A ‘nondecimated’ lifting transform

Classical nondecimated wavelet transforms are attractive for many applications. When the data comes from complex or irregular designs, the use of second generation wavelets in nonparametric regression has proved superior to that of classical wavelets. However, the construction of a nondecimated second generation wavelet transform is not obvious. In this paper we propose a new ‘nondecimated’ lifting transform, based on the lifting algorithm which removes one coefficient at a time, and explore its behavior. Our approach also allows for embedding adaptivity in the transform, i.e. wavelet functions can be constructed such that their smoothness adjusts to the local properties of the signal. We address the problem of nonparametric regression and propose an (averaged) estimator obtained by using our nondecimated lifting technique teamed with empirical Bayes shrinkage. Simulations show that our proposed method has higher performance than competing techniques able to work on irregular data. Our construction also opens avenues for generating a ‘best’ representation, which we shall explore.     

A new alternative to the standard F test for clustered data

The data collection process and the inherent population structure are the main causes for clustered data. The observations in a given cluster are correlated, and the magnitude of such correlation is often measured by the intra-cluster correlation coefficient. The intra-cluster correlation can lead to an inflated size of the standard F test in a linear model. In this paper, we propose a solution to this problem. Unlike previous adjustments, our method does not require estimation of the intra-class correlation, which is problematic especially when the number of clusters is small. Our simulation results show that the new method outperforms the existing methods.     

A re-evaluation of random-effects meta-analysis

Summary.  Meta-analysis in the presence of unexplained heterogeneity is frequently undertaken by using a random-effects model, in which the effects underlying different studies are assumed to be drawn from a normal distribution. Here we discuss the justification and interpretation of such models, by addressing in turn the aims of estimation, prediction and hypothesis testing. A particular issue that we consider is the distinction between inference on the mean of the random-effects distribution and inference on the whole distribution. We suggest that random-effects meta-analyses as currently conducted often fail to provide the key results, and we investigate the extent to which distribution-free, classical and Bayesian approaches can provide satisfactory methods. We conclude that the Bayesian approach has the advantage of naturally allowing for full uncertainty, especially for prediction. However, it is not without problems, including computational intensity and sensitivity to a priori judgements. We propose a simple prediction interval for classical meta-analysis and offer extensions to standard practice of Bayesian meta-analysis, making use of an example of studies of 'set shifting' ability in people with eating disorders.     

Women–Family in Quality Perspective

Bringing together women and family in quality perspective bring about interesting discussions in this paper. By integrating previous studies and considering expert opinions, we determinate the variables and dimensions with respect to women’s existence regarding their roles both in the family and at work. Many activities carried out by women represent the consumer aspects of their role. Women undertake these activities to fulfill their needs, which can be classified in the order of preference using Maslow’s Hierarchy of Need. Women success can be measured based on their ability to perform their roles successfully. We identify women’s performance by using quality approach of Personal Quality Maintenance (PQM), which is widely applied in many organizations in order to maintain the service delivery, which meets the customer satisfaction. The effort to enhance women’s satisfaction for their success in playing the multirole in the family and at work is our main consideration. This study may contribute a new point of view regarding for the women’s welfare and existence.     

FOR MONEY OR GLORY? COMMERCIALIZATION,COMPETITION, AND SECRECY IN THE ENTREPRENEURIAL UNIVERSITY

Scholars have grown concerned that the commercialization of academic science is increasing secrecy at the expense of cooperation and information sharing. Using data from comparable surveys of academic scientists in three fields (experimental biology, mathematics, and physics), we test whether scientists have become more competitive and more secretive over the last 30 years. We also use the recent survey to test a multivariate model of the effects of scientific competition and commercialization (patenting, industry funding, and industry collaboration) on scientific secrecy. We find that secrecy has increased, and has increased particularly for experimental biologists. Only 13 percent of experimental biologists in 1998 felt safe discussing their ongoing research with all others doing similar work. Our multivariate analysis shows that this secrecy is most related to concerns about being anticipated (scientific competition). We find that patenting is associated with increased secrecy among mathematicians and physicists, but not for experimental biologists. We find that industry funding is associated with more secrecy, while industry collaboration is associated with less secrecy, across fields. Our results suggest that the recent concern over increasing scientific secrecy has merit. However, this increased secrecy seems to result from a combination of increasing commercial linkages and increased pressures from scientific competition. Our research highlights the central role that scientists' competition for priority plays in the system of science and that, while such competition spurs effort, it also produces negative effects that recent trends toward commercialization of academic science seem to be exacerbating.     

Acculturation and Enculturation Trajectories Among Mexican‐American Adolescent Offenders

This study examines changes over time in ethnic affirmation/belonging and ethnic identity achievement, Spanish language use, English language use, Mexican/Mexican‐American affiliation/identification and Anglo affiliation/identification in a sample of Mexican‐American adolescents participating in a longitudinal study of juvenile offenders. The Multigroup Ethnic Identity Measure and the Acculturation Rating Scale for Mexican Americans‐II were completed by the Mexican‐American adolescents 7 times over a 3‐year period. The findings from longitudinal growth modeling analyses and growth mixture modeling analyses indicate that there is heterogeneity in the initial scores and changes over time on these variables that are related to markers for the cultural qualities of the home environment (i.e., generational status and mother's most frequent language use). In contrast to expectations, marginalized or assimilated acculturation trajectories/types were not overrepresented in this sample of adolescent offenders. Implications for our understanding of the nature of acculturation and enculturation processes and the way these processes are studied are discussed.     

Making Implementation More Democratic through Action Implementation Research

Public administration scholars often associate increased publicparticipation in policy implementation with the advancementof democratic values. Likewise, scholars engaged in the variousforms of action research (AR) promote their efforts as a meansto democratizing the research process or contributing to improvedsocial outcomes. Despite these common interests in participationand democracy, there have been few attempts to apply AR methodsto policy implementation problems. Drawing on Matland's conflict-ambiguitymatrix, we develop a framework for matching particular AR methodsto specific policy implementation contexts.     

Comparing the U.S. Decennial Census Coverage Estimates for Children from Demographic Analysis and Coverage Measurement Surveys

Following every U.S. decennial census since 1960, the U.S. Census Bureau has evaluated the completeness of coverage using two different methods. Demographic analysis (DA) compares the census counts to a set of independent population estimates to infer coverage differences by age, sex, and race. The survey-based approach (also called dual system estimation or DSE) provides coverage estimates based on matching data from a post-enumeration survey to census records. This paper reviews the fundamentals of the two methodological approaches and then initially examines the results of these two methods for the 2010 decennial census in terms of consistency and inconsistency for age groups. The authors find that the two methods produce relatively consistent results for all age groups, except for young children. Consequently, the paper focuses on the results for children. Results of the 1990, 2000, and 2010 decennial censuses are shown for the overall population in this age group and by demographic detail (age, race, and Hispanic origin). Among children, the DA and DSE results are most inconsistent for the population aged 0–4 and most consistent for ages 10–17. Results also show that DA and DSE are more consistent for Black than non-Black populations. The authors discuss possible explanations for the differences in the two methods for young children and conclude that the DSE approach may underestimate the net undercount of young children due to correlation bias.