VALIDITY
Validity refers to the extent to which a concept, conclusion, measurement, or design accurately represents
the phenomenon it claims to represent or measures what it intends to measure. In various contexts such as
research, testing, and argumentation, validity is essential for ensuring that the results or conclusions drawn
from a study or process are meaningful, reliable, and applicable to the real world.
TYPES OF VALIDITY
INTERNAL VALIDITY
Internal validity refers to the extent to which a research study accurately measures or tests the relationship
between variables without the influence of confounding factors or biases. In other words, it assesses
whether the observed changes in the dependent variable are actually caused by manipulation of the
independent variable(s) and not due to other factors.
Achieving high internal validity involves designing the study in a way that minimizes potential sources of bias
or error.
Internal validity is crucial for drawing accurate conclusions about cause-and-effect relationships within a
study. Without strong internal validity, it becomes difficult to confidently attribute changes in the dependent
variable(s) to the independent variable(s) being studied.
Threats to internal validity
History: Changes that occur over time, unrelated to the experimental manipulation, may affect the
dependent variable(s). For example, if a study is conducted over several weeks, other events or experiences
during that time period could influence participants' responses.
This threat could be mitigated by using a control group.
Maturation: Natural changes or development within participants that occur over time can impact the
dependent variable(s). This is particularly relevant in longitudinal studies or studies involving children or
aging populations.
This threat could also be mitigated by using a control group.
Testing: Participants' responses may be influenced by repeated exposure to the measurement instrument
or by previous testing experiences. This can lead to changes in performance or behavior unrelated to the
experimental manipulation. People may become aware of the objective or purpose of the study and this may
make bias the responses.
Instrumentation: Changes in the measurement instruments or procedures used to assess the dependent
variable(s) over the course of the study can introduce inconsistencies or errors into the data. If the tool used
during pretest and post test are different then it may become difficult to infer changes to the intervention
of interest.
, Regression to the mean: Extreme scores on a measure tend to move closer to the average (mean) upon
retesting, regardless of any experimental manipulation. This can lead to the illusion of a treatment effect
when, in fact, the change is simply due to statistical fluctuations.
This threat can be controlled by using an adequate sample size.
Selection bias: Differences in the characteristics of participants assigned to different groups may affect the
dependent variable(s), making it difficult to determine whether any observed effects are due to the
independent variable(s) or pre-existing differences between groups.
Attrition or dropout: Loss of participants over the course of a study can introduce bias if the characteristics
of those who drop out differ systematically from those who remain in the study.
This can be controlled by increasing the sample size by maybe 10% in order to account for the attrition.
Experimental mortality: Participants dropping out of the study due to factors related to the experimental
manipulation can affect the results, particularly if dropout rates are unequal across experimental
manipulations.
EXTERNAL VALIDITY
External validity refers to the extent to which the results of a research study can be generalized or applied
to settings, populations, or conditions beyond the specific context of the study.
Dimensions of external validity
Population validity: Refers to the extent to which the findings of a study can be generalized to other
populations beyond the one studied. For example, if a study is conducted with college students, can the
results be generalized to adults.
Ecological validity: Refers to the extent to which the experimental conditions and procedures in a study
reflect real-world settings and behaviors.
Temporal validity: Also known as historical validity, it refers to the extent to which the findings of a study
remain relevant over time. For instance, if a study was conducted in the 1980s, can its findings still be
applicable today given potential changes in societal norms, technology, or other factors.
Situational validity: Refers to the extent to which the findings of a study can be generalized across different
situations or contexts. For example, if a study was conducted in a laboratory setting, can its findings be
applied to real-life situations outside of the laboratory.
Threats to external validity
Population characteristics: If the sample used in the study is not representative of the larger population to
which the findings are intended to be applied, generalization may be limited. This could occur if the sample
is too homogeneous or if certain groups are excluded from the study.
Validity refers to the extent to which a concept, conclusion, measurement, or design accurately represents
the phenomenon it claims to represent or measures what it intends to measure. In various contexts such as
research, testing, and argumentation, validity is essential for ensuring that the results or conclusions drawn
from a study or process are meaningful, reliable, and applicable to the real world.
TYPES OF VALIDITY
INTERNAL VALIDITY
Internal validity refers to the extent to which a research study accurately measures or tests the relationship
between variables without the influence of confounding factors or biases. In other words, it assesses
whether the observed changes in the dependent variable are actually caused by manipulation of the
independent variable(s) and not due to other factors.
Achieving high internal validity involves designing the study in a way that minimizes potential sources of bias
or error.
Internal validity is crucial for drawing accurate conclusions about cause-and-effect relationships within a
study. Without strong internal validity, it becomes difficult to confidently attribute changes in the dependent
variable(s) to the independent variable(s) being studied.
Threats to internal validity
History: Changes that occur over time, unrelated to the experimental manipulation, may affect the
dependent variable(s). For example, if a study is conducted over several weeks, other events or experiences
during that time period could influence participants' responses.
This threat could be mitigated by using a control group.
Maturation: Natural changes or development within participants that occur over time can impact the
dependent variable(s). This is particularly relevant in longitudinal studies or studies involving children or
aging populations.
This threat could also be mitigated by using a control group.
Testing: Participants' responses may be influenced by repeated exposure to the measurement instrument
or by previous testing experiences. This can lead to changes in performance or behavior unrelated to the
experimental manipulation. People may become aware of the objective or purpose of the study and this may
make bias the responses.
Instrumentation: Changes in the measurement instruments or procedures used to assess the dependent
variable(s) over the course of the study can introduce inconsistencies or errors into the data. If the tool used
during pretest and post test are different then it may become difficult to infer changes to the intervention
of interest.
, Regression to the mean: Extreme scores on a measure tend to move closer to the average (mean) upon
retesting, regardless of any experimental manipulation. This can lead to the illusion of a treatment effect
when, in fact, the change is simply due to statistical fluctuations.
This threat can be controlled by using an adequate sample size.
Selection bias: Differences in the characteristics of participants assigned to different groups may affect the
dependent variable(s), making it difficult to determine whether any observed effects are due to the
independent variable(s) or pre-existing differences between groups.
Attrition or dropout: Loss of participants over the course of a study can introduce bias if the characteristics
of those who drop out differ systematically from those who remain in the study.
This can be controlled by increasing the sample size by maybe 10% in order to account for the attrition.
Experimental mortality: Participants dropping out of the study due to factors related to the experimental
manipulation can affect the results, particularly if dropout rates are unequal across experimental
manipulations.
EXTERNAL VALIDITY
External validity refers to the extent to which the results of a research study can be generalized or applied
to settings, populations, or conditions beyond the specific context of the study.
Dimensions of external validity
Population validity: Refers to the extent to which the findings of a study can be generalized to other
populations beyond the one studied. For example, if a study is conducted with college students, can the
results be generalized to adults.
Ecological validity: Refers to the extent to which the experimental conditions and procedures in a study
reflect real-world settings and behaviors.
Temporal validity: Also known as historical validity, it refers to the extent to which the findings of a study
remain relevant over time. For instance, if a study was conducted in the 1980s, can its findings still be
applicable today given potential changes in societal norms, technology, or other factors.
Situational validity: Refers to the extent to which the findings of a study can be generalized across different
situations or contexts. For example, if a study was conducted in a laboratory setting, can its findings be
applied to real-life situations outside of the laboratory.
Threats to external validity
Population characteristics: If the sample used in the study is not representative of the larger population to
which the findings are intended to be applied, generalization may be limited. This could occur if the sample
is too homogeneous or if certain groups are excluded from the study.
