[讨论]Manova in SPSS by Programming

Example

MANOVA A, B, C, D, E BY FAC( 1,4) WITH F G

/ ANALYSIS = ( A, B / C / D WITH E) WITH F G

/ DESIGN.

A final covariate list WITH F G is specified outside the parentheses. The covariates apply to every list within the parentheses.

The first analysis uses A and B, with F and G as covariates.

The second analysis uses C, with F and G as covariates.

The third analysis uses D, with E, F, and G as covariates.

Factoring out F and G is the only way to use them as covariates in all three analyses, since no variable can be named more than once on an ANALYSIS subcommand.

Example MANOVA A B C BY FAC( 1,3)

/ ANALYSIS( CONDITIONAL) = ( A WITH B / C)

/ DESIGN.

In the first analysis, A is the dependent variable, B is a covariate, and C is not used.

In the second analysis, C is the dependent variable, and both A and B are covariates.

Example

MANOVA Y1 TO Y4 BY GROUP( 1,2)

/ WSFACTORS= YEAR( 4)

/ CONTRAST( YEAR)= POLYNOMIAL

/ RENAME= CONST, LINEAR, QUAD, CUBIC

/ PRINT= TRANSFORM PARAM( ESTIM)

/ WSDESIGN= YEAR / DESIGN= GROUP.

WSFACTORS immediately follows the MANOVA variable list and specifies a repeated measures analysis in which the four dependent variables represent a single variable measured at four levels of the within- subjects factor. The within- subjects factor is called YEAR for the duration of the MANOVA procedure.

CONTRAST requests polynomial contrasts for the levels of YEAR. Because the four variables, Y1, Y2, Y3, and Y4, in the working data file represent the four levels of YEAR, the effect is to perform an orthonormal polynomial transformation of these variables.

RENAME assigns names to the dependent variables to reflect the transformation.

PRINT requests that the transformation matrix and the parameter estimates be displayed.

WSDESIGN specifies a within- subjects design that includes only the effect of the YEAR within- subjects factor. Because YEAR is the only within- subjects factor specified, this is the default design, and WSDESIGN could have been omitted.

DESIGN specifies a between- subjects design that includes only the effect of the GROUP between- subjects factor. This subcommand could have been omitted.

Example

MANOVA MATH1 TO MATH4 BY METHOD( 1,2) WITH PHYS1 TO PHYS4 ( SES)

/ WSFACTORS= SEMESTER( 4).

The four dependent variables represent a score measured four times ( corresponding to the four levels of SEMESTER).

The four varying covariates PHYS1 to PHYS4 represents four measurements of another score.

SES is a constant covariate. Its value does not change over the time covered by the four levels of SEMESTER.

Default contrast ( POLYNOMIAL) is used.

MANOVA X1Y1 X1Y2 X2Y1 X2Y2 X3Y1 X3Y2 BY TREATMNT( 1,5) GROUP( 1,2)

/ WSFACTORS= X( 3) Y( 2)

/ DESIGN.

The MANOVA variable list names six dependent variables and two between- subjects factors, TREATMNT and GROUP.

WSFACTORS identifies two within- subjects factors whose levels distinguish the six dependent variables. X has three levels and Y has two. Thus, there are cells in the within- subjects design, corresponding to the six dependent variables.

Variable X1Y1 corresponds to levels 1,1 of the two within- subjects factors; variable X1Y2 corresponds to levels 1,2; X2Y1 to levels 2,1; and so on up to X3Y2, which corresponds to levels 3,2. The first within- subjects factor named, X, varies most slowly, and the last within- subjects factor named, Y, varies most rapidly on the list of dependent variables.

Because there is no WSDESIGN subcommand, the within- subjects design will include all main effects and interactions: X, Y, and X by Y.

Likewise, the between- subjects design includes all main effects and interactions: TREATMNT, GROUP, and TREATMNT by GROUP.

In addition, a repeated measures analysis always includes interactions between the withinsubjects factors and the between- subjects factors. There are three such interactions for each of the three within- subjects effects.

Example

MANOVA SCORE1 SCORE2 SCORE3 BY GROUP( 1,4)

/ WSFACTORS= ROUND( 3)

/ CONTRAST( ROUND)= DIFFERENCE / CONTRAST( GROUP)= DEVIATION

/ PRINT= TRANSFORM PARAM( ESTIM).

? This analysis has one between- subjects factor, GROUP, with levels 1, 2, 3, and 4, and one within- subjects factor, ROUND, with three levels that are represented by the three dependent variables.

? The first CONTRAST subcommand specifies difference contrasts for ROUND, the withinsubjects factor.

? There is no WSDESIGN subcommand, so a default full factorial within- subjects design is assumed. This could also have been specified as WSDESIGN= ROUND, or simply WSDESIGN.

? The second CONTRAST subcommand specifies deviation contrasts for GROUP, the between- subjects factor. This subcommand could have been omitted because deviation contrasts are the default.

? PRINT requests the display of the transformation matrix generated by the within- subjects contrast and the parameter estimates for the model.

? There is no DESIGN subcommand, so a default full factorial between- subjects design is assumed. This could also have been specified as DESIGN= GROUP, or simply DESIGN.

Example

MANOVA JANLO, JANHI, FEBLO, FEBHI, MARLO, MARHI BY SEX( 1,2)

/ WSFACTORS MONTH( 3) STIMULUS( 2)

/ WSDESIGN MONTH, STIMULUS

/ WSDESIGN / DESIGN SEX.

? There are six dependent variables, corresponding to three months and two different levels of stimulus.

? The dependent variables are named on the MANOVA variable list in such an order that the level of stimulus varies more rapidly than the month. Thus, STIMULUS is named last on the WSFACTORS subcommand.

? The first WSDESIGN subcommand specifies only the main effects for within- subjects factors. There is no MONTH by STIMULUS interaction term.

? The second WSDESIGN subcommand has no specifications and, therefore, invokes the default within- subjects design, which includes the main effects and their interaction.

We can use MWITHIN on either the WSDESIGN or the DESIGN subcommand in a model with both between- and within- subjects factors to estimate simple effects for factors nested within factors of the opposite type.

Example

MANOVA WEIGHT1 WEIGHT2 BY TREAT( 1,2)

/ WSFACTORS= WEIGHT( 2)

/ DESIGN= MWITHIN TREAT( 1) MWITHIN TREAT( 2) MANOVA WEIGHT1 WEIGHT2 BY TREAT( 1,2)

/ WSFACTORS= WEIGHT( 2)

/ WSDESIGN= MWITHIN WEIGHT( 1) MWITHIN WEIGHT( 2)

/ DESIGN.

? The first DESIGN tests the simple effects of WEIGHT within each level

? The second DESIGN tests the simple effects of TREAT within each level of WEIGHT.

Example

MANOVA TEMP1 TO TEMP6, WEIGHT1 TO WEIGHT6 BY GROUP( 1,2)

/ WSFACTORS= DAY( 3) AMPM( 2)

/ MEASURE= TEMP WEIGHT

/ WSDESIGN= DAY, AMPM, DAY BY AMPM

/ PRINT= SIGNIF( HYPOTH AVERF)

/ DESIGN.

? There are 12 dependent variables: 6 temperatures and 6 weights, corresponding to morning and afternoon measurements on three days.

? WSFACTORS identifies the two factors ( DAY and AMPM) that distinguish the temperature and weight measurements for each subject. These factors define six within- subjects cells.

? MEASURE indicates that the first group of six dependent variables correspond to TEMP and the second group of six dependent variables correspond to WEIGHT.

? These labels, TEMP and WEIGHT, are used on the output requested by PRINT.

? WSDESIGN requests a full factorial within- subjects model. Because this is the default, WSDESIGN could have been omitted.

Example

MANOVA LOW1 LOW2 LOW3 HI1 HI2 HI3

/ WSFACTORS= LEVEL( 2) TRIAL( 3)

/ CONTRAST( TRIAL)= DIFFERENCE

/ RENAME= CONST LEVELDIF TRIAL21 TRIAL312 INTER1 INTER2

/ PRINT= TRANSFORM

/ DESIGN.

? This analysis has two within- subjects factors and no between- subjects factors.

? Difference contrasts are requested for TRIAL, which has three levels.

? Because all orthonormal contrasts produce the same F test for a factor with two levels, there is no point in specifying a contrast type for LEVEL.

? New names are assigned to the transformed variables based on the transformation matrix. These names correspond to the meaning of the transformed variables: the mean or constant, the average difference between levels, the average effect of trial 2 compared to 1, the average effect of trial 3 compared to 1 and 2; and the two interactions between LEVEL and TRIAL.