Question

Question Completion Status:

Scenario:
ign
A researcher collected data from male and female Caucasian, African American, and Hispanic graduate
students using quantitative measures of extraversion, academic self-efficacy, dissertation anxiety, and
math anxiety, each on a scale of 0 to 100 with higher scores indicating more of the specific latent
attribute (e.g., more anxiety). Each participant was also asked if they intended to conduct a qualitative
or quantitative dissertation study and how many months they estimated it would take to complete the
dissertation.
Note: The following 4 items refer to the scenario.
Select the statistical procedure that would be used to test each of the following null hypotheses:
Dissertation anxiety and academic self-efficacy are not related.
a. Correlation
b. Paired samples t-test
c. Analysis of Variance (ANOVA)
d. Multiple linear regression
e. Chi-square test of independence

Answer 1

To determine if dissertation anxiety and academic self-efficacy are related, a correlation statistical test should be used to assess the strength and direction of the relationship between these two continuous variables.

Step-by-step explanation:

To test the null hypothesis that dissertation anxiety and academic self-efficacy are not related, the appropriate statistical procedure would be a correlation. this statistical test assesses the strength and direction of the relationship between two continuous variables. in this case, those variables are dissertation anxiety and academic self-efficacy, both measured on a scale of 0 to 100.

Correlation coefficients range from -1 to 1. A coefficient close to -1 or 1 indicates a strong relationship, whereas a coefficient close to 0 suggests no linear relationship. a positive coefficient signifies a positive relationship, meaning as one variable increases, so does the other. A negative coefficient suggests an inverse relationship. to calculate the correlation, one might use Pearson's r if the data are normally distributed and meet other assumptions such as homoscedasticity. If the data do not meet these criteria, a Spearman's rank correlation might be more appropriate.