|
| ||
|
|
||
Causal Interpretations (causal + interpretation)
Selected AbstractsTaking a Position: A Reinterpretation of the Theory of Planned BehaviourJOURNAL FOR THE THEORY OF SOCIAL BEHAVIOUR, Issue 2 2005ANDREW J. COOK This paper examines methodological issues associated with the theory of planned behaviour and explains that an alternative account of data used to support this theory can be provided by positioning theory. A case is presented that shows tests of the theory of planned behaviour fail to eliminate the possibility of alternative explanations for co-variation in its data. An agency or person-centered alternative shows how a causal interpretation can be reinterpreted as evidence of the actions of a person. Unlike the conceptualisation of the individual as behaving in keeping with postulated underlying cognitive laws or rules we assume that the person has, through socialisation, acquired the skills necessary to initiate and manage their own actions. Unlike the interest in TPB data as a causal explanation of action we draw attention to the interpretation of patterns in these data as an aggregate of each person using a common mode of explanation to justify and explain their intentions. [source] Effort,reward imbalance at work and self-rated health of Las Vegas hotel room cleanersAMERICAN JOURNAL OF INDUSTRIAL MEDICINE, Issue 4 2010Niklas Krause MD Abstract Background This study investigates the relationship between effort-reward-imbalance (ERI) at work and self-rated health (SF-36) among 941 Las Vegas hotel room cleaners (99% female, 84% immigrant). Methods Logistic regression models adjust for age, health behaviors, physical workload and other potential confounders. Results 50% reported ERI and 60% poor or fair general health. Significant associations were found between ERI and all SF-36 health measures. Workers in the upper quartile of the efforts/rewards ratio were 2,5 times more likely to experience poor or fair general health, low physical function, high levels of pain, fatigue, and role limitations due to physical and mental health problems. Conclusions The cross-sectional design limits causal interpretation of these associations. However, the development of interventions to reduce ERI and to improve general health among room cleaners deserves high priority considering that both high ERI and low self-rated health have predicted chronic diseases and mortality in prospective studies. Am. J. Ind. Med. 53:372,386, 2010. © 2009 Wiley-Liss, Inc. [source] Using Experiments to Estimate the Effects of Education on Voter TurnoutAMERICAN JOURNAL OF POLITICAL SCIENCE, Issue 1 2010Rachel Milstein Sondheimer The powerful relationship between education and voter turnout is arguably the most well-documented and robust finding in American survey research. Yet the causal interpretation of this relationship remains controversial, with many authors suggesting that the apparent link between education and turnout is spurious. In contrast to previous work, which has relied on observational data to assess the effect of education on voter turnout, this article analyzes two randomized experiments and one quasi-experiment in which educational attainment was altered exogenously. We track the children in these experiments over the long term, examining their voting rates as adults. In all three studies, we find that exogenously induced changes in high school graduation rates have powerful effects on voter turnout rates. These results imply that the correlation between education and turnout is indeed causal. We discuss some of the pathways by which education may transmit its influence. [source] FC02.4 Meteorological factors and standard series patch test reactionsCONTACT DERMATITIS, Issue 3 2004Janice Hegewald The existence of seasonal patterns to patch test reactions has been described, but with conflicting causal interpretations. The potential seasonality of patch tests may be due to irritation, changes to skin barrier or changes to immunological functions caused by meteorological fluctuations. For example, increased skin irritability due to cold winter weather and low humidity may cause an increase in irritative/doubtful and weak positive (false positive) reactions. To investigate the extent of the association between weather and patch test results, consecutive patients (N = 73691) patch tested with the standard series of the German Contact Dermatitis Research Group (DKG) at German or Austrian IVDK (http://www.ivdk.de) centres were matched with weather data collected at a nearby (30 km radius) weather station. Temperature and absolute humidity (AH) on the day of patch test application and the two preceding days were averaged to represent the environment most likely to have influenced the skin condition at the time of testing. The results of 24 standard series substances were analyzed with multivariate logistic regression. Half of the standard series substances examined, including fragrance mix, nickel sulphate, and formaldehyde, exhibited evidence of a relationship with meteorological conditions. Fragrance mix and p-Phenylene diamine exhibited the strongest evidence of an association to weather, with the odds of the reactions in all three reaction categories (ir/?, +, ++/+++) increasing during winter conditions. Due to the association between weather and patch test reactivity, the potential effect of meteorological conditions should be considered in the interpretation of patch test reactions. [source] Differential effects of high-quality child careJOURNAL OF POLICY ANALYSIS AND MANAGEMENT, Issue 4 2002Jennifer Hill In policy research a frequent aim is to estimate treatment effects separately by subgroups. This endeavor becomes a methodological challenge when the subgroups are defined by post-treatment, rather than pre-treatment, variables because if analyses are performed in the same way as with pre-treatment variables, causal interpretations are no longer valid. The authors illustrate a new approach to this challenge within the context of the Infant Health and Development Program, a multisite randomized study that provided at-risk children with intensive, center-based child care. This strategy is used to examine the differential causal effects of access to high-quality child care for children who would otherwise have participated in one of three child care options: no non-maternal care, home-based non-maternal care, and center-based care. Results of this study indicate that children participating in the first two types of care would have gained the most from high-quality center-based care and, moreover, would have more consistently retained the bulk of these positive benefits over time. These results may have implications for policy, particularly with regard to the debate about the potential implications of providing universal child care. © 2002 by the Association for Public Policy Analysis and Management. [source] Chain graph models and their causal interpretations,JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 3 2002Steffen L. Lauritzen Chain graphs are a natural generalization of directed acyclic graphs and undirected graphs. However, the apparent simplicity of chain graphs belies the subtlety of the conditional independence hypotheses that they represent. There are many simple and apparently plausible, but ultimately fallacious, interpretations of chain graphs that are often invoked, implicitly or explicitly. These interpretations also lead to flawed methods for applying background knowledge to model selection. We present a valid interpretation by showing how the distribution corresponding to a chain graph may be generated from the equilibrium distributions of dynamic models with feed-back. These dynamic interpretations lead to a simple theory of intervention, extending the theory developed for directed acyclic graphs. Finally, we contrast chain graph models under this interpretation with simultaneous equation models which have traditionally been used to model feed-back in econometrics. [source] A Bayesian Approach to Surrogacy Assessment Using Principal Stratification in Clinical TrialsBIOMETRICS, Issue 2 2010Yun Li Summary A surrogate marker (S) is a variable that can be measured earlier and often more easily than the true endpoint (T) in a clinical trial. Most previous research has been devoted to developing surrogacy measures to quantify how well,S,can replace,T,or examining the use of,S,in predicting the effect of a treatment (Z). However, the research often requires one to fit models for the distribution of,T,given,S,and,Z. It is well known that such models do not have causal interpretations because the models condition on a postrandomization variable,S. In this article, we directly model the relationship among,T,,S, and,Z,using a potential outcomes framework introduced by Frangakis and Rubin (2002,,Biometrics,58, 21,29). We propose a Bayesian estimation method to evaluate the causal probabilities associated with the cross-classification of the potential outcomes of,S,and,T,when,S,and,T,are both binary. We use a log-linear model to directly model the association between the potential outcomes of,S,and,T,through the odds ratios. The quantities derived from this approach always have causal interpretations. However, this causal model is not identifiable from the data without additional assumptions. To reduce the nonidentifiability problem and increase the precision of statistical inferences, we assume monotonicity and incorporate prior belief that is plausible in the surrogate context by using prior distributions. We also explore the relationship among the surrogacy measures based on traditional models and this counterfactual model. The method is applied to the data from a glaucoma treatment study. [source] | |||||||||||||