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.     

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.     

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.     

Analytical approximations for iterated bootstrap confidence intervals

Standard algorithms for the construction of iterated bootstrap confidence intervals are computationally very demanding, requiring nested levels of bootstrap resampling. We propose an alternative approach to constructing double bootstrap confidence intervals that involves replacing the inner level of resampling by an analytical approximation. This approximation is based on saddlepoint methods and a tail probability approximation of DiCiccio and Martin (1991). Our technique significantly reduces the computational expense of iterated bootstrap calculations. A formal algorithm for the construction of our approximate iterated bootstrap confidence intervals is presented, and some crucial practical issues arising in its implementation are discussed. Our procedure is illustrated in the case of constructing confidence intervals for ratios of means using both real and simulated data. We repeat an experiment of Schenker (1985) involving the construction of bootstrap confidence intervals for a variance and demonstrate that our technique makes feasible the construction of accurate bootstrap confidence intervals in that context. Finally, we investigate the use of our technique in a more complex setting, that of constructing confidence intervals for a correlation coefficient.