Statistical Analysis and Interpretation Flashcards
7 cards from real RA practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Statistical Analysis and Interpretation flashcards as text
In multiple regression analysis, what does multicollinearity refer to?
Answer: High correlation among two or more predictor variables in the model
Multicollinearity occurs when predictor variables in a regression model are highly correlated with each other, making it difficult to isolate the independent contribution of each predictor.
Effect size in research primarily communicates:
Answer: The practical magnitude or importance of an observed difference or relationship
Effect size quantifies the practical or real-world magnitude of a research finding, providing information beyond statistical significance, which can be influenced by sample size.
Which of the following is the correct interpretation of a statistically non-significant result (p > 0.05)?
Answer: There is insufficient evidence to reject the null hypothesis
A non-significant result means the data do not provide sufficient evidence against the null hypothesis; it does not prove the null hypothesis is true or that no effect exists.
Data transformation in statistical analysis (e.g., log transformation) is most commonly used to:
Answer: Improve normality or stabilize variance in skewed data
Log or other transformations are applied to skewed data to better approximate a normal distribution or achieve homogeneity of variance, meeting the assumptions of parametric tests.
A Likert scale used in research surveys is best classified as which level of measurement?
Answer: Ordinal
Likert scales produce ordered categories (e.g., Strongly Agree to Strongly Disagree) without equal or meaningful intervals between points, classifying them as ordinal data.
In research measurement, internal consistency reliability is most commonly assessed using:
Answer: Cronbach's alpha
Cronbach's alpha is the standard measure of internal consistency, evaluating how closely related a set of items in a scale are to each other as a group.
In regression analysis, the R-squared (R²) value represents:
Answer: The proportion of variance in the outcome variable explained by the predictor(s)
R² indicates what percentage of the variability in the dependent variable is accounted for by the independent variable(s) in the regression model, ranging from 0 to 1.