and (4) the robustness of ANOVA is independent of the normality and variance congruence of the analyzed data. This study made us realize that the repeatable study factors in psychology are more complex than we expected them to be.

Factors associated with nurse well-being in relation to electronic health record use: A systematic review

OBJECTIVE

Although nurses comprise the largest group of health professionals and electronic health record (EHR) user base, it is unclear how EHR use has affected nurse well-being. This systematic review assesses the multivariable (ie, organizational, nurse, and health information technology [IT]) factors associated with EHR-related nurse well-being and identifies potential improvements recommended by frontline nurses.

MATERIALS AND METHODS

We searched MEDLINE, Embase, CINAHL, PsycINFO, ProQuest, and Web of Science for literature reporting on EHR use, nurses, and well-being. A quality appraisal was conducted using a previously developed tool.

RESULTS

Of 4583 articles, 12 met inclusion criteria. Two-thirds of the studies were deemed to have a moderate or low risk of bias. Overall, the studies primarily focused on nurse- and IT-level factors, with 1 study examining organizational characteristics. That study found worse nurse well-being was associated with EHRs compared with paper charts. Studies on nurse-level factors suggest that personal digital literacy is one modifiable factor to improving well-being. Additionally, EHRs with integrated displays were associated with improved well-being. Recommendations for improving EHRs suggested IT-, organization-, and policy-level solutions to address the complex nature of EHR-related nurse well-being.

CONCLUSIONS

The overarching finding from this synthesis reveals a critical need for multifaceted interventions that better organize, manage, and display information for clinicians to facilitate decision making. Our study also suggests that nurses have valuable insight into ways to reduce EHR-related burden. Future research is needed to test multicomponent interventions that address these complex factors and use participatory approaches to engage nurses in intervention development.

A systematic review of contributing factors of and solutions to electronic health record-related impacts on physician well-being

OBJECTIVE

Physicians often describe the electronic health record (EHR) as a cumbersome impediment to meaningful work, which has important implications for physician well-being. This systematic review (1) assesses organizational, physician, and information technology factors associated with EHR-related impacts on physician well-being; and (2) highlights potential improvements to EHR form and function, as recommended by frontline physicians.

MATERIALS AND METHODS

The MEDLINE, Embase, CINAHL, PsycINFO, ProQuest, and Web of Science databases were searched for literature describing EHR use by physicians and markers of well-being.

RESULTS

After reviewing 7388 article, 35 ultimately met the inclusion criteria. Multiple factors across all levels were associated with EHR-related well-being among physicians. Notable predictors amenable to interventions include (1) total EHR time, (2) after-hours EHR time, (3) on-site EHR support, (4) perceived EHR usability, (5) in-basket burden, and (6) documentation burden. Physician recommendations also echoed these themes.

CONCLUSIONS

There are multiple complex factors involved in EHR-related well-being among physicians. Our review shows physicians have recommendations that span from federal regulations to organizational policies to EHR modifications. Future research should assess multipronged interventions that address these factors. As primary stakeholders, physicians should be included in the planning and implementation of such modifications to ensure compatibility with physician needs and clinical workflows.

A Viable Approach to Mitigating Irreproducibility

In a recent article, Trafimow suggested the usefulness of imagining an ideal universe where the only difference between original and replication experiments is the operation of randomness. This contrasts with replication in the real universe where systematicity, as well as randomness, creates differences between original and replication experiments. Although Trafimow showed (a) that the probability of replication in the ideal universe places an upper bound on the probability of replication in the real universe, and (b) how to calculate the probability of replication in the ideal universe, the conception is afflicted with an important practical problem. Too many participants are needed to render the approach palatable to most researchers. The present aim is to address this problem. Embracing skewness is an important part of the solution.

Socioeconomic Status, Health and Lifestyle Settings as Psychosocial Risk Factors for Road Crashes in Young People: Assessing the Colombian Case

The social determinants of health influence both psychosocial risks and protective factors, especially in high-demanding contexts, such as the mobility of drivers and non-drivers. Recent evidence suggests that exploring socioeconomic status (SES), health and lifestyle-related factors might contribute to a better understanding of road traffic crashes (RTCs). Thus, the aim of this study was to construct indices for the assessment of crash rates and mobility patterns among young Colombians who live in the central region of the country. The specific objectives were developing SES, health and lifestyle indices, and assessing the self-reported RTCs and mobility features depending on these indices. A sample of 561 subjects participated in this cross-sectional study. Through a reduction approach of Principal Component Analysis (PCA), three indices were constructed. Mean and frequency differences were contrasted for the self-reported mobility, crash rates, age, and gender. As a result, SES, health and lifestyle indices explained between 56.3–67.9% of the total variance. Drivers and pedestrians who suffered crashes had higher SES. A healthier lifestyle is associated with cycling, but also with suffering more bike crashes; drivers and those reporting traffic crashes have shown greater psychosocial and lifestyle-related risk factors. Regarding gender differences, men are more likely to engage in road activities, as well as to suffer more RTCs. On the other hand, women present lower healthy lifestyle-related indices and a less active implication in mobility. Protective factors such as a high SES and a healthier lifestyle are associated with RTCs suffered by young Colombian road users. Given the differences found in this regard, a gender perspective for understanding RTCs and mobility is highly suggestible, considering that socio-economic gaps seem to differentially affect mobility and crash-related patterns.

Making the A Priori Procedure Work for Differences Between Means

Previous researchers have proposed the a priori procedure, whereby the researcher specifies, prior to data collection, how closely she wishes the sample means to approach corresponding population means, and the degree of confidence of meeting the specification. However, an important limitation of previous research is that researchers sometimes are interested in differences between means, rather than in the means themselves. To address this limitation, we propose additional equations that expand the a priori procedure to handle differences between means, both in matched and in independent samples. Finally, implications are discussed.

How Many Participants Do We Have to Include in Properly Powered Experiments? A Tutorial of Power Analysis with Reference Tables

Given that an effect size of d = .4 is a good first estimate of the smallest effect size of interest in psychological research, we already need over 50 participants for a simple comparison of two within-participants conditions if we want to run a study with 80% power. This is more than current practice. In addition, as soon as a between-groups variable or an interaction is involved, numbers of 100, 200, and even more participants are needed. As long as we do not accept these facts, we will keep on running underpowered studies with unclear results. Addressing the issue requires a change in the way research is evaluated by supervisors, examiners, reviewers, and editors. The present paper describes reference numbers needed for the designs most often used by psychologists, including single-variable between-groups and repeated-measures designs with two and three levels, two-factor designs involving two repeated-measures variables or one between-groups variable and one repeated-measures variable (split-plot design). The numbers are given for the traditional, frequentist analysis with p < .05 and Bayesian analysis with BF > 10. These numbers provide researchers with a standard to determine (and justify) the sample size of an upcoming study. The article also describes how researchers can improve the power of their study by including multiple observations per condition per participant.