Survivorship bias occurs when researchers focus on individuals, groups, or cases that have passed some sort of selection process while ignoring those who did not. Survivorship bias can lead researchers to form incorrect conclusions due to only studying a subset of the population. Survivorship bias is a type of selection bias.
Selection bias refers to situations where research bias is introduced due to factors related to the study’s participants. Selection bias can be introduced via the methods used to select the population of interest, the sampling methods, or the recruitment of participants. It is also known as the selection effect.
Selection bias may threaten the validity of your research, as the study population is not representative of the target population.
The Pygmalion effect refers to situations where high expectations lead to improved performance and low expectations lead to worsened performance. Although the Pygmalion effect was originally observed in the classroom, it also has been applied to in the fields of management, business, and sports psychology.
The Pygmalion effect is also known as the Rosenthal effect, after the researcher who first observed the phenomenon.
The Hawthorne effect refers to people’s tendency to behave differently when they become aware that they are being observed. As a result, what is observed may not represent “normal” behavior, threatening the internal and external validity of your research.
The Hawthorne effect is also known as the observer effect and is closely linked with observer bias.
Inclusion and exclusion criteria determine which members of the target population can or can’t participate in a research study. Collectively, they’re known as eligibility criteria, and establishing them is critical when seeking study participants for clinical trials.
This allows researchers to study the needs of a relatively homogeneous group (e.g., people with liver disease) with precision. Examples of common inclusion and exclusion criteria are:
Study-specific variables: Type and stage of disease, previous treatment history, presence of chronic conditions, ability to attend follow-up study appointments, technological requirements (e.g., internet access)
Predictive validity refers to the ability of a test or other measurement to predict a future outcome. Here, an outcome can be a behavior, performance, or even disease that occurs at some point in the future.
Predictive validity is a subtype of criterion validity. It is often used in education, psychology, and employee selection.
Concurrent validity shows you the extent of the agreement between two measures or assessments taken at the same time. It compares a new assessment with one that has already been tested and proven to be valid.
Concurrent validity is a subtype of criterion validity. It is called “concurrent” because the scores of the new test and the criterion variables are obtained at the same time.
Establishing concurrent validity is particularly important when a new measure is created that claims to be better in some way than existing measures: more objective, faster, cheaper, etc.
Criterion validity (or criterion-related validity) evaluates how accurately a test measures the outcome it was designed to measure. An outcome can be a disease, behavior, or performance. Concurrent validity measures tests and criterion variables in the present, while predictive validity measures those in the future.
To establish criterion validity, you need to compare your test results to criterion variables. Criterion variables are often referred to as a “gold standard” measurement. They comprise other tests that are widely accepted as valid measures of a construct.
The researcher can then compare the college entry exam scores of 100 students to their GPA after one semester in college. If the scores of the two tests are close, then the college entry exam has criterion validity.
When your test agrees with the criterion variable, it has high criterion validity. However, criterion variables can be difficult to find.