Question

What kind of parametric test is suitable for this design?
R X O
R O
dv = interval
multi-subscales

A. ANOVA

B. ANCOVA

C. MANOVA

D. MANCOVA

Answer 1

The RXO and RO are also known as the Posttest-only control group design.

This design would be used when you are not able to do pretesting but you can get a comparison or control group. A serious limitation with this design is that because you do not have a random assignment to the groups you cannot assume that the groups are similar on many variables, and therefore, it is highly risky to conclude that any difference observed on the posttest is the result of the treatments. The key threats to the internal validity of this design (in addition to the problem I just mentioned) are differential selection, differential attrition, and additive and interactive effects.

You can use this design when you don’t need real strong evidence about cause and effect and when you want to measure whether people change over time due to treatment. For example, if you want to train the workforce in your organization to understand their retirement system, you could pretest them, give the training, and then posttest them and look for an increase in knowledge about the retirement system. The key threats to internal validity for this design are history, maturation, testing, instrumentation, and regression artifacts.

This design is so poor that I would recommend never using it. The biggest problem is that you have little evidence that what is observed at the posttest is due to the treatment because you have no pretest to use it as a baseline measure (i.e., you don’t know where they started out). This is the weakest of all experimental designs. The only possible situation I can think of where this design might be of some use would be where the posttest measures something that you are pretty sure they all knew nothing about previously so that you can assume that if a pretest had been given then they all would have scored very low. Occasionally in some training situations, this assumption might be made. Still, you can very much improve this design by adding a pretest (i.e., transforming it into the one-group pretest-posttest design). The key threats to this design (in addition to the problems I just mentioned) are history and maturation

Since it is not vulnerable to testing-treatment interaction so we need not go interaction testing.

There it is enough to use the ANOVA in this kind of design.