Random effects regression mixtures for analyzing infant habituation

Random effects regression mixture models are a way to classify longitudinal data (or trajectories) having possibly varying lengths. The mixture structure of the traditional random effects regression mixture model arises through the distribution of the random regression coefficients, which is assumed to be a mixture of multivariate normals. An extension of this standard model is presented that accounts for various levels of heterogeneity among the trajectories, depending on their assumed error structure. A standard likelihood ratio test is presented for testing this error structure assumption. Full details of an expectation-conditional maximization algorithm for maximum likelihood estimation are also presented. This model is used to analyze data from an infant habituation experiment, where it is desirable to assess whether infants comprise different populations in terms of their habituation time.     

Small Town in Mass Society Revisited1

Abstract The case study of a small New York town that dramatized the thesis that the secular expansion of macro forces—urbanization, industrialization, bureaucratization—has permanently reduced the autonomy of all small communities is an example of a special type of discovery/persuasion strategy in the social sciences: the “opposition case study.” In contrast to the more rigorous “competitive test” or the atheoretical “negative case,” opposition case studies confront the dominant perspective with a qualitative illustration of a new theory in the context of a zero-sum game. When they are successful, opposition cases meet four criteria: the dominant view is immediately rendered obsolete; the origin of the new idea supports its plausibility; the new perspective is shown to be testable; and the new perspective quickly generates new lines of research. Small Town in Mass Society meets the first criterion, and may have been heuristic, but its probable origin in populist ideology undermines its testability.     

Bayesian model selection and parameter estimation for possibly asymmetric and non-stationary time series using a reversible jump Markov chain Monte Carlo approach

A Markov chain Monte Carlo (MCMC) approach, called a reversible jump MCMC, is employed in model selection and parameter estimation for possibly non-stationary and non-linear time series data. The non-linear structure is modelled by the asymmetric momentum threshold autoregressive process (MTAR) of Enders & Granger (1998) or by the asymmetric self-exciting threshold autoregressive process (SETAR) of Tong (1990). The non-stationary and non-linear feature is represented by the MTAR (or SETAR) model in which one ( 𝜌 1 ) of the AR coefficients is greater than one, and the other ( 𝜌 2 ) is smaller than one. The other non-stationary and linear, stationary and nonlinear, and stationary and linear features, represented respectively by ( 𝜌 1 = 𝜌 2 = 1 ), ( 𝜌 1 p 𝜌 2 < 1 ) and ( 𝜌 1 = 𝜌 2 < 1 ), are also considered as possible models. The reversible jump MCMC provides estimates of posterior probabilities for these four different models as well as estimates of the AR coefficients 𝜌 1 and 𝜌 2 . The proposed method is illustrated by analysing six series of US interest rates in terms of model selection, parameter estimation, and forecasting.     

The Impact of Question and Respondent Characteristics on Comprehension and Mapping Difficulties

Behavior coding is one technique researchers use to detect problemsin survey questions, but it has been primarily explored as apractical tool rather than a source of insight into the theoreticalunderstanding of the cognitive processes by which respondentsanswer survey questions. The latter is the focus of the currentinvestigation. Using data from a large study in which face-to-faceinterviews were taped and extensive behavior coding was done,we tested whether sets of respondent behavior codes could beused to distinguish respondent difficulties with comprehensionof the question from difficulties associated with mapping ajudgment onto the response format provided, and whether characteristicsof the survey questions and respondents could be used to predictwhen and for whom such difficulties would occur. Sets of behaviorcodes were identified that reflected comprehension and mappingdifficulties, and these two types of difficulties were associatedwith different question and respondent characteristics. Thisevidence suggests that behavior coding shows promise as a toolfor researchers studying the cognitive processes involved inanswering survey questions.     

Goal Ambiguity and Organizational Performance in U.S. Federal Agencies

In spite of numerous observations that government organizationshave high levels of organizational goal ambiguity that exertmajor influences on their other characteristics, few researchershave measured goal ambiguity and tested these frequent assertions.In previous research, we developed measures of four dimensionsof goal ambiguity: mission comprehension ambiguity, directivegoal ambiguity, evaluative goal ambiguity, and priority goalambiguity. Confirming hypotheses developed from the literatureon public organizations, the latter three variables showed relationsto such organizational characteristics as organizational age,financial publicness (proportion of funding from governmentallocations), and regulatory status. This article reports asecond analytical step of examining the relations between thegoal ambiguity dimensions and indicators of organizational performancebased on responses to the 2000 National Partnership for ReinventingGovernment Survey of federal employees. The performance variablesincluded managerial effectiveness, customer service orientation,productivity, and work quality. Regression analyses with numerouscontrol variables found that directive, evaluative, and prioritygoal ambiguity related negatively to managerial effectiveness.All four performance indicators showed significant negativerelationships with evaluative goal ambiguity and directive goalambiguity. The results provide further evidence of the viabilityof the new measures of goal ambiguity, support theory-basedbut previously untested hypotheses, and further indicate thefeasibility and value of analyzing goal ambiguity of governmentorganizations.     

Estimation of global temperature fields from scattered observations by a spherical-wavelet-based spatially adaptive method

Summary.  The paper considers the problem of estimating the entire temperature field for every location on the globe from scattered surface air temperatures observed by a network of weather-stations. Classical methods such as spherical harmonics and spherical smoothing splines are not efficient in representing data that have inherent multiscale structures. The paper presents an estimation method that can adapt to the multiscale characteristics of the data. The method is based on a spherical wavelet approach that has recently been developed for a multiscale representation and analysis of scattered data. Spatially adaptive estimators are obtained by coupling the spherical wavelets with different thresholding (selective reconstruction) techniques. These estimators are compared for their spatial adaptability and extrapolation performance by using the surface air temperature data.     

Native American drinking: a neglected subject of study and research

Although Native Americans are plagued by high rates of alcoholism, violence, suicide and early death, these social and clinical problems are usually given little more than cursory treatment in textbooks. A content analysis of twenty-six textbooks on alcoholism and substance misuse revealed that only four provided a detailed discussion of Native American drinking. Greater attention needs to be given to the cultural, psychological, and biological issues of drinking and drunkenness among this special population.