Shared Concepts

Multimember Effects

EFFECT effect-name=MULTIMEMBER (var-list </ mm-options>);

EFFECT effect-name=MM (var-list </ mm-options>);

A multimember effect is formed from one or more classification variables in such a way that each observation can be associated with one or more levels of the union of the levels of the classification variables. In other words, a multimember effect is a classification-type effect with possibly more than one nonzero column entry for each observation. Multimember effects are useful, for example, in modeling the following:

nurses’ effects on patient recovery in hospitals
teachers’ effects on student scores
lineage effects in genetic studies

The levels of a multimember effect consist of the union of formatted values of the variables that define this effect. Each such level contributes one column to the design matrix. For each observation, the value that corresponds to each level of the multimember effect in the design matrix is the number of times that this level occurs for the observation.

For example, the following data provide teacher information and end-of-year test scores for students after two semesters:

Student	Score	Teacher1	Teacher2
Mary	87	Tobias	Cohen
Tom	89	Rodriguez	Tobias
Fred	82	Cohen	Cohen
Jane	88	Tobias	.
Jack	99	.	.

For example, Mary had different teachers in the two semesters, Fred had the same teacher in both semesters, and Jane received instruction only in the first semester.

You can model the effect of the teachers on student performance by specifying a multimember effect as follows:

CLASS teacher1 teacher2;
EFFECT teacher = MM(teacher1 teacher2);

The levels of the teacher effect are Cohen, Rodriguez, and Tobias, and the associated design matrix columns are as follows:

Student	Cohen	Rodriguez	Tobias
Mary	1	0	1
Tom	0	1	1
Fred	2	0	0
Jane	0	0	1
Jack	.	.	.

You can specify the following mm-options after a slash (/):

DETAILS

displays a table that shows the levels of the multimember effect.

NOEFFECT

specifies that, for observations of the multimember variables whose levels are all missing, the values in the corresponding design matrix columns be set to 0. If, in the preceding example, the teacher effect is defined by the following statement, then the associated design matrix columns’ values for Jack are all 0:

EFFECT teacher = MM(teacher1 teacher2 / noeffect);

This statement enables you to include Jack in the analysis even though there is no effect of teachers on his performance.

A situation where it is important to designate observations as having no effect that can be attributed to a classification variable is the analysis of crossover designs, where lagged treatment levels are used to model the carryover effects of treatments between periods. Because there is no carryover effect for the first period, the treatment lag effect in a crossover design can be modeled by using a multimember effect that consists of a single classification variable and the NOEFFECT option, as in the following statements:

CLASS Treatment lagTreatment;
EFFECT Carryover = MM(lagTreatment / noeffect);

The lagTreatment variable contains a missing value for the first period. Otherwise, it contains the value of the treatment variable for the preceding period.

STDIZE

specifies that for each observation, the entries in the design matrix that corresponds to the multimember effect be scaled to have a sum of 1.

WEIGHT=weight-list

specifies numeric variables used to weigh the contributions of each of the classification effects that define the constructed multimember effect. The number of variables in weight-list must match the number of classification variables that define the effect.

Last updated: November 08, 2022