"Validity is the main extent to which a concept, conclusion or measurement is well-founded and likely corresponds accurately to the real world."
The extent to which a measurement tool or experimental design is measuring what it is intended to measure.
Types of validity: There are different types of validity such as construct validity, internal validity, external validity, and statistical conclusion validity, each emphasizing different aspects of the measurement of variables in experimental psychology.
Construct validity: The extent to which a measure accurately captures the theoretical construct a researcher seeks to study.
Internal validity: The extent to which the experiment demonstrates that an observed effect is not due to extraneous factors, but caused specifically by the manipulation of the independent variable.
External validity: The extent to which an experiment can be generalized to a larger population beyond the sample.
Statistical conclusion validity: The extent to which the researcher can make inferences about the relationship between the independent variable and dependent variable based on statistical analyses.
Threats to validity: These are factors that might affect internal and external validity. Examples include participant selection biases, demand characteristics, experimenter expectations, and situational factors.
Control measures: These are measures that the researcher can employ to reduce the impact of threats to internal and external validity. Examples include random assignment, blinding, manipulation checks, and repeated measures.
Reliability: The degree to which a measure produces consistent and stable results over time.
Test-retest reliability: The extent to which a measure produces consistent results when administered multiple times to an individual.
Inter-rater reliability: The extent to which different raters or observers produce the same results when observing and coding the same data.
Validity coefficients: The correlation coefficients between a measure and a criterion measure that assesses the same construct or variable.
Criterion validity: The extent to which a measure aligns with external variables or criteria that it is logically associated with.
Convergent and discriminant validity: The extent to which two measures show a strong positive correlation with each other if they are measuring the same construct (convergent), and the extent to which two measures show a low correlation when they are measuring different constructs (discriminant).
Face validity: The degree to which the measure appears to measure what it claims to measure.
Content validity: The extent to which a measure covers all aspects or dimensions of the construct it is measuring.
Factor analysis: A statistical technique used to identify the underlying factors or dimensions that explain the correlations between multiple measures of a construct.
Multitrait-multimethod analysis: A statistical technique used to assess the convergent and discriminant validity of measures by analyzing the correlation patterns between multiple measures of different traits and methods.
Validity in qualitative research: The concept of validity also applies to qualitative research, but it may be less clear-cut due to the nature of the research method. Qualitative researchers must ensure that their findings are unbiased, credible, transferable, and confirmable.
Internal validity: Refers to the degree to which a study is able to establish a causal relationship between the independent and dependent variables, while ruling out alternative explanations for the results. This involves controlling for extraneous variables that may affect the outcome of the experiment.
External validity: Refers to the degree to which the findings of a study can be extended or generalized to other populations, settings, and situations beyond the ones studied. This involves ensuring that the sample size, selection criteria, and other factors are representative of the population being studied.
Construct validity: Refers to the degree to which a study measures what it is intended to measure, and whether the theoretical constructs being studied are accurately operationalized.
Content validity: Refers to the degree to which a measure covers all aspects of the construct being studied, while excluding irrelevant factors that may affect the outcome of the study.
Face validity: Refers to whether a measure appears to accurately measure what it is intended to measure, based on a subjective evaluation of the construct by the researcher or participants.
Concurrent validity: Refers to the degree to which a measure is able to accurately predict an outcome that is occurring at the same time as the measure is being taken.
Predictive validity: Refers to the degree to which a measure is able to accurately predict a future outcome that is not directly observable at the time of the measure being taken.
Convergent validity: Refers to the degree to which a measure is able to measure the same construct as other measures of the same construct.
Discriminant validity: Refers to the degree to which a measure is able to differentiate between two or more separate constructs, which may be confounded with each other.
"The word 'valid' is derived from the Latin validus, meaning strong."
"The validity of a measurement tool is the degree to which the tool measures what it claims to measure."
"Validity is based on the strength of a collection of different types of evidence (e.g. face validity, construct validity, etc.) described in greater detail below."
"In psychometrics, validity has a particular application known as test validity: 'the degree to which evidence and theory support the interpretations of test scores' ('as entailed by proposed uses of tests')."
"It is generally accepted that the concept of scientific validity addresses the nature of reality in terms of statistical measures and as such is an epistemological and philosophical issue as well as a question of measurement."
"The use of the term in logic is narrower, relating to the relationship between the premises and conclusion of an argument. In logic, validity refers to the property of an argument whereby if the premises are true then the truth of the conclusion follows by necessity."
"By contrast, 'scientific or statistical validity' is not a deductive claim that is necessarily truth-preserving, but is an inductive claim that remains true or false in an undecided manner."
"This is why 'scientific or statistical validity' is a claim that is qualified as being either strong or weak in its nature, it is never necessary nor certainly true."
"Validity is important because it can help determine what types of tests to use, and help to make sure researchers are using methods that are not only ethical, and cost-effective, but also a method that truly measures the idea or constructs in question."