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Is political affiliation an independent variable?
It may or may not be: it depends on the study. In a study of how people intend to vote in an election, the political affiliation would be an independent variable - although probably very highly correlated with the voting intention. On the other hand in the study of the political affiliation of the people who voted in favour of (or against) a particular person, it will be a dependent variable.
What is the advantages and disadvantages of a pilot survey?
1. It permits preliminary testing of the hypotheses that leads to testing more precise hypotheses in the main study. It may lead to changing some hypotheses, dropping some, or developing new hypotheses. 2. It often provides the researcher with ideas, approaches, and clues you may not have foreseen before conducting the pilot study. Such ideas and clues increase the chances of getting clearer findings in the main study. 3. It permits a thorough check of the planned statistical and analytical procedures, giving you a chance to evaluate their usefulness for the data. You may then be able to make needed alterations in the data collecting methods, and therefore, analyze data in the main study more efficiently. 4. It can greatly reduce the number of unanticipated problems because you have an opportunity to redesign parts of your study to overcome difficulties that the pilot study reveals. 5. It may save a lot of time and money. Unfortunately, many research ideas that seem to show great promise are unproductive when actually carried out. The pilot study almost always provides enough data for the researcher to decide whether to go ahead with the main study. 6. In the pilot study, the researcher may try out a number of alternative measures and then select those that produce the clearest results for the main study.
How would you interpret the findings of a correlation study that reported a linear correlation coefficient of -0.13?
There is not enough information to say much. To start with, the correlation may not be significant. Furthermore, a linear relationship may not be an appropriate model. If you assume that a linear model is appropriate and if you assume that there is evidence to indicate that the correlation is significant (by this time you might as well assume anything you want!) then you could say that the dependent variable decreases by 0.13 units for every unit change in the independent variable - within the range of the independent variable.
Relationship between level of significance and hypothesis testing?
A physician wishes to study the relationship between hypertension and smoking habits. From a random sample of 180 individuals, the following results were obtainedAt the 5% level of significance, test whether the absence of hypertension is independent of smoking habits.HypertensionSmoking habitNon-smokersModerate smokersHeavy smokersYes213630No482619
What is the repeated measure?
The repeated measures design (also known as a within-subjects design) uses the same subjects with every condition of the research, including the control.[1] For instance, repeated measures are collected in a longitudinal study in which change over time is assessed. Other studies compare the same measure under two or more different conditions. For instance, to test the effects of caffeine on cognitive function, a subject's math ability might be tested once after they consume caffeine and another time when they consume a placebo.(Source Reference: - http://en.wikipedia.org/wiki/Repeated_measures )
