|
⇤ ← Revision 1 as of 2008-10-16 10:52:30
Size: 1847
Comment:
|
Size: 2065
Comment:
|
| Deletions are marked like this. | Additions are marked like this. |
| Line 1: | Line 1: |
| = Inappropriate use of a constant covariate in repeated measures ANCOVA = | |
| Line 3: | Line 2: |
| When a covariate which only varies between subjects (e.g. age) is in a repeated measures ANCOVA it has no effect on the ''main effect'' of any repeated measures factor e.g. time. It can, however, influence the factor by being present in a factor by covariate interaction e.g. age by time. An ANCOVA assumes there is ''no'' covariate by w subs factor interaction but some packages such as SPSS automaticallyfit such a term. If there is ''no'' covariate by time interaction then the term may be dropped from the model using, for example, the ''custom mode'' option in the SPSS GLM procedure. | = Inappropriate use of a constant covariate in repeated measures ANCOVA = |
| Line 5: | Line 5: |
| There is some debate about what to do if there is such an interaction. Some authors advocate dropping the w subs factor by subject interaction and interpreting the main effect of the within subjects factor with no interaction present even if the interactio is statistically significant (see [http://journals.cambridge.org/download.php?file=%2FINS%2FINS13_02%2FS1355617707070464a.pdf&code=5f2a2f071eb28ffc6ca4bcf617ba24df here).] |
When a covariate which only varies between subjects (e.g. age) is in a repeated measures ANCOVA it has no effect on the ''main effect'' of any repeated measures factor e.g. time. It can, however, influence the factor by being present in a factor by covariate interaction e.g. age by time. An ANCOVA assumes there is ''no'' covariate by w subs factor interaction but some packages such as SPSS automaticallyfit such a term. If there is ''no'' covariate by time interaction then the term may be dropped from the model using, for example, the ''custom model'' option in the SPSS GLM procedure. This can also be achieved using ''SPSS MANOVA'' OR ''SPSS MIXED'' syntax although the latter requires a reformatting of the data. There is some debate about what to do if there is such an interaction. Some authors advocate dropping the w subs factor by subject interaction and interpreting the main effect of the within subjects factor with no interaction present even if the interaction is statistically significant (see [http://journals.cambridge.org/download.php?file=%2FINS%2FINS13_02%2FS1355617707070464a.pdf&code=5f2a2f071eb28ffc6ca4bcf617ba24df here).] This approach is supported by SPSS (A copy of this is [:FAQ/res22133 here.)] |
| Line 9: | Line 11: |
| It is a good idea to ''centre'' the covariate (by subtracting the overall sample mean from each data point) before entering it into the repeated measures ANCOVA. This has the advantage of correctly leaving unaltered the within subjects factor sum of squares in the presence of the covariate. The error sum of squares for the within subjects factor (ignoring the covariate) is divided into a sums of squares for the factor by covariate interaction and an error sum of squares representing the diff | It is a good idea to ''centre'' the covariate (by subtracting the overall sample mean from each data point) before entering it into the repeated measures ANCOVA. This has the advantage over using raw covariate values of correctly leaving unaltered the within subjects factor sum of squares in the presence of the covariate. The error sum of squares for the within subjects factor (ignoring the covariate) is divided into a sums of squares for the factor by covariate interaction and a remainder term. |
= Inappropriate use of a constant covariate in repeated measures
- ANCOVA =
When a covariate which only varies between subjects (e.g. age) is in a repeated measures ANCOVA it has no effect on the main effect of any repeated measures factor e.g. time. It can, however, influence the factor by being present in a factor by covariate interaction e.g. age by time. An ANCOVA assumes there is no covariate by w subs factor interaction but some packages such as SPSS automaticallyfit such a term. If there is no covariate by time interaction then the term may be dropped from the model using, for example, the custom model option in the SPSS GLM procedure. This can also be achieved using SPSS MANOVA OR SPSS MIXED syntax although the latter requires a reformatting of the data.
There is some debate about what to do if there is such an interaction. Some authors advocate dropping the w subs factor by subject interaction and interpreting the main effect of the within subjects factor with no interaction present even if the interaction is statistically significant (see [http://journals.cambridge.org/download.php?file=%2FINS%2FINS13_02%2FS1355617707070464a.pdf&code=5f2a2f071eb28ffc6ca4bcf617ba24df here).] This approach is supported by SPSS (A copy of this is [:FAQ/res22133 here.)] Others suggest ignoring the main within subjects effect altogether and explaining the interaction (see [http://journals.cambridge.org/download.php?file=%2FINS%2FINS13_02%2FS135561770707049Xa.pdf&code=f1fdb8258ee7f4da74f024857fd4885c here.)]
It is a good idea to centre the covariate (by subtracting the overall sample mean from each data point) before entering it into the repeated measures ANCOVA. This has the advantage over using raw covariate values of correctly leaving unaltered the within subjects factor sum of squares in the presence of the covariate. The error sum of squares for the within subjects factor (ignoring the covariate) is divided into a sums of squares for the factor by covariate interaction and a remainder term.