scholarly journals Differences in Sickness Allowance Receipt between Swedish Speakers and Finnish Speakers in Finland

2017 ◽  
Vol 52 ◽  
pp. 43-58 ◽  
Author(s):  
Kaarina S. Reini ◽  
Jan Saarela
Keyword(s):  
Regression Models ◽  
Health Condition ◽  
Logistic Regression Models ◽  
Individual Level ◽  
Objective Health ◽  
Language Groups ◽  
Health Measure ◽  
All Cause Mortality ◽  
The Difference ◽  
The Individual

Previous research has documented lower disability retirement and mortality rates of Swedish speakers as compared with Finnish speakers in Finland. This paper is the first to compare the two language groups with regard to the receipt of sickness allowance, which is an objective health measure that reflects a less severe poor health condition. Register-based data covering the years 1988-2011 are used. We estimate logistic regression models with generalized estimating equations to account for repeated observations at the individual level. We find that Swedish-speaking men have approximately 30 percent lower odds of receiving sickness allowance than Finnish-speaking men, whereas the difference in women is about 15 percent. In correspondence with previous research on all-cause mortality at working ages, we find no language-group difference in sickness allowance receipt in the socially most successful subgroup of the population.

scholarly journals Predicting Violence in Merseyside: a Network-Based Approach Using No Demographic Information

2020 ◽  
Vol 4 (3-4) ◽  
pp. 89-102
Author(s):  
Paolo Campana ◽  
Andrea Giovannetti
Keyword(s):  
Regression Models ◽  
Predictive Power ◽  
Demographic Information ◽  
Data Set ◽  
Logistic Regression Models ◽  
Individual Level ◽  
The Individual ◽  
High Level ◽  
Very High ◽  
Individual Information

Abstract Purpose We explore how we can best predict violent attacks with injury using a limited set of information on (a) previous violence, (b) previous knife and weapon carrying, and (c) violence-related behaviour of known associates, without analysing any demographic characteristics. Data Our initial data set consists of 63,022 individuals involved in 375,599 events that police recorded in Merseyside (UK) from 1 January 2015 to 18 October 2018. Methods We split our data into two periods: T1 (initial 2 years) and T2 (the remaining period). We predict “violence with injury” at time T2 as defined by Merseyside Police using the following individual-level predictors at time T1: violence with injury; involvement in a knife incident and involvement in a weapon incident. Furthermore, we relied on social network analysis to reconstruct the network of associates at time T1 (co-offending network) for those individuals who have committed violence at T2, and built three additional network-based predictors (associates’ violence; associates’ knife incident; associates’ weapon incident). Finally, we tackled the issue of predicting violence (a) through a series of robust logistic regression models using a bootstrapping method and (b) through a specificity/sensitivity analysis. Findings We found that 7720 individuals committed violence with injury at T2. Of those, 2004 were also present at T1 (27.7%) and co-offended with a total of 7202 individuals. Regression models suggest that previous violence at time T1 is the strongest predictor of future violence (with an increase in odds never smaller than 123%), knife incidents and weapon incidents at the individual level have some predictive power (but only when no information on previous violence is considered), and the behaviour of one’s associates matters. Prior association with a violent individual and prior association with a knife-flagged individual were the two strongest network predictors, with a slightly stronger effect for knife flags. The best performing regressors are (a) individual past violence (36% of future violence cases correctly identified); (b) associates’ past violence (25%); and (c) associates’ knife involvement (14%). All regressors are characterised by a very high level of specificity in predicting who will not commit violence (80% or more). Conclusions Network-based indicators add to the explanation of future violence, especially prior association with a knife-flagged individual and association with a violent individual. Information about the knife involvement of associates appears to be more informative than a subject’s own prior knife involvement.

Preference Voting in Italy

1985 ◽  
Vol 18 (2) ◽  
pp. 229-249 ◽  
Author(s):  
RICHARD S. KATZ
Keyword(s):  
Political Culture ◽  
Regression Models ◽  
Contextual Effects ◽  
Full Account ◽  
Logistic Regression Models ◽  
Individual Level ◽  
Representation Systems ◽  
Preference Voting ◽  
Important Possibility ◽  
The Individual

Intraparty preference voting is a potentially important possibility for voters in many proportional representation systems, especially the Italian system. Three hypotheses—that preference voting is an indicator of traditionalism or the voto di scambio, sophistication or the voto d'opinione, and mobilization or the voto d'appartenenza—are considered using survey data and logistic regression models. All three hypotheses are supported by the data. Although the support for the individual-level traditionalism account is weakest, the data suggest that traditional political culture may contribute to the contextual prerequisites for sophistication or mobilization to lead to preference voting. Overall, it is suggested that the three explanations are complementary rather than contradictory, and that contextual effects must be considered in a full account of preference voting.

Fair-Wages for People With Disabilities: Barriers and Facilitators

2020 ◽  
Vol 31 (3) ◽  
pp. 152-163
Author(s):  
Carli Friedman ◽  
Mary C. Rizzolo
Keyword(s):  
Regression Models ◽  
People With Disabilities ◽  
Binary Logistic Regression ◽  
Service Organizations ◽  
Organizational Level ◽  
Logistic Regression Models ◽  
Individual Level ◽  
Problematic Issue ◽  
Fair Wages ◽  
The Individual

Subminimum wage is a prominent and problematic issue affecting the lives of many people with disabilities. For this reason, the aim of this study was to identify the correlates of fair-wages (at least minimum wage) for people with disabilities—which factors facilitate and hinder people with disabilities’ access to fair-wages. We utilized Personal Outcome Measures® interview data from approximately 1,500 people with disabilities to examine how individual, employment, and organizational-level factors correlate with people with disabilities’ access to fair-wages. Binary logistic regression models revealed at the individual-level support needs, guardianship, and residence type all significantly correlate with people with disabilities’ odds of receiving fair-wages. In addition, the ability to experience a number of employment options, as well as decide where to work, produce higher odds of having fair-wages. Finally, our findings also revealed the key role service organizations can play in facilitating people with disabilities’ access to fair-wages. Attention to the facilitators that promote access to fair-wages for people with disabilities, and the barriers that hinder this access is one of the first steps toward ending this discrimination against people with disabilities.

scholarly journals Low HbA1c levels and all-cause or cardiovascular mortality among people without diabetes: the US National Health and Nutrition Examination Survey 1999–2015

2020 ◽  
Author(s):  
Kosuke Inoue ◽  
Roch Nianogo ◽  
Donatello Telesca ◽  
Atsushi Goto ◽  
Vahe Khachadourian ◽  
...  
Keyword(s):  
Cardiovascular Mortality ◽  
Regression Models ◽  
Machine Learning Algorithms ◽  
Logistic Regression Models ◽  
Health And Nutrition ◽  
Increased Risk ◽  
The Us ◽  
All Cause Mortality ◽  
Hba1c Levels

Abstract Objective It is unclear whether relatively low glycated haemoglobin (HbA1c) levels are beneficial or harmful for the long-term health outcomes among people without diabetes. We aimed to investigate the association between low HbA1c levels and mortality among the US general population. Methods This study includes a nationally representative sample of 39 453 US adults from the National Health and Nutrition Examination Surveys 1999–2014, linked to mortality data through 2015. We employed the parametric g-formula with pooled logistic regression models and the ensemble machine learning algorithms to estimate the time-varying risk of all-cause and cardiovascular mortality by HbA1c categories (low, 4.0 to <5.0%; mid-level, 5.0 to <5.7%; prediabetes, 5.7 to <6.5%; and diabetes, ≥6.5% or taking antidiabetic medication), adjusting for 72 potential confounders including demographic characteristics, lifestyle, biomarkers, comorbidities and medications. Results Over a median follow-up of 7.5 years, 5118 (13%) all-cause deaths, and 1116 (3%) cardiovascular deaths were observed. Logistic regression models and machine learning algorithms showed nearly identical predictive performance of death and risk estimates. Compared with mid-level HbA1c, low HbA1c was associated with a 30% (95% CI, 16 to 48) and a 12% (95% CI, 3 to 22) increased risk of all-cause mortality at 5 years and 10 years of follow-up, respectively. We found no evidence that low HbA1c levels were associated with cardiovascular mortality risk. The diabetes group, but not the prediabetes group, also showed an increased risk of all-cause mortality. Conclusions Using the US national database and adjusting for an extensive set of potential confounders with flexible modelling, we found that adults with low HbA1c were at increased risk of all-cause mortality. Further evaluation and careful monitoring of low HbA1c levels need to be considered.

scholarly journals Asymmetric Fixed Effects Models for Panel Data

2018 ◽  
Author(s):  
Paul D Allison
Keyword(s):  
Logistic Regression ◽  
Panel Data ◽  
Opposite Direction ◽  
Difference Method ◽  
Fixed Effects ◽  
Regression Models ◽  
Social Phenomena ◽  
Logistic Regression Models ◽  
Fixed Effects Models ◽  
The Difference

Standard fixed effects methods presume that effects of variables are symmetric: the effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light (2017) showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this paper, I show that there are several aspects of their method that need improvement. I also develop a data generating model that justifies the first-difference method but can be applied in more general settings. In particular, it can be used to construct asymmetric logistic regression models.

Workplace Location and ICTs Substituting Travel

2010 ◽  
pp. 205-225 ◽  
Author(s):  
Greger Henriksson ◽  
Minna Räsänen
Keyword(s):  
Case Studies ◽  
Environmental Sustainability ◽  
Relevant Literature ◽  
Social Practices ◽  
Time Service ◽  
Individual Level ◽  
The Difference ◽  
The Individual ◽  
Relations Of Power ◽  
Shed Light

This chapter is based on the assumption that keeping the number and length of business and commuting trips at reasonable levels could contribute to reaching targets of environmental sustainability. The authors highlight a couple of options for reducing or avoiding business trips and commuting through workplace location or improved use of communications. They present case studies concerning travel and communications, carried out by using diaries and interviews. They also present relevant literature on social practices and sustainability goals in relation to use of ICT. The aim is to shed light on variation in the use of travel and communications on an individual level in work life. The case studies illustrate that such variation is mainly due to the concrete practices involved in execution of professional duties and roles. Duties that involve a clearly defined end result or product being delivered regularly by the member of staff are correlated to clearly defined needs for communications. Less clearly defined end results of the work duties seem to make it harder for the individual to plan and perform communication and travel in a more energy saving way. The difference in professional duties can thus be expressed in terms of clarity and maturity. Another factor that affect who can replace travel with ICTs is relations of power, e.g., when a purchaser dictates the terms for a subcontractor concerning how and where to “deliver” his working time, service or product. The importance of clarity, maturity and power aspects means that professional practices need to be studied at a detailed level to find out who could substitute ICTs for travel and how this could be done.

scholarly journals Asymmetric Fixed-effects Models for Panel Data

2019 ◽  
Vol 5 ◽  
pp. 237802311982644 ◽  
Author(s):  
Paul D. Allison
Keyword(s):  
Logistic Regression ◽  
Panel Data ◽  
Opposite Direction ◽  
Difference Method ◽  
Fixed Effects ◽  
Regression Models ◽  
Social Phenomena ◽  
Logistic Regression Models ◽  
Fixed Effects Models ◽  
The Difference

Standard fixed-effects methods presume that effects of variables are symmetric: The effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this article, I show that there are several aspects of their method that need improvement. I also develop a data-generating model that justifies the first-difference method but can be applied in more general settings. In particular, it can be used to construct asymmetric logistic regression models.

Monitoring Players’ Readiness Using Predicted Heart-Rate Responses to Soccer Drills

2018 ◽  
Vol 13 (10) ◽  
pp. 1273-1280 ◽  
Author(s):  
Mathieu Lacome ◽  
Ben Simpson ◽  
Nick Broad ◽  
Martin Buchheit
Keyword(s):  
Heart Rate ◽  
Global Positioning System ◽  
Daily Basis ◽  
Internal Load ◽  
Individual Level ◽  
Multiple Stepwise Regression Analysis ◽  
Global Positioning ◽  
Fitness Criterion ◽  
The Difference ◽  
The Individual

Purpose: To examine the ability of multivariate models to predict the heart-rate (HR) responses to some specific training drills from various global positioning system (GPS) variables and to examine the usefulness of the difference in predicted vs actual HR responses as an index of fitness or readiness to perform. Method: All data were collected during 1 season (2016–17) with players’ soccer activity recorded using 5-Hz GPS and internal load monitored using HR. GPS and HR data were analyzed during typical small-sided games and a 4-min standardized submaximal run (12 km·h−1). A multiple stepwise regression analysis was used to identify which combinations of GPS variables showed the largest correlations with HR responses at the individual level (HRACT, 149 [46] GPS/HR pairs per player) and was further used to predict HR during individual drills (HRPRED). Then, HR predicted was compared with actual HR to compute an index of fitness or readiness to perform (HRΔ, %). The validity of HRΔ was examined while comparing changes in HRΔ with the changes in HR responses to a submaximal run (HRRUN, fitness criterion) and as a function of the different phases of the season (with fitness being expected to increase after the preseason). Results: HRPRED was very largely correlated with HRACT (r = .78 [.04]). Within-player changes in HRΔ were largely correlated with within-player changes in HRRUN (r = .66, .50–.82). HRΔ very likely decreased from July (3.1% [2.0%]) to August (0.8% [2.2%]) and most likely decreased further in September (−1.5% [2.1%]). Conclusions: HRΔ is a valid variable to monitor elite soccer players’ fitness and allows fitness monitoring on a daily basis during normal practice, decreasing the need for formal testing.

scholarly journals Creativity Versus Innovativeness: Exploring the Differences between the Two Constructs May Lead to Greater Innovation in Large Firms

2015 ◽  
Vol 10 (11) ◽  
pp. 83 ◽  
Author(s):  
John Cocco ◽  
Majdi Quttainah
Keyword(s):  
Motivational Orientation ◽  
Individual Level ◽  
Sustained Competitive Advantage ◽  
Starting Point ◽  
Seeking Behavior ◽  
Study Support ◽  
The Difference ◽  
Primary Focus ◽  
The Individual ◽  
The Right

<p>Several individuals from top management seem to be confused about the difference between creativity and innovativeness. Amabile (1997) suggests that while innovation begins with creative ideas, creativity by individuals and teams is only a starting point for innovation. Individual creativity is necessary but not sufficient to yield breakthrough innovation in organizations. This can sometimes cause confusion in employee development efforts and actions taken by management. Companies often look for ways to hire and retain creative employees and at the same time they are also interested in establishing a creative environment for knowledge workers… but should creativity be the primary focus? These firms hope that creativity enhancing steps will eventually lead to greater innovation and therefore help it to achieve sustained competitive advantage. This paper attempts to demonstrate that there are potentially other dimensions beyond creativity related to innovativeness, which should be considered at the individual level in order to foster innovation in firms. Empirical results in this study support the idea that intrinsic motivational orientation, sociability and political astuteness are enhancers to employee innovativeness while perfection seeking behavior detracts employee innovativeness. These findings may serve to extend Amabile’s (1997) componential framework to center on the “innovativeness” construct versus creativity to help explain how firms need to hire, cultivate and retain the right talent.</p>

Performance simulations for categorical mediation: Analyzing khb estimates of mediation in ordinal regression models

2019 ◽  
Vol 19 (4) ◽  
pp. 913-930 ◽  
Author(s):  
E. Keith Smith ◽  
Michael G. Lacy ◽  
Adam Mayer
Keyword(s):  
Logistic Regression ◽  
Nonlinear Regression ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Experimental Conditions ◽  
Logistic Regression Models ◽  
Nonlinear Regression Models ◽  
Size Number ◽  
Confidence Interval Coverage ◽  
The Difference

Standard mediation techniques for fitting mediation models cannot readily be translated to nonlinear regression models because of scaling issues. Methods to assess mediation in regression models with categorical and limited response variables have expanded in recent years, and these techniques vary in their approach and versatility. The recently developed khb technique purports to solve the scaling problem and produce valid estimates across a range of nonlinear regression models. Prior studies demonstrate that khb performs well in binary logistic regression models, but performance in other models has yet to be investigated. In this article, we evaluate khb‘s performance in fitting ordinal logistic regression models as an exemplar of the wider set of models to which it applies. We examined performance across 38,400 experimental conditions involving sample size, number of response categories, distribution of variables, and amount of mediation. Results indicate that under all experimental conditions, khb estimates the difference (mediation) coefficient and its associated standard error with little bias and that the nominal confidence interval coverage closely matches the actual. Our results suggest that researchers using khb can assume that the routine reasonably approximates population parameters.

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