EXAM 2 TERMS

External Validity

External Validity

How well the findings of a study can be generalized to other situations, people or settings

Variability

How spread out or different the data points are from each other in a data set

Nominal Scale

A scale that names or categorizes without any quantitative value (e.g. gender, color)

Reliability

Consistency of a test or measurement across time or different conditions

Content Validity

The extent to which a test covers ALL ASPECTS of the concept it’s meant to measure

Discrete Variable

A variable that can take on specific, separate values (e.g. number of pets can be 1,2,3 not 1.5, number of children can be 1,2,3 not 2.5)

Ordinal Scale

Ranking scale that shows order but the intervals between them aren’t exactly equal (e.g. race positions)

Ratio Scale

Scale of measurement with a true zero

Construct

A concept or characteristic that is measured indirectly like: intelligence or happiness

Internal Validity

The extent to which a study shows a clear cause-and-effect relationship

Validity

The extent to how accurately the test/measurement tool measures what it’s supposed to

Interval Scale

Scale of measurement between values but NO TRUE ZERO

Construct Validity

Ensures that a test or measurement accurately reflects the concept or trait it’s supposed to measure

Confound

A factor that confuses or distorts the results of an experiment, making it unclear whether the effects are due to the independent variable or another factor

Covariance

A measure of how two variables change together

Confidence Interval

A range of values that estimate where a population parameter (like a mean) is likely to fail with a certain level of confidence (e.g. 95%: which means we can be 95% confident that the true value falls within that range.)

Power

The probability that a statistical test will correctly reject a false null hypothesis (e.g detect a true effect)

HIGH Power

Higher chance of finding a significant result if there is one

Type I Error

FALSE POSITIVE the mistake of rejecting a true null hypothesis; concluding there IS an effect when there IS NOT

Type II Error

FALSE NEGATIVE the mistake of failing to reject a false null hypothesis; concluding that there ISN”T an effect when there IS

Statistical Significance

Indicates whether the result of a test is unlikely to have occurred by chance, typically evaluated with a p-value

P-Value

a number that helps us determine how likely it is that our results happened by chance. A low p-value (usually below 0.05) suggests that the results are unlikely to have occurred by chance, meaning there is likely a real effect or difference.

Experimental Manipulation

The intentional change of one or more independent variables in an experiment to examine its effect on the dependent variable

Staged Manipulation

a form of experimental manipulation where events or interactions are staged (often w/ an “undercover researcher”) to create specific experimental conditions

Independent Sample T-Test

A statistical measure that compares the means of two different groups to determine if they are statistically different from each other

Test Statistic

A value calculated from sample data used in hypothesis testing to determine whether to reject the null hypothesis (e.g. t, F, or chi-square value)

Error Variance

Variability in data that cannot be explained by the independent variable usually due to random factors or measurement error

Between Subjects Design

A research design where different groups of participants are exposed to different conditions of the independent variable, with each participant experiencing only one condition

Testing Effect

A threat to internal validity where a participant’s score increases due to familiarity with the test after taking the test repeatedly

Attrition

Study drop outs

Heterogeneous Attrition

When participants drop out of different groups in an experiment at unequal rates, which can bias the results

History

A threat to internal validity where external events that occur between the pretest and postest that can affect the outcome of the study

Regression Toward the Mean

The tendency for extreme scores to move closer to the average on subsequent measures, due to random variability

Maturation

A threat to internal validity where changes within participants (growing older/more experiences) naturally occur overtime, affecting the outcome of the study