Pyschology Validity

independent variables

manipulated variables

dependent variables

measured outcomes

constant variables

variables that remain the same throughout the experiment

Confounding variables

change simultaneously with the independent variable, introducing unwanted alternative explanations

Random errors

represent unpredictable noise during testing^[3]

conceptual variable

is a concept defined at an abstract or theoretical level, such as anxiety

operational variable

is the specific way that abstract construct is measured or manipulated in a given study^[5]

Frequency claims

focus on a single variable to describe a rate or percentage, which is the focus of descriptive research

Association claims

a correlation between 2 variables in a correlation study

Causal claims

a change in one variable directly causes a change in another, which requires experimental research

Validity

appropriateness of a claim^[2]. A valid claim is reasonable, accurate, and justifiable

Pillar 1: Construct Validity

This reflects how well a theoretical construct is operationalized, measured, or manipulated in a study

Threats: Key threats include an inadequate operational definition (e.g., a test that measures an absence of self-confidence rather than anxiety) and mono-operation bias^[7]

Pillar 2: External Validity

This measures how well the results generalize to individuals, settings, places, and times outside the specific study

Major threats include selection biases in sampling, as well as study conditions where the setting, place (e.g., testing memory in a stuffy classroom), or time (e.g., assessing attitudes toward meat right after a disease scandal) differ significantly from other contexts^[8][9]

Pillar 3: Internal Validity

n causal research, internal validity is the extent to which we can rule out alternative explanations for the relationship between two variables

Threats:

• Maturation, history, testing, and instrumentation effects^[10].

• Regression to the mean, where extreme initial scores naturally shift closer to the average on repeated testing^[10].

• Attrition (experimental mortality), which occurs when participants drop out of a study^[11].

• Selection effects (such as Simpson's paradox)^[4].

• Design confounds, where another variable unintentionally varies systematically with the independent variable^[4].

• Observer bias, demand characteristics, and placebo effects

Pillar 4: Statistical Validity

This assesses the extent to which a study's statistical conclusions are accurate and reasonable

Threats: Key threats include violated statistical assumptions, fishing and error rate problems ("seeing things that aren't there"), low statistical power ("missing the needle in the haystack"), and unreliability of measures