Do you consider that the existence of substantive interactions can influence your understanding of main effects? Or, as an instance, an important
hank you for your clear and well-structured explanation of the differences among statistical techniques. I appreciate how you have explained all the differences between the various statistical methods in a clear and well-organised manner. This is well done describing how a research design (especially the volume that requires independent variables and their nature) can dictate the choice of the test to utilize (Yu et al., 2022). I really liked how you logically led me to t-tests, to ANOVA procedures, and finally to mixed factorial ANOVA, which is a good way of showing how analysis tools show development in answering more complex research issues.
After exploring the use of mixed designs what I think you mentioned is worth going into more was this. I came to hear them referred to as a powerful one because they combine both within subject and between subject factors as you mentioned. They also have interpretative problems, however, when there exist interpersonal interactions. Questions Do you consider that the existence of substantive interactions can influence your understanding of main effects? Or, as an instance, an important interaction may indicate that the principal action of one variable is only ever true at particular levels of the other, which can take the results of a study radically new.
As well, because your source (Yu et al., 2022) focuses on the impending transition to traditional tools of statistical analysis (t-tests and ANOVA) in favour of mixed-effects tools, I am asking: how will the future of statistical analysis change in your daily practice or area of practice? Mixed-effects models are becoming more popular as they are more flexible in terms of handling hierarchical or nested data structure, and missing data. Curiosorily would you say that you have encountered them or do you already view them having a place in your research?
Thank you for providing and fully educational and solid foundation contributing to my development. Thank you for sharing your insights!
Mario
Reference
Yu, Z., Guindani, M., Grieco, S. F., Chen, L., Holmes, T. C., & Xu, X. (2022). Beyond t-test and ANOVA: Applications of mixed-effects models for more rigorous statistical analysis in neuroscience research. Neuron, 110(1), 21–35. https://doi.org/10.1016/j.neuron.2021.10.031
Kindell Fincher
6 hours ago, at 2
1
Description of the Differences Between Techniques
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Description of the Differences Between Techniques
The primary differences between these statistical tests stem from the design it was conceived to investigate; the number of independent variables, whether group comparisons are qualitative or quantitative, and the number of dependent variables. The simplest test is a one-sample t-test. That is, it operates by itself and compares the mean of a single group with some prespecified population value, where there is no manipulation of the independent variable. With one independent variable and only two levels, the corresponding test is a t-test (Yu et al., 2022). If either group consists of different subjects, the test must be an independent samples t-test; if the same subjects are measured twice, then the test is a paired samples t-test.
The ANOVA procedure or analysis must be adopted when more than two groups or variables are compared. A one-way ANOVA is merely the step-up test into an independent samples t-test when one independent variable has three or more levels or groups arrived at through different subjects. Repeated measures ANOVA is the extension of the paired samples t-test when the same subjects undergo measurement on three or more occasions or under three or more levels of a single independent variable. A two-way ANOVA must be used when two independent variables are present in a research design. It can test for the main effect of each independent variable on the dependent variable and for the interaction effect of the independent variables being tested, checking if the effect of one variable depends on the levels of the other variable (Yu et al., 2022). The most complex design involves both within-subject and between-subject factors and is analyzed via a mixed factorial ANOVA. This two-way mixed factor ANOVA can distinguish the main effects of single factors and their interaction while considering the dependency among the repeated measurements.
Yu, Z., Guindani, M., Grieco, S. F., Chen, L., Holmes, T. C., & Xu, X. (2022). Beyond t-test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research. Neuron, 110(1), 21–35.
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