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The role of selfefficacy and selfconcept beliefs in mathematical problemsolving: A path analysis
 Journal of Educational Psychology
, 1994
"... Path analysis was used to test the predictive and mediational role of selfefficacy beliefs in mathematical problem solving. Results revealed that math selfefficacy was more predictive of problem solving than was math selfconcept, perceived usefulness of mathematics, prior experience with mathema ..."
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Path analysis was used to test the predictive and mediational role of selfefficacy beliefs in mathematical problem solving. Results revealed that math selfefficacy was more predictive of problem solving than was math selfconcept, perceived usefulness of mathematics, prior experience with mathematics, or gender (N = 350). Selfefficacy also mediated the effect of gender and prior experience on selfconcept, perceived usefulness, and problem solving. Gender and prior experience influenced selfconcept, perceived usefulness, and problem solving largely through the mediational role of selfefficacy. Men had higher performance, selfefficacy, and selfconcept and lower anxiety, but these differences were due largely to the influence of selfefficacy, for gender had a direct effect only on selfefficacy and a prior experience variable. Results support the hypothesized role of selfefficacy in A. Bandura's (1986) social cognitive theory. Social cognitive theory suggests that selfefficacy, "people's judgments of their capabilities to organize and execute courses of action required to attain designated types of performances " (Bandura, 1986, p. 391), strongly influences the choices people make, the effort they expend, and how
From association to causation: Some remarks on the history of statistics
 Statist. Sci
, 1999
"... The “numerical method ” in medicine goes back to Pierre Louis ’ study of pneumonia (1835), and John Snow’s book on the epidemiology of cholera (1855). Snow took advantage of natural experiments and used convergent lines of evidence to demonstrate that cholera is a waterborne infectious disease. More ..."
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The “numerical method ” in medicine goes back to Pierre Louis ’ study of pneumonia (1835), and John Snow’s book on the epidemiology of cholera (1855). Snow took advantage of natural experiments and used convergent lines of evidence to demonstrate that cholera is a waterborne infectious disease. More recently, investigators in the social and life sciences have used statistical models and significance tests to deduce causeandeffect relationships from patterns of association; an early example is Yule’s study on the causes of poverty (1899). In my view, this modeling enterprise has not been successful. Investigators tend to neglect the difficulties in establishing causal relations, and the mathematical complexities obscure rather than clarify the assumptions on which the analysis is based. Formal statistical inference is, by its nature, conditional. If maintained hypotheses A, B, C,... hold, then H can be tested against the data. However, if A, B, C,... remain in doubt, so must inferences about H. Careful scrutiny of maintained hypotheses should therefore be a critical part of empirical work—a principle honored more often in the breach than the observance. Snow’s work on cholera will be contrasted with modern studies that depend on statistical models and tests of significance. The examples may help to clarify the limits of current statistical techniques for making causal inferences from patterns of association. 1.
From association to causation via regression
 Indiana: University of Notre Dame
, 1997
"... For nearly a century, investigators in the social sciences have used regression models to deduce causeandeffect relationships from patterns of association. Path models and automated search procedures are more recent developments. In my view, this enterprise has not been successful. The models tend ..."
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For nearly a century, investigators in the social sciences have used regression models to deduce causeandeffect relationships from patterns of association. Path models and automated search procedures are more recent developments. In my view, this enterprise has not been successful. The models tend to neglect the difficulties in establishing causal relations, and the mathematical complexities tend to obscure rather than clarify the assumptions on which the analysis is based. Formal statistical inference is, by its nature, conditional. If maintained hypotheses A, B, C,... hold, then H can be tested against the data. However, if A, B, C,... remain in doubt, so must inferences about H. Careful scrutiny of maintained hypotheses should therefore be a critical part of empirical work a principle honored more often in the breach than the observance.
On specifying graphical models for causation, and the identification problem
 Evaluation Review
, 2004
"... This paper (which is mainly expository) sets up graphical models for causation, having a bit less than the usual complement of hypothetical counterfactuals. Assuming the invariance of error distributions may be essential for causal inference, but the errors themselves need not be invariant. Graphs c ..."
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Cited by 20 (2 self)
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This paper (which is mainly expository) sets up graphical models for causation, having a bit less than the usual complement of hypothetical counterfactuals. Assuming the invariance of error distributions may be essential for causal inference, but the errors themselves need not be invariant. Graphs can be interpreted using conditional distributions, so that we can better address connections between the mathematical framework and causality in the world. The identification problem is posed in terms of conditionals. As will be seen, causal relationships cannot be inferred from a data set by running regressions unless there is substantial prior knowledge about the mechanisms that generated the data. There are few successful applications of graphical models, mainly because few causal pathways can be excluded on a priori grounds. The invariance conditions themselves remain to be assessed.
Causation, Statistics, and Sociology
 EUROPEAN SOCIOLOGICAL REVIEW,VOL. 17 NO. 1, 1^20
, 2001
"... Three different understandings of causation, each importantly shaped by the work of statisticians, are examined from the point of view of their value to sociologists: causation as robust dependence, causation as consequential manipulation, and causation as generative process. The last is favoured as ..."
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Three different understandings of causation, each importantly shaped by the work of statisticians, are examined from the point of view of their value to sociologists: causation as robust dependence, causation as consequential manipulation, and causation as generative process. The last is favoured as the basis for causal analysis in sociology. It allows the respective roles of statistics and theory to be clarified and is appropriate to sociology as a largely nonexperimental social science in which the concept of action is central.
The path analysis controversy: A new statistical approach to strong apWALLER AND MEEHL336 praisal of verisimilitude
 Psychological Methods
, 2002
"... A new approach for using path analysis to appraise the verisimilitude of theories is described. Rather than trying to test a model’s truth (correctness), this method corroborates a class of path diagrams by determining how well they predict intradata relations in comparison with other diagrams. The ..."
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A new approach for using path analysis to appraise the verisimilitude of theories is described. Rather than trying to test a model’s truth (correctness), this method corroborates a class of path diagrams by determining how well they predict intradata relations in comparison with other diagrams. The observed correlation matrix is partitioned into disjoint sets. One set is used to estimate the model parameters, and a nonoverlapping set is used to assess the model’s verisimilitude. Computer code was written to generate competing models and to test the conjectured model’s superiority (relative to the generated set) using diagram combinatorics and is available on the Web
LargeScale Functional Connectivity in Associative Learning: Interrelations of the Rat Auditory, Visual, and Limbic Systems
 Journal of Neurophysiology
, 1998
"... this article were defrayed in part by the that the behavioral relevance of a stimulus impacts on the payment of page charges. The article must therefore be hereby marked auditory system at the very earliest stages of processing. "advertisement" in accordance with 18 U.S.C. Section 1734 sol ..."
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Cited by 7 (1 self)
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this article were defrayed in part by the that the behavioral relevance of a stimulus impacts on the payment of page charges. The article must therefore be hereby marked auditory system at the very earliest stages of processing. "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. The interactions between parallel auditory pathways also 3148 00223077/98 $5.00 Copyright q 1998 The American Physiological Society J2368 / 9k2f$$de38 111898 17:07:16 neupa LPNeurophys FUNCTIONAL CONNECTIVITY IN AUDITORY LEARNING 3149 appear to change with learning. This was especially evident to explore the interactions in terms of covariance patterns between two or more brain regions. The second technique in the case where the behavioral relevance of an auditory stimulus depended on a visual stimulus (McIntosh and Gon incorporates additional information, such as anatomic connections, to quantify explicitly the effect one brain region zalezLima 1995) . Two groups of rats received pairings of a tone (conditioned excitor: T / ) with a mild footshock. has on another. These two approaches are known as functional and effective connectivity, respectively. Both terms Group TL 0 was trained in a Pavlovian conditioned inhibition paradigm (T / /TL 0 ) where the tonelight compound sig were introduced in the context of electrophysiological recordings from multiple cells (Aertsen et al. 1989; Gerstein naled the absence of footshock, making the light an inhibitor (L 0 ). Group TL 0 was trained with the tone as the excitor et al. 1978) . More recently, they have been used in reference to neuroimaging data (Friston 1994) . In most of our previ and the light as a "neutral" stimulus in that it predicted the absence of footshock on only 50% of trials. During fluo...
The relative validity of inferences about mediation as a function of research design characteristics
 Organizational Research Methods, PE: PLS INSERT VOLUME NUMBER, PE: PLS INSERT
, 2008
"... The online version of this article can be found at: ..."
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The online version of this article can be found at:
Exploring user data from a gamelike math tutor: a case study in causal modeling
"... We have used causal modeling to understand data from a gamelike math tutor, Monkey’s Revenge. We collected student data of various types such as their attitude and enjoyment via surveys, performance within tutor via logging, and learning as measured by a pre/post test. Although the data are observa ..."
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We have used causal modeling to understand data from a gamelike math tutor, Monkey’s Revenge. We collected student data of various types such as their attitude and enjoyment via surveys, performance within tutor via logging, and learning as measured by a pre/post test. Although the data are observational, we want to understand the causal relationships between the variables we have collected. We contrast the causal modeling approach to the results we achieve with traditional approaches such as correlation matrix and multiple regression. Relative to traditional approaches, we found that causal modeling did a better job at detecting and representing spurious association, and direct and indirect effects. We found that the causal model, particularly one augmented with domain knowledge about likely causal relationships, resulted in much more plausible and interpretable model. We present a case study for blending exploratory results from causal modeling with randomized controlled studies to validate hypotheses.
A Rejoinder to Berk
 Blalock, and Mason.” Sociological Methodology
, 1991
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you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, noncommercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at.