The analysis of policy implementation models in higher education: the case study of Indonesia

This paper aims to analyse and understand the models of policy implementation in Indonesian state universities during the ongoing process of market-driven reforms and how academics respond to these models. The research undertaken was in the form of qualitative study using semi-structured interviews as instruments to collect data. Interviews were conducted with 30 academics in three state universities in Indonesia. The research demonstrated that policy implementation in Indonesian state universities takes largely a dialectical form which takes place both at macro and micro levels, suggesting a blended application of top-down and bottom-up approaches. The results of this study also confirmed that the interaction between policy and local systems plays an important role for the successful implementation of the policy. Indonesian academics perceived that each of these models has its strength and weaknesses particularly in relation to the aspect of collegiality. It is anticipated that the study will both contribute to a better understanding of the dynamics between governments as policy makers and academics as street level bureaucrats in the implementation of a policy, and offer guidance for policy makers in the formulation and enactment of relevant policies in higher education sectors both in Indonesia and other contexts implementing policy implementation.     

Compromise allocation in multivariate stratified surveys with stochastic quadratic cost function

In many real life situations the linear cost function does not approximate the actual cost incurred adequately. The cost of traveling between the units selected in the sample within a stratum is significant, instead of linear cost function. In this paper, we have considered the problem of finding a compromise allocation for a multivariate stratified sample survey with a significant travel cost within strata is formulated as a problem of non-linear stochastic programming with multiple objective functions. The compromise solutions are obtained through Chebyshev approximation technique, D ₁- distance and goal programming. A numerical example is presented to illustrate the computational details of the proposed methods.     

Using the Diffusion of Innovation Concept to Explain the Factors That Contribute to the Adoption Rate of E-journal Publishing

This paper reports on the factors that contribute to the adoption rate of e-journal publishing based upon responses from 82 Malaysian journal publishers. Drawing upon the theory of innovation diffusion, the study examines the role of awareness, three organization variables (publication size, age of the journal, and experience of editors), and attributes of e-journals (relative advantage, compatibility, complexity, trialability and observability) as influences to the rate of e-journal publishing adoption. Findings show that only two attributes—complexity and trialability—emerge as significant contributors to e-journal adoption rates, and all three organization variables—journal's age, publication size, and editorial experience—are significant. Collectively, these five significant variables explained 57.8% of the variance in adoption rate. We discuss the results and implication of the findings with respect to the wider context of e-journal publishing.     

Mixed ranked set sampling design

The main focus of agricultural, ecological and environmental studies is to develop well designed, cost-effective and efficient sampling designs. Ranked set sampling (RSS) is one method that leads to accomplish such objectives by incorporating expert knowledge to its advantage. In this paper, we propose an efficient sampling scheme, named mixed RSS (MxRSS), for estimation of the population mean and median. The MxRSS scheme is a suitable mixture of both simple random sampling (SRS) and RSS schemes. The MxRSS scheme provides an unbiased estimator of the population mean, and its variance is always less than the variance of sample mean based on SRS. For both symmetric and asymmetric populations, the mean and median estimators based on SRS, partial RSS (PRSS) and MxRSS schemes are compared. It turns out that the mean and median estimates under MxRSS scheme are more precise than those based on SRS scheme. Moreover, when estimating the mean of symmetric and some asymmetric populations, the mean estimates under MxRSS scheme are found to be more efficient than the mean estimates with PRSS scheme. An application to real data is also provided to illustrate the implementation of the proposed sampling scheme.     

Improved best linear unbiased estimators for the simple linear regression model using double ranked set sampling schemes

ABSTRACT

In this paper, we consider the best linear unbiased estimators (BLUEs) based on double ranked set sampling (DRSS) and ordered DRSS (ODRSS) schemes for the simple linear regression model with replicated observations. We assume three symmetric distributions for the random error term, i.e., normal, Laplace and some scale contaminated normal distributions. The proposed BLUEs under DRSS (BLUEs-DRSS) and ODRSS (BLUEs-ODRSS) are compared with the BLUEs based on ordered simple random sampling (OSRS), ranked set sampling (RSS), and ordered RSS (ORSS) schemes. These estimators are compared in terms of relative efficiency (RE), RE of determinant (RED), and RE of trace (RET). It is found that the BLUEs-ODRSS are uniformly better than the BLUEs based on OSRS, RSS, ORSS, and DRSS schemes. We also compare the estimators based on imperfect RSS (IRSS) schemes. It is worth mentioning here that the BLUEs under ordered imperfect DRSS (OIDRSS) are better than their counterparts based on IRSS, ordered IRSS (OIRSS), and imperfect DRSS (IDRSS) methods. Moreover, for sensitivity analysis of the BLUEs, we calculate REs and REDs of the BLUEs under the assumption of normality when in fact the parent distribution follows a non normal symmetric distribution. It turns out that even under violation of normality assumptions, BLUEs of the intercept and the slope parameters are found to be unbiased with equal REs under each sampling scheme. It is also observed that the BLUEs under ODRSS are more efficient than the existing BLUEs.     

Assessing Schools’ Academic Performance Using a Belief Structure

A lot of attention has been given to education as it is the most practical way of improving social mobility. In order to be progressive and resilient, there has to be an overall transformation of society as evidenced in the annual budget allocations which has educational implications to it. Therefore, schools will come to term that they are under the watching eyes of interested parties who want them to perform academically. Thus, this will lead the stakeholders of education to make assessment and comparison between schools. However, the process of assessing schools is not an easy task as it involves many attributes. The problem of aggregating a large number of attributes will be a challenge if we use the traditional Multi Criteria Decision Making (MCDM) method. Hence, in this paper an innovative approach called Evidential Reasoning (ER) will be used to assess school performance in a multilevel or hierarchical setting which involved indirect measurement of quality by using standardized examination results, rather than directly measuring the quality of the processes unfolding within the schools. The approach is different from most conventional decision making modeling methods in that it employs a belief structure to represent an assessment as a distribution. By using this method, we are able to assess and rank the schools involved by giving an appropriate weight and merit point for each attribute and grade, respectively. For easy comprehension of the method, a numerical example was given on how to utilize the proposed method in order to obtain a single performance figure for school ranking purposes. Finally, by comparing the school ranking reveals that there is little similarity with the normal practice currently adopted.     

How does paid work affect who does the childcare? An analysis of the time use of Australian couples

This paper analyzes the effect of paid work by coupled parents of young children on the joint decisions to spend time engaged in childcare. We explore this using Australian Time-Use Survey data from 2006. We examine the effect of paid work in terms of the effect that total work time on a given day has on the amount of time spent on childcare; the allocation of time on activities across work and non-work days; and the effect of non-traditional work hours. The results show that mothers perform a large share of childcare, irrespective of their earning power or their partner’s availability to take on some of these tasks. The use of formal and informal childcare by others allows the mother to balance the competing demands of work and her own childcare; an effect that does not hold for fathers. These effects on childcare are also almost solely concentrated in the routine component of childcare (e.g. preparing meals, changing nappies), with each parent ‘protecting’ interactive childcare from the effect of both paid work and the relative availability of their partner to take on some of this childcare.     

Estimation of the distribution function under hybrid ranked set sampling

Recently, a hybrid ranked set sampling (HRSS) scheme has been proposed in the literature. The HRSS scheme encompasses several existing ranked set sampling (RSS) schemes, and it is a cost-effective alternative to the classical RSS and double RSS schemes. In this paper, we propose an improved estimator for estimating the cumulative distribution function (CDF) using HRSS. It is shown, both theoretically and numerically, that the CDF estimator under HRSS scheme is unbiased and its variance is always less than the variance of the CDF estimator with simple random sampling (SRS). An unbiased estimator of the variance of CDF estimator using HRSS is also derived. Using Monte Carlo simulations, we also study the performances of the proposed and existing CDF estimators under both perfect and imperfect rankings. It turns out that the proposed CDF estimator is by far a superior alternative to the existing CDF estimators with SRS, RSS and L-RSS schemes. For a practical application, a real data set is considered on the bilirubin level of babies in neonatal intensive care.     

NeoLiberalism and the Challenges of Managing Labour Migration in Urban Malaysia

Labour migration into Malaysia has increased rapidly in recent decades and this has affected Malaysia's government policy in managing migrants’ movement. Interestingly, Malaysia has attracted a high degree of unskilled labour, accompanied by unabated rise of undocumented migrant workers. Mitigating undocumented migration is the main aim of Malaysia's labour migration policy and therefore the focus of Malaysian government. This has impacted on how enforcement agencies work out strategies. These agencies are the forefront of Malaysia's labour migration policy but they faced a number of challenges, such as documentation, finance and manpower capability, and political intervention, which impede their ability to optimize their capabilities in enforcing the Malaysian government labour migration policy. Resolving these challenges and moving towards a long‐term labour migration policy will benefit the Malaysian state, its citizens and the labour migrants.