The effect of non-normality on estimating the variance of the combined ratio estimate in complex surveys

The performance of the balanced half-sample, jackknife and linearization methods for estimating the variance of the combined ratio estimate is studied by means of a computer simulation using artificially generated non-normally distributed populations.

The results of this investigation demonstrate that the variance estimates for the combined ratio estimate may be highly biased and unstable when the underlying distributions are non-normal. This is particularly true when the number of observations available from each stratum is small. The jack-     

Reciprocal Family,Friendship and Church Support Networks of African Americans: Findings from the National Survey of American Life

This study examined reciprocal support networks involving extended family, friends and church members among African Americans. Our analysis examined specific patterns of reciprocal support (i.e., received only, gave only, both gave and received, neither gave or received), as well as network characteristics (i.e., contact and subjective closeness) as correlates of reciprocal support. The analysis is based on the African American subsample of the National Survey of American Life. Overall, our findings indicate that African Americans are very involved in reciprocal support networks with their extended family, friends and church members. Respondents were most extensively involved in reciprocal supports with extended family members, followed closely by friends and church networks. Network characteristics (i.e., contact and subjective closeness) were significantly and consistently associated with involvement with reciprocal support exchanges for all three networks. These and other findings are discussed in detail. This study complements previous work on the complementary roles of family, friend and congregational support networks, as well as studies of racial differences in informal support networks.     

The Pursuit of Productivity

This study names a pantheon of entrepreneurs and managers who have introduced a range of far‐reaching productivity innovations throughout modern history. The thread tying together all of the innovations, in whatever sector of the economy one examines, is the theory of swift, even flow. The study argues why swift, even flow explains the power and long‐lasting nature of these innovations and why other factors thought by some to affect productivity fall short.     

Relationships among JIT practices: an interpretive modeling approach

Abstract

This research develops a model of relationships among components of Total-JIT, including JIT-information, JIT-manufacturing, JIT-purchasing, and JIT-selling, to establish an implementation hierarchy based on relative importance. The data collected relates to the relationships among JIT components and two performance measures, supply chain competency and organizational performance. Two groups are used in the research, one group of five operations management academics and another group of 30 practicing operations managers working in U.S. manufacturing firms. An interpretive structural modelling methodology is used to develop alternative structural models. The academics’ data show JIT-information emerging as lynchpin of relationships, directly impacting all other JIT practices and both performance measures. The practitioners’ data indicates that all JIT practices and performance measures are interactive as components and outcomes. This study is the first to apply interpretive structural modelling to investigate the interplay among total-JIT components and the performance measures of supply chain competency and organizational performance.     

A Comparison of Some Methods for the Selection of a Common Eigenvector Model for the Covariance Matrices of Two Groups

Two new nonparametric common principal component model selection procedures based on bootstrap distributions of the vector correlations of all combinations of the eigenvectors from two groups are proposed. The performance of these methods is compared in a simulation study to the two parametric methods previously suggested by Flury in 1988, as well as modified versions of two nonparametric methods proposed by Klingenberg in 1996 and then by Klingenberg and McIntyre in 1998. The proposed bootstrap vector correlation distribution (BVD) method is shown to outperform all of the existing methods in most of the simulated situations considered.     

Testing Hypotheses in Nonparametric Models of Production

Data envelopment analysis (DEA) and free disposal hull (FDH) estimators are widely used to estimate efficiency of production. Practitioners use DEA estimators far more frequently than FDH estimators, implicitly assuming that production sets are convex. Moreover, use of the constant returns to scale (CRS) version of the DEA estimator requires an assumption of CRS. Although bootstrap methods have been developed for making inference about the efficiencies of individual units, until now no methods exist for making consistent inference about differences in mean efficiency across groups of producers or for testing hypotheses about model structure such as returns to scale or convexity of the production set. We use central limit theorem results from our previous work to develop additional theoretical results permitting consistent tests of model structure and provide Monte Carlo evidence on the performance of the tests in terms of size and power. In addition, the variable returns to scale version of the DEA estimator is proved to attain the faster convergence rate of the CRS-DEA estimator under CRS. Using a sample of U.S. commercial banks, we test and reject convexity of the production set, calling into question results from numerous banking studies that have imposed convexity assumptions. Supplementary materials for this article are available online.     

Power analysis for clustered non-continuous responses in multicenter trials

Power analysis for multi-center randomized control trials is quite difficult to perform for non-continuous responses when site differences are modeled by random effects using the generalized linear mixed-effects model (GLMM). First, it is not possible to construct power functions analytically, because of the extreme complexity of the sampling distribution of parameter estimates. Second, Monte Carlo (MC) simulation, a popular option for estimating power for complex models, does not work within the current context because of a lack of methods and software packages that would provide reliable estimates for fitting such GLMMs. For example, even statistical packages from software giants like SAS do not provide reliable estimates at the time of writing. Another major limitation of MC simulation is the lengthy running time, especially for complex models such as GLMM, especially when estimating power for multiple scenarios of interest. We present a new approach to address such limitations. The proposed approach defines a marginal model to approximate the GLMM and estimates power without relying on MC simulation. The approach is illustrated with both real and simulated data, with the simulation study demonstrating good performance of the method.     

Investigating impacts of complex sampling on latent growth curve modelling

We investigate the impacts of complex sampling on point and standard error estimates in latent growth curve modelling of survey data. Methodological issues are illustrated with empirical evidence from the analysis of longitudinal data on life satisfaction trajectories using data from the British Household Panel Survey, a national representative survey in Great Britain. A multi-process second-order latent growth curve model with conditional linear growth is used to study variation in the two perceived life satisfaction latent factors considered. The benefits of accounting for the complex survey design are considered, including obtaining unbiased both point and standard error estimates, and therefore correctly specified confidence intervals and statistical tests. We conclude that, even for the rather elaborated longitudinal data models that were considered, estimation procedures are affected by variance-inflating impacts of complex sampling.     

Sampling Telephone Numbers and Adults, Interview Length, and Weighting in The California Health Interview Survey Cell Phone Pilot Study

This article describes several features included in a CaliforniaHealth Interview Survey cell phone pilot study that differ fromearlier cell phone surveys conducted in the United States. Onedifference is that the study used a screening design and onlyadults living in cell-only households were interviewed. Mostof the previous studies used dual frame designs. Another differencewas the development and implementation of a within-householdadult sampling procedure to cover adults when cell phones wereshared in the household. The study was also intended to determineif conducting a cell phone interview of the same scope and lengthas a regular telephone interview was feasible. Most previoussurveys were focused on cell phone topics and were shorter thanthe comparable landline surveys. We then explore some interestingproblems that arise in weighting a cell phone survey using ascreening design. We conclude with a discussion of planninga new cell phone survey in 2007 that incorporates findings fromthis study.     

Protective and risk influences of drug use among a multiethnic sample of adolescent boys

A measurement model was analyzed to demonstrate a differential distribution and cumulative exposure to psychosocial risk and protective influences of adolescent drug use among ethnically-diverse adolescent samples. The sample included U.S.-born (US) Latino (N = 837), foreign-born (FB) Latino (N = 447), White (N = 632), and African American (N = 618) adolescent boys. Psychological Distress, Peer Drug Use, and Adolescent Drug Use were significantly positively correlated with Peer Drug Use and Adolescent Drug Use. In contrast, Family, School, law abidance, and guilt were significantly negatively correlated with Peer Drug Use and Adolescent Drug Use. Multiple group analyses indicated significant similarities and differences among US and FB Latinos, and Whites. Implications for early prevention, multi-level interventions, and suggestions for future research are discussed.