multilevel modeling: other topics

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Multilevel Modeling: Other Topics David A. Kenny January 7, 2014

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Multilevel Modeling: Other Topics. David A. Kenny. Outline. Centering and the Three Effects Multiple Correlation Convergence issues in SPSS Tau matrix Significance Testing Non-normal outcomes GEE. Centering and the Three Effects. The Three Effects of X (a level 1 variable) on Y - PowerPoint PPT Presentation

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Page 1: Multilevel Modeling: Other Topics

Multilevel Modeling:Other Topics

David A. Kenny

January 7, 2014

Page 2: Multilevel Modeling: Other Topics

2

Outline• Centering and the Three Effects

• Multiple Correlation

• Convergence issues in SPSS

• Tau matrix

• Significance Testing

• Non-normal outcomes

• GEE

Page 3: Multilevel Modeling: Other Topics

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Centering and the Three Effects

• The Three Effects of X (a level 1 variable) on Y–Within: effect of X on Y estimated for

each level 2 unit and then averaged

–Between: effect of mean X on Y

–Pooled: an “average” of the two

Page 4: Multilevel Modeling: Other Topics

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Example: Effect of Daily Stress on Mood

• Within: the effect of daily stress on mood computed for each person and then averaged

• Between: Stress is averaged for each person and then average stress is used to predict mood.

• Pooled: Stress is used to predict mood using all people and all days.

Page 5: Multilevel Modeling: Other Topics

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Types of Centering and the Effect

• Grand mean centering– X effect: Pooled estimate

• Grand mean centering with mean X as a predictor– X effect: Within estimate– Mean X effect: Between minus within estimate

• Group (or person) mean– X effect: Within estimate

Page 6: Multilevel Modeling: Other Topics

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Multiple Correlation• Not outputted by any MLM program.

• Estimate a second model without any fixed effects besides the intercept, the empty model.

• Measure the relative changes in variances with predictors in and out of the model.– sE

2 from the empty model; sE2 from the model

– (sE2 - sM

2)/sE2

– If negative, report as zero.

– Sometimes called pseudo R2.

Page 7: Multilevel Modeling: Other Topics

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Illustration: Légaré Variances >

Terma Empty Model Model R2

DD 0.105 0.102 .040

DP 0.131 0.131 .004

PD 0.009 0.008 .054

PP 0.184 0.184 .000aDP implies that the respondent is the doctor and that level is that of the patient.

Page 8: Multilevel Modeling: Other Topics

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Tau Matrix• Whenever there is more than one

random effect, there is a variance-covariance matrix of random effects.

• That matrix is called the “tau matrix” in the program HLM.

• Different programs make different restriction on this matrix.

Page 9: Multilevel Modeling: Other Topics

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Programs• HLM: Unstructured only

• SPSS and R’s nlme: Allows various possibilities but not any matrix.

• SAS and MLwiN: User can enter own matrix which gives maximal flexibility.

Page 10: Multilevel Modeling: Other Topics

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Example: Growth Curve Model with Indistinguishable Dyad Members

Slope P1 (1) a

Int. P1 (2) c b

Slope P2 (3) d e a

Int. 2 (4) e f c b

(1) (2) (3) (4)

Letters symbolize different elements of the tau matrix, some of which are set equal.

Page 11: Multilevel Modeling: Other Topics

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Significance Testing• SPSS uses the Wald test for

variances.

• The likelihood ratio test involving deviance differences is used by other programs and provides a more powerful and accurate test of significance.

Page 12: Multilevel Modeling: Other Topics

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Non-normal Outcomes• Types

–Dichotomous or binary outcomes–Counts

• For these cases, the error variance is not an additional parameter.

• Basic model is often multiplicative.• Can access in SPSS: Mixed Models,

Generalized Linear

Page 13: Multilevel Modeling: Other Topics

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GEE: Generalized Estimating Equations

• An alternative to MLM

• Does not test variance components, but rather using a “working model.”

• Weaker assumptions about the distribution of random variables.

• Used often in medical research.

• Used also with non-normal outcomes.