Ulf Böckenholt

Social capital and core network ties: A validation study of individual-level social capital measures and their association with extra- and intra-neighborhood ties, and self-rated health

Research on social capital and health has assumed that measures of trust, participation, and perceived cohesion capture aspects of peoples neighborhood social connections. This study uses data on the personal networks of 2707 Montreal adults in 300 different neighborhoods to examine the association of socio-demographic and social capital variables with the likelihood of having core ties, core neighborhood ties, and high self-rated health (SRH). Persons with higher household income were more likely to have core ties, but less likely to have core neighborhood ties. Persons with greater diversity in extra-neighborhood network capital were more likely to have core ties, and persons with greater diversity in intra-neighborhood network capital were more likely to have core neighborhood ties. Generalized trust, perceived neighborhood cohesion, and extra-neighborhood network diversity were shown associated with high SRH. Conventional measures of social capital may not capture network mechanisms. Findings suggest a critical appraisal of the mechanisms linking social capital and health, and the further delineation of network and psychosocial mechanisms in understanding these links.

Structural Equation Modeling of Paired-Comparison and Ranking Data.

L. L. Thurstones (1927) model provides a powerful framework for modeling individual differences in choice behavior. An overview of Thurstonian models for comparative data is provided, including the classical Case V and Case III models as well as more general choice models with unrestricted and factor-analytic covariance structures. A flow chart summarizes the model selection process. The authors show how to embed these models within a more familiar structural equation modeling (SEM) framework. The different special cases of Thurstones model can be estimated with a popular SEM statistical package, including factor analysis models for paired comparisons and rankings. Only minor modifications are needed to accommodate both types of data. As a result, complex models for comparative judgments can be both estimated and tested efficiently.

Modeling Methods for Discrete Choice Analysis

This paper introduces new forms, sampling and estimation approaches fordiscrete choice models. The new models include behavioral specifications oflatent class choice models, multinomial probit, hybrid logit, andnon-parametric methods. Recent contributions also include new specializedchoice based sample designs that permit greater efficiency in datacollection. Finally, the paper describes recent developments in the use ofsimulation methods for model estimation. These developments are designed toallow the applications of discrete choice models to a wider variety ofdiscrete choice problems.

Moderation of Preference Reversals in the Long Run

The evaluation of a monetary gamble requires consideration of two basic types of information: (a) the probabilities associated with winning and losing, and (b) the amounts to be won and lost. The well-documented phenomenon of preference reversals provides strong evidence that the processing of this information depends on how the evaluation is expressed (Goldstein & Einhorn, 1987; Grether & Plott, 1979; Lichtenstein & Slovic, 1971, 1973; Lindman, 1971; Slovic & Lichtenstein, 1983). When presented with two gambles close in expected value, many subjects choose a bet with a high probability of winning a modest amount of money (P-bet) over a bet with a low probability of winning a large amount (

Response Styles in Affect Ratings: Making a Mountain Out of a Molehill

-bet), even though they place a higher minimum selling price on the

Bayesian estimation of Thurstonian ranking models based on the Gibbs sampler

-bet. When confronted with the apparent inconsistency in their behavior and given the chance to change either of their responses, the majority of these subjects stand by their decisions (Slovic, 1986). In the typical preference reversal task, response modes may vary in two ways: (a) subjects either make a choice between two lotteries or make a judgment of each separately, and (b) the focus is either on the monetary value of the gamble or on its attractiveness. Goldstein and Einhorn (1987) manipulated these two factors independently and demonstrated preference reversals associated with each. Although recent research (Tversky, Sattath, & Slovic, 1988) has demonstrated preference reversals for nonprobabilistic stimuli, the vast majority of preference reversal studies have used gambles as stimuli. In this article, we explore the possibility that these preference reversals are largely due to the way in which people evaluate probabilities for unique, one-shot events. Two experiments test this hypothesis by varying the number of times a gamble

Canonical analysis of contingency tables with linear constraints

Ratings of affect words are the most commonly used method to assess pleasant affect (PA) and unpleasant affect (UA). The reliance on self-reports would be problematic if affect ratings were heavily influenced by response styles. Several recent publications have indeed suggested (a) that the influence of response styles on affect ratings is pervasive, (b) that this influence can be controlled by variations of the response format using multitrait-multimethod models, and (c) the discriminant validity of PA and UA is spurious. In this article, we examined the evidence for these claims. We demonstrate that (a) response styles have a negligible effect on affect ratings, (b) multiple response formats produce the same results as a single response format, and (c) the discriminant validity of PA and UA is not a method artifact. Rather, evidence against discriminant validity is due to the use of inappropriate response formats that respondents interpreted as bipolar scales.

Modeling Multiple Response Processes in Judgment and Choice

This paper presents a Gibbs sampler for the estimation of Thurstonian ranking models. This approach is useful for the analysis of ranking data with a large number of options. Approaches for assessing the goodness-of-fit of Thurstonian ranking models based on posterior predictive distributions are also discussed. Two simulation studies and two ranking studies are presented to illustrate that the Gibbs sampler is a promising solution to the numerical problems that previously plagued the estimation of Thurstonian ranking models.

Comparative Judgments as an Alternative to Ratings: Identifying the Scale Origin.

A generalized least squares approach is presented for incorporating linear constraints on the standardized row and column scores obtained from a canonical analysis of a contingency table. The method is easy to implement and may simplify considerably the interpretation of a data matrix. The approach is compared to a restricted maximum likelihood procedure.

Mixed inar(1) poisson regression models : Analyzing heterogeneity and serial dependencies in longitudinal count data

In this article, I show how item response models can be used to capture multiple response processes in psychological applications. Intuitive and analytical responses, agree-disagree answers, response refusals, socially desirable responding, differential item functioning, and choices among multiple options are considered. In each of these cases, I show that the response processes can be measured via pseudoitems derived from the observed responses. The estimation of these models via standard software programs that allow for missing data is also discussed. The article concludes with two detailed applications that illustrate the prevalence of multiple response processes.