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Which type you use for your data depends on the type of measurement scale used and how your collected data are distributed. T-Test Assumptions The t-test is a parametric statistic and perhaps one of the simplest analyses used in dissertation and thesis research.
Prior to using the t-test, you must make sure that your data does not violate any of the three assumptions underlying the t-test: The scores in your data represent a random sample from the population under study. The distribution of the mean of your sample is normal. The variances of the different groups studied are very similar.
If your data violates one or more of these assumptions, you may be committing a Type I error more or less often than the alpha probability Old test research papers set either.
This bias may undermine the value of the t-test, and therefore, the results of your dissertation. Types of T-Tests The t-test is used when your data has only two levels of the independent variable.
There is a t-test for dissertations involving experimental designs with randomized groups independent samplesand another t-test for dissertations with experimental designs involving correlated groups matched pairs or within-subjects designs. Knowing what kind of sample you have is key to selecting the appropriate t-test for your analyses.
Let's say that your dissertation involves two groups of people. If you obtained your subjects from multiple locations and assigned each person to be in one group or the other randomly, say through the use of a random numbers table, then you would use the t-test for independent samples in your analysis.
If, however, your dissertation is looking at men versus women in an undergraduate introductory psychology course at your school, you must use the t-test for correlated samples in your analysis. Independent Samples For example, let's suppose that your dissertation involves two random groups of people, an experimental group and a control group.
All of your subjects listen to the same song. The experimental group sees the artist's face before hearing the song, while the control group does not.
You then collect data from the two groups about how well they liked the song on a scale of For your analysis, you compute the mean of each group and find that the experimental group's mean is 5.
For this analysis, you would use the t-test for independent means. The crux of your paper is determining whether the 1. Correlated Samples Using the above example, let's say your work involved one group of subjects, but each subject listened to the song first, without seeing the artist's face, then rated how much they liked it.
Then, the same subject saw the artist's face and listened to the song again. For your analysis, you would use the t-test for correlated samples, because each person in your sample made two observations.
Obviously, the ratings for this sample are correlated, because they came from the same individual. This type of experimental design is called a "within-subjects" design. Calculating Degrees of Freedom Once you have calculated the t-score for your groups, you need to know whether these t values are large enough to assume that the difference you found between the two groups is significant.
Most statistical packages used for analyses SPSS, etc. If, for your dissertation, you have set your significance level at. Dissertation committees and dissertation chairs love significant results!
If you do not have a statistical package, you must first find the degrees of freedom for your sample.Others allow students to choose their own research paper topics, but they may ask for a certain style. Some examples of common research paper styles include: Argumentative Research Papers.
This “IQ test” doesn’t really test your IQ! But it does test your ability to turn in good research papers and can predict how well you’ll do on your papers.
EXAMPLE OF A RESEARCH PAPER START OF EXAMPLE The experiment will test whether a person can keep administering painful electric shocks to another person just because they are ordered to do so. and we're also okay with people reprinting in publications like books, blogs, newsletters, course-material, papers, wikipedia and.
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