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Apparent mental causation: Sources of the experience of will
 American Psychologist
, 1999
"... The experience of willing an act arises from interpreting one's thought as the cause of the act. Conscious will is thus experienced as a function of the priority, consistency, and exclusivity of the thought about the action. The thought must occur before the action, be consistent with the action, an ..."
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Cited by 62 (7 self)
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The experience of willing an act arises from interpreting one's thought as the cause of the act. Conscious will is thus experienced as a function of the priority, consistency, and exclusivity of the thought about the action. The thought must occur before the action, be consistent with the action, and not be accompanied by other causes. An experiment illustrating the role of priority found that people can arrive at the mistaken belief that they have intentionally caused an action that in fact they were forced to perform when they are simply led to think about the action just before its occurrence. Conscious will is a pervasive human experience. We all have the sense that we do things, that we cause our acts, that we are agents. As William James (1890) observed, "the whole sting and excitement of our voluntary life... depends on our sense that in it things are really being decided from one moment to another, and that it is not the dull rattling off of a chain that was forged innumerable ages ago " (p. 453). And yet, the very notion of the will seems to contradict the core assumption of psychological science. After all, psychology examines how behavior is caused by mechanisms—the rattling off of genetic, unconscious, neural, cognitive, emotional, social, and yet other chains that lead, dully or not, to the things people do. If the things we do are caused by such mechanisms, how is it that we nonetheless experience willfully doing them? Our approach to this problem is to look for yet another chain—to examine the mechanisms that produce the experience of conscious will itself. In this article, we do this by exploring the possibility that the experience of will is a result of the same mental processes that people use in the perception of causality more generally. Quite simply, it may be that people experience conscious will when they interpret their own thought as the cause of their action. This idea means that people can experience conscious will quite independent of any actual causal connection between
Applications of randomeffects patternmixture models for missing data in longitudinal studies. Psychol Methods
, 1997
"... Randomeffects regression models have become increasingly popular for analysis of longitudinal data. A key advantage of the randomeffects approach is that it can be applied when subjects are not measured at the same number of timepoints. In this article we describe use of randomeffects patternmix ..."
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Cited by 25 (4 self)
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Randomeffects regression models have become increasingly popular for analysis of longitudinal data. A key advantage of the randomeffects approach is that it can be applied when subjects are not measured at the same number of timepoints. In this article we describe use of randomeffects patternmixture models to further handle and describe the influence of missing data in longitudinal studies. For this approach, subjects are first divided into groups depending on their missingdata pattern and then variables based on these groups are used as model covariates. Tn this way, researchers are able to examine the effect of missingdata patterns on the outcome (or outcomes) of interest. Furthermore, overall estimates can be obtained by averaging over the missingdata patterns. A psychiatric clinical trials data set is used to illustrate the randomeffects patternmixture approach to longitudinal data analysis with missing data. Longitudinal studies occupy an important role in psychological and psychiatric research. In these studies the same individuals are repeatedly measured on a number of important variables over a series of timepoints. As an example, a longitudinal design is often used to determine whether a particular therapeutic agent can produce changes in clinical status over the course of an illness. Another application for the longitudinal study is to assess potential indicators of a change, in the subject's clinical status; for example, the assessment of whether drug plasma level measurements indicate clinical outcome. Even in wellcontrolled situations, missing data invariably occur in longitudinal studies. Subjects can be
Missing Data: Our View of the State
 of the Art.” Psychological Methods
"... Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missingdata problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random ..."
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Cited by 4 (0 self)
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Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missingdata problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art. Why do missing data create such difficulty in scientific research? Because most data analysis procedures were not designed for them. Missingness is usually a nuisance, not the main focus of inquiry, but
The Covariance Between Level and Shape in the Latent Growth Curve Model With Estimated Basis Vector Coefficients
, 1998
"... A LISREL representation of the Latent Growth Curve Model is used to generate the set of equations for the expectation of the vector of means and the covariance matrix in terms of the unknown population parameters. A dependency between the covariance of the level and shape parameters and the scal ..."
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Cited by 4 (1 self)
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A LISREL representation of the Latent Growth Curve Model is used to generate the set of equations for the expectation of the vector of means and the covariance matrix in terms of the unknown population parameters. A dependency between the covariance of the level and shape parameters and the scalings of the shape basis vector is shown. A simulation is presented based on data presented by McArdle and Hamagami (1991) to show how the covariance changes as the basis vector coecients are rescaled. The relationship is explained in terms of the algebraic solution to a system of equations. Keywords: Latent growth curves, structural equation modeling, LISREL. 1 Introduction The latent growth curve model has become a popular method used to analyze repeatedly measured data (McArdle and Epstein, 1987; McArdle and Aber, 1990) when the interest is modeling "individual change as a function of time" (McArdle and Epstein, 1987, p. 110). As originally described the method combines ideas from bo...
The Rainbow Project: Enhancing the SAT through assessments of analytical, practical, and creative skills
, 2006
"... This article describes the formulation and execution of the Rainbow Project, Phase I, funded by the College Board. Past data suggest that the SAT is a good predictor of performance in college. But in terms of the amount of variance explained by the SAT, there is room for improvement, as there would ..."
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This article describes the formulation and execution of the Rainbow Project, Phase I, funded by the College Board. Past data suggest that the SAT is a good predictor of performance in college. But in terms of the amount of variance explained by the SAT, there is room for improvement, as there would be for virtually any single test battery. Phase I of the Rainbow Project, described here, uses Sternberg's triarchic theory of successful intelligence as a basis to provide a supplementary assessment of analytical skills, as well as tests of practical and creative skills, to augment the SAT in predicting college performance. This assessment is delivered through a modification of the Sternberg Triarchic Abilities Test (STAT) and the development of new assessment devices. Results from Phase I of the Rainbow Project support the construct validity of the theory of successful intelligence and suggest its potential for use in college admissions as an enhancement to the SAT. In particular, the results indicated that the triarchically based Rainbow measures enhanced predictive validity for college GPA relative to high school grade point average (GPA) and the SAT and also reduced ethnic group differences. The data suggest that measures such as these potentially could increase diversity and equity in the admissions process.
POPULATIONS WHEN CLASS MEMBERSHIP IS UNKNOWN: DEFINING AND DEVELOPING THE LATENT CLASSIFICATION DIFFERENTIAL CHANGE MODEL
, 2005
"... by Kenneth Kelley III Standard methods for analyzing change generally assume that the population of interest is homogeneous or that heterogeneity is known. When a population consists of unknown subpopulations, the parameters within each of the latent classes may be unique to that particular class. I ..."
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by Kenneth Kelley III Standard methods for analyzing change generally assume that the population of interest is homogeneous or that heterogeneity is known. When a population consists of unknown subpopulations, the parameters within each of the latent classes may be unique to that particular class. In such a situation the results of standard techniques for analyzing change are misleading, because such methods ignore unobserved heterogeneity and treat the population as if it were homogeneous. The growth mixture model (GMM; Muthén, 2001a; Muthén, 2001b; Muthén, 2002) partly addresses the problem of unknown heterogeneity because the parameters of the GMM are conditional on latent class membership. However, the GMM is necessarily restricted to models of change linear in their parameters (such as polynomial change models). The latent classification
Latent Differential Equation Modeling with Multivariate MultiOccasion Indicators
, 2003
"... Please do not cite or quote A number of models for estimating the coefficients of dynamical systems have been proposed. One is the exact discrete model, another is the latent differences model or proportional change model, and a third is a continuous time manifest variable differential structural mo ..."
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Please do not cite or quote A number of models for estimating the coefficients of dynamical systems have been proposed. One is the exact discrete model, another is the latent differences model or proportional change model, and a third is a continuous time manifest variable differential structural model. These models differ from standard growth curve modeling in that they intend to illuminate processes generating individual trajectories over time rather than just estimate an aggregate best trajectory. The current work proposes a novel approach to the modeling of multivariate change by first creating a lagged covariance matrix. Then rather than using factor loadings to estimate average growth characteristics as in other growth curve modeling related methods, confirmatory factor loadings are fixed across the time dimension and freed across the variables dimension, in such a way as to force the factor scores to estimate instantaneous derivatives. Then, the factor covariances are structured as a regression that allows the estimation of parameters of differential equation models of dynamical systems theories proposed to account for the data. Results of a simulation will be presented illustrating strengths and weaknesses of the latent differential equation model.
Formal and Informal Model Selection with Incomplete Data
, 808
"... Abstract. Model selection and assessment with incomplete data pose challenges in addition to the ones encountered with complete data. There are two main reasons for this. First, many models describe characteristics of the complete data, in spite of the fact that only an incomplete subset is observed ..."
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Abstract. Model selection and assessment with incomplete data pose challenges in addition to the ones encountered with complete data. There are two main reasons for this. First, many models describe characteristics of the complete data, in spite of the fact that only an incomplete subset is observed. Direct comparison between model and data is then less than straightforward. Second, many commonly used models are more sensitive to assumptions than in the completedata situation and some of their properties vanish when they are fitted to incomplete, unbalanced data. These and other issues are brought forward using two key examples, one of a continuous and one of a categorical nature. We argue that model assessment ought to consist of two parts: (i) assessment of a model’s fit to the observed data and (ii) assessment of the sensitivity of inferences to unverifiable assumptions, that is, to how a model described the unobserved data given the observed ones. Key words and phrases: Interval of ignorance, linear mixed model, missing at random, missing not at random, multivariate normal, sensitivity analysis. 1.
10 ANALYSIS WITH MISSING DATA IN PREVENTION RESEARCH
"... Missing data are pervasive in alcohol and drug abuse prevention evaluation efforts: Researchers administer surveys, and some items are left unanswered. Slow readers often leave large portions incomplete at the end of the survey. Researchers administer the surveys at several points in time, and peopl ..."
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Missing data are pervasive in alcohol and drug abuse prevention evaluation efforts: Researchers administer surveys, and some items are left unanswered. Slow readers often leave large portions incomplete at the end of the survey. Researchers administer the surveys at several points in time, and people fail to show up at one or more waves of measurement. Researchers often design their measures to include a certain amount of “missingness”; some measures are so expensive (in money or time) that researchers can afford to administer them only to some respondents. Missing data problems have been around for years. Until recently, researchers have fumbled with partial solutions and put up only the weakest counterarguments to the admonitions of the critics of prevention and applied psychological research. Things have changed, however. Statistically sound solutions are now available for virtually every missing data problem,
Thomas W. PullumA Comparison of Latent Growth Models for Constructs Measured by Multiple Indicators
"... Maria do Rosário de Lana Leite, my sisters Janise and Flávia, and my first academic mentor, Iris GoulartAcknowledgements First, I’d like to thank the two professors that have helped with my journey through graduate school from the moment I joined The University of Texas at Austin until the moment I ..."
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Maria do Rosário de Lana Leite, my sisters Janise and Flávia, and my first academic mentor, Iris GoulartAcknowledgements First, I’d like to thank the two professors that have helped with my journey through graduate school from the moment I joined The University of Texas at Austin until the moment I completed my dissertation: Laura Stapleton and Tasha Beretvas. Laura, as my supervisor, has always been available to talk to me about my dissertation and her excellent feedback allowed my initial research ideas to grow into a complete dissertation. I am also very thankful for learning structural equation modeling with her, which not only has provided a very interesting topic for my dissertation but has also indicated the initial direction of my academic career. Tasha is the person without whom I would not have become a quantitative methods student. I need to thank her for offering me the opportunity to do research with her when I was an exchange student at UT, and also for giving me the incentive to join the quantitative methods program as a doctoral student. I am in debt with Tasha for working with me in my first publication, which was a