One-way analysis of variance (ANOVA) is a useful research tool. ANOVA compares the means of three or more unrelated variables (Kim, 2017). The null hypothesis assumes that there is no significant statistical difference between the means of the independent variables, while the alternative hypothesis assumes a significant statistical difference between the means. If we reject the null hypothesis, the means of at least two of the independent variables are different. The results’ unspecific nature shows that one-way ANOVA is an omnibus test, and a post hoc analysis is needed to determine the different variables if the null hypothesis is rejected (Kim, 2017). One-way ANOVA is used when a researcher collects a sample and then divides the sample into groups based on the independent variable characteristics such as education level. The developed groups are then measured based on a similar dependent variable, such as scores obtained in a test.
Research question
The research wishes to compare the efficiency of three different methods of memorizing information. The research question is to determine whether there exist significant differences between the efficiency of repetition, imagery, and mnemonics in memorizing information. The memorization method’s efficiency is measured by the test scores administered to the three groups that used repetition, imagery, and mnemonics to memorize the document provided. Therefore, the research seeks to establish whether there exist significant differences between the mean scores for repetition, imagery, and mnemonics groups.
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Hypothesis
The null hypothesis assumes that the mean score for the groups that used repetition, imagery, and mnemonics are the same. On the other hand, the alternative hypothesis assumes a significant statistical difference between the mean score for at least two groups.
Null hypothesis, :
Alternative hypothesis, :
Independent variables
The researcher collected a sample after which the participants were randomly assigned to the three different memorization methods: repetition, imagery, and mnemonics. The independent variable is, therefore, the memorization method.
Dependent variable
After categorization, participants in the three groups were assigned a document and a test issued to determine the different memorization methods’ effectiveness. The dependent variable used to evaluate the independent variable is the scores obtained in the issued test.
Sample size and groups
The sample is categorized into three groups based on the memorization method used by the participants. The three groups are, therefore that participants that used repetition, imagery, and mnemonics. The degrees of freedom for the sum of squares within is equal to the number of groups less one (3-1=2) (Kim, 2017). Similarly, the degrees of freedom for the sum of squares within are the total number of participants less number of groups (45-3 = 42), and the degrees of freedom for the sum of squares total are the total numbers of participants less one (45-1= 44) (Kim, 2017). Therefore, the sample size collected was 45 participants, and each group consisted of 15 participants.
Assumptions
One way ANOVA comes with three main assumptions: Normality, homogeneity of variances, and independence of observations. ANOVA assumes that each of the respective groups’ dependent variable data are normally distributed (Blanca et al., 2017). In this case, the assumption is, therefore, that the tests score for participants in each of the respective groups (repetition, imagery, and mnemonics) are normally distributed. The second assumptions are that the population variances for the independent groups are equal (Blanca et al., 2017). Therefore the tests assume the homogeneity of the population variances of test scores for the three groups (repetition, imagery, and mnemonics). The third assumptions refer to the study’s design, which must ensure that the groups are unrelated (Blanca et al., 2017). Therefore, the ANOVA tests assume that the research methods ensure that the groups (repetition, imagery, and mnemonics) are independent.
Limitations
If there is a significant difference between the group means, the One-way ANOVA results do not provide specific information about the significantly different groups. Post hoc tests such as Bonferroni Procedure and Tukey’s Honest Significant Difference (HSD) are needed to tests the specific differences between groups (Blanca et al., 2017). Post hoc tests are only run if the ANOVA tests prove a statistically significant difference in group means. Besides, ANOVA test results are significantly influenced if the assumptions of homogeneity of population variances, normality of data, and groups’ independence are violated.
ANOVA Results
We reject the null hypothesis if the p-value < the assumed level of significance. Since the p-value is less than the assumed level of significance, 0.05, the one way ANOVA F ((2, 42) = 19.74, p < 0.0001) means that we reject the null hypothesis and conclude that there exists sufficient statistical evidence to conclude that there exists a significant difference between at least two of the groups. A post hoc test would be needed to determine the specific groups that are different.
In conclusion, the efficiency of the three different methods of memorizing information vary. There exist significant mean differences between at least two memorization methods (repetition, imagery, and mnemonics).
References
Blanca, M. J., Alarcón, R., Arnau, J., Bono, R., & Bendayan, R. (2017). Non-normal data: Is ANOVA still a valid option?
Kim, T. K. (2017). Understanding one-way ANOVA using conceptual figures. Korean journal of anesthesiology.
