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17 Cards in this Set
- Front
- Back
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refers to the characteristics of description for interval or ratio scales - for example the level of temperature
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levels
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refers to the characteristics of description for nominal or ordinal scales - EX: buyers vs. non-buyers
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labels
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two variables are associated, but only in a very general sense; don't know "direction" of relationship, but we do know that the presence (or absence) of one variable is associated with the presence (or absence) of another
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nonmonotonic
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general direction of a relationship between two variables is known
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monotonic
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"straight-line" association between two variables
- knowledge of one variable will yield knowledge of another variable |
linear
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some smooth curve pattern describes the association
EX: research shows that job satisfaction is high when one first starts to work for a company - goes down after a few years - back up after workers have been with the company for many years - U shaped relationship |
curvilinear
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consists of rows and columns defined by the categories classifying each variable...used for nonmonotonic relationships
EX: is there a relationship between studying and test performance? |
cross-tabulations
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examination of frequencies for two nominal-scaled variables in a cross-tabulation table to determine whether the variables have a significant relationship
- the null hypothesis is that the two variables are not related |
chi-square analysis
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chi-square yields the amount of support for the null hypothesis if the researcher repeated the study many, many times with independent samples
EX: if the chi-square analysis yielded a .02 significance level for the null hypothesis, the researcher would conclude that only 2% of the time he or she would find evidence to support the null hypothesis. Since the null hypothesis is not supported, this means there is a significant association |
how to interpret chi-square
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is an index number, constrained to fall between the range of -1.0 and +1.0
- communicates both the strength and the direction of the linear relationship between two metric variables |
correlation coefficient
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amount of change in one variable systematically associated with a change in another variable
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covariation
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when you want to know if there is an association between two variables and both of those variables have nominal (or ordinal) scales
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when to use cross tabs and chi-square tests
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determine if there is a significant association - the p value should be examined first. If it is significant, there is a significant association. If not, then there is not association
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presence
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The correlation coefficient (r) is a number ranging from -1.0 to +1.0. The closer to 1.00 (+ or -), the stronger the association
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strength
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+ or - indicates the relationship
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direction
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measures the degree of linear association between the two variables
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pearson product moment correlation
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1. use correlations only for metric variables (interval or ratio scaling)
2. correlation assumes that only the two variables involved are relevant: all other variables and factors are considered to be constant 3. correlation does not indicate cause and effect, only covariance between two variables is being analyzed. 4. correlation expresses only the linear relationship between the two variables |
four caveats of correlation
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