[讨论]Manova in SPSS by Programming

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

Hi, Lister:

Could you show me how to conduct Manova in SPSS programming?

Thanks!

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

分享0 收藏0 回帖

关键词：Programming Program MANOVA ANOVA anov 讨论 SPSS Programming MANOVA

Example 1

* Analysis of Variance

MANOVA RESULT BY TREATMNT( 1,4) GROUP( 1,2).

Example 2

* Analysis of Covariance

MANOVA RESULT BY TREATMNT( 1,4) GROUP( 1,2) WITH RAINFALL.

Example 3

* Repeated Measures Analysis

MANOVA SCORE1 TO SCORE4 BY CLASS( 1,2) / WSFACTORS= MONTH( 4).

Example 4

* Parallelism Test with Crossed Factors

MANOVA YIELD BY PLOT( 1,4) TYPEFERT( 1,3) WITH FERT

/ ANALYSIS YIELD

/ DESIGN FERT, PLOT, TYPEFERT, PLOT BY TYPEFERT, FERT BY PLOT + FERT BY TYPEFERT + FERT BY PLOT BY TYPEFERT.

MANOVA ( multivariate analysis of variance) is a generalized procedure for analysis of variance and covariance. MANOVA is a powerful analysis- of- variance procedure and can be used for both univariate and multivariate designs. MANOVA allows you to perform the following tasks:

? Specify nesting of effects.

? Specify individual error terms for effects in mixed- model analyses.

? Estimate covariate- by- factor interactions to test the assumption of homogeneity of regressions.

? Obtain parameter estimates for a variety of contrast types, including irregularly spaced polynomial contrasts with multiple factors.

? Test user- specified special contrasts with multiple factors.

? Partition effects in models. ? Pool effects in models.

MANOVA YIELD BY SEED( 1,4) FERT( 1,3) WITH RAINFALL

/ PRINT= CELLINFO( MEANS) PARAMETERS( ESTIM)

/ DESIGN.

YIELD is the dependent variable; SEED ( with values 1, 2, 3, and 4) and FERT ( with values 1, 2, and 3) are factors; RAINFALL is a covariate.

The PRINT subcommand requests the means of the dependent variable for each cell and the default deviation parameter estimates.

Example

MANOVA DEPENDNT BY FACTOR1 ( 1,3) FACTOR2, FACTOR3 ( 1,2).

In this example, three factors are specified.

FACTOR1 has values 1, 2, and 3, while FACTOR2 and FACTOR3 have values 1 and 2.

A default full factorial model is used for the analysis.

Example

MANOVA DEP BY A( 1,2) B( 1,4)

/ ERROR = 1

/ DESIGN = A, B, A BY B = 1 VS WITHIN

/ DESIGN = A, B.

ERROR defines error term 1 as the default error term.

In the first design, A by B is defined as error term 1 and is therefore used to test the A and B effects. The A by B effect itself is explicitly tested against the within- cells error.

In the second design, no term is defined as error term 1, so no significance tests are carried out. Hypothesis sums of squares are displayed for A and B.

Example

MANOVA DEP BY FAC( 1,5)

/ CONTRAST( FAC)= DIFFERENCE

/ DESIGN= FAC( 1) FAC( 2) FAC( 3) FAC( 4).

The factor FAC has five categories and therefore four degrees of freedom.

CONTRAST requests DIFFERENCE contrasts, which compare each level ( except the first) with the mean of the previous levels.

Each of the four degrees of freedom is tested individually on the DESIGN subcommand.

Example

MANOVA OUTCOME BY TREATMNT( 1,12)

/ PARTITION( TREATMNT) = ( 3* 2,4)

/ DESIGN TREATMNT( 2).

The factor TREATMNT has 12 categories, hence 11 degrees of freedom.

PARTITION divides the effect of TREATMNT into four partitions, containing, respectively, 2, 2, 2, and 4 degrees of freedom. A fifth partition is formed to contain the remaining 1 degree of freedom.

DESIGN specifies a model in which only the second partition of TREATMNT is tested. This partition contains the third and fourth degrees of freedom.

Since the default contrast type for between- subjects factors is DEVIATION, this second partition represents the deviation of the third and fourth levels of TREATMNT from the grand mean.

Example MANOVA DEP BY A B C ( 1,4)

/ METHOD= NOCONSTANT

/ DESIGN= A, B, C

/ METHOD= CONSTANT SEQUENTIAL

/ DESIGN.

For the first design, a main- effects model, the METHOD subcommand requests the model to be fitted with no constant.

The second design requests a full factorial model to be fitted with a constant and with a sequential decomposition of sums of squares.

Example

MANOVA DEP BY A( 1,3) B( 1,2)

/ OMEANS= TABLES( A, B)

/ DESIGN.

Because there is no VARIABLES specification on the OMEANS subcommand, observed means are displayed for all continuous variables. DEP is the only dependent variable here, and there are no covariates.

The TABLES specification on the OMEANS subcommand requests tables of observed means for each of the three categories of A ( collapsing over B) and for both categories of B ( collapsing over A).