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James Gerhart , Ph.D. Rush University Medical Center

An Introduction to Modeling Causality with Repeated Measurement Designs: Guest Lecture for Experimental Psychology Tulane University 11/21/13. James Gerhart , Ph.D. Rush University Medical Center. Agenda.

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James Gerhart , Ph.D. Rush University Medical Center

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  1. An Introduction to Modeling Causality with Repeated Measurement Designs:Guest Lecture for Experimental Psychology Tulane University 11/21/13 James Gerhart, Ph.D. Rush University Medical Center

  2. Agenda • Discuss methods for assessing statistical causality1and mechanisms of change2in longitudinal designs • Ecological Momentary Assessment (Diary Data) • Treatment Outcome Research

  3. Statistical Causality and Mechanisms of Change • Psychologists are interested in explaining behavior and promoting socially valid changes in behavior. • Thus, we are often interested in identifying causes of behavior. • Philosophers continue to argue about causality. • Today we’ll focus on a few suggested methods for estimating causality.

  4. John Stuart Mill’s Criteria • Establish temporal precedence • Cause precedes effect • Establish co-variation- • The cause and effect are related • Rule out alternative explanations

  5. Statistical Causality and Mechanisms of Change • We can attempt to meet these criteria with Ecological Momentary Assessment (diary data), and treatment outcome data. • We can attempt behavioral change more efficiently.

  6. Benefits of Diary Data • Diary studies assess multiple behaviors in the context of day-to-day life (ecologically valid) • Repeated measurements throughout the day allows us to study changes • Easy to use with smart phone/tablets • The structure of the data allow us to test for temporal precedence (1), co-variation (2), and rule out some potential confounds (3).

  7. Structure of Diary Data Note. All data are illustrative and not from research subjects

  8. Structure of Diary Data Between subject variance Within subject variance

  9. Structure of Diary Data-Measuring Confounds

  10. Missing Data • Missing data used to preclude statistical analysis.

  11. Missing Data • Data replacement techniques have been developed so that data can be estimated and used • Mixed Models • Data Imputation • Data should be missing at random • Data could be biased if angrier folks skip more

  12. Variance Components Who is angriest? How does anger fluctuate? Between subject variance Within subject variance

  13. Setting up the Diary Model How does change in anger relate to change in later pain? Pain = Prior Anger+PriorPain+Time+Covariates Reverse the Model Anger =Prior Pain+PriorAnger+Time+Covariates

  14. Mechanisms of Change • For years psychologists have argued back and forth about theoretical orientations • Meta-analytic research shows that for many disorders (anxiety, anger, depression, PTSD) treatments intended to work, usually do • But psychodynamic, cognitive, and behavioral models differ drastically

  15. Mechanisms of Change • There are many explanations for the similarity of effects. • Behaviors are multiply determined • Maybe psychodynamic treatment changes one part of distress, and behavioral changes another • Treatments overlap on key processes • Hope, expectation of change, normalization, behavioral activation, insight, placebo effects, unconscious learning. • We need to measure these possibilities regularly throughout treatment.

  16. Revisiting John Stuart Mill’s Criteria • Establish temporal precedence • The mechanism should change before outcome changes • Establish co-variation • Change in mechanism should correlate with change in outcome • Rule out alternative explanations • Measure and analyze other potential mechanisms of change

  17. Structure of Treatment Data

  18. Setting up a Mechanism Models 1. Pain = Prior Pain Attitude+Prior Pain+Time+Covariates 2. Pain = Prior Hope+Prior Pain+ Time+Covariates 3. Pain = Prior Mindfulness+Prior Pain+ Time+Covariates 4. …

  19. Contact info: James Gerhart, Ph.D. Rush University Medical Center James_Gerhart@rush.edu

  20. References • 1. Duckworth, A.L., Tsukayama, E. & May, H. (2010). Establishing causality using longitudinal hierarchical linear modeling: An illustration predicting achievement from self-control. Social psychological and personality science, 1, 311-317. • 2. Kazdin, A.E. (2007). Mediators and mechanisms of change in psychotherapy research. Annual review of clinical psychology, 3, 1-27.

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