Streamlining the Johnson-Neyman Technique for Two-Way Interactions

When trying to determine the effect of one variable upon another, it is often the case that the strength of the effect depends on a third variable. For example, it is well known that alcohol has an effect on reaction time, but the size of that effect depends greatly on the body mass of the individual. These types of situations are the subject of moderation analysis, also known as interaction analysis. One of the techniques for studying these interactions is the Johnson-Neyman technique. While the statistical concepts are fairly simple, the computations are tedious and involved, and most software packages do not include a standard implementation. Furthermore, even if a software package does contain functions implementing the Johnson-Neyman technique, it may require an expensive license or programming abilities many researchers do not possess. Our goal is to produce a freely-available spreadsheet that makes implementing the Johnson-Neyman technique as simple as possible for researchers in the social sciences. The end-user need only enter the original data and the significance level. The spreadsheet will automate the data transformations, estimation of regression coefficients, covariance calculations, and creation of two high-quality figures for describing the interaction. In this presentation, we will describe the underlying statistics, give an overview of how the spreadsheet implements the Johnson-Neyman technique, and show the resulting figures from an example data set

Streamlining the Johnson-Neyman Technique for Two-Way Interactions

When trying to determine the effect of one variable upon another, it is often the case that the strength of the effect depends on a third variable. For example, it is well known that alcohol has an effect on reaction time, but the size of that effect depends greatly on the body mass of the individual. These types of situations are the subject of moderation analysis, also known as interaction analysis. One of the techniques for studying these interactions is the Johnson-Neyman technique. While the statistical concepts are fairly simple, the computations are tedious and involved, and most software packages do not include a standard implementation. Furthermore, even if a software package does contain functions implementing the Johnson-Neyman technique, it may require an expensive license or programming abilities many researchers do not possess. Our goal is to produce a freely-available spreadsheet that makes implementing the Johnson-Neyman technique as simple as possible for researchers in the social sciences. The end-user need only enter the original data and the significance level. The spreadsheet will automate the data transformations, estimation of regression coefficients, covariance calculations, and creation of two high-quality figures for describing the interaction. In this presentation, we will describe the underlying statistics, give an overview of how the spreadsheet implements the Johnson-Neyman technique, and show the resulting figures from an example data set

Exploration Using Without-Replacement Sampling of Actions Is Sometimes Inferior

In many statistical and machine learning applications, without-replacement sampling is considered superior to with-replacement sampling. In some cases, this has been proven, and in others the heuristic is so intuitively attractive that it is taken for granted. In reinforcement learning, many count-based exploration strategies are justified by reliance on the aforementioned heuristic. This paper will detail the non-intuitive discovery that when measuring the goodness of an exploration strategy by the stochastic shortest path to a goal state, there is a class of processes for which an action selection strategy based on without-replacement sampling of actions can be worse than with-replacement sampling. Specifically, the expected time until a specified goal state is first reached can be provably larger under without-replacement sampling. Numerical experiments describe the frequency and severity of this inferiority

CAHOST Facilitating the Johnson-Neyman Technique for Two-Way Interactions in Multiple Regression

When using multiple regression, researchers frequently wish to explore how the relationship between two variables is moderated by another variable; this is termed an interaction. Historically, two approaches have been used to probe interactions: the pick-a-point approach and the Johnson-Neyman (JN) technique. The pick-a-point approach has limitations that can be avoided using the JN technique. Currently, the software available for implementing the JN technique and creating corresponding figures lacks several desirable features–most notably, ease of use and figure quality. To fill this gap in the literature, we offer a free Microsoft Excel 2013 workbook, CAHOST (a concatenation of the first two letters of the authors’ last names), that allows the user to seamlessly create publication-ready figures of the results of the JN technique

Cronbach’s Alpha Under Insufficient Effort Responding: An Analytic Approach

Surveys commonly suffer from insufficient effort responding (IER). If not accounted for, IER can cause biases and lead to false conclusions. In particular, Cronbach’s alpha has been empirically observed to either deflate or inflate due to IER. This paper will elucidate how IER impacts Cronbach’s alpha in a variety of situations. Previous results concerning internal consistency under mixture models are extended to obtain a characterization of Cronbach’s alpha in terms of item validities, average variances, and average covariances. The characterization is then applied to contaminating distributions representing various types of IER. The discussion will provide commentary on previous simulation-based investigations, confirming some previous hypotheses for the common types of IER, but also revealing possibilities from newly considered responding patterns. Specifically, it is possible that the bias can change from negative to positive (and vice versa) as the proportion of contamination increases

Individual Difference Correlates of Being Sexually Unrestricted Yet Declining an HIV Test

Which individual differences accurately predict one’s decision to get tested for human immunodeficiency virus (HIV), and do individuals who have regular short-term sex get tested at higher rates? Two studies—one lab study (total valid N = 69, with n = 20 who were tested) and one involving a student health center (valid N = 250, n = 4 who were tested)—involved participants (total valid N = 319, with n = 24 who got tested) taking a number of personality and individual difference measures, including the dark triad (Machiavellianism, narcissism, and psychopathy). Then, in both studies, participants had the opportunity to actually get tested for HIV. After analyzing data from Study 1, for Study 2 we preregistered the prediction that narcissistic participants would tend to (a) show disinclination to get tested for HIV, and (b) show proclivity for unrestricted short-term sexual behavior, manifesting in (c) a significant difference between these two correlations. As predicted, such a difference in correlations was evident for narcissism as well as psychopathy (the latter, however, was not predicted), suggesting that such individuals are not likely to seek HIV diagnostic information, but are taking more sexual risks. A research synthesis was consistent with these ideas (although controlling for demographic factors diminished the effects). Narcissistic and psychopathic individuals may be undetected hubs in the network of sexually active individuals with HIV. These results are silent on whether the typical HIV patient is narcissistic or psychopathic; the results merely implicate narcissistic and psychopathic traits in the spread of the virus

Mitoxantrone, cisplatin, and methyl-glyoxal bis-guanylhydrazone chemotherapy for refractory malignant lymphoma: A Southwest Oncology Group phase II trial

A phase II trial of combination chemotherapy with mitoxantrone, cisplatin, and methyl-glyoxal bix-guanylhydrazone (MGBG) was conducted in 32 patients with unfavorable histology malignant lymphoma. All patients had relapsed after only one prior chemotherapy regimen (CHOP — 56%; mBACOD — 28%). There were three complete and eight partial responses (overall response rate — 34%) among 32 eligible patients. The median duration of remission was 6.0 months. Severe granulocytopenia was common, with 19/32 patients (63%) suffering life-threatening, and 1/32 (3%) suffering fatal, granulocytopenia.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45311/1/10637_2004_Article_BF00170868.pd

Genomic assessment of quarantine measures to prevent SARS-CoV-2 importation and transmission

Mitigation of SARS-CoV-2 transmission from international travel is a priority. We evaluated the effectiveness of travellers being required to quarantine for 14-days on return to England in Summer 2020. We identified 4,207 travel-related SARS-CoV-2 cases and their contacts, and identified 827 associated SARS-CoV-2 genomes. Overall, quarantine was associated with a lower rate of contacts, and the impact of quarantine was greatest in the 16–20 age-group. 186 SARS-CoV-2 genomes were sufficiently unique to identify travel-related clusters. Fewer genomically-linked cases were observed for index cases who returned from countries with quarantine requirement compared to countries with no quarantine requirement. This difference was explained by fewer importation events per identified genome for these cases, as opposed to fewer onward contacts per case. Overall, our study demonstrates that a 14-day quarantine period reduces, but does not completely eliminate, the onward transmission of imported cases, mainly by dissuading travel to countries with a quarantine requirement

Changes in symptomatology, reinfection, and transmissibility associated with the SARS-CoV-2 variant B.1.1.7: an ecological study

Background The SARS-CoV-2 variant B.1.1.7 was first identified in December, 2020, in England. We aimed to investigate whether increases in the proportion of infections with this variant are associated with differences in symptoms or disease course, reinfection rates, or transmissibility. Methods We did an ecological study to examine the association between the regional proportion of infections with the SARS-CoV-2 B.1.1.7 variant and reported symptoms, disease course, rates of reinfection, and transmissibility. Data on types and duration of symptoms were obtained from longitudinal reports from users of the COVID Symptom Study app who reported a positive test for COVID-19 between Sept 28 and Dec 27, 2020 (during which the prevalence of B.1.1.7 increased most notably in parts of the UK). From this dataset, we also estimated the frequency of possible reinfection, defined as the presence of two reported positive tests separated by more than 90 days with a period of reporting no symptoms for more than 7 days before the second positive test. The proportion of SARS-CoV-2 infections with the B.1.1.7 variant across the UK was estimated with use of genomic data from the COVID-19 Genomics UK Consortium and data from Public Health England on spike-gene target failure (a non-specific indicator of the B.1.1.7 variant) in community cases in England. We used linear regression to examine the association between reported symptoms and proportion of B.1.1.7. We assessed the Spearman correlation between the proportion of B.1.1.7 cases and number of reinfections over time, and between the number of positive tests and reinfections. We estimated incidence for B.1.1.7 and previous variants, and compared the effective reproduction number, Rt, for the two incidence estimates. Findings From Sept 28 to Dec 27, 2020, positive COVID-19 tests were reported by 36 920 COVID Symptom Study app users whose region was known and who reported as healthy on app sign-up. We found no changes in reported symptoms or disease duration associated with B.1.1.7. For the same period, possible reinfections were identified in 249 (0·7% [95% CI 0·6–0·8]) of 36 509 app users who reported a positive swab test before Oct 1, 2020, but there was no evidence that the frequency of reinfections was higher for the B.1.1.7 variant than for pre-existing variants. Reinfection occurrences were more positively correlated with the overall regional rise in cases (Spearman correlation 0·56–0·69 for South East, London, and East of England) than with the regional increase in the proportion of infections with the B.1.1.7 variant (Spearman correlation 0·38–0·56 in the same regions), suggesting B.1.1.7 does not substantially alter the risk of reinfection. We found a multiplicative increase in the Rt of B.1.1.7 by a factor of 1·35 (95% CI 1·02–1·69) relative to pre-existing variants. However, Rt fell below 1 during regional and national lockdowns, even in regions with high proportions of infections with the B.1.1.7 variant. Interpretation The lack of change in symptoms identified in this study indicates that existing testing and surveillance infrastructure do not need to change specifically for the B.1.1.7 variant. In addition, given that there was no apparent increase in the reinfection rate, vaccines are likely to remain effective against the B.1.1.7 variant. Funding Zoe Global, Department of Health (UK), Wellcome Trust, Engineering and Physical Sciences Research Council (UK), National Institute for Health Research (UK), Medical Research Council (UK), Alzheimer's Society

Genomic epidemiology of SARS-CoV-2 in a UK university identifies dynamics of transmission

AbstractUnderstanding SARS-CoV-2 transmission in higher education settings is important to limit spread between students, and into at-risk populations. In this study, we sequenced 482 SARS-CoV-2 isolates from the University of Cambridge from 5 October to 6 December 2020. We perform a detailed phylogenetic comparison with 972 isolates from the surrounding community, complemented with epidemiological and contact tracing data, to determine transmission dynamics. We observe limited viral introductions into the university; the majority of student cases were linked to a single genetic cluster, likely following social gatherings at a venue outside the university. We identify considerable onward transmission associated with student accommodation and courses; this was effectively contained using local infection control measures and following a national lockdown. Transmission clusters were largely segregated within the university or the community. Our study highlights key determinants of SARS-CoV-2 transmission and effective interventions in a higher education setting that will inform public health policy during pandemics.</jats:p