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Toward a brighter future for psychology as an observation oriented science.

Grice JW, Barrett PT, Schlimgen LA, Abramson CI - Behav Sci (Basel) (2012)

Bottom Line: These concerns are echoed in the current paper, and Observation Oriented Modeling (OOM) is presented as an alternative approach toward data conceptualization and analysis for the social and life sciences.This approach is rooted in philosophical realism and an attitude toward data analysis centered around causality and common sense.Three example studies and accompanying data analyses are presented and discussed to demonstrate a number of OOM's advantages over current researcher practices.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Oklahoma State University, 116 North Murray, Stillwater OK 74074, USA; E-Mails: liz.schlimgen@gmail.com (L.S.), charles.abramson@okstate.edu (C.A.).

ABSTRACT
Serious criticisms of psychology's research practices and data analysis methods date back to at least the mid-1900s after the Galtonian school of thought had thoroughly triumphed over the Wundtian school. In the wake of Bem's (2011) recent, highly publicized study on psi phenomena in a prestigious journal, psychologists are again raising serious questions about their dominant research script. These concerns are echoed in the current paper, and Observation Oriented Modeling (OOM) is presented as an alternative approach toward data conceptualization and analysis for the social and life sciences. This approach is rooted in philosophical realism and an attitude toward data analysis centered around causality and common sense. Three example studies and accompanying data analyses are presented and discussed to demonstrate a number of OOM's advantages over current researcher practices.

No MeSH data available.


Standard mediation model showing both direct and indirect effects.
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behavsci-02-00001-f003: Standard mediation model showing both direct and indirect effects.

Mentions: Recent years have witnessed a growing trend among quantitative psychologists and applied researchers toward attempting to infer causation from statistical methods. Drawing causal inferences from Structural Equation Modeling (SEM) has particularly been a topic of interest and debate among methodologists. Most notably, Pearl [17] has suffused the standard equations of SEM with particular assumptions and joined the equations to nodal graphing techniques in an attempt to create a causal inference engine. In a particular effort to derive a generic method for testing mediation models, Pearl [18] presents contrived data to demonstrate his approach. His example entails three dichotomous variables labeled X, Z, and Y. The first variable, X, represents a drug treatment (drug/no drug), the second variable, Z, stands for the presence of a certain enzyme in the blood stream (enzyme/no enzyme), and the third variable, Y, represents physical recovery from an ailment (cured/not cured). Drug treatment is the initial cause, the enzyme is the mediator, and recovery is the outcome. The three variables are linked in a standard mediation model format showing both direct and indirect connections between X and Y, as can be seen in Figure 3.


Toward a brighter future for psychology as an observation oriented science.

Grice JW, Barrett PT, Schlimgen LA, Abramson CI - Behav Sci (Basel) (2012)

Standard mediation model showing both direct and indirect effects.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4217578&req=5

behavsci-02-00001-f003: Standard mediation model showing both direct and indirect effects.
Mentions: Recent years have witnessed a growing trend among quantitative psychologists and applied researchers toward attempting to infer causation from statistical methods. Drawing causal inferences from Structural Equation Modeling (SEM) has particularly been a topic of interest and debate among methodologists. Most notably, Pearl [17] has suffused the standard equations of SEM with particular assumptions and joined the equations to nodal graphing techniques in an attempt to create a causal inference engine. In a particular effort to derive a generic method for testing mediation models, Pearl [18] presents contrived data to demonstrate his approach. His example entails three dichotomous variables labeled X, Z, and Y. The first variable, X, represents a drug treatment (drug/no drug), the second variable, Z, stands for the presence of a certain enzyme in the blood stream (enzyme/no enzyme), and the third variable, Y, represents physical recovery from an ailment (cured/not cured). Drug treatment is the initial cause, the enzyme is the mediator, and recovery is the outcome. The three variables are linked in a standard mediation model format showing both direct and indirect connections between X and Y, as can be seen in Figure 3.

Bottom Line: These concerns are echoed in the current paper, and Observation Oriented Modeling (OOM) is presented as an alternative approach toward data conceptualization and analysis for the social and life sciences.This approach is rooted in philosophical realism and an attitude toward data analysis centered around causality and common sense.Three example studies and accompanying data analyses are presented and discussed to demonstrate a number of OOM's advantages over current researcher practices.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Oklahoma State University, 116 North Murray, Stillwater OK 74074, USA; E-Mails: liz.schlimgen@gmail.com (L.S.), charles.abramson@okstate.edu (C.A.).

ABSTRACT
Serious criticisms of psychology's research practices and data analysis methods date back to at least the mid-1900s after the Galtonian school of thought had thoroughly triumphed over the Wundtian school. In the wake of Bem's (2011) recent, highly publicized study on psi phenomena in a prestigious journal, psychologists are again raising serious questions about their dominant research script. These concerns are echoed in the current paper, and Observation Oriented Modeling (OOM) is presented as an alternative approach toward data conceptualization and analysis for the social and life sciences. This approach is rooted in philosophical realism and an attitude toward data analysis centered around causality and common sense. Three example studies and accompanying data analyses are presented and discussed to demonstrate a number of OOM's advantages over current researcher practices.

No MeSH data available.