design approaches to causal inference l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Design Approaches to Causal Inference PowerPoint Presentation
Download Presentation
Design Approaches to Causal Inference

Loading in 2 Seconds...

play fullscreen
1 / 13

Design Approaches to Causal Inference - PowerPoint PPT Presentation


  • 120 Views
  • Uploaded on

Design Approaches to Causal Inference. Statistical mediation analysis answers the following question, “How does a researcher use measures of the hypothetical intervening process to increase the amount of information from a research study?”

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Design Approaches to Causal Inference' - kevork


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
design approaches to causal inference

Design Approaches to Causal Inference

Statistical mediation analysis answers the following question, “How does a researcher use measures of the hypothetical intervening process to increase the amount of information from a research study?”

Another question is, “What is the best next study or studies to conduct after a statistical mediation analysis to further test mediation theory.”

Five general approaches: (1) double randomization, (2) blockage, (3) enhancement, (4) purification, (5) pattern matching for multiple variables, subgroups, settings, time, and alternative manipulations (Mark, 1986).

1 double randomization

(1) Double Randomization

If the problem with the b path is that M is not randomly assigned, then how about randomizing both X in the X to M relation and randomizing M in the M to Y relation.

Say X was randomized and there was a significant effect of X on M in Study 1. In Study 2, an experiment was set up so that M was randomized to levels defined by how X changed M in Study 1. If there was a significant relation of M to Y in Study 2, then there is more evidence for mediation.

wood et al 1974 overview

Wood et al. (1974) Overview

Study of self-fulfilling prophecy in interviews cited in Spencer et al., (2005).

Race (X) predicts quality of interview (M) and quality of interview predicts performance (Y).

Confederate—Person assisting with the experiment. The confederates are used to manipulate factors. Confederate applicants were used in Study 1 for the X to M relation and confederate interviewers were used in Study 2 for the M to Y relation.

wood et al 1974

Wood et al., (1974)

Study 1. White participants interviewed either Black or White confederate applicants (X). The dependent variable M, was interview quality and participants with Black confederate applicants gave poorer quality interviews (M).

Study 2. Confederates gave either an interview (M) like White applicants were interviewed in Study 1 or like Black applicants in Study 1. This manipulation had a significant effect on applicant performance (Y).

So randomization was used for the X to M relation and the M to Y relation.

prevention example mackinnon et al 2002

Prevention Example (MacKinnon et al., 2002)

Norms increase exercise which decreases depression.

Study 1, X to M: Similar to existing prevention studies, participants either receive a social norm manipulation to increase exercise or not (X) and exercise is measured (M).

Study 2, M to Y: Participants are randomly assigned to conduct an amount of exercise (M) obtained in the program group or the control from Study 1 and depression is measured (Y).

double randomization problems

Double Randomization Problems

Most problems center around the randomization of the mediator so that it corresponds to the change in the mediator in the X to M study.

Study 2 is a mediation model with a manipulation (X) that should change M in the same way as X changed M in Study 1. So Study 2 data is analyzed with statistical mediation analysis with the same problems of interpretation.

2 blockage designs

(2) Blockage Designs

The goal of blockage designs is to test a mediation relation with a manipulation that blocks the mediator from operating.

For example, lets say that an exercise program appears to reduce depression by increasing endorphin levels-- the hypothesized mediator. A blockage manipulation would administer a drug to prevent endorphin production so that persons receiving the exercise program would no longer experience reduced depression if the endorphin level is the mediator.

3 enhancement designs

(3) Enhancement Designs

The goal of enhancement designs is to deliver interventions that enhance the effects of a hypothesized mediator.

For example, lets say that an addiction treatment program reduces remission by improving social support. An enhancement design would increase social support even more to demonstrate a larger effect on remission. Social support may be increased by more exposure to a therapist, additional contact with friends and family etc.

4 purification designs

(4) Purification Designs

The goal of purification designs is to reduce a manipulation to its critical ingredients.

For example, in drug prevention research, it appears that changes in norms, beliefs about positive consequences of drugs, and intentions to avoid drugs appear to be important mediators of drug prevention programs. A purification design would retain only those program components that address these mediators to test whether the purer program changes drug use.

5 pattern matching

(5) Pattern Matching

The goal of pattern matching is to specify patterns of results based on mediation theory. Different types of studies and information are used to assess whether the pattern of results is consistent with mediation theory.

Multiple variables: a mediation relation is observed for one variable but not another. For example, change in beliefs about positive consequences of alcohol use is a mediator for alcohol use but not for tobacco use. Changes in beliefs about positive consequences is a statistical mediator but changes in beliefs about negative consequences is not.

more pattern matching examples

More Pattern Matching Examples

Moderators: For example, prevention program effects are most effective for persons low on the mediator at baseline.

Setting: An intervention to change norms to change behavior should be more successful in a setting where more norm change may occur.

Different Manipulations: A different manipulation that should change the same theoretical mediator should lead to the same results.

goals of caps presentation
Goals of CAPS Presentation
  • Describe many mediating variable examples.
  • Describe reasons for mediation analysis--it can help improve prevention programs and reduce their cost. It is also useful for testing theories.
  • Describe the latest methods to assess mediation.
  • Describe limitations of mediation analysis.
  • Describe experimental as well as non-experimental designs to investigate mediating variables.
summary of workshop

Summary of Workshop

Described methods for multilevel, categorical, longitudinal, and multiple mediator data. Moderators and potential designs to assess mediation were discussed.

New methods have more power and are more accurate than older methods, e.g., distribution of the product methods.

Mediation can be investigated in the analysis of any design that includes mediating variable measures.

Mediation analysis provides a way to extract more information from a research study, e.g., action theory and conceptual theory. Can improve programs.