Modeling population and subject-specific growth in a latent trait measured by multiple instruments over time using a hierarchical Bayesian framework

Psychometric growth curve modeling techniques are used to describe a person’s latent ability and how that ability changes over time based on a specific measurement instrument. However, the same instrument cannot always be used over a period of time to measure that latent ability. This is often the case when measuring traits longitudinally in children. Reasons may be that over time some measurement tools that were difficult for young children become too easy as they age resulting in floor effects or ceiling effects or both. We propose a Bayesian hierarchical model for such a scenario. Within the Bayesian model we combine information from multiple instruments used at different age ranges and having different scoring schemes to examine growth in latent ability over time. The model includes between-subject variance and within-subject variance and does not require linking item specific difficulty between the measurement tools. The model’s utility is demonstrated on a study of language ability in children from ages one to ten who are hard of hearing where measurement tool specific growth and subject-specific growth are shown in addition to a group level latent growth curve comparing the hard of hearing children to children with normal hearing.KEYWORDS: Bayesian hierarchical models, psychometric modeling, language ability, growth curve modeling, longitudinal analysis     

Unmet Need for Family Planning in Developing Countries and Implications for Population Policy

Unmet need for family planning has been a core concept in international population discourse for several decades. This article reviews the history of unmet need and the development of increasingly refined methods of its empirical measurement and delineates the main questions that have been raised about unmet need during the past decade, some of which concern the validity of the concept and others its role in policy debates. The discussion draws heavily on empirical research conducted during the 1990s, much of it localized, in‐depth studies combining quantitative and qualitative methodologies. Of the causes of unmet need other than those related to access to services, three emerge as especially salient: lack of necessary knowledge about contraceptive methods, social opposition to their use, and health concerns about possible side effects. The article argues that the concept of unmet need for family planning, by joining together contraceptive behavior and fertility preferences, encourages an integration of family planning programs and broader development approaches to population policy. By focusing on the fulfillment of individual aspirations, unmet need remains a defensible rationale for the formulation of population policy and a sensible guide to the design of family planning programs.     

Women and Poverty in Spain (1981--1991)

Social Indicators Research -     

Assessing attitude toward same-sex marriage: scale development and validation

This paper reports the results of three studies conducted to develop, refine, and validate a scale which assessed heterosexual adults' attitudes toward same-sex marriage, the Attitude Toward Same-Sex Marriage Scale (ASSMS). The need for such a scale is evidenced in the increasing importance of same-sex marriage in the political arena of the United States and other nations, as well as the growing body of empirical research examining same-sex marriage and related issues (e.g., Lannutti, 2005; Solomon, Rothblum, & Balsam, 2004). The results demonstrate strong reliability, convergent validity, and predictive validity for the ASSMS and suggest that the ASSMS may be adapted to measure attitudes toward civil unions and other forms of relational recognition for same-sex couples. Gender comparisons using the validated scale showed that in college and non-college samples, women had a significantly more positive attitude toward same-sex marriage than did men.     

Understanding changes in Māori incomes and income inequality 1997–2003

This paper examines recent changes in weekly income levels and dispersion for Māori, New Zealand’s indigenous ethnic group. Changes in the Māori income distribution between 1997 and 2003 reflect rapid increases in economic growth and employment rate. A reduced proportion of people had zero or benefit-level incomes and a higher proportion had high incomes. Income inequality declined for working-aged Māori and was stable for employed Māori. The average income gap between Māori and Europeans declined. The increased Māori employment rate during this period was the single most important driver of changes in the Māori income distribution.

Emotional Regulation and Display in Classroom Victims of Bullying: Characteristic Expressions of Affect, Coping Styles and Relevant Contextual Factors

Research suggests that victims of bullying may lack skills in emotional regulation, a process which facilitates coping with provocative situations to lessen the stress of negative emotions (Cicchetti, Ackerman, & Izard, 1995). The present study examined the emotional regulation and display patterns of victims during classroom bullying episodes. Children in grades one through six were observed during free play in the winter and spring of three consecutive school years. Results of the study indicated that the coping styles observed in victims of bullying can be grouped into two distinct clusters: 1) problem-solving strategies that are associated with the de-escalation and resolution of bullying episodes; and 2) aggressive strategies that tend to perpetuate and escalate the bullying interaction. Parallels were found between victims' and bullies' emotional displays. Results are discussed in the context of how maladaptive emotional regulation processes may act as risk factors for chronic victimization.     

On the minimum expected quantity for the validity of the chi-squared test in 2 2 2 tables

A 2 2 2 contingency table can often be analysed in an exact fashion by using Fisher's exact test and in an approximate fashion by using the chi-squared test with Yates' continuity correction, and it is traditionally held that the approximation is valid when the minimum expected quantity E is E S 5. Unfortunately, little research has been carried out into this belief, other than that it is necessary to establish a bound E>E*, that the condition E S 5 may not be the most appropriate (Martín Andrés et al., 1992) and that E* is not a constant, but usually increasing with the growth of the sample size (Martín Andrés & Herranz Tejedor, 1997). In this paper, the authors conduct a theoretical experimental study from which they ascertain that E* value (which is very variable and frequently quite a lot greater than 5) is strongly related to the magnitude of the skewness of the underlying hypergeometric distribution, and that bounding the skewness is equivalent to bounding E (which is the best control procedure). The study enables estimating the expression for the above-mentioned E* (which in turn depends on the number of tails in the test, the alpha error used, the total sample size, and the minimum marginal imbalance) to be estimated. Also the authors show that E* increases generally with the sample size and with the marginal imbalance, although it does reach a maximum. Some general and very conservative validity conditions are E S 35.53 (one-tailed test) and E S 7.45 (two-tailed test) for alpha nominal errors in 1% h f h 10%. The traditional condition E S 5 is only valid when the samples are small and one of the marginals is very balanced; alternatively, the condition E S 5.5 is valid for small samples or a very balanced marginal. Finally, it is proved that the chi-squared test is always valid in tables where both marginals are balanced, and that the maximum skewness permitted is related to the maximum value of the bound E*, to its value for tables with at least one balanced marginal and to the minimum value that those marginals must have (in non-balanced tables) for the chi-squared test to be valid.     

Designing and integrating composite networks for monitoring multivariate gaussian pollution fields

Networks of ambient monitoring stations are used to monitor environmental pollution fields such as those for acid rain and air pollution. Such stations provide regular measurements of pollutant concentrations. The networks are established for a variety of purposes at various times so often several stations measuring different subsets of pollutant concentrations can be found in compact geographical regions. The problem of statistically combining these disparate information sources into a single 'network' then arises. Capitalizing on the efficiencies so achieved can then lead to the secondary problem of extending this network. The subject of this paper is a set of 31 air pollution monitoring stations in southern Ontario. Each of these regularly measures a particular subset of ionic sulphate, sulphite, nitrite and ozone. However, this subset varies from station to station. For example only two stations measure all four. Some measure just one. We describe a Bayesian framework for integrating the measurements of these stations to yield a spatial predictive distribution for unmonitored sites and unmeasured concentrations at existing stations. Furthermore we show how this network can be extended by using an entropy maximization criterion. The methods assume that the multivariate response field being measured has a joint Gaussian distribution conditional on its mean and covariance function. A conjugate prior is used for these parameters, some of its hyperparameters being fitted empirically.