1. Threats to Construct Validity

2. Inadequate Pre-Operational Explication of Constructs
Preoperational = before translating constructs into measures or treatments
In other words, you didn't do a good enough job of defining (operationally) what you mean by the construct

3. Mono-Operation Bias Pertains to the treatment or program
Used only one version of the treatment or program

4. Mono-Method Bias Pertains especially to the measures or outcomes
Only operationalized measures in one way
For instance, only used paper-and-pencil tests

5. Hypothesis Guessing
People guess the hypothesis and respond to it rather than respond "naturally“.
People want to look good or look smart.
This is a construct validity issue because the "cause" will be mislabeled. You'll attribute effect to treatment rather than to good guessing.

6. Evaluation Apprehension
People make themselves look good because they know they're in a study.
Perhaps their apprehension makes them consistently respond poorly -- you mislabel this as a negative treatment effect.

7. Experimenter Expectancies
The experimenter can bias results consciously or unconsciously.
Bias becomes confused (mixed up with) the treatment; you mislabel the results as a treatment effect.

8. Confounding Constructs and Levels of Constructs
Conclude that the treatment has no effect when it is only that level of the treatment which has none
Really a dosage issue -- related to mono-operation because you only looked at one or two levels.

9. Interaction of Different Treatments
People get more than one treatment .
This happens all the time in social ameliorative studies.
Again, the construct validity issue is largely a labeling issue.

10. Interaction of Testing and Treatment
Does the testing itself make the groups more sensitive or receptive to the treatment?
This is a labeling issue.
It differs from testing threat to internal validity; here, the testing interacts with the treatment to make it more effective; there, it is not a treatment effect at all (but rather an alternative cause).

11. Restricted Generalizability Across Constructs
You didn't measure your outcomes completely.
You didn't measure some key affected constructs at all (for example, unintended effects).