Lecture 7 Flashcards
interval variables
these are variables where the distances between the categories are identical across the range of categories
ratio variables
interval variables with a fixed zero point
ordinal variables
variables whose categories can be rank ordered (as in the case of interval/ratio variables) but the distances between the categories are not equal across the range.
nominal variables
categorical variables, variables that cannot be rank ordered.
Dichotomous variables
variables that contain data that only have two categories
univariate analysis
refers to the analysis of one variable at a time
bivariate analysis
Analysis of two variables at the same time to discover whether or not the two are related
pearson’s r
a method for examining relationships between interval/ratio variables (value [-1]-0-1, 0=no relationship, 1 perfect positive relationship etc.)
Coefficient of determination r2
expresses how much of the variation in one variable is due to the other variable. The higher it is, the stronger is the correlation between the two variables.
multivariate analysis
simultaneous analysis of three or more variables
spurious relationship
there appears to be a relationship between two variables but the relationship is not real: it is being produced because each variable is itself related to a third variable.
intervening variable
explains the process through which two variables are related.
moderating variable
affects the strength and direction of that relationship.
test of statistical significance
allows the analyst to estimate how confident he or she can be that the results deriving from a study based on a randomly selected sample are generalizable to the population from which the sample was drawn.
null hypothesis
says that the two variables are not related in the population
statistical significance
the level of risk that you are prepared to take that you are inferring that there is a relationship between two variables in the population from which the sample was taken when in fact no such relationship exists.
when do we reject the null hypothesis?
when the p-value is smaller than the level of significance.
type 1 error
rejecting the nut hypothesis when it is true
type 2 error
not rejecting the null hypothesis when it is false
what are three basic methods for testing hypotheses?
Chi-square, Correlation analysis, regression analysis
chi-square test
examines the probability that observed frequencies are randomly distributed of the independent variable
what is the chi-square test affected by?
the magnitude of the effect and the number of categories/values of each variable
