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Mediation
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1.Definition 2. Testing mediation using multiple regression Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182. (over 7000 citations) 3. Testing mediation using bootstrapping Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36(4), 717-731.SPSS and SAS procedures for estimating indirect effects in simple mediation models 4. Extensions (Moderated-Mediation and Mediated-Moderation) http://davidakenny.net/cm/mediate.htm http://www.psych.ku.edu/preacher/
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Example IV: Helping context (Alone vs. W/others) DV: Help giving (1= no help, 7 = a lot of help) Finding: A person is more likely to provide help when alone Why is it that people are more likely to help when they are alone? (MEDIATOR) When will this effect become weaker/stronger/disappear? (MODERATOR)
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Example IV: Stress (1-7 scale) DV: Depression (1-7 scale) Finding: Stressed people are more depressed Why are stressed people more depressed? What is the mechanism that explains that? (MEDIATOR) Under what conditions will the effect of stress on depression be weaker/stronger/disappear? (MODERATOR)
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Definitions (Baron & Kenny, 1986) Mediator – Answers the “why” question (gets at the process) - A variable that is caused by the IV, and in turn, causes the DV IV: Helping Context DV: Help Giving Mediator Responsibility
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Definitions (Baron & Kenny, 1986) Mediator – Answers the “why” question (gets at the process) - A variable that is caused by the IV, and in turn, causes the DV Helping contextHelp Responsibility
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1. the IV predicts the DV (c = total effect) Predictor: Helping contexts, DV: Help giving 2. the IV predicts the MED (a) Predictor: Helping contexts, DV: Responsibility 3. the MED predicts the DV (controlling for the IV) (b) Predictors: Helping contexts, Responsibility, DV: Help giving 4. c is reduced (to zero/not) controlling for the MED (c’=direct effect) Same equation as # 3 IV: Helping context DV: Help giving Med: responsibility a c’ c’ b c Establishing Mediation using Multiple Regression
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Full Mediation – Controlling for the mediator, the IV no longer affects the DV. Hence, c’ = 0 (β is no longer sig different from zero) (e.g. if you “take out” responsibility you lose the effect of helping context on help giving) Partial mediation – Controlling for the mediator, the effect of the IV on the DV (c’) is reduced, but still different from 0 (β is still sig) (e.g. if you “take out” responsibility you still get an effect of helping context on help giving) (OUTPUT) IV DV Mediator a b Full Mediation vs. Partial Mediation c’ c’ c
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Purpose: Test that a*b (or c-c’) is different from o. Important to examine also when full mediation is obtained Run a Sobel test: Z = a*b/ √(b²SEa² + a²SEb²) Use Preacher’s website a c’ c’ b c Testing the Indirect effect IV: Helping context DV: Help giving Med: responsibility
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Specification Error (do you have the right model?) The mediator may be caused by the outcome Solutions: - Makes sense theoretically? - Design issues (temporal order: measure mediator before DV) - Test the alternative model (run the model with switching the Med with the DV and test mediation again) Measurement error in mediator Solution: Measure with high reliability Mediator and outcome are sharing variance due to method effect (self reports) Solution: Use different methods to measure mediator and DV Omitted variable (causing both Med and DV) Solution: Specify, measure and control for it
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The a*b sampling distribution is assumed to be normal - but the distribution of products is usually positively skewed The Sobel test runs the risk of violating assumptions of normality resulting in less power (less chance to find an effect that exists in the population) Sobel’s test works best in large samples (N>200) Solution: bootstrapping (for testing the indirect effect) No distributional assumptions (a*b can be non-normally distributed) Not a large-sample technique (can be applied to small samples with more confidence) Preacher will do it for you (again!) (WEBSITE) a c’ c’ b c Limitation of Sobel test IV: Helping context DV: Help giving Med: responsibility
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Download the syntax command from preacher’s website Run it in SPSS (highlight the entire text and click run) Add a line at the end of the syntax file: SOBEL y=yvar / x = xvar / m = mvar / boot=z. yvar = the name of the dependent variable xvar = the name of the independent variable mvar = the name of the proposed mediating variable, z = specifies the number of bootstrap resamples desired (3000) a c’ c’ b c Bootstrapping IV: Helping context DV: Help giving Med: responsibility
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Extensions
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Moderator (Baron & Kenny, 1986) Moderator – Answers the “when” question -A variable that affects the direction and/or strength of the relation between an IV and a DV. - A cause (IV) and not an Outcome (DV) – (ideally measured before IV and not correlated with it) Establishing moderation: The moderator should interact with the predictor (IV) to cause the outcome (DV) IV: Helping context DV: Help giving Moderator: Target’s age
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Mediated Moderation -Purpose: To examine possible reasons (mediators) for a particular interaction effect -Same logic as simple mediation, only here the effect to be explained involves an interaction (moderation) -In the mediation model, the IV is the interaction term (IV*Moderator). -Effect: When the target is a child people tend to help regardless of context. - Why? Helping Target’s Context * age Help Responsibility
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Moderated Mediation Purpose: Examine whether the mediation effect remains constant across different groups; Preacher, Rucker, & Hayes, 2007 for SPSS macro) Conditional Indirect Effect -E.g., The mediation model doesn’t work for people who served at the Peace Corps Peace Corps Non-Peace Corps Helping Context Help Responsibility a c’ c’ b c Help Responsibility a c’ c’ b c Helping Context
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