Problems in Path Analysis and Causal Inference

1969 ◽  
Vol 1 ◽  
pp. 38 ◽  
Author(s):  
David R. Heise
Keyword(s):  
Causal Inference ◽  
Path Analysis

Are Illusory Contours a Cause or a Consequence of Apparent Differences in Brightness and Depth in the Kanizsa Square?

Perception ◽  
1988 ◽  
Vol 17 (4) ◽  
pp. 513-521 ◽  
Author(s):  
Takeo Watanabe ◽  
Tadasu Oyama
Keyword(s):  
Causal Inference ◽  
Path Analysis ◽  
Illusory Contour ◽  
Apparent Depth ◽  
Inference Method ◽  
Illusory Contours ◽  
Partial Correlations

The causal flows between the processes responsible for illusory contour clarity, brightness, and apparent depth in the Kanizsa square were examined. The sixty-four stimuli used consisted of all possible combinations of eight disk luminances and eight centre-to-centre separations between nearest disks. Ten subjects were instructed to rate the clarity of the illusory contour and the brightness and apparent depth differences between the Kanizsa square and its surround in each stimulus. On the basis of results obtained with the causal inference method, using partial correlations and path analysis, it is suggested that clarity of illusory contour can be influenced directly by disk separation, and that the output from the process responsible for illusory contour clarity has some effect on the processes responsible for the apparent depth and brightness differences.

Factorial Modeling in Support of Causal Inference

1979 ◽  
Vol 16 (4) ◽  
pp. 323-340 ◽  
Author(s):  
Paul R. Lohnes
Keyword(s):  
Natural Language ◽  
Causal Inference ◽  
Path Analysis ◽  
Canonical Correlation ◽  
Correlation Matrix ◽  
Structural Equation ◽  
Simple Algebra ◽  
Social Scientists ◽  
Type Analysis ◽  
Commonality Analysis

This paper describes and justifies a new method for analyzing correlations in support of causal inference. Named Factorial Modeling (FaM), its motivations are (1) Social scientists have an obligation to hypothesize the probable causes of the phenomena they seek to explain, and (2) In the interests of discipline and parsimony, causes should be operationalized as uncorrelated variables. Applying a simple algebra to the correlation matrix, FaM produces a structural equation for each variate in the research, thus analyzing all the variances and covariances. The advantage of the FaM method is that the natural language of domains of measurement which are known to be relevant can be respected in the hypothesizing of causes. Since the algorithm does not attempt to maximize or minimize anything, a loose fit to the data will be obtained; but it is suggested that such loose-fitting models may travel well to other situations to which generalization is attempted. The FaM method is illustrated on a small example, for which path analysis, LISREL-type analysis, canonical correlation, and commonality analysis results are also given to provide comparisons with other methods of modeling. Predictions of the impacts of policy manipulations under a model obtained from FaM are demonstrated.

Beyond metaphors and semantics: A framework for causal inference in neuroscience

2019 ◽  
Vol 42 ◽  
Author(s):  
Roberto A. Gulli
Keyword(s):  
Causal Inference ◽  
Causal Power

Abstract The long-enduring coding metaphor is deemed problematic because it imbues correlational evidence with causal power. In neuroscience, most research is correlational or conditionally correlational; this research, in aggregate, informs causal inference. Rather than prescribing semantics used in correlational studies, it would be useful for neuroscientists to focus on a constructive syntax to guide principled causal inference.

An Input–Process–Output Model of Pilot Core Competencies

2017 ◽  
Vol 7 (2) ◽  
pp. 78-85 ◽  
Author(s):  
Heikki Mansikka ◽  
Don Harris ◽  
Kai Virtanen
Keyword(s):  
Path Analysis ◽  
Team Performance ◽  
Core Competencies ◽  
Core Competency ◽  
Principal Component ◽  
Analysis Model ◽  
Input Process ◽  
The Core ◽  
Performance Framework ◽  
Process Output

Abstract. The aim of this study was to investigate the relationship between the flight-related core competencies for professional airline pilots and to structuralize them as components in a team performance framework. To achieve this, the core competency scores from a total of 2,560 OPC (Operator Proficiency Check) missions were analyzed. A principal component analysis (PCA) of pilots’ performance scores across the different competencies was conducted. Four principal components were extracted and a path analysis model was constructed on the basis of these factors. The path analysis utilizing the core competencies extracted adopted an input–process–output’ (IPO) model of team performance related directly to the activities on the flight deck. The results of the PCA and the path analysis strongly supported the proposed IPO model.

Sign in / Sign up

Export Citation Format

Share Document