Hi,
will appreciate expert opinion on the below:
1. Can 'impacting' or 'relationship' hypothesis be formulated for descriptive research?
2. Can correlation, regression and chi-square tests be used to test hypothesis in descriptive research?
3. Is it mandatory to 'validate' the data being used for 'conclusive' or 'causal' research? or measuring 'reliability' only is also okay.
Regards,
1) If you do have a specific hypothesis, it's not descriptive.
2) see 1)
3) How will you determine "reliability" if not through validation?
Hi,
1) You should start reviewing the relevant body of literature. There you will find evidence in order to create and support Null (H0) and Alternative (H1) Hypotheses for your research. Hypothesis tests usually are examined after the descriptive statistics section of an essay.
2) Those methods you mention can be applied. It depends on the nature of the variables (ordinal, nominal, scale) and the approach you wish to have.
3)You can have a reliability analysis by using alpha cronbach. Also you should discuss about the sample size based on the pilot study.
https://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=8&ved=0ahUKEwiKgOC-z5jaAhUHyKQKHXrSA50QFghcMAc&url=http%3A%2F%2Frobertmatthews.org%2Fwp-content%2Fuploads%2F2016%2F03%2FRM-storks-paper.pdf&usg=AOvVaw3OlMhwy9_YweVxGVDnfCTP
Yes, Nina, that's what it is. And the p-value is the probability of |r| > 0.62 under the hypothesized model (which is nonsense, because both, number of stork breeding pairs and the birth rate depend on a common confounder: the size of the country, that was not considered in the model). The technical interpretation of t (or F or chi²) and p is always the same; it refers to the technical aspects of the test (it is based on the model and its assumptions), but the practical (scientific) interpretation goes astray when the model or the assumptions are obviousely non-sensical. Garbage in -> garbage out.
That's why the interpretation of "significance" requires to understand the whole story. Knowing that p
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