Answer to Question 1
ANS: A
If one or both of two study variables do not meet the assumptions for a Pearson's correlation, or if the variables are scaled on an ordinal scale of measurement (rather than interval or ratio), both the Spearman rank order correlation and Kendall's tau are more appropriate statistics. The Spearman rank-order correlation and Kendall's tau calculations involve converting the data to ranks, thereby discarding any variance or normality issues associated with the original values.
Answer to Question 2
ANS: A, B, F, G
Correlational analysis provides two pieces of information about the data: the nature or direction of the linear relationship (positive or negative) between the two variables and the magnitude (or strength) of the linear relationship. No direction of relationship is stated heremerely the fact that the variables are strongly correlated, meaning that as one varied, the other varied. Since the relationship between these two variables was statistically significant, no type II error occurred. The probability of failing to reject the null hypothesis when it is in fact false is called type II error, and it is related to using a sample that is of insufficient size. Operationalization of a variable indicates how it will be measured or manipulated in a study; without operationalization, no measurement can occur.