The bridge between design and analysis

The overall purpose of the ‘Statistical Points and Pitfalls’ series is to help readers and researchers alike increase awareness of how to use statistics and why/how we fall into inappropriate choices or interpretations. We hope to help readers understand common misconceptions and give clear guidance on how to avoid common pitfalls by offering simple tips to improve your reporting of quantitative research findings. Each entry discusses a commonly encountered inappropriate practice and alternatives from a pragmatic perspective with minimal mathematics involved. We encourage readers to share comments on or suggestions for this section on Twitter, using the hashtag: #mededstats

Are differences between groups different at different occasions?

The overall purpose of the 'Statistical Points and Pitfalls' series is to help readers and researchers alike increase awareness of how to use statistics and why/how we fall into inappropriate choices or interpretations. We hope to help readers understand common misconceptions and give clear guidance on how lo avoid common pitfalls by offering simple tips to improve your reporting of quantitative research findings. Each entry discusses a commonly encountered inappropriate practice and alternatives from a pragmatic perspective with minimal mathematics involved. We encourage readers to share comments on or suggestions for this section on Twitter. using the hashtag: #mededstat

Effect size – large, medium, and small

The overall purpose of the ‘Statistical Points and Pitfalls’ series is to help readers and researchers alike increase awareness of how to use statistics and why/how we fall into inappropriate choices or interpretations. We hope to help readers understand common misconceptions and give clear guidance on how to avoid common pitfalls by offering simple tips to improve your reporting of quantitative research findings. Each entry discusses a commonly encountered inappropriate practice and alternatives from a pragmatic perspective with minimal mathematics involved. We encourage readers to share comments on or suggestions for this section on Twitter, using the hashtag: #mededstats

Development of an instrument for measuring different types of cognitive load

According to cognitive load theory, instructions can impose three types of cognitive load on the learner: intrinsic load, extraneous load, and germane load. Proper measurement of the different types of cognitive load can help us understand why the effectiveness and efficiency of learning environments may differ as a function of instructional formats and learner characteristics. In this article, we present a ten-item instrument for the measurement of the three types of cognitive load. Principal component analysis on data from a lecture in statistics for PhD students (n = 56) in psychology and health sciences revealed a three-component solution, consistent with the types of load that the different items were intended to measure. This solution was confirmed by a confirmatory factor analysis of data from three lectures in statistics for different cohorts of bachelor students in the social and health sciences (ns = 171, 136, and 148), and received further support from a randomized experiment with university freshmen in the health sciences (n = 58)

On variation and uncertainty

The overall purpose of the 'Statistical Points and Pitfalls' series is to help readers and researchers alike increase awareness of how to use statistics and why/how we fall into inappropriate choices or interpretations. We hope to help readers understand common misconceptions and give clear guidance on how to avoid common pitfalls by offering simple tips to improve your reporting of quantitative research findings. Each entry discusses a commonly encountered inappropriate practice and alternatives from a pragmatic perspective with minimal mathematics involved. We encourage readers to share comments on or suggestions for this section on Twitter, using the hashtag: #mededstats.</p

Problematic internet use (PIU): Associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry.

Problematic internet use is common, functionally impairing, and in need of further study. Its relationship with obsessive-compulsive and impulsive disorders is unclear. Our objective was to evaluate whether problematic internet use can be predicted from recognised forms of impulsive and compulsive traits and symptomatology. We recruited volunteers aged 18 and older using media advertisements at two sites (Chicago USA, and Stellenbosch, South Africa) to complete an extensive online survey. State-of-the-art out-of-sample evaluation of machine learning predictive models was used, which included Logistic Regression, Random Forests and Naïve Bayes. Problematic internet use was identified using the Internet Addiction Test (IAT). 2006 complete cases were analysed, of whom 181 (9.0%) had moderate/severe problematic internet use. Using Logistic Regression and Naïve Bayes we produced a classification prediction with a receiver operating characteristic area under the curve (ROC-AUC) of 0.83 (SD 0.03) whereas using a Random Forests algorithm the prediction ROC-AUC was 0.84 (SD 0.03) [all three models superior to baseline models p < 0.0001]. The models showed robust transfer between the study sites in all validation sets [p < 0.0001]. Prediction of problematic internet use was possible using specific measures of impulsivity and compulsivity in a population of volunteers. Moreover, this study offers proof-of-concept in support of using machine learning in psychiatry to demonstrate replicability of results across geographically and culturally distinct settings.This research received internal departmental funds of the Department of Psychiatry at the University of Chicago.This is the final version of the article. It first appeared from Elsevier at http://dx.doi.org/10.1016/j.jpsychires.2016.08.010

The impact of COVID-19 on research

This article is made available for unrestricted research re-use and secondary analysis in any form or be any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.Coronavirus disease 2019 (COVID-19) has swept across the globe causing hundreds of thousands of deaths, shutting down economies, closing borders and wreaking havoc on an unprecedented scale. It has strained healthcare services and personnel to the brink in many regions and will certainly deeply mark medical research both in the short and long-term

Does comorbidity matter in body-focused repetitive behavior disorders?

$\textbf{BACKGROUND:}$ Trichotillomania (TTM) and skin-picking disorder (SPD) have been characterized as body-focused repetitive behavior disorders (BFRBs). Because BFRBs frequently co-occur, we sought to discover the similarities and differences for individuals having both TTM and SPD as opposed to 1 of these disorders. $\textbf{METHODS:}$ Participants with primary TTM (N = 421) were evaluated regarding the comorbidity of SPD, and participants with primary SPD (N = 124) were evaluated regarding the comorbidity of TTM. The effects of comorbidity overlap on demographic and clinical measures were evaluated. $\textbf{RESULTS:}$ Of the 421 participants with primary TTM, 61 (14.5%) had co-occurring SPD. Of 124 participants with primary SPD, 21 (16.9%) had comorbid TTM. Participants with primary TTM and comorbid SPD had significantly more severe trichotillomania symptoms and were more likely to have major depressive disorder than those with TTM alone. Participants with primary SPD and comorbid TTM reported significantly more severe skin-picking symptoms than those who had only SPD. $\textbf{CONCLUSIONS:}$ Individuals with co-occurring TTM and SPD may have more problematic symptoms with the primary repetitive behavior. Hair pullers with comorbid SPD were more likely to have comorbid depression. Evaluating patients for multiple BFRBs may be important to assess the severity of symptoms and may have treatment implications.This study was funded by the Trichotillomania Learning Center and its BFRB Precision Medicine Initiative