1. The Two-intercept Approach in Multilevel Modeling with SPSS
David A. Kenny

2. *
Presumed Background
Multilevel Modeling
Nested Design *

3. Problem Have multilevel data and have two variables*
Problem
Have multilevel data and have two variables
How to simultaneously examine effects for each of the two variables and allow for correlations of effects?
How can we fool a method that estimates one equation to estimate two equations? *

4. Examples “Simple” bivariate analysis Data from two people*
Examples
“Simple” bivariate analysis
Data from two people
Two growth curves
Two variables
Two people *

5. *
Illustrative Example
Data originally collected by Campbell, C., Lockyer, J., Laidlaw, T., & Macleod, H. (2007). Assessment of a matchedpair instrument to examine doctor–patient communication skills in practising doctors. Medical Education, 41, 123–129.
A study in which each physician (level 2) has multiple patients (level 1).
Outcome: Decision Conflict Scale or DCS
Uncertainty from both the patient and the doctor (hence the two variables)
Sixteen-item scale from 1 to 5 *

6. Download
Data
Syntax
Output

7. Predictors MD_Age from 23 to 63 Patient Age PT_Age from 13 to 99*
Predictors
Physician Age
MD_Age
from 23 to 63
Patient Age
PT_Age
from 13 to 99
Both centered at age 40. *

8. Sample Sizes 162 physicians with 2 to 38 patients*
Sample Sizes
162 physicians with 2 to 38 patients
93 physicians with 20 patients
2682 patients in all *

9. Data Preparation Stack the data*
Data Preparation
Stack the data
Instead of having patient and doctor’s response on the same record for each patient, have two records one for the patient and one for the doctor. *

10. Three New Variables Create two dummy variables. Each is 0 and 1*
Three New Variables
Create two dummy variables.
Each is 0 and 1
One called MD (a 1 if from the doctor and 0 otherwise)
The other called PT (a 1 if from the patient and 0 otherwise)
Note that MD and PT correlate -1!
Also create the variable “Role” with two levels MD and PT (or 1 and 2). *

12. *
Downloads
Data
Syntax
Output *

13. Model: Intercepts Drop the ordinary intercept in the model.*
Model: Intercepts
Drop the ordinary intercept in the model.
Have MD and PT as predictors.
They correspond to the individual intercepts for each of the two variables. *

14. Fixed Effects Predictors*
Fixed Effects
Predictors
Role
Role*MD_AgeC
Role*PT_AgeC
Note that if just MD_AgeC is put in the model, you are fixing the effect of doctor age to be the same for doctor’s the patient’s DCS. *

15. Syntax MIXED dcs BY role WITH pt md pt_agec md_agec*
Syntax
MIXED
dcs BY role WITH pt md pt_agec md_agec
/FIXED = role role*pt_agec role*md_agec | NOINT
/PRINT = SOLUTION TESTCOV
/RANDOM role | SUBJECT(md_id) COVTYPE(UNR)
/REPEATED = role | SUBJECT(md_id*pt_id) COVTYPE(UNR) . *

18. Unchecked!

19. Unchecked!

20. Written as One Equation for patients than doctors.
Doctors experience less decision conflict if they are older and their patients are younger.
Written as One Equation
DCSMD = PT_AGEC MD_AGEC + eMD
DCSPT = PT_AGEC MD_AGEC + ePT
Intercept much lower
for patients than doctors.

21. (1) is MD and (2) is PT (alphabetical).
“Repeated Measures” are the error variances.
“role[…]” are the random intercepts

22. Variances and Correlations*
Doctors with generally low decisional conflict do not have patients with low decisional conflict.
Variances and Correlations
Level Level 2
Patent Doctor
Doctor:
I I r
Patient:
Some doctors generally think that there is low decisional conflict whereas others think there is more decisional conflict.
Patients of the same doctor agree somewhat as to the doctor’s level of decisional conflict. *

23. Variances and Correlations*
If a doctor thinks there was low decisional conflict with a particular patient, that patient very slightly agrees.
Variances and Correlations
Level Level 2
Patent Doctor
Doctor:
I I r
I I
Patient:
Variance in how much a patient especially experiences decision conflict.
Variance in how much a doctor who thinks that there is particular low or high decisional conflict with a given patient. *

24. Alternative Formulation*
Alternative Formulation
Instead of Doctor and Patient Age, we could have Age of the respondent and age of the other person.
This formulation is the Actor-partner Interdependence Model or APIM. *

25. Thank You Dr. Campbell & Dr. France Légaré!*
Thank You Dr. Campbell & Dr. France Légaré! *

26. *
Other Webinars
References (pdf)
Crossed Design
Advanced Topics *