2 Inadequate Pre-Operational Explication of Constructs Preoperational = before translating constructs into measures or treatmentsIn 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 wayFor instance, only used paper-and-pencil tests
5 Hypothesis GuessingPeople 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 noneReally 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).