EFFECTIVENESS OF EDUCATION AND BASIC TRAINING EXPERT AGRICULTURE 2014 IN SECRETARIAT OF EXTENSION COORDINATION BENGKULU PROVINCE

The objectives of this research to measure the effectiveness and analyze the factors that influence the effectiveness of basic training agricultural extension experts organized by the Secretariat Bakorluh Bengkulu Province. This research was conducted in March through June 2015 and used slovin methods. The study population was defined as many as 36 people of 40 people from the extension of existing civil servants. Data analysis was done by using tobit models. The results showed that : 1) some characteristics of extension that is age and work experience significantly influence the effectiveness of training 2) several factors including the method of training, facilities and trainers significantly influence the effectiveness of training. 3) The level of effectiveness basic education and training of agricultural extension experts by secretariat of Bakorluh Bengkulu province in middle category or sufficiently effective;.

Using Bayesian Tobit Models to Understand the Impact of Mobile Automated Enforcement on Collision and Crime Rates

The Data Driven Approaches to Crime and Traffic Safety approach identifies opportunities where a single enforcement deployment can achieve multiple objectives: reduce collision and crime rates. Previous research focused on modeling both events separately despite evidence suggesting a high correlation. Additionally, there is a limited understanding of the impact of Mobile Automated Enforcement (MAE) on crime or the impact of changing a deployment strategy on collision and crime dates. For this reason, this study categorized MAE deployment into three different clusters. A random-parameter multivariate Tobit model was developed under the Bayesian framework to understand the impact of changing the deployment on collision and crime rates in a neighborhood. Firstly, the results of the analysis quantified the high correlation between collision and crime rates (0.86) which suggest that locations with high collision rates also coincide with locations with high crime rates. The results also demonstrated the safety effectiveness (i.e., reduced crime and collision rates) increased for the clusters that are associated with an increased enforcement duration at a neighborhood level. Understanding how changing the deployment strategy at a macro-level affects collision and crime rates provides enforcement agencies with the opportunity to maximize the efficiency of their existing resources.

Plasma Metabolomic Profiling in 1391 Subjects with Overweight and Obesity from the SPHERE Study

Overweight and obesity have high prevalence worldwide and assessing the metabolomic profile is a useful approach to study their related metabolic processes. In this study, we assessed the metabolomic profile of 1391 subjects affected by overweight and obesity, enrolled in the frame of the SPHERE study, using a validated LC–MS/MS targeted metabolomic approach determining a total of 188 endogenous metabolites. Multivariable censored linear regression Tobit models, correcting for age, sex, and smoking habits, showed that 83 metabolites were significantly influenced by body mass index (BMI). Among compounds with the highest association, aromatic and branched chain amino acids (in particular tyrosine, valine, isoleucine, and phenylalanine) increased with the increment of BMI, while some glycerophospholipids decreased, in particular some lysophosphatidylcholines (as lysoPC a C18:2) and several acylalkylphosphatidylcholines (as PC ae C36:2, PC ae C34:3, PC ae C34:2, and PC ae C40:6). The results of this investigation show that several endogenous metabolites are influenced by BMI, confirming the evidence with the strength of a large number of subjects, highlighting differences among subjects with different classes of obesity and showing unreported associations between BMI and different phosphatidylcholines.

The Effect of Rural Poverty on Access to Land in Ethiopia

Land ownership and its persistent gap between rich and poor is one of the pressing development challenges in Africa. Access to land has fundamental implications for a poor and agrarian African economy like Ethiopia, where most people depend on agriculture for their livelihood. Empirical literatures suggest that access to land is a cause and effect of poverty—at the same time, the role of poverty status of the household in gaining or limiting access to land has received only a passing attention from researchers. This study investigates the effect of ‘being poor’ on access to land using ordered probit and censored tobit models. Three wave panel data of Ethiopian Rural Socioeconomic Survey (ERSS) collected between 2011–12 and 2015–16 are used for the analysis. The study result confirms that poverty does have significant effect on household’s participation and intensity of participation on both sides of the rental market. It is found that being poor, as compared to non-poor counterpart, leads to an increase in the likelihood of rent-in land by 0.068 hectare and reduce the likelihood of rent-out land by 0.046 hectare at 1% and 5% significance levels, respectively. The tenants are not characterised as economically disadvantaged reflecting the existence of reverse tenancy among rural poor in Ethiopia.

On the use of the Box-Cox transformation in censored and truncated regression models

In this paper we revisit some issues related to the use of the Box-Cox transformation in censored and truncated regression models, which have been overlooked by the econometric and statistical literature. We first analyze the shape of the density function of the random variable which, rescaled by a Box-Cox transformation, leads to a normal random variable. Then, we identify the value ranges of the Box-Cox scale parameter for which a regular expectation of the derived random variable does not exist. This result calls for an extension of the concept of expectation, which can be computed regardless of the value of the scale parameter. For this purpose, we extend the concept of mean of a rescaled series of observations to the case of a random variable. Finally, we run estimates of censored and truncated Box-Cox standard Tobit models to determine the range of the scale parameter most relevant for empirical demand analyzes. These estimates highlight significant deviations from the assumption of normality of the dependent variable towards highly right skewed and leptokurtic distributions with no expectation.

Analysis of change in patient-reported outcome measures with floor and ceiling effects using the multilevel Tobit model: a simulation study and an example from a National Joint Register using body mass index and the Oxford Hip Score

ObjectivesThis study has three objectives. (1) Investigate the association between body mass index (BMI) and the efficacy of primary hip replacement using a patient-reported outcome measure (PROMs) with a measurement floor and ceiling, (2) Explore the performance of different estimation methods to estimate change in PROMs score following surgery using a simulation study and real word data where data has measurement floors and ceilings and (3) Lastly, develop guidance for practising researchers on the analysis of PROMs in the presence of floor and ceiling effects.DesignSimulation study and prospective national medical device register.SettingNational Register of Joint Replacement and Medical Devices.MethodsUsing a Monte Carlo simulation study and data from a national joint replacement register (162 513 patients with pre- and post-surgery PROMs), we investigate simple approaches for the analysis of outcomes with floor and ceiling effects that are measured at two occasions: linear and Tobit regression (baseline adjusted analysis of covariance, change-score analysis, post-score analysis) in addition to linear and multilevel Tobit models.Primary outcomeThe primary outcome of interest is change in PROMs from pre-surgery to 6 months post-surgery.ResultsAnalysis of data with floor and ceiling effects with models that fail to account for these features induce substantial bias. Single-level Tobit models only correct for floor or ceiling effects when the exposure of interest is not associated with the baseline score. In observational data scenarios, only multilevel Tobit models are capable of providing unbiased inferences.ConclusionsInferences from pre- post-studies that fail to account for floor and ceiling effects may induce spurious associations with substantial risk of bias. Multilevel Tobit models indicate the efficacy of total hip replacement is independent of BMI. Restricting access to total hip replacement based on a patients BMI can not be supported by the data.