exam 202 #2

Independent Variable (IV)

The variable that is manipulated or categorized to observe its effect on the dependent variable.

Dependent Variable (DV)

The outcome or response measured in the study; it depends on the manipulation of the independent variable.

Independent Variable (IV)

The variable that is manipulated or categorized to observe its effect on the dependent variable.

Dependent Variable (DV)

The outcome or response measured in the study; it depends on the manipulation of the independent variable.

Levels of IVs

Different variations or categories of the independent variable.

Confounded Variables

Variables that are unintentionally related to both the independent and dependent variables, making it difficult to draw clear conclusions.

Measured Variables

Variables that are observed or recorded without intervention (e.g., height, mood).

Manipulated Variables

Variables that are intentionally altered in an experiment (e.g., dosage of a drug).

Quantitative Variables

Variables that represent a measurable quantity (e.g., height, weight, time).

Categorical Variables

Variables that categorize or classify data into distinct groups (e.g., gender, type of therapy).

Continuous Variables

Variables that can take any value within a range (e.g., temperature, time).

Discrete Variables

Variables that have distinct, separate values (e.g., number of children, number of cars).

Nominal Scale

Categorical data with no order (e.g., gender, colors).

Ordinal Scale

Categorical data with a meaningful order but no fixed intervals between categories (e.g., rankings).

Interval Scale

Numeric data with equal intervals but no true zero (e.g., temperature in Celsius).

Ratio Scale

Numeric data with equal intervals and a true zero (e.g., weight, height).

Reliability

The consistency or stability of a measure.

Validity

The degree to which a measure assesses what it claims to measure.

Face Validity

Whether a measure appears to measure what it's supposed to measure.

Content Validity

Whether the measure covers all aspects of the concept being measured.

Criterion Validity

Whether a measure predicts an outcome it should theoretically predict.

Concurrent Validity

The measure's correlation with a relevant outcome at the same time.

Predictive Validity

The ability of the measure to predict future outcomes.

Error Bars

Lines indicating the variability of data in a graph, usually representing the standard deviation or confidence interval.

Histograms

A bar graph for continuous data, showing frequency distribution.

Pie Chart

A circular chart divided into slices to represent proportions of a whole.

Scattergrams (Scatter Plots)

A graph that shows the relationship between two continuous variables.

Line Graphs

A graph that shows data points connected by lines, often used for showing trends over time.

Internal Validity

The extent to which the study accurately measures the relationship between the IV and DV, without influence from confounding variables.

Construct Validity

The extent to which a measure truly measures the concept it intends to measure.

External Validity

The extent to which the findings of a study can be generalized to other contexts, populations, or settings.

Statistical Validity

The degree to which the statistical analysis used in the study is accurate and appropriate.

Ecological Validity

The extent to which the findings of a study can be generalized to real-world settings.

Sample Size

The number of participants or observations in a study. Larger sample sizes typically yield more reliable results.

Variance

A measure of how much scores in a dataset differ from the mean.

Effect Size

The magnitude of the relationship or difference between variables, showing how meaningful a result is.

p-values

A statistical measure that helps determine the significance of the results.