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Strong inferences are drawn from police-worn body camera (BWC) footage, frequently without an assessment of reliability. Unique characteristics of BWC footage (i.e., capturing trends and less frequent behavior, a focal actor (officer) is absent) suggest a specific reliability strategy. A three-step strategy of selecting appropriate reliability indexes, providing salient reliability categories, and ranking the reliability categories was applied to BWC footage. Five interrater agreement ( A C 1 , α ^ K , B − P coefficient, r wg , ad . m ), two interrater consistency ICC 1 , 1 , ICC 2 , 1 , and three internal consistency ( ω t , ω h , GLB ) indexes were applied to police BWC footage. A focus was to ascertain the upper limits of reliability for BWC footage. Item development and rater training were conducted to optimize rating reliability. Using a within design and confidence intervals, the relatively stronger and weaker reliabilities across the six domains of video completeness, respect (passive, active, discourse), threats, and behavioral stance were assessed. Applied to the admissibility of court evidence, central aspects of video completeness have relatively stronger reliabilities. For research, lower reliabilities have a cost of limited generalizability and ecological validity. Policy recommendations include the usage of a standardized scale with multiple ratings to determine what information should be used in high-stake decision-making based on BWC footage. The three-step strategy integrated the reliability indexes into a single figure to reflect a reliability summary of each component of BWC footage. Weighted rankings found the Overall Audio Quality (-4.9) and Empathy (-4.9) items to have the weakest reliabilities and the Clarification (5.1) and Physical Resistance (4.9) items to have the strongest reliabilities.

This paper introduces and presents a first analysis of a uniquely curated dataset of misinformation, disinformation, and rumors spreading on Twitter about the 2020 U.S. Election. Previous research on misinformation — an umbrella term for false and misleading content — has largely focused either on broad categories, using a finite set of keywords to cover a complex topic, or on a few, focused case studies, with increased precision but limited scope. Our approach, by comparison, leverages real-time reports collected from September through November 2020 to develop a comprehensive dataset of tweets connected to 456 distinct misinformation stories from the 2020 US Presidential election (our ElectionMisinfo2020 dataset), 307 of which sowed doubt in the legitimacy of the election. By relying on real-time incidents and streaming data, we generate a curated dataset that not only provides more granularity than a large collection based on a finite number of search terms, but also an improved opportunity for generalization compared to a small set of case studies. Though the emphasis is on misleading content, not all of the tweets linked to a misinformation story are false: some are questions, opinions, corrections, or factual content that nonetheless contributes to misperceptions. Along with a detailed description of the data, this paper provides an analysis of a critical subset of election-delegitimizing misinformation in terms of size, content, temporal diffusion, and partisanship. We label key ideological clusters of accounts within interaction networks, describe common misinformation narratives, and identify those accounts which repeatedly spread misinformation. We document the asymmetry of misinformation spread: accounts associated with support for President Biden shared stories in ElectionMisinfo2020 far less than accounts supporting his opponent. That asymmetry remained among the accounts who were repeatedly influential in the spread of misleading content that sowed doubt in the election: all but two of the top 100 ‘repeat spreader’ accounts were supporters of then-President Trump. These findings support the implementation and enforcement of ‘strike rules’ on social media platforms, directly addressing the outsized role of repeat spreaders.

Protein engineering is a growing field with a variety of experimental techniques available for altering protein function. However, creating an enzyme de novo is still in its infancy, so far yielding enzymes of modest catalytic efficiency. In this study, a system of artificial retro-aldolase enzymes found to have chemistry coupled to protein dynamics was examined. The original design was created computationally, and this protein was then subjected to directed evolution to improve the initial low catalytic efficiency. We found that this re-engineering of the enzyme resulted in rapid density fluctuations throughout the enzyme being reshaped via alterations in the hydrogen bonding network. This work also led to the discovery of a second important motion which aids in the release of an intermediate product. These results provide compelling evidence that to engineer efficient protein catalysts, fast protein dynamics need to be considered in the design.

Background Cardioversion in patients with atrial fibrillation (AF) can cause cardioembolic stroke, and effective clinical management is necessary to reduce morbidity and mortality. Currently, transesophageal echocardiography (TEE) is the accepted standard to diagnose cardiogenic thromboemboli; however, a negative TEE does not eliminate the possibility of left atrial thrombus. The objective of this study was to evaluate the diagnostic value of supplementing the TEE with additional noninvasive markers to ensure thrombus absence. Methods A prospective study was conducted on 59 patients who underwent TEE for suspected intra‐cardiac thrombi. The TEE indications included acute ischemic stroke (45.7%) and AF or flutter (59.3%). D‐dimer level and white blood cell counts were assessed. Results A negative D‐dimer level (

Following the development and implementation of mainstream social media platforms’ election-related speech policies, a renewed wave of criticism emerged from the U.S. ideological right. Several months before the 2020 U.S. presidential election, conservative politicians, pundits, and “patriots” alleged that their speech was being censored by “Big Tech.” This resulted in right-leaning influencers, and many of their followers, migrating to alternative online platforms to avoid moderation. Alternative social media, such as Parler, Bitchute, Gab, and Gettr, describe themselves as unmoderated hubs for “free speech,” signalling an invitation for users to voice everything from unpopular opinions, to misinformation, to hate speech. Yet when pushed by technology infrastructure platforms like Apple’s App Store and Google’s Play Store to address missing or substandard moderation practices, “alt-tech” platforms were forced to create or adapt ad hoc, often minimalistic, content moderation policies.Our research explores and evaluates these policies in comparison to mainstream platforms, and analyzes how moderation policies interact with the ideological framework asserted at an alternative platform’s nascence. Our work provides necessary insight into the potential motivations for one potential source of internet platform oversight. With few immediately available regulatory options, assessing the viability of alternatives is crucial. This is particularly true as severe legislative gridlock stalls meaningful reform to the federal law perhaps capable of improving platforms’ moderation practices. Because private regulation appears to be the most immediate solution to address new breeding grounds for mis- and disinformation, inquiry into alternative platforms’ adoption and enforcement of moderation policies is needed. Our paper concludes with questions for future research into the efficacy of alternative platforms’ policy implementation; it is imperative to distinguish legitimate moderation from mere shells constructed to retain profit in parallel with ideological posturing.

The 2018 U.S. pivot in information and cyberspace degraded Russian operations in the 2018 election. Following pervasive Russian information power operations during the U.S. 2016 elections, the United States progressed from a policy of preparations and defense in information and cyberspace to a policy of forward engagement. U.S recognition of renewed great power competition coupled with Russia’s inability to compete diplomatically, militarily (conventionally), or economically, inspires Russia to continues to concentrate on information power operations. This great game in cyberspace was virtually uncontested by the U.S. prior to 2017. Widespread awareness of Russian aggression in 2016 served as a catalyst which highlighted the enormity of Russian campaigns and the crippling constraints on U.S. information power. This catalyst pivoted the U.S. from a passive policy of preparations and defense in information and cyberspace to a policy of forward engagement that successfully attenuated Russian efforts in 2018. By examining information power from theory development and Russian practice to recent reports and primary sources we find that the U.S. demonstrated the capability and willingness to defend forward successfully during the 2018 elections. Going forward, the U.S. must continue and expand efforts to contest cyberspace and counter disinformation to secure our democracy and the U.S. 2020 presidential election.

Nearly two centuries ago, the British Parliament established the legal basis for the London Metropolitan Police Department by passing the Metropolitan Police Act of 1829 (Klockars 1985; Uchida 1997...

The design of artificial enzymes is an emerging field of research. Although progress has been made, the catalytic proficiency of many designed enzymes is low compared to natural enzymes. Nevertheless, recently Hilvert et al. ( Nat. Chem. 2017, 9, 50-56) created a series of five artificial retro-aldolase enzymes via directed evolution, with the final variant exhibiting a rate comparable to the naturally occurring enzyme fructose 1,6 bisphosphate aldolase. We present a study of this system in atomistic detail that elucidates the effects of mutational changes on the chemical step. Transition path sampling is used to create ensembles of reactive trajectories, and committor analysis is used to identify the stochastic separatrix of each ensemble. The application of committor distribution analysis to constrained trajectories allows the identification of changes in important protein motions coupled to reaction across the generated series of the artificial retro-aldolases. We observed two different reaction mechanisms and analyzed the role of the residues participating in the reaction coordinate of each enzyme. However, only in the most evolved variant we identified a fast motion that promotes catalysis, suggesting that this rate promoting vibration was introduced during directed evolution. This study provides further evidence that protein dynamics must be taken into account in designing efficient artificial enzymes.

When we have data with missing values, the assumption that data are missing at random is very convenient. It is, however, sometimes questionable because some of the missing values could be strongly related to the underlying true values. We introduce methods for nonignorable multivariate missing data, which assume that missingness is related to the variables in question, and to the additional covariates, through a latent variable measured by the missingness indicators. The methodology developed here is useful for investigating the sensitivity of one's estimates to untestable assumptions about the missing-data mechanism. A simulation study and data analysis are conducted to evaluate the performance of the proposed method and to compare to that of MAR-based alternatives.

Earlier age of drinking is a well-known predictor for a variety of adverse public health consequences in the United States and worldwide. In longitudinal research on early-onset drinkers, a great deal of attention has been paid to the identification of subgroups of individuals who follow similar sequential patterns of drinking behaviours. However, research on the sequential development of drinking behaviour can be challenging in part because it may not be possible to directly observe the particular drinking behaviour stage at a given point in time. To address this difficulty, one can use a latent class analysis (LCA) approach, which provides a set of principles for the systematic identification of homogeneous subgroups of individuals. We apply an LCA approach in an investigation of stage-sequential patterns of drinking behaviours among early-onset drinkers from early to late adolescence, using data from the ‘National Longitudinal Survey of Youth 1997.’ In this work, an identification procedure is used to sort different patterns of drinking behaviours into a small number of classes, based on responses to questions about drinking at each measurement occasion; with class assignment, next the class sequencing of early-onset drinkers over the entire set of time points is evaluated in order to identify two or more homogeneous subgroups. A condition to be satisfied is that all early-onset drinkers in a subgroup should exhibit a similar sequence of class memberships over time. This approach uncovers four common drinking behaviours in early-onset drinkers over three measurements from 1997 through 2003. The sequences of drinking behaviours can be grouped into three groups of sequential patterns representing the most probable progression of early-onset drinking behaviours.

Popular cultures in Western societies have long espoused the notion that phases of the moon influence human behavior. In particular, there is a common belief the full moon increases incidents of aberrant, deviant, and criminal behavior. Using police, astronomical, and weather data from a major southwestern American city, this study assessed whether lunar cycles related with rates of reported crime. The findings fail to support popular lore, which has suggested that lunar phase influenced the volume of crime reported to the police. Future research directions examining qualitative rather than quantitative aspects of this problem may yield further inform the understanding of whether lunar cycles appreciably influence demands for policing services.

It has long been acknowledged that police officers have substantial levels of discretion in their day-to-day activities. There is a well developed body of literature that considers how this discretion is exercised across a broad array of situations including the decision to arrest, use force, and grant citizen requests for official action. Using both social disorganization and conflict theories as conceptual models, the purpose of this study was to determine if neighborhood characteristics affect police reporting behavior across a wide cross-section of reported call types. The findings indicated that reporting behavior widely varies across crime types with a greater percentage of more serious crimes translated into official crime. Neighborhood characteristics did affect reporting practices, but surprisingly only for more serious forms of disorder where discretion was perceived to be less. The findings lent support for both social disorganization and conflict theories. Theoretical implications are discussed.

The present study examined the effects of institutional pressures on homeland security preparedness among law enforcement agencies in Illinois. The data come from the Illinois Homeland Security Survey (IHSS). Specifically, the study employed three theories to explain homeland security preparedness: contingency theory, resource dependence theory, and institutional theory. We hypothesized that institutional pressures will lead to isomorphism as agencies attempt to conform to institutional expectations about appropriate activities in a homeland security era. To evaluate these theories and their impact on homeland security practices, the authors used confirmatory factor analysis. The IHSS data lend strong support to the application of organizational theory as a lens through which homeland security preparedness can be understood. Institutional pressures, such as professional and government publications, training, professional associations, and the actions of peer agencies, significantly influenced municipal an...

When an assumption of missing at random is untenable, it becomes necessary to model missing-data indicators, which carry information about the parameters of the complete-data population. Within a given application, however, researchers may believe that some aspects of missingness are ignorable but others are not. We argue that there are two different ways to formalize the notion that only part of the missingness is ignorable. These approaches correspond to assumptions that we call partially missing at random and latently missing at random. We explain these concepts and apply them in a latent-class analysis of survey questions with item nonresponse. Copyright 2009, Oxford University Press.

Latent class analysis (LCA) is a statistical method used to identify a set of discrete, mutually exclusive latent classes of individuals based on their responses to a set of observed categorical variables. In multiple-group LCA, both the measurement part and structural part of the model can vary across groups, and measurement invariance across groups can be empirically tested. LCA with covariates extends the model to include predictors of class membership. In this article, we introduce PROC LCA, a new SAS procedure for conducting LCA, multiple-group LCA, and LCA with covariates. The procedure is demonstrated using data on alcohol use behavior in a national sample of high school seniors.

SummaryAnalysing the use of marijuana is challenging in part because there is no widely accepted single measure of individual use. Similarly, there is no single response variable that effectively captures attitudes toward its social and moral acceptability. One approach is to view the joint distribution of multiple use and attitude indicators as a mixture of latent classes. Pooling items from the annual ‘Monitoring the future’ surveys of American high school seniors from 1977 to 2001, we find that marijuana use and attitudes are well summarized by a four-class model. Secular trends in class prevalences over this period reveal major shifts in use and attitudes. Applying a multinomial logistic model to the latent response, we investigate how class membership relates to demographic and life style factors, political beliefs and religiosity over time. Inferences about the parameters of the latent class logistic model are obtained by a combination of maximum likelihood and Bayesian techniques.

Likelihood functions from finite mixture models have many unusual features. Maximum likelihood (ML) estimates may behave poorly over repeated samples, and the abnormal shape of the likelihood often makes it difficult to assess the uncertainty in parameter estimates. Bayesian inference via Markov chain Monte Carlo (MCMC) can be a useful alternative to ML, but the component labels may switch during the MCMC run, making the output difficult to interpret. Two basic methods for handling the label-switching problem have been proposed: imposing constraints on the parameter space and cluster-based relabeling of the simulated parameters. We have found that label switching may also be reduced by supplying small amounts of prior information that are asymmetric with respect to the mixture components. Simply assigning one observation to each component a priori may effectively eliminate the problem. Using a very simple example—a univariate sample from a mixture of two exponentials—we evaluate the performance of likelih...

Multiple imputation replaces an incomplete dataset with m > 1 simulated complete versions that are analyzed separately by standard methods. We present a natural extension of multiple imputation for handling the dual problems of nonresponse and response error. This extension, which we call multiple edit/multiple imputation (MEMI), replaces an observed dataset containing missing values and errors with m > 1 simulated versions of the ideal dataset that is complete and error-free. These ideal data sets are analyzed separately, and the results are combined using the same rules as for multiple imputation. The resulting inferences simultaneously reflect uncertainty due to nonresponse and response error. MEMI may be an attractive alternative to deterministic or quasi-statistical edit and imputation procedures used by many data-collecting agencies. Producing MEMI's requires assumptions about the distribution of the ideal data, the nature of nonresponse, and a model for the response error mechanism. However, fittin...

This article presents new computational techniques for multivariate longitudinal or clustered data with missing values. Current methodology for linear mixed-effects models can accommodate imbalance or missing data in a single response variable, but it cannot handle missing values in multiple responses or additional covariates. Applying a multivariate extension of a popular linear mixed-effects model, we create multiple imputations of missing values for subsequent analyses by a straightforward and effective Markov chain Monte Carlo procedure. We also derive and implement a new EM algorithm for parameter estimation which converges more rapidly than traditional EM algorithms because it does not treat the random effects as “missing data,” but integrates them out of the likelihood function analytically. These techniques are illustrated on models for adolescent alcohol use in a large school-based prevention trial.

Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.

A semicontinuous variable has a portion of responses equal to a single value (typically 0) and a continuous, often skewed, distribution among the remaining values. In cross-sectional analyses, variables of this type may be described by a pair of regression models; for example, a logistic model for the probability of nonzero response and a conditional linear model for the mean response given that it is nonzero. We extend this two-part regression approach to longitudinal settings by introducing random coefficients into both the logistic and the linear stages. Fitting a two-part random-effects model poses computational challenges similar to those found with generalized linear mixed models. We obtain maximum likelihood estimates for the fixed coefficients and variance components by an approximate Fisher scoring procedure based on high-order Laplace approximations. To illustrate, we apply the technique to data from the Adolescent Alcohol Prevention Trial, examining reported recent alcohol use for students in g...

In this article we present analytic techniques for inference from a dataset in which missing values have been replaced by predictive means derived from an imputation model. The derivations are based on asymptotic expansions of point estimators and their associated variance estimators, and the resulting formulas can be thought of as first-order approximations to standard multiple-imputation procedures with an infinite number of imputations for the missing values. Our method, where applicable, may require substantially less computational effort than creating and managing a multiply imputed database; moreover, the resulting inferences can be more precise than those derived from multiple imputation, because they do not rely on simulation. Our techniques use components of the standard complete-data analysis, along with two summary measures from the fitted imputation model. If the imputation and analysis phases are carried out by the same person or organization, then the method provides a quick assessm...

This review article looks at a small part of the picture of the interrelationship between statistical theory and computational algorithms, especially the Gibbs sampler and the Accept-Reject algorithm. We pay particular attention to how the methodologies affect and complement each other.

In the process of collecting Post-Enumeration Survey (PES) data to evaluate census coverage, it is inevitable that there will be some individuals whose enumeration status (outcome in the census-PES match) remains unresolved even after extensive field follow-up operations. Earlier work developed a logistic regression framework for imputing the probability that unresolved individuals were enumerated in the census, so that the probability of having been enumerated is allowed to depend on covariates. The covariates may include demographic characteristics, geographic information, and census codes that summarize information on the characteristics of the match (e.g., the before-follow-up match code assigned by clerks to describe the type of match between PES and census records). In the production of 1990 undercount estimates, the basic logistic regression model was expanded into a mixed hierarchical model to allow for the presence of group-specific effects, where groups are characterized by common befor...

When outcomes are missing for reasons beyond an investigator's control, there are two different ways to adjust a parameter estimate for covariates that may be related both to the outcome and to missingness. One approach is to model the relationships between the covariates and the outcome and use those relationships to predict the missing values. Another is to model the probabilities of missingness given the covariates and incorporate them into a weighted or stratified estimate. Doubly robust (DR) procedures apply both types of model simultaneously and produce a consistent estimate of the parameter if either of the two models has been correctly specified. In this article, we show that DR estimates can be constructed in many ways. We compare the performance of various DR and non-DR estimates of a population mean in a simulated example where both models are incorrect but neither is grossly misspecified. Methods that use inverse-probabilities as weights, whether they are DR or not, are sensitive to misspecification of the propensity model when some estimated propensities are small. Many DR methods perform better than simple inverse-probability weighting. None of the DR methods we tried, however, improved upon the performance of simple regression-based prediction of the missing values. This study does not represent every missing-data problem that will arise in practice. But it does demonstrate that, in at least some settings, two wrong models are not better than one., This paper commented in: [arXiv:0804.2962], [arXiv:0804.2965], [arXiv:0804.2969], [arXiv:0804.2970]. Rejoinder in [arXiv:0804.2973]. Published in at http://dx.doi.org/10.1214/07-STS227 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Rejoinder to ``Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data'' [arXiv:0804.2958], Comment: Published in at http://dx.doi.org/10.1214/07-STS227REJ the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

A new class of longitudinal data has emerged with the use of technological devices for scientific data collection. This class of data is called intensive longitudinal data (ILD). This volume features state-of-the-art applied statistical modelling strategies developed by leading statisticians and methodologists working in conjunction with behavioural scientists.

Survey attrition and nonresponse, particularly when selective, present unique challenges to researchers interested in studying developmental processes and longitudinal change. Four distinct patterns of nonresponse on children's psychosocial adjustment and lifetime poverty experiences and family histories are identified using principal components analysis. In turn, membership in these four groups is significantly predicted by the child's demographic characteristics, family experiences, and previous values on adjustment variables, indicating selective nonresponse and raising the possibility of biased estimates based on listwise deletion of missing data. We then examine a set of latent growth curve models that interrelate children's family experiences and psychosocial adjustment using listwise deletion (LD) and multiple imputation (MI) procedures. Implications for treatment of nonresponse in national longitudinal surveys are discussed.

The effectiveness of Swareflex Wildlife Reflectors in reducing deer-vehicle collision rates was tested on SR 395 in eastern Washington State where high mortality rates of white-tailed deer (Odocoileus virginianus) had previously been recorded. Reflectors were placed in four test sections and alternately covered and uncovered at regular intervals during the late fall to early spring period from 1981 to 1984. During this period, 52 deer were killed at night in test sections where the reflectors were covered and six deer were killed at night when the reflectors were uncovered. This difference in deer-vehicle collision rates between the covered and uncovered periods is significant (p less than .005), indicating that the reflectors were effective on this highway during this time period. (Author)