The Psychological research question for my analysis is: Does student anxiety for an exam increase the study hours?
Null hypothesis (H0) states that student anxiety for an exam has no significant effect on the number of study hours.
Alternative hypothesis (H1) states that student anxiety for an exam has a significant effect on the number of study hours.
Correlation does not depend on any statistical variables. The aforementioned statement makes it unique in giving unbiased comparison between variable units (Anselin & Rey, 1997).
To calculate the correlation between student anxiety scores and number of study hours, we use the formula below: Let X represent student scores and Y represent number of study hours.
Where N = 10
First, we calculate the means of the student anxiety scores and the study hours respectively.
Given that alpha is 0.05%, the effect size is 0.3. According to this, the effect size is greater than the critical value thus; we accept the null hypothesis that student anxiety for an exam has no significant effect on the number of study hours.
How to conduct a t and ANOVA tests
The t and ANOVA tests examine the ratio of the data groups analyzed in relation to each other. They allow researchers to differentiate a clear signal of statistical change and an appearance of similarity that otherwise does not count as significant change. A huge variability of data increases the difficulty of noticing statistical differences, whereas a mean difference serves as a signal for a statistical significance in the two groups of data. Researchers use two-tailed tests to analyze a non-directional hypothesis. Under a non-directional hypothesis testing, results are interesting in both directions of the mean. The null hypothesis is a specific value and the alternative hypothesis only states that the results will be different. ANOVA uses the effect size of F test to draw results by chance. To clarify whether this is true, the researcher looks up the F-test value in a table of significance specifically to check whether the ratio is larger than a given value for chance (Anselin & Rey, 1997).
The this risk level, also known as alpha level, is set to 0.05 implying that chance would account for five out of one hundred tests. A researcher will also need the degrees of freedom, which is the sum of the number of data values in all the groups after a subtraction (Vicky, 2009). The three values namely, the alpha value, the degrees of freedom value and the F-test value form a complete set for looking up in a standard table of significance.
Behavioral research situation
The research data given are essential in choosing the tests of significance to be applied in a research study when working out a solution. Pearson coefficient and chi square research study works well with discrete counted data (Vicky, 2009). It is applicable in a situation where there are distinct categories i.e. male and female or in a psychological question which seeks to look into artificial attributes like tall and short.
Anselin, L. & Rey, S. (1997). Introduction to special issue on spatial econometrics. International Regional Science Review, 20(1-2).
Vicky R. N. (2009). T-tests and One-Way ANOVA. Web.