scholarly journals Returning to the Fight: Addressing the Drivers and Likelihood of Terrorist Disengagement and Re-Engagement

Proceedings ◽  
2021 ◽  
Vol 77 (1) ◽  
pp. 19
Author(s):  
Mary Beth Altier
Keyword(s):  
Small Sample ◽  
Sunk Costs ◽  
Investment Model ◽  
Terrorist Group ◽  
Pull Factors ◽  
Role Exit ◽  
Terrorist Groups ◽  
Traditional Work ◽  
Using Data ◽  
Small Sample Sizes

Recent interest in terrorist risk assessment and rehabilitation reveals that the likelihood and risk factors for terrorist disengagement, re-engagement, and recidivism are poorly understood. In this presentation, I review related literature on criminal desistance, disaffiliation from new religious movements, commitment, and turnover in traditional work organizations, role exit, and the investment model to develop a series of theoretical starting points for gauging the likelihood and predictors of risk, which can help inform evaluation efforts. I then highlight key findings from the existing literature on terrorist disengagement and re-engagement/recidivism as well as key differences across samples and the methodological challenges associated with such research—mainly the absence of control groups, relatively small sample sizes, the need for a lengthy time horizon, and inconsistencies in what constitutes re-engagement and recidivism. Then, using data collected on 185 terrorist engagement events for 85 individuals representing over 70 unique terrorist groups, I present my and my colleagues’ findings on the drivers of terrorist disengagement and re-engagement. We find that terrorist disengagement is a lengthy process more commonly driven by “push” rather than “pull” factors, specifically disillusionment with the strategy or actions of the terrorist group, disillusionment with leaders or other members, disillusionment with one’s day-to-day tasks, burnout, difficulty living a clandestine lifestyle, difficulty coping with attacks, and psychological distress. Importantly, “de-radicalization” is only cited as playing a “large role” in just 16% of disengagement events in our sample. I then discuss how one’s role within a terrorist group offers insight into the disengagement process. Our research shows that leaders and violent operatives have a harder time disengaging than those in logistical or support roles because of the sunk costs associated with their involvement and/or the fewer opportunities available to them. We also find that individuals in certain roles are more/less likely to experience certain push/pull factors for disengagement. I conclude by discussing our research on terrorist re-engagement, which shows that in the short term, a deep commitment to the ideology, maintaining ties to individuals still involved in terrorism, and being young increase the likelihood one will return to terrorism.

Confidence intervals for a difference between lognormal means in cluster randomization trials

2014 ◽  
Vol 26 (2) ◽  
pp. 598-614 ◽  
Author(s):  
Julia Poirier ◽  
GY Zou ◽  
John Koval
Keyword(s):  
Confidence Intervals ◽  
Community Acquired Pneumonia ◽  
Small Sample ◽  
Cluster Randomization ◽  
Critical Pathway ◽  
Arithmetic Means ◽  
Multiple Parameters ◽  
The Difference ◽  
Using Data ◽  
Small Sample Sizes

Cluster randomization trials, in which intact social units are randomized to different interventions, have become popular in the last 25 years. Outcomes from these trials in many cases are positively skewed, following approximately lognormal distributions. When inference is focused on the difference between treatment arm arithmetic means, existent confidence interval procedures either make restricting assumptions or are complex to implement. We approach this problem by assuming log-transformed outcomes from each treatment arm follow a one-way random effects model. The treatment arm means are functions of multiple parameters for which separate confidence intervals are readily available, suggesting that the method of variance estimates recovery may be applied to obtain closed-form confidence intervals. A simulation study showed that this simple approach performs well in small sample sizes in terms of empirical coverage, relatively balanced tail errors, and interval widths as compared to existing methods. The methods are illustrated using data arising from a cluster randomization trial investigating a critical pathway for the treatment of community acquired pneumonia.

Prevalence of tuberculosis, brucellosis and trypanosomiasis in cattle in Tanzania: a systematic review and meta-analysis

2016 ◽  
Vol 17 (1) ◽  
pp. 16-27 ◽  
Author(s):  
Silvia Alonso ◽  
Ian Dohoo ◽  
Johanna Lindahl ◽  
Cristobal Verdugo ◽  
Isaiah Akuku ◽  
...  
Keyword(s):  
Systematic Review ◽  
Production Systems ◽  
Strong Predictor ◽  
Meta Analysis ◽  
Small Sample ◽  
Skin Tests ◽  
Prevalence Studies ◽  
Prevalence Estimates ◽  
Using Data ◽  
Small Sample Sizes

AbstractA meta-analysis was performed to derive prevalence estimates for Brucella spp., Mycobacterium spp. and Trypanosoma spp. in cattle in Tanzania using data derived from a systematic review of zoonotic hazards in cattle production systems. Articles published before 2012 reporting prevalence and considered at least moderate in quality were included in the analysis. Results showed high heterogeneity between studies, with wide ranges in the reported prevalence: Brucella (0.3–60.8%), Mycobacterium (0.1–13.2%) and Trypanosoma (0.82–33.3%). Overall meta-analytic mean prevalence estimates were 8.2% (95% CI 6.5–10.2), 1.28% (95% CI 0.35–4.58) and 10.3% (95% CI 6.20–16.70) respectively, for Brucella spp., Mycobacterium spp. and Trypanosoma spp. Time and region were predictors of variability of Brucella spp. prevalence, while diagnostic test was a strong predictor of Mycobacterium spp. prevalence, with higher prevalence estimates given by skin tests compared with post-mortem inspection. None of the studied factors were associated with prevalence of Trypanosoma spp. The small sample sizes, range of study locations, study designs and diagnostics used, contributed to high variability among prevalence estimates. Larger and more robust prevalence studies are needed to adequately support risk assessment and management of animal and public health threats.

scholarly journals Neuropathological Associates of Multiple Cognitive Functions in Two Community-Based Cohorts of Older Adults

2010 ◽  
Vol 17 (4) ◽  
pp. 602-614 ◽  
Author(s):  
N. Maritza Dowling ◽  
Sarah Tomaszewski Farias ◽  
Bruce R. Reed ◽  
Joshua A. Sonnen ◽  
Milton E. Strauss ◽  
...  
Keyword(s):  
Older Adults ◽  
Lewy Bodies ◽  
Lewy Body Disease ◽  
Small Sample ◽  
Cognitive Testing ◽  
Brain Weight ◽  
Cognitive Domains ◽  
Using Data ◽  
Small Sample Sizes ◽  
Independent Effects

AbstractStudies of neuropathology-cognition associations are not common and have been limited by small sample sizes, long intervals between autopsy and cognitive testing, and lack of breadth of neuropathology and cognition variables. This study examined domain-specific effects of common neuropathologies on cognition using data (N = 652) from two large cohort studies of older adults. We first identified dimensions of a battery of 17 neuropsychological tests, and regional measures of Alzheimer’s disease (AD) neuropathology. We then evaluated how cognitive factors were related to dimensions of AD and additional measures of cerebrovascular and Lewy Body disease, and also examined independent effects of brain weight. All cognitive domains had multiple neuropathology determinants that differed by domain. Neocortical neurofibrillary tangles were the strongest predictors of most domains, while medial temporal tangles showed a weaker relationship with episodic memory. Neuritic plaques had relatively strong effects on multiple domains. Lewy bodies and macroscopic infarcts were associated with all domains, while microscopic infarcts had more limited associations. Brain weight was related to all domains independent of specific neuropathologies. Results show that cognition is complexly determined by multiple disease substrates. Neuropathological variables and brain weight contributed approximately a third to half of the explained variance in different cognitive domains. (JINS, 2011, 17, 602–614).

scholarly journals Characterizing the Urban Mine—Simulation-Based Optimization of Sampling Approaches for Built-in Batteries in WEEE

Recycling ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 19
Author(s):  
Paul Martin Mählitz ◽  
Nathalie Korf ◽  
Kristine Sperlich ◽  
Olivier Münch ◽  
Matthias Rösslein ◽  
...  
Keyword(s):  
Small Sample ◽  
Data Sets ◽  
Distributed Data ◽  
Sample Sizes ◽  
Representative Sampling ◽  
Comprehensive Knowledge ◽  
Simulation Based ◽  
Using Data ◽  
Small Sample Sizes ◽  
Almost All

Comprehensive knowledge of built-in batteries in waste electrical and electronic equipment (WEEE) is required for sound and save WEEE management. However, representative sampling is challenging due to the constantly changing composition of WEEE flows and battery systems. Necessary knowledge, such as methodologically uniform procedures and recommendations for the determination of minimum sample sizes (MSS) for representative results, is missing. The direct consequences are increased sampling efforts, lack of quality-assured data, gaps in the monitoring of battery losses in complementary flows, and impeded quality control of depollution during WEEE treatment. In this study, we provide detailed data sets on built-in batteries in WEEE and propose a non-parametric approach (NPA) to determine MSS. For the pilot dataset, more than 23 Mg WEEE (6500 devices) were sampled, examined for built-in batteries, and classified according to product-specific keys (UNUkeys and BATTkeys). The results show that 21% of the devices had battery compartments, distributed over almost all UNUkeys considered and that only about every third battery was removed prior to treatment. Moreover, the characterization of battery masses (BM) and battery mass shares (BMS) using descriptive statistical analysis showed that neither product- nor battery-specific characteristics are given and that the assumption of (log-)normally distributed data is not generally applicable. Consequently, parametric approaches (PA) to determine the MSS for representative sampling are prone to be biased. The presented NPA for MSS using data-driven simulation (bootstrapping) shows its applicability despite small sample sizes and inconclusive data distribution. If consistently applied, the method presented can be used to optimize future sampling and thus reduce sampling costs and efforts while increasing data quality.

scholarly journals MISSING VALUE IMPUTATION VIA GRAPH COMPLETION IN QUESTIONNAIRE SCORES FROM PERSONS WITH DEMENTIA

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S972-S972
Author(s):  
Chen Kan ◽  
Won Hwa Kim ◽  
Ling Xu ◽  
Noelle L Fields
Keyword(s):  
Missing Data ◽  
Statistical Power ◽  
Missing Values ◽  
Small Sample Size ◽  
Small Sample ◽  
Small Scale ◽  
Assessment And Treatment ◽  
Using Data ◽  
Small Sample Sizes ◽  
Clinical Conditions

Abstract Background: Questionnaires are widely used to evaluate cognitive functions, depression, and loneliness of persons with dementia (PWDs). Successful assessment and treatment of dementia hinge on effective analysis of PWDs’ answers. However, many studies, especially pilot ones, are with small sample sizes. Further, most of them contain missing data as PWDs skip some study sessions due to their clinical conditions. Conventional imputation strategies are not well-suited as bias will be introduced because of insufficient samples. Method: A novel machine learning framework was developed based on harmonic analysis on graphs to robustly handle missing values. Participants were first embedded as nodes in the graph with edges derived by their similarities based on demographic information, activities of daily living, etc. Then, questionnaire scores with missing values were regarded as a function on the nodes, and they were estimated based on spectral analysis of the graph with a smoothness constraint. The proposed approach was evaluated using data from our pilot study of dementia subjects (N=15) with 15% data missing. Result: A few complete variables (binary or ordinal) were available for all participants. For each variable, we randomly removed 5 scores to mimic missing values. With our approach, we could recover all missing values with 90% accuracy on average. We were also able to impute the actual missing values in the dataset within reasonable ranges. Conclusion: Our proposed approach imputes missing values with high accuracy despite the small sample size. The proposed approach will significantly boost statistical power of various small-scale studies with missing data.

scholarly journals Repression Hurts: Coercive Government Responses and the Demise of Terrorist Campaigns

2012 ◽  
Vol 43 (3) ◽  
pp. 559-577 ◽  
Author(s):  
Ursula E. Daxecker ◽  
Michael L. Hess
Keyword(s):  
Group Dynamics ◽  
Authoritarian Regimes ◽  
Government Policies ◽  
Terrorist Group ◽  
Regime Type ◽  
Terrorist Organizations ◽  
Terrorist Groups ◽  
The Government ◽  
Using Data

The question of how coercive government policies affect the duration and outcome of terrorist campaigns has only recently started to attract scholarly interest. This article argues that the effect of repression on terrorist group dynamics is conditional on the country's regime type. Repression is expected to produce a backlash effect in democracies, subsequently lengthening the duration of terrorist organizations and lowering the probability of outcomes favourable to the government. In authoritarian regimes, however, coercive strategies are expected to deter groups’ engagement in terrorism, thus reducing the lifespan of terrorist groups and increasing the likelihood of government success. These hypotheses are examined using data on terrorist groups for the 1976–2006 period; support is found for these conjectures on terrorist group duration and outcomes.

A comparison of several measures of genetic distance and population structure with microsatellite data: bias and sampling variance

1998 ◽  
Vol 55 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Daniel E Ruzzante
Keyword(s):  
Genetic Distance ◽  
Gadus Morhua ◽  
Genetic Distances ◽  
Small Sample ◽  
Sampling Variance ◽  
Sample Sizes ◽  
Allele Size ◽  
Using Data ◽  
Small Sample Sizes ◽  
Structure Metrics

Because of their rapid mutation rate and resulting large number of alleles, microsatellite DNA are well suited to examine the genetic or demographic structure of fish populations. However, the large number of alleles imply that large sample sizes are required for accurate reflection of genotypic frequencies. Estimates of genetic distance are often biased at small sample sizes, and biases and sampling variances can be affected by the number of, and distances between, alleles. Using data from a large collection of larval cod (Gadus morhua) from a single area, I examined the effect of sample size on seven genetic distance and two structure metrics. Pairs of samples (equal or unequal) of various sizes were drawn at random from a pool of 856 individuals scored for six microsatellite loci. ( delta µ)2, DSW, RST, and FST were the best performers in terms of bias and variance. Sample sizes of 50 <= N <= 100 individuals were generally necessary for precise estimation of genetic distances and this value depended on number of loci, number of alleles, and range in allele size. ( delta µ)2 and DSW were biased at small sample sizes.

Integrating Visual and Bayesian Statistical Analyses in Single Case Experimental Research to Evaluate the Effectiveness and Magnitude of a Comprehensive Behavioral Intervention

2018 ◽  
Author(s):  
Prathiba Natesan ◽  
Smita Mehta
Keyword(s):  
Behavioral Intervention ◽  
Effect Size ◽  
Rate Ratio ◽  
Visual Analysis ◽  
Single Case ◽  
Small Sample ◽  
Statistical Analyses ◽  
Data Types ◽  
Ratio Effect ◽  
Small Sample Sizes

Single case experimental designs (SCEDs) have become an indispensable methodology where randomized control trials may be impossible or even inappropriate. However, the nature of SCED data presents challenges for both visual and statistical analyses. Small sample sizes, autocorrelations, data types, and design types render many parametric statistical analyses and maximum likelihood approaches ineffective. The presence of autocorrelation decreases interrater reliability in visual analysis. The purpose of the present study is to demonstrate a newly developed model called the Bayesian unknown change-point (BUCP) model which overcomes all the above-mentioned data analytic challenges. This is the first study to formulate and demonstrate rate ratio effect size for autocorrelated data, which has remained an open question in SCED research until now. This expository study also compares and contrasts the results from BUCP model with visual analysis, and rate ratio effect size with nonoverlap of all pairs (NAP) effect size. Data from a comprehensive behavioral intervention are used for the demonstration.

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