bluuebunny/crossref_metadata_2025 huggingface dataset:
Speaking Stata: A Set of Utilities for Managing Missing Values
Authors: Cox Nicholas J.
Published: November 2018

Identifying frequencies and patterns of missing values and preliminary cleaning of missing data are common and fundamental parts of statistical data management. I offer a new command, missings, as a replacement for, and extension of, my previous commands nmissing (Cox 1999, Stata Technical Bulletin 49: 7–8; 2001a, Stata Technical Bulletin 60: 2–3; 2003, Stata Journal 3: 449; 2005, Stata Journal 5: 607) and dropmiss (Cox 2001b, Stata Technical Bulletin 60: 7–8; 2008, Stata Journal 8: 594). I also make comparisons with similar commands.
Estimation of Multivariate Probit Models via Bivariate Probit
Authors: Mullahy John
Published: November 2018

In this article, I suggest the utility of fitting multivariate probit models using a chain of bivariate probit estimators. This approach is based on Stata's biprobit and suest commands and is driven by a Mata function, bvpmvp(). I discuss two potential advantages of the approach over the mvprobit command (Cappellari and Jenkins, 2003, Stata Journal 3: 278–294): significant reductions in computation time and essentially unlimited dimensionality of the outcome set. Computation time is reduced because the approach does not rely on simulation methods; unlimited dimensionality arises because only pairs of outcomes are considered at each estimation stage. This approach provides a consistent estimator of all the multivariate probit model's parameters under the same assumptions required for consistent estimation via mvprobit, and simulation exercises I provide suggest no loss of estimator precision relative to mvprobit.
Diff: Simplifying the Estimation of Difference-in-differences Treatment Effects
Authors: Villa Juan M.
Published: November 2018

In this article, I present the features of the user-written command diff, which estimates difference-in-differences (DID) treatment effects. diff simplifies the DID analysis by allowing the conventional DID setting to be combined with other nonexperimental evaluation methods. The command is equipped with an attractive set of options: the single DID with covariates, the kernel propensity-score matching DID, and the quantile DID. Specific options are included to obtain DID estimation on a repeated cross-section setting and to test the general balancing properties of the model. I illustrate the features of diff using a sample of the dataset from the pioneering implementation of DID by Card and Krueger (1994, American Economic Review 84: 772–793).
Quantifying the Uptake of user-written Commands over Time
Authors: Choodari-Oskooei Babak , Morris Tim P.
Published: November 2018

A major factor in the uptake of new statistical methods is the availability of user-friendly software implementations. One attractive feature of Stata is that users can write their own commands and release them to other users via Statistical Software Components at Boston College. Authors of statistical programs do not always get adequate credit, because programs are rarely cited properly. There is no obvious measure of a program's impact, but researchers are under increasing pressure to demonstrate the impact of their work to funders. In addition to encouraging proper citation of software, the number of downloads of a user-written package can be regarded as a measure of impact over time. In this article, we explain how such information can be accessed for any month from July 2007 and summarized using the new ssccount command.
Conindex: Estimation of Concentration Indices
Authors: O'Donnell Owen , O'Neill Stephen , Van Ourti Tom , Walsh Brendan
Published: November 2018

Concentration indices are frequently used to measure inequality in one variable over the distribution of another. Most commonly, they are applied to the measurement of socioeconomic-related inequality in health. We introduce the user-written command conindex, which provides point estimates and standard errors of a range of concentration indices. The command also graphs concentration curves (and Lorenz curves) and performs statistical inference for the comparison of inequality between groups. We offer an accessible introduction to the various concentration indices that have been proposed to suit different measurement scales and ethical responses to inequality. We also demonstrate the command's capabilities and syntax by analyzing wealth-related inequality in health and health care in Cambodia.
Estimating Polling Accuracy in Multiparty Elections Using Surveybias
Authors: Arzheimer Kai , Evans Jocelyn
Published: November 2018

Any rigorous discussion of bias in opinion surveys requires a scalar measure of survey accuracy. Martin, Traugott, and Kennedy (2005, Public Opinion Quarterly 69: 342-369) propose such a measure A for the two-party case, and Arzheimer and Evans (2014, Political Analysis 22: 31-44) demonstrate how measures A'i, B, and Bw for the more common multiparty case can be derived. We describe the commands surveybias, surveybiasi, and surveybiasseries, which enable the fast computation of these binomial and multinomial measures of bias in opinion surveys. While the examples are based on pre-election surveys, the methodology applies to any multinomial variable whose true distribution in the population is known (for example, through census data).
Creating LaTeX Documents from within Stata using Texdoc
Authors: Jann Ben
Published: November 2018

I discuss the use of texdoc for creating LATEX documents from within Stata. Specifically, texdoc provides a way to embed LATEX code directly in a do-file and to automate the integration of results from Stata in the final document. One can use the command, for example, to assemble automatic reports, write a Stata Journal article, prepare slides for classes, or put together solutions for homework assignments.
Simpler Standard Errors for Two-stage Optimization Estimators
Authors: Terza Joseph V.
Published: November 2018

Aiming to lessen the analytic and computational burden faced by practitioners seeking to correct the standard errors of two-stage estimators, I offer a heretofore unexploited simplification of the conventional formulation for the most commonly encountered cases in empirical application—two-stage estimators that involve maximum likelihood or pseudomaximum likelihood estimation. With the applied researcher in mind, I focus on the two-stage residual inclusion estimator designed for nonlinear regression models involving endogeneity. I demonstrate the analytics and Stata and Mata code for implementing my simplified standard-error formula by applying the two-stage residual inclusion method to the birthweight model of Mullahy (1997, Review of Economics and Statistics 79: 586–593) using his original data.
Igmobil: A Command for Intergenerational Mobility Analysis in Stata
Authors: Savegnago Marco
Published: November 2018

In this article, I describe a new command, igmobil, that computes up to 20 intergenerational mobility (IGM) indices for continuous (that is, income or years of education) or discrete (that is, educational or occupational level) variables. I consider three classes of IGM indices: 1) single-stage indices, 2) indices derived from a transition matrix between parents’ and children's socioeconomic status, and 3) indices based on inequality measures. Users may add a fourth class to specify any possible IGM index not included in igmobil. Standard errors and confidence intervals are calculated using a bootstrap procedure. Users can customize many aspects of the program output, including the type and dimension of the transition matrix, the parameters for some IGM indices (like the ones involving generalized entropy measures and the Atkinson index), and how standard errors and confidence intervals are calculated.
Speaking Stata: Multiple bar Charts in Table form
Authors: Cox Nicholas J.
Published: November 2018

Tables that are one way, two way, or three way in structure may often be helpfully represented as multiple bar charts. The one, two, or three variables that define the structure of the table thus determine rows, columns, and panels in which bars are arranged. The merits of this design include easy focus on individual values or groups of values; leaving space for numeric information to be shown as in a table; and convenient axis or panel labeling through text rather than through a key or legend. A Stata command for these purposes, tabplot, is discussed systematically.
Hot and Cold Spot Analysis Using Stata
Authors: Kondo Keisuke
Published: November 2018

Spatial analysis is attracting more attention from Stata users because of the increasing availability of regional data. In this article, I present an implementation of hot and cold spot analysis using Stata. I introduce the new command getisord, which calculates the Getis–Ord G*i ( d) statistic. To implement this command, one only needs the latitude and longitude of regions as the additional required information. In combination with shape files, the results obtained from the getisord command can be visually displayed in Stata. In this article, I also offer an interesting illustration to explain how the getisord command works.
Strmst2 and Strmst2pw: New Commands to Compare Survival Curves Using the Restricted Mean Survival time
Authors: Cronin Angel , Tian Lu , Uno Hajime
Published: November 2018

We present strmst2, a new command to implement k-sample comparisons using the restricted mean survival time (RMST) as the summary measure of the survival-time distribution. Unlike model-based summary measures such as the hazard ratio, the validity of which relies on the adequacy of the proportional-hazards assumption, the measures based on the RMST (that is, the difference in RMST, the ratio of RMST, and the ratio of the restricted mean time lost) provide more robust and clinically interpretable results about the between-group differences. strmst2 performs analysis of covariance-type adjusted analyses for k-sample comparisons as well as the corresponding unadjusted analyses. Pairwise comparisons for the adjusted analyses are summarized using the new postestimation command strmst2pw. We briefly describe the issues of the hazard ratio, introduce details of the method for the RMST, and then illustrate how to use the new commands.
Implementing Rubin's Alternative Multiple-imputation Method for Statistical Matching in Stata
Authors: Alpman Anil
Published: November 2018

This article introduces two new commands, smpc and smmatch, that implement the statistical matching procedure proposed by Rubin (1986, Journal of Business and Economic Statistics 4: 87-94). The purpose of statistical matching in Rubin's procedure is to generate a single dataset from various datasets, where each dataset contains a specific variable of interest and all contain some variables in common. For two variables of interest that are not observed jointly for any unit, smpc generates the predicted values of each as a function of the other variable of interest and a set of control variables by assuming a partial correlation value (defined by the user) between the two variables of interest (other statistical matching procedures assume that they are conditionally independent given the control variables). The smmatch command, on the other hand, matches observations of different datasets according to their predicted values (using a minimum distance criterion) conditional on a set of control variables, and it imputes the observed value of the match for the missing.
A Test for Exogeneity in the Presence of Nonlinearities
Authors: Babington Michael P. , Cano-Urbina Javier
Published: November 2018

We provide a command, locmtest, that implements a test for exogeneity that is robust when the true relationship between the outcome variable and a discrete potentially endogenous variable is nonlinear. This test was developed in Lochner and Moretti (2015, Review of Economics and Statistics 97: 387–397), and it can be implemented even when only a single valid instrument is available. We present the motivation and general idea of the test. We also describe locmtest, which calculates the test, and provide empirical applications of the test and the command.
Speaking Stata: Shading Zones on Time Series and Other Plots
Authors: Cox Nicholas J.
Published: November 2018

Background shading of time series and other plots is a common need. For example, we often wish to identify particular periods of recession or war or other distinct conditions. The shading should be laid down before the data elements (for example, data points or lines). Several methods of shading can be effective. Tips are included for automating the production of a series of similar graphs.
Support Vector Machines
Authors: Guenther Nick , Schonlau Matthias
Published: November 2018

Support vector machines are statistical- and machine-learning techniques with the primary goal of prediction. They can be applied to continuous, binary, and categorical outcomes analogous to Gaussian, logistic, and multinomial regression. We introduce a new command for this purpose, svmachines. This package is a thin wrapper for the widely deployed libsvm (Chang and Lin, 2011, ACM Transactions on Intelligent Systems and Technology 2(3): Article 27). We illustrate svmachines with two examples.
Strcs: A Command for Fitting Flexible Parametric Survival Models on the Log-hazard Scale
Authors: Bower Hannah , Crowther Michael J. , Lambert Paul C.
Published: November 2018

In this article, we describe strcs, a user-written command for fitting flexible parametric survival models on the log-hazard scale. strcs is an extension of the user-written stgenreg command (Crowther and Lambert, 2013b, Journal of Statistical Software 53(12): 1–17), which fits general parametric models with user-defined hazard functions using numerical integration. strcs implements a two-step method that incorporates both analytical and numerical integration to estimate the cumulative hazard function required for the log-likelihood function. This method improves the accuracy of the fully numeric estimation implemented in stgenreg. Time-dependent effects can be incorporated, and excess mortality models can be fit by using the available options. We also describe some of the extensive postestimation commands that are easily implemented after fitting an strcs model.
Quasi–maximum Likelihood Estimation of Linear Dynamic Short-T panel-data Models
Authors: Kripfganz Sebastian
Published: November 2018

In this article, I describe the xtdpdqml command for the quasi–maximum likelihood estimation of linear dynamic panel-data models when the time horizon is short and the number of cross-sectional units is large. Based on the theoretical groundwork by Bhargava and Sargan (1983, Econometrica 51: 1635–1659) and Hsiao, Pesaran, and Tahmiscioglu (2002, Journal of Econometrics 109: 107–150), the marginal distribution of the initial observations is modeled as a function of the observed variables to circumvent a short- T dynamic panel-data bias. Both random-effects and fixed-effects versions are available.
A Comprehensive set of Postestimation Measures to Enrich Interrupted Time-series Analysis
Authors: Linden Ariel
Published: November 2018

While the primary goal of interrupted time-series analysis (ITSA) is to evaluate whether there is a change in the level or trend of an outcome following an interruption (for example, policy change, intervention initiation), a series of additional measures may be relevant to the analysis. In this article, I seek to fill a gap in the ITSA literature by describing a comprehensive set of measures that can be computed following ITSA models, including those that fulfill the primary goal and those that provide supplementary information about trends. These measures can be calculated using the itsa command; this article therefore serves as a complement to “Conducting interrupted time-series analysis for single and multiple group comparisons” (Linden, 2015, Stata Journal 15: 480–500), which introduced the itsa command. Specific ITSA postestimation measures described in this article include individual trend lines, comparisons between multiple interventions, and comparisons with a counterfactual.
The Estimation and Modeling of Cause-specific Cumulative Incidence Functions Using Time-dependent Weights
Authors: Lambert Paul C.
Published: November 2018

Competing risks occur in survival analysis when an individual is at risk of more than one type of event and one event's occurrence precludes another's. The cause-specific cumulative incidence function (CIF) is a measure of interest with competing-risks data. It gives the absolute (or crude) risk of having the event by time t, accounting for the fact that it is impossible to have the event if a competing event occurs first. The user-written command stcompet calculates nonparametric estimates of the cause-specific CIF, and the official Stata command stcrreg fits the Fine and Gray (1999, Journal of the American Statistical Association 94: 496–509) model for competing-risks data. Geskus (2011, Biometrics 67: 39–49) has recently shown that standard software can estimate some of the key measures in competing risks by restructuring the data and incorporating weights. This has a number of advantages because any tools developed for standard survival analysis can then be used to analyze competing-risks data. In this article, I describe the stcrprep command, which restructures the data and calculates the appropriate weights. After one uses stcrprep, a number of standard Stata survival analysis commands can then be used to analyze competing risks. For example, sts graph, failure will give a plot of the cause-specific CIF, and stcox will fit the Fine and Gray (1999) proportional subhazards model. Using stcrprep together with stcox is computationally much more efficient than using stcrreg. In addition, stcrprep opens up new opportunities for competing-risk models. I illustrate this by fitting flexible parametric survival models to the expanded data to directly model the cause-specific CIF.
Estimation of Unit Values in Household Expenditure Surveys without Quantity Information
Authors: Menon Martina , Perali Federico , Tommasi Nicola
Published: November 2018

In this article, we present the pseudounit command, which estimates pseudounit values in cross-sections of household expenditure surveys without quantity information. Household surveys traditionally record only expenditure information. The lack of information about quantities purchased precludes the possibility of deriving household-specific unit values. We use a theoretical result developed by Lewbel (1989, Review of Economic Studies 56: 311–316) to construct pseudounit values by first reproducing cross-sectional price variation, then adding this variability to the aggregate price indexes published by national statistical institutes. We illustrate the method with an example that uses a time series of cross-sections of Italian household budgets.
Estimating Inverse-probability Weights for Longitudinal Data with Dropout or Truncation: The Xtrccipw Command
Authors: Daza Eric J. , Hudgens Michael G. , Herring Amy H.
Published: November 2018

Individuals may drop out of a longitudinal study, rendering their outcomes unobserved but still well defined. However, they may also undergo truncation (for example, death), beyond which their outcomes are no longer meaningful. Kurland and Heagerty (2005, Biostatistics 6: 241–258) developed a method to conduct regression conditioning on nontruncation, that is, regression conditioning on continuation (RCC), for longitudinal outcomes that are monotonically missing at random (for example, because of dropout). This method first estimates the probability of dropout among continuing individuals to construct inverse-probability weights (IPWs), then fits generalized estimating equations (GEE) with these IPWs. In this article, we present the xtrccipw command, which can both estimate the IPWs required by RCC and then use these IPWs in a GEE estimator by calling the glm command from within xtrccipw. In the absence of truncation, the xtrccipw command can also be used to run a weighted GEE analysis. We demonstrate the xtrccipw command by analyzing an example dataset and the original Kurland and Heagerty (2005) data. We also use xtrccipw to illustrate some empirical properties of RCC through a simulation study.
Fitting Bayesian item response models in Stata and Stan
Authors: Grant Robert L. , Furr Daniel C. , Carpenter Bob , Gelman Andrew
Published: November 2018

Stata users have access to two easy-to-use implementations of Bayesian inference: Stata's native bayesmh command and StataStan, which calls the general Bayesian engine, Stan. We compare these implementations on two important models for education research: the Rasch model and the hierarchical Rasch model. StataStan fits a more general range of models than can be fit by bayesmh and uses a superior sampling algorithm, that is, Hamiltonian Monte Carlo using the no-U-turn sampler. Furthermore, StataStan can run in parallel on multiple CPU cores, regardless of the flavor of Stata. Given these advantages and given that Stan is open source and can be run directly from Stata do-files, we recommend that Stata users interested in Bayesian methods consider using StataStan.
Instantaneous Geometric Rates via Generalized Linear Models
Authors: Discacciati Andrea , Bottai Matteo
Published: November 2018

The instantaneous geometric rate represents the instantaneous probability of an event of interest per unit of time. In this article, we propose a method to model the effect of covariates on the instantaneous geometric rate with two models: the proportional instantaneous geometric rate model and the proportional instantaneous geometric odds model. We show that these models can be fit within the generalized linear model framework by using two nonstandard link functions that we implement in the user-defined link programs log_igr and logit_igr. We illustrate how to fit these models and how to interpret the results with an example from a randomized clinical trial on survival in patients with metastatic renal carcinoma.
Multilevel Multiprocess Modeling with Gsem
Authors: Bartus Tamás
Published: November 2018

Multilevel multiprocess models are simultaneous equation systems that include multilevel hazard equations with correlated random effects. Demographers routinely use these models to adjust estimates for endogeneity and sample selection. In this article, I demonstrate how multilevel multiprocess models can be fit with the gsem command. I distinguish between two classes of multilevel multiprocess models: nonrecursive systems of hazard equations without observed endogenous variables and recursive systems that include a hazard equation with observed endogenous qualitative variables. I illustrate the estimation of both classes of models using sample datasets shipped with the statistical software aML. I pay special attention to identifying structural coefficients in nonrecursive simultaneous systems.
Covariate-constrained Randomization Routine for Achieving Baseline Balance in Cluster-randomized Trials
Authors: Lorenz Eva , Gabrysch Sabine
Published: November 2018

In cluster-randomized trials, groups or clusters of individuals, rather than individuals themselves, are randomly allocated to intervention or control. In this article, we describe a new command, ccrand, that implements a covariate-constrained randomization procedure for cluster-randomized trials. It can ensure balance of one or more baseline covariates between trial arms by restriction to allocations that meet specified balance criteria. We provide a brief overview of the theoretical background, describe ccrand and its options, and illustrate it using an example.
Literate Data Analysis with Stata and Markdown
Authors: Rodríguez Germán
Published: November 2018

In this article, I introduce markstat, a command for combining Stata code and output with comments and annotations written in Markdown into a beautiful webpage or PDF file, thus encouraging literate programming and reproducible research. The command tangles the input separating Stata and Markdown code, runs the Stata code, relies on Pandoc to process the Markdown code, and then weaves the outputs into a single file. HTML documents may include inline and display math using MathJax. Generating PDF output requires access to LATEX and a style file from Stata but works with the same input file.
Model Selection for Univariable Fractional Polynomials
Authors: Royston Patrick
Published: November 2018

Since Royston and Altman's 1994 publication ( Journal of the Royal Statistical Society, Series C 43: 429–467), fractional polynomials have steadily gained popularity as a tool for flexible parametric modeling of regression relationships. In this article, I present fp_select, a postestimation tool for fp that allows the user to select a parsimonious fractional polynomial model according to a closed test procedure called the fractional polynomial selection procedure or function selection procedure. I also give a brief introduction to fractional polynomial models and provide examples of using fp and fp_select to select such models with real data.
Dealing with Misfits in Random Treatment Assignment
Authors: Carril Alvaro
Published: November 2018

In this article, I discuss the “misfits” problem, a practical issue that arises in random treatment assignment whenever observations cannot be neatly distributed among treatments. I also introduce the randtreat command, which performs random assignment of unequal treatment fractions and provides several methods to deal with misfits.
Fitting and Interpreting Correlated Random-coefficient Models Using Stata
Authors: Cabanillas Oscar Barriga , Michler Jeffrey D. , Michuda Aleksandr , Tjernström Emilia
Published: November 2018

In this article, we introduce the community-contributed command randcoef, which fits the correlated random-effects and correlated random-coefficient models discussed in Suri (2011, Econometrica 79: 159–209). While this approach has been around for a decade, its use has been limited by the computationally intensive nature of the estimation procedure that relies on the optimal minimum distance estimator. randcoef can accommodate up to five rounds of panel data and offers several options, including alternative weight matrices for estimation and inclusion of additional endogenous regressors. We also present postestimation analysis using sample data to facilitate understanding and interpretation of results.
Heckroccurve: ROC Curves for Selected Samples
Authors: Cook Jonathan A. , Rajbhandari Ashish
Published: November 2018

Receiver operating characteristic (ROC) curves can be misleading when they are constructed with selected samples. In this article, we describe heckroccurve, which implements a recently developed procedure for plotting ROC curves with selected samples. The command estimates the area under the ROC curve and a graphical display of the curve. A variety of plot options are available, including the ability to add confidence bands to the plot.
Panel Unit-root Tests for Heteroskedastic Panels
Authors: Herwartz Helmut , Maxand Simone , Raters Fabian H. C. , Walle Yabibal M.
Published: November 2018

In this article, we describe the command xtpurt, which implements the heteroskedasticity-robust panel unit-root tests suggested in Herwartz and Siedenburg (2008, Computational Statistics and Data Analysis 53: 137–150), Demetrescu and Hanck (2012a, Economics Letters 117: 10–13), and, recently, Herwartz, Maxand, and Walle (2017, Center for European, Governance and Economic Development Research Discussion Papers 314). While the former two tests are robust to time-varying volatility when the data contain only an intercept, the latter test is unique because it is asymptotically pivotal for trending heteroskedastic panels. Moreover, xtpurt incorporates lag-order selection, prewhitening, and detrending procedures to account for serial correlation and trending data.
Speaking Stata: Logarithmic Binning and Labeling
Authors: Cox Nicholas J.
Published: November 2018

Histograms on logarithmic scale cannot be produced by an option like xscale(log). You need first to transform the variable concerned with a logarithm function. That raises small choices: how to select bin start, bin width, and informative axis labels and titles? Problems and solutions are discussed here in detail.

In contrast, for logarithmic scales on other graphs, options xscale(log) and yscale(log) may do most of what you want. But there is usually still scope for “nicer” axis labels than are given by default and indeed scope for differing tastes on what “nice” means. This column introduces the niceloglabels command for helping (even automating) label choice.

Historical notes and references are sprinkled throughout.
Eq5Dmap: A Command for Mapping between EQ-5D-3L and EQ-5D-5L
Authors: Hernández-Alava Mónica , Pudney Stephen
Published: November 2018

In this article, we describe a new command, eq5dmap, for conditional prediction of the utility values of EQ-5D-5L (EQ-5D-3L) from observed or specified values of EQ-5D-3L (EQ-5D-5L) conditional on age and gender. Predictions can be made either from the five-item health descriptions or from the (exact or approximate) utility score. The prediction process is based on a joint statistical model of the two variants of EQ-5D that have been fit to alternative reference datasets (the National Data Bank for Rheumatic Diseases and a EuroQol Group coordinated data-collection study). The underlying model is a system of ordinal regressions with a flexible residual distribution specified as Gaussian or as a copula mixture. Use of the command is illustrated with an application that includes an investigation of the sensitivity of the mapping outcomes to the choice of reference dataset.
Qfactor: A Command for Q-methodology Analysis
Authors: Akhtar-Danesh Noori
Published: November 2018

In this article, I introduce qfactor, a new command for Q-methodology analysis. Q-methodology is a combination of qualitative and quantitative techniques for studying subjectivity. Its quantitative component is based on a by-person factor analysis, usually followed by a factor-rotation technique. Currently, only a handful of programs with limited capability are available for Q-methodology analysis, and none of them are in the major commercial statistical programs such as Stata, SPSS, and SAS. qfactor offers an attractive set of options, including different factor-extraction and factor-rotation techniques in Stata. The use of qfactor is illustrated using a dataset representing 40 individuals’ perceptions on marijuana legalization.
Disentangling How Coworkers and Supervisors Influence Employee Cyberloafing: What Normative Information Are Employees Attending To?
Authors: Askew Kevin L. , Ilie Alexandra , Bauer Jeremy A. , Simonet Daniel V. , Buckner John E. , Robertson Thomas A.
Published: November 2018

Cyberloafing—the use of an electronic device at work for an activity that an immediate supervisor would not consider work-related—is now the most common way that employees waste time at work. It is well established that social norms play a role in cyberloafing, but it is unknown what specific normative information employees attend to when deciding whether or not to cyberloaf. In Study 1, we tested which of four types of normative information could underlie the observed correlation between social norms and cyberloafing. We found that both perceptions of supervisor cyberloafing and perceptions of coworker cyberloafing accounted for unique variance in cyberloafing, and also discovered some evidence that the approval of these referents also had the same effect. In Study 2, we cross-validated these results from Study 1 using a sample that was reasonably representative of the general working population—supporting the generalizability of our findings from Study 1. Furthermore, we conducted supplemental analyses (relative weights analysis and polynomial regression) to untangle nuances in how normative data relates to cyberloafing. In Study 1, we also examined the role of actual norms—as opposed to perceived norms—and found evidence that actual supervisor cyberloafing does influence cyberloafing through employee perceptions of supervisor cyberloafing. Overall, this investigation serves to clarify how social influence plays a role in the cyberloafing phenomenon.
Concept development for comprehensive operations support with modeling and simulation
Authors: Veldhuis Guido A , de Reus Nico M , Keijser Bas MJ
Published: November 2018

Modern warfighting is not only conducted on the physical battlefield but also engages actors in the information and human landscapes. Across these landscapes many types of actors characterize a comprehensive environment. A commander and staff follow the operations planning process to translate a desired end-state to tactical effects and activities. Understanding the interactions between actors and factors that shape conflict and understanding how they can be influenced is highly challenging. Modeling and simulation methods that aim to describe and understand the behavior of complex systems can serve to structure information, derive problem insights, and identify effective interventions. These methods are not, however, common as part of operations planning and intelligence processes. This paper documents findings based on reported experiences from practice and based on concept development and experimentation workshops conducted with Dutch Armed Forces stakeholders. We identify eight challenges to the adoption of modeling and simulation in operations planning. We then present a decision support concept that incorporates several elements to counter the challenges identified in the form of a prototype approach called Comprehensive Operations Support with Modeling and Simulation (COSMOS). We describe the approach along several process steps, illustrated by examples from the concept development and experimentation case studies. We identify several avenues for future research assisting adoption of modeling and simulation in the operations planning process.
Cold cranking viscosity of used synthetic oils originating from vehicles operated under similar driving conditions
Authors: Wolak Artur , Zając Grzegorz
Published: November 2018

This article assesses the performance and reduction level of five engine oils available from various manufacturers. The trend and intensity of the cranking viscosity changes as measured in the cold cranking simulator were thoroughly analysed. In the presented experiment, alterations in engine oils appearing during actual operation were noted. The tests were conducted under conditions which can be depicted as ‘harsh’, that is, multiplied starting of the engine, extended engine idling and short stretch driving. All of the engine oil samples were collected from passenger cars of a homogeneous fleet of 25 vehicles. The dynamic (cranking) viscosity was determined according to the ASTM D5293-15. In all analysed cases, there was a dangerously rapid increase (36%–69%) in the cranking viscosity, and the limit values (7000 mPa·s) were reached very quickly (for the mileages in the range of 3000–13,000 km). The obtained results have led to the development of a statistical model, allowing vehicle users/drivers to choose a better engine oil in winter, thus improving the engine’s ability to cold start and protecting it from excessive wear or damage. The test results may help to predict the performance of the engine oil during operation, its service life and an oil-change interval.
Arteriovenous fistulas ipsilateral to internal jugular catheters for hemodialysis have decreased patency rates
Authors: Ozpak Berkan , Yilmaz Yeliz
Published: November 2018

Purpose

This study aimed to evaluate the effects of hemodialysis catheters on the survival of subsequent arteriovenous fistulas, according to the relative localization to the catheters.

Methods

A total of 201 patients who initiated dialysis therapy using a hemodialysis catheter were eligible for this retrospective study. Arteriovenous fistulas were created on the nondominant upper extremity after the placement of hemodialysis catheters. The catheters were removed after four consecutive successful dialyses via arteriovenous fistulas. The effective factors on arteriovenous fistula failure were determined in and the prognostic factors for survival were modeled by regression analysis.

Results

The relative placement of catheters as ipsi- or contralateral was found to significantly affect the survival of the arteriovenous fistulas. The overall survival was significantly longer in the contralateral arteriovenous fistula group (778.7 ± 28.8 vs. 247.3 ± 26.1 days; p < 0.001). The independent predictors of arteriovenous fistula survival were found to be relative side of arteriovenous fistula and hemodialysis catheter, age, and the presence of hypertension in multivariate analyses.

Conclusion

This study showed that ipsilateral localization of the catheters and arteriovenous fistulas negatively affect the cumulative arteriovenous fistula survival. Therefore, to improve vascular access survival, side of catheters or arteriovenous fistulas should always be considered.
Minimum standards of clinical practice for physiotherapists working in critical care settings in the United Kingdom: A modified Delphi technique
Authors: Twose Paul , Jones Una , Cornell Gareth
Published: November 2018

Introduction

Across the United Kingdom, physiotherapy for critical care patients is provided 24 h a day, 7 days per week. There is a national drive to standardise the knowledge and skills of physiotherapists which will support training and reduce variability in clinical practice.

Methods

A modified Delphi technique using a questionnaire was used. The questionnaire, originally containing 214 items, was completed over three rounds. Items with no consensus were included in later rounds along with any additional items suggested.

Results

In all, 114 physiotherapists from across the United Kingdom participated in the first round, with 102 and 92 completing rounds 2 and 3, respectively. In total, 224 items were included: 107 were deemed essential as a minimum standard of clinical practice; 83 were not essential and consensus was not reached for 34 items.

Analysis/Conclusion

This study identified 107 items of knowledge and skills that are essential as a minimum standard for clinical practice by physiotherapists working in United Kingdom critical care units.
Origins of Intraindividual Genetic Variation in Human Fetuses
Authors: Bazrgar Masood , Gourabi Hamid , Karimpour-Fard Anis , Boroujeni Parnaz Borjian , Anisi Khadije , Movaghar Bahar , Valojerdi Mojtaba Rezazadeh
Published: November 2018

Background:

Intraindividual copy number variation (CNV) origin is largely unknown. They might be due to aging and/or common genome instability at the preimplantation stage while contribution of preimplantation in human intraindividual CNVs occurrence is unknown. To address this question, we investigated mosaicism and its origin in the fetuses of natural conception.

Methods:

We studied normal fetuses following therapeutic abortion due to maternal indications. We analyzed the genome of 22 tissues of each fetus by array comparative genomic hybridization for intraindividual CNVs. Each tissue was studied in 2 microarray experiments; the reciprocal aberrations larger than 40 Kb, identified by comparing tissues of each fetus, were subsequently validated using quantitative polymerase chain reaction.

Results:

Through intraindividual comparison, frequency of reciprocal events varied from 2 to 9. According to the distribution pattern of the frequent CNV in derivatives of different germ layers, we found that its origin is early development including preimplantation, whereas CNVs with low frequency have occurred in later stages. Shared CNVs in both fetuses were belonged to thymus and related to the functional role of genes located in these CNVs.

Conclusions:

The origin of some of fetal CNVs is preimplantation stage. Each organ might inherit CNVs with an unpredictable pattern due to the extensive cell mixing/migration in embryonic development.
A Ciliary Motility Index for Activity Measurement in Cell Cultures With Respiratory Syncytial Virus
Authors: Parrilla Eduardo , Armengot Miguel , Mata Manuel , Carda Carmen , Cortijo Juilo , Moratal David , Ginestar Damián , Hueso José L. , Riera Jaime
Published: November 2018

Background

The respiratory epithelium is frequently infected by the respiratory syncytial virus, resulting in inflammation, a reduction in cilia activity and an increase in the production of mucus.

Methods

In this study, an automatic method has been proposed to characterize the ciliary motility from cell cultures by means of a motility index using a dense optical flow algorithm. This method allows us to determine the ciliary beat frequency (CBF) together with a ciliary motility index of the cells in the cultures. The object of this analysis is to automatically distinguish between normal and infected cells in a culture.

Results

The method was applied in 2 stages. It was concluded from the first stage that the CBF is not a good enough indicator to discriminate between the control and infected cultures. However, the ciliary motility index does succeed in discriminating between the control and infected cultures using the t test with a value t = 6.46 and P < .001. In the second stage, it has been shown that the ciliary motility index did not differ significantly between patients, and the analysis of variance test gives α = 0.05, F = 1.61, P = .20. A threshold for this index has been determined using a receiver operating characteristics analysis that gives an area under the curve of 0.93.

Conclusions

We have obtained a ciliary motility index that is able to discriminate between control and infected cultures after the eighth postinfection day. After infection, there is a rapid cilia loss of the cells and the measured CBF corresponds to the remaining noninfected cells. This is why the CBF does not discriminate between the control and the infected cells.
Goals, commitment and peer effects as tools for improving the behavioural outcomes of financial education
Authors: Riitsalu Leonore
Published: November 2018

Financial education has been criticised for its limited effect on behaviour. To explore ways of improving behaviour in managing personal finances, interpretive methodology and an intervention were used in five financial education courses in Estonia. It was hypothesised that applying behavioural insights into the design of financial education helps the participants to improve their financial behaviours. The intervention employed goal setting, partitioning, commitment to achieving the goal, feedback, deadlines, peer pressure and advice. These five case studies reveal that promotional goals are more motivating than prevention oriented goals, peer effects can be utilised only in homogeneous groups and that partitioning needs to be incentivised. The results provide support for the hypothesis; furthermore, positive changes in behaviour were noted also 6 months after the end of the course. However, the intervention and its elements would benefit from further testing.
Social media and microtargeting: Political data processing and the consequences for Germany
Authors: Papakyriakopoulos Orestis , Hegelich Simon , Shahrezaye Morteza , Serrano Juan Carlos Medina
Published: November 2018

Amongst other methods, political campaigns employ microtargeting, a specific technique used to address the individual voter. In the US, microtargeting relies on a broad set of collected data about the individual. However, due to the unavailability of comparable data in Germany, the practice of microtargeting is far more challenging. Citizens in Germany widely treat social media platforms as a means for political debate. The digital traces they leave through their interactions provide a rich information pool, which can create the necessary conditions for political microtargeting following appropriate algorithmic processing. More specifically, data mining techniques enable information gathering about a people's general opinion, party preferences and other non-political characteristics. Through the application of data-intensive algorithms, it is possible to cluster users in respect of common attributes, and through profiling identify whom and how to influence. Applying machine learning algorithms, this paper explores the possibility to identify micro groups of users, which can potentially be targeted with special campaign messages, and how this approach can be expanded to large parts of the electorate. Lastly, based on these technical capabilities, we discuss the ethical and political implications for the German political system.
“Before I die I want to …”: An experience of death education among university students of social service and psychology
Authors: Testoni Ines , Iacona Erika , Fusina Sonia , Floriani Maddalena , Crippa Matteo , Maccarini Andrea , Zamperini Adriano
Published: November 2018

The censorship of death and dying has removed the “memento mori” practices, and in order to reintroduce this practice, some “Before I die” projects have been increasingly implemented. Running in parallel, in the syllabi of social service and psychology students, some experiences of death education has commenced. This study illustrates the results of a qualitative research conducted on the “Before I die I want to …” Polaroid® Project (BIDIWT), which is divided into two phases. The first phase entails an analysis of the wishes collected from the United States, Japan, India, and Italy. The second phase refers to the analysis of the captions of the BIDIWT realized from two groups of undergraduates, with regard to the effect of such experience on their religiosity, representation of death, and fear of death.
“A Two Glass of Wine Shift”: Dominant Discourses and the Social Organization of Nurses’ Substance Use
Authors: Ross Charlotte A. , Jakubec Sonya L. , Berry Nicole S. , Smye Victoria
Published: November 2018

We undertook an institutional ethnography utilizing the expert knowledge of nurses who have experienced substance-use problems to discover: (a) What are the discourses embedded in the talk among nurses in their everyday work worlds that socially organize their substance-use practices and (b) how do those discourses manage these activities? Data collection included interviews, researcher reflexivity, and texts that were critically analyzed with a focus on institutional features. Analysis revealed dominant moralistic and individuated discourses in nurses’ workplace talk that socially organized their substance-use practices, subordinated and silenced experiences of work stress, and erased employers’ roles in managing working conditions. Conclusions included that nurses used substances in ways that enabled them to remain silent and keep working. Nurses’ education did not prepare them regarding nurses’ substance-use problems or managing emotional labor. Nurses viewed alcohol as an acceptable and encouraged coping strategy for nurses to manage emotional distress.