Yes, a Repeated Measures Covariance Analysis (RM-ANCOVA) is the appropriate method in SPSS for your scenario. It's designed for analyzing the effects of within-subject factors (repeated measures) on a continuous dependent variable while controlling for the influence of a continuous covariate (your single continuous independent variable).

Rahul Banerjee Hi Rahul. Thank you for the kind explanation.

Apart from the influence of the 4 categorical variables, I also want to explore the main effect of this continuous dependent variable on the dependent variable. You gave a quite clear explanation of the RM-ANCOVA, does that mean it could only offer the main effect of the 4 categorical variables, as well as the interactions between each categorical variable and this continuous dependent variable, as shown by my previous SPSS results? If RM-ANCOVA could not provide the main effect of this continuous dependent variable, which statistical method could do that? Thank you.

Xiaolan Gu

somehow the designs is not really clear to me. What is the repeated measures in your design?

If your rm-ANCOVA model is the right choice cannot be answered without way more information about the variables and the model. Especially the residuals may help to assess this. Not all forms of DVs are suitable for OLS regression/ANOVA models, but that is a totally different topic.

If we assume that everything is correctly specified and the model is suitable, you will not get the requested output by default, since the main focus of a ANCOVA is not the influence of the metric covariable. But the approach is correct (given all assumptions are met) and should give you the same result as a multiple regression approach. To get the main and interactions of the metric (co-)variable in your model you need to tell SPSS directly.

In the ANOVA dialogue, you need to specify all relevant variables and then go to "Model..."

Under "Specify Model" the default is "Full Factorial", but this will only show the main and interaction effects for the nominal factors. Click "Build terms" and build all effects you want to consider in your model, including all effects with the metric (co)variable. Now you should find all effects in the output.

Based on your description, a repeated measures analysis of covariance (ANCOVA) seems appropriate for assessing the effects of multiple independent variables on a continuous dependent variable when there are repeated measurements. However, you’re not seeing the main effect of the continuous variable, consider the following:1. Check the model specification:Ensure that the continuous variable is correctly included as covariate in the model. It should be listed as predictor variable, not just an interaction term.2. Interaction Terms: verify if the model is set to test interactions correctly. Sometimes, specifying terms or main effects can affect the visibility of certain effects.3. Ensure that your data meets the assumptions of ANCOVA, including normality and homogeneity of variances.4. statistical software settings: Double- check the settings in SPSS to make sure that all variables are included in the analysis as intended.If these steps don’t resolve the issue, you might also consider alternative approaches like mixed- effects models, which can handle repeated measures and multiple types of predictors more flexibly.

1. Repeated Measures ANCOVA

in SPSS

Analyze > General Linear Model > Repeated Measures,Define your within-subjects factors

If you have categorical variables that vary between subjects, include these in the "Between-Subjects Factors" section

If your continuous variable is a covariate

you should specify it in the "Covariates" section under Analyze > General Linear Model > Univariate@Xiaolan Gu