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Related Concept Videos

Correlation and Causation01:27

Correlation and Causation

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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
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Correlations02:20

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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Cause and Effect01:53

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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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Criteria for Causality: Bradford Hill Criteria - II01:28

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The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
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Causality in Epidemiology01:21

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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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Criteria for Causality: Bradford Hill Criteria - I01:30

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The Bradford Hill criteria are a group of principles that provide a framework to determine a causal relationship between a specific factor and a disease. There are nine criteria that are pivotal in assessing causality in epidemiological studies. Here's a closer look at Strength, Consistency, Specificity, and Temporality criteria with definitions and examples:
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Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal
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Concluding causation from correlation: comment on Burns and Spangler (2000).

N Kazantzis1, K R Ronan, F P Deane

  • 1School of Psychology at Albany and Waitemata District Health Board Cognitive Therapy Center, Massey University, Auckland, New Zealand. n.kazantzis@massey.ac.nz

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Compliance with therapy homework aids patient outcomes, but causal links require cautious interpretation. Further research should use prospective, experimental designs for stronger evidence.

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Area of Science:

  • Psychology
  • Clinical Psychology
  • Research Methodology

Background:

  • Critiques a study on homework compliance and therapy outcomes using structural equation modeling (SEM).
  • Highlights the limitations of correlational data and retrospective accounts in establishing causality.
  • Emphasizes the need to consider therapist competence in homework administration.

Discussion:

  • Advocates for cautious interpretation of causal relationships derived from SEM with cross-sectional, retrospective data.
  • Contrasts correlational findings with the gold standard of prospective, experimental research for causal inference.
  • Acknowledges that findings align with existing evidence on homework's positive impact on therapeutic outcomes.

Key Insights:

  • Structural equation modeling (SEM) is a valuable tool but requires careful application.
  • Correlational and retrospective data limit definitive causal claims in psychotherapy research.
  • Therapist factors, such as competence in homework delivery, warrant further investigation.

Outlook:

  • Recommends prospective, experimental studies for robust causal inferences in psychotherapy research.
  • Suggests refining methodologies to better assess the causal impact of therapeutic interventions like homework.
  • Underscores the importance of rigorous research designs to advance clinical psychology.