|What you need to know||
Before selecting the correct statistical analysis, you need to
A hypothesis is the researcher's prediction, derived from theory or speculation, about how two or more measured variables are related to one another.
Testable hypotheses often state
H1: There is a significant relationship between (continuous variable) and (continuous variable).
These are the two most basic types of hypotheses. Review quantitative research studies for examples of many more types of testable hypotheses.
Operationalizing your variable means that you know how you are going to measure it. For example, we operationalize 'gender' by asking a person if they are male or female.
Is your variable
Nominal level (one or more mutually exclusive categories)
Ordinal level (ranked data where the differences in value are not equal - categories are mutually exclusive and exhaustive)
Interval level (there are meaningful amounts of differences between the data values but there is no absolute zero)
Ratio (equal distances between data points (e.g., between 1 and 2, 2 and 3, etc.) and there is an absolute zero
Nominal level variables are always categorical level
Ordinal level variables are usually considered categorical, but if you sum a series of Likert-type ordinal level questions, you will end up with a continuous variable
Interval/ratio level variables are always continuous variables
|Steps to choosing your statistical analysis||
Follow this link to a presentation that will ask you a series of questions. Each answer will lead you closer to a recommended data analysis. Before you begin, you must know
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by Diane M. Dusick, Ph.D.
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