Testing for Differences in Communication Research Methods

These flashcards cover important vocabulary and concepts related to inferential statistics, Chi-Square tests, and t-Tests in the context of Communication Research Methods.

Inferential Statistics

Used to draw conclusions about a population by examining the sample.

Chi-Square

A statistical test that determines if differences among categories are statistically significant, comparing observed frequency with expected frequency.

t-Test

A statistical test used to determine if differences between two groups on a dependent variable are significant.

Null Hypothesis

A statement that there is no effect or no difference, serving as a default position that indicates no statistical significance.

Alternative Hypothesis

The hypothesis that indicates the presence of an effect or a difference.

Degree of Freedom (df)

The number of independent values or variables that can vary in an analysis.

Significance Level (alpha)

A threshold that determines whether a statistically significant result is present, commonly set at 0.05.

One-Dimensional Chi-Square

A Chi-Square test that assesses differences across categories of one nominal variable.

Two-Way ANOVA

A statistical test that examines the interaction between two nominal independent variables on a continuous dependent variable.

Factorial ANOVA

An analysis that determines all possible interaction effects of more than two independent variables on a continuous dependent variable.

Independent Sample t-Test

Compares mean scores of the dependent variable for two different groups.

Paired Comparison t-Test

Compares mean scores of two paired or matched dependent variable scores from the same participants.

Causal Relationship

An association between two variables where one variable is affected by the other.

Contingency Analysis

A statistical analysis that determines the relationship between two nominal variables.