The Best and Worst Times of Life: Narratives and Assessments of Subjective Well-Being by Anamnestic Comparative Self Assessment (ACSA) in the Eastern Cape,South Africa

The Anamnestic Comparative Self Assessment (ACSA) measure of subjective well-being (SWB) aims to reduce the problems of cultural bias and relativity to external standards by allowing people to define the endpoints or ‘anchors’ of the measurement scale. In medical terminology anamnestic denotes ‘based on memory’. The ACSA uses subjects’ memories of the best and worst periods in their lives to define the anchors of the scale. They then assess their current quality of life relative to these personal anchors. The South African pilot study tested the match between self-assessment of SWB with ACSA and the conventional single-item measures of life satisfaction and happiness used in the South African Quality of Life Trends Study and analysed the narratives of the best and worst times of life. The quota sample of 46 consisted of 26 residents of Makana district in the Eastern Cape Province, South Africa, and 20 patients undergoing treatment in the local TB hospital. Mean SWB ratings with all three measures of life satisfaction, happiness and ACSA were between 5 and 6 on a 0–10-point scale. Ratings on all three scales were positively correlated. However, on ACSA the TB patients rated their current SWB 1.84 points lower than the community respondents, suggesting a greater sensitivity of this measure. It was observed that the starting points of the life stories produced by respondents to define the anchor periods for ACSA were related to their current assessment of SWB. A typology was developed that combined the starting point of the life stories with current SWB. The majority of community respondents matched the ‘Achiever’ type who scored positively on ACSA (i.e., above the mid-point of the scale) and whose life stories started with the worst period of their lives and proceeded to the best period. The TB patients were the only respondents to represent the ‘Survivor’ type whose morale had recovered after misfortune in life. ‘Survivors’ started their narratives with the best period in their lives, then moved to the worst (often health-related) one, and gave positive ACSA ratings. Based on the qualitative analysis of narratives, it is concluded that ACSA is a sensitive measurement instrument and therefore particularly useful for monitoring the effects of treatments and social interventions in longitudinal studies. However, further research is required to verify its cross-cultural validity.

The Perceptions of Women’s Roles and Progress: A Study of Malay Women

This study examines the general perceptions of women towards their roles, their interpretation of progress, as well as the facilitating factors and barriers to their progress. Questionnaires were distributed to 1,000 Malay women in Malaysia from rural and urban areas, from various age and income groups. Interviews were also carried out on the selected sample. Using both quantitative and qualitative methods of analyses, the results showed that women were perceived to play a strong, influential, and supportive role in their families. The results also showed how women perceive progress. These findings are discussed in relation to culture, socialization, values and norms of the Malay society.     

A semi-parametric approach to robust parameter design

Parameter design or robust parameter design (RPD) is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes. The goal of parameter design is to choose the levels of the control variables that optimize a defined quality characteristic. An essential component of RPD involves the assumption of well estimated models for the process mean and variance. Traditionally, the modeling of the mean and variance has been done parametrically. It is often the case, particularly when modeling the variance, that nonparametric techniques are more appropriate due to the nature of the curvature in the underlying function. Most response surface experiments involve sparse data. In sparse data situations with unusual curvature in the underlying function, nonparametric techniques often result in estimates with problematic variation whereas their parametric counterparts may result in estimates with problematic bias. We propose the use of semi-parametric modeling within the robust design setting, combining parametric and nonparametric functions to improve the quality of both mean and variance model estimation. The proposed method will be illustrated with an example and simulations.     

Adaptive tests of regression functions via multiscale generalized likelihood ratios

Many applications of nonparametric tests based on curve estimation involve selecting a smoothing parameter. The author proposes an adaptive test that combines several generalized likelihood ratio tests in order to get power performance nearly equal to whichever of the component tests is best. She derives the asymptotic joint distribution of the component tests and that of the proposed test under the null hypothesis. She also develops a simple method of selecting the smoothing parameters for the proposed test and presents two approximate methods for obtaining its P‐value. Finally, she evaluates the proposed test through simulations and illustrates its application to a set of real data.     

Empirical Non-Parametric Control Charts: Estimation Effects and Corrections

Owing to the extreme quantiles involved, standard control charts are very sensitive to the effects of parameter estimation and non-normality. More general parametric charts have been devised to deal with the latter complication and corrections have been derived to compensate for the estimation step, both under normal and parametric models. The resulting procedures offer a satisfactory solution over a broad range of underlying distributions. However, situations do occur where even such a large model is inadequate and nothing remains but to consider non- parametric charts. In principle, these form ideal solutions, but the problem is that huge sample sizes are required for the estimation step. Otherwise the resulting stochastic error is so large that the chart is very unstable, a disadvantage that seems to outweigh the advantage of avoiding the model error from the parametric case. Here we analyse under what conditions non-parametric charts actually become feasible alternatives for their parametric counterparts. In particular, corrected versions are suggested for which a possible change point is reached at sample sizes that are markedly less huge (but still larger than the customary range). These corrections serve to control the behaviour during in-control (markedly wrong outcomes of the estimates only occur sufficiently rarely). The price for this protection will clearly be some loss of detection power during out-of-control. A change point comes in view as soon as this loss can be made sufficiently small.     

Markov chain Monte Carlo methods for high dimensional inversion in remote sensing

Summary.  We discuss the inversion of the gas profiles (ozone, NO₃, NO₂, aerosols and neutral density) in the upper atmosphere from the spectral occultation measurements. The data are produced by the 'Global ozone monitoring of occultation of stars' instrument on board the Envisat satellite that was launched in March 2002. The instrument measures the attenuation of light spectra at various horizontal paths from about 100 km down to 10–20 km. The new feature is that these data allow the inversion of the gas concentration height profiles. A short introduction is given to the present operational data management procedure with examples of the first real data inversion. Several solution options for a more comprehensive statistical inversion are presented. A direct inversion leads to a non-linear model with hundreds of parameters to be estimated. The problem is solved with an adaptive single-step Markov chain Monte Carlo algorithm. Another approach is to divide the problem into several non-linear smaller dimensional problems, to run parallel adaptive Markov chain Monte Carlo chains for them and to solve the gas profiles in repetitive linear steps. The effect of grid size is discussed, and we present how the prior regularization takes the grid size into account in a way that effectively leads to a grid-independent inversion.     

Interactionism,Social Organization,and Social Processes: Looking Back and Moving Ahead

With its roots in American pragmatism, symbolic interactionism has created a distinctive perspective and produced numerous important contributions and now offers significant prospects for the future. In this article, I review my intellectual journey with this perspective over forty years. This journey was initiated within the American society, sociology, and symbolic interaction of circa 1960. I note many of the contributions made by interactionists since that time, with particular focus on those who have contributed to the study of social organization and social process. I offer an agenda for the future based on currently underdeveloped areas that have potential. These are inequality orders, institutional analysis, collective action across space and time, and the integration of temporal and spatial orders. The article concludes with calls for further efforts at cross‐perspective dialogues, more attention to feminist scholars, and an elaborated critical pragmatism.     

Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances

We study the properties of the quasi-maximum likelihood estimator (QMLE) and related test statistics in dynamic models that jointly parameterize conditional means and conditional covariances, when a normal log-likelihood os maximized but the assumption of normality is violated. Because the score of the normal log-likelihood has the martingale difference property when the forst two conditional moments are correctly specified, the QMLE is generally Consistent and has a limiting normal destribution. We provide easily computable formulas for asymptotic standard errors that are valid under nonnormality. Further, we show how robust LM tests for the adequacy of the jointly parameterized mean and variance can be computed from simple auxiliary regressions. An appealing feature of these robyst inference procedures is that only first derivatives of the conditional mean and variance functions are needed. A monte Carlo study indicates that the asymptotic results carry over to finite samples. Estimation of several AR and AR-GARCH time series models reveals that in most sotuations the robust test statistics compare favorably to the two standard (nonrobust) formulations of the Wald and IM tests. Also, for the GARCH models and the sample sizes analyzed here, the bias in the QMLE appears to be relatively small. An empirical application to stock return volatility illustrates the potential imprtance of computing robust statistics in practice.