Final answer:
Selection Bias is a threat to validity in cross-sectional designs because it can lead to non-representative samples and non-generalizable results, affecting the study's internal validity.
Step-by-step explanation:
A threat to validity with cross-sectional designs is Selection Bias. Cross-sectional designs can be described as observational studies that analyze data from a population, or a representative subset, at a specific point in time. Selection bias occurs when the sample is not representative of the population being studied, which can lead to inaccurate or non-generalizable results.
In the context of quasi-experimental design, selection bias can undermine the internal validity of a study, as it makes it difficult to determine if the outcomes are due to the factors being studied or because of pre-existing differences between groups. Unlike a controlled experiment, where participants are randomly assigned to treatment conditions, quasi-experimental designs typically do not have that level of control over participant allocation, thus increasing the risk of selection biases affecting the results.