category of statistics that includes: pearson product-moment correlation, spearman rank-order correlation, and simple chi-square

association statistics

name of the test that produces a parametric measure of the relationship between two variables

pearson product-moment correlation

name of the test that determines direction, reliability, and strength of the relationship between two variables

pearson product-moment correlation

the pearson correlation coefficient, r, describes the relationship between two variables where +1.00 is a strong positive correlation, -1.00 is a strong negative correlation, and 0.00 indicates ___

no relationship between the two variables

in order to compute the ___ the numerals must be equal interval, ratio, or log, it CANNOT be performed on ordinal or nominal data, and the data in the sample should be drawn from a population where the attributes are normally distributed

pearson product-moment correlation

once the descriptive statistic, r, is computed, the next step is to test for ___ using a little z test (if the sample has ___ or more subjects OR a t test (if the sample has fewer than ___ subjects

significance, 30, 30

name of the test where the null represents "no relationship" and and the two-tailed alternative hypotheses show that "the relationship is positive or negative" Ho: r = 0 Ha1: +1.00 (strong positive) Ha2: -1:00 (strong negative)

pearson product-moment correlation

what do you do once you've accepted the null hypothesis (r = 0.00) using the pearson product-moment correlation?

stop; the relationship between the two variables is random on the exam: show computation of the r-value, compare it to the critical value, and make the decision to accept the null

r^2, read as a percentage, shows the ___ of the relationship between two variables

magnitude / strength

re: r^2 9% : no useful relationship :: 25% : weak correlation :: 64% : moderate correlation :: 81% : ___ correlation

strong

name of the test that produces a non-parametric measure of the relationship between two variables

spearman rank-order correlation

because you cannot mathematically manipulate ordinal data, the main difference between the spearman rho and the pearson r is that the spearman rho requires ___ of the detain the two variables under consideration

relative ranking

the name of the descriptive statistic indicating whether variables appear to be positively or negatively related between two variables

rho

if the null hypothesis (Ho: rho = 0.00) is ___, then the two variables are not related

accepted

rho^2, read as a percentage, shows the ___ of the relationship between two variables

magnitude / strength it is the percentage of the first variable's relationship to the second

name of the test that "involves the unique situation of a non-parametric association between two binomial variables" (i.e. the two variables each have only two possibilities - for example, pass / fail, normal / abnormal)

simple chi-square

in the simple chi-square 2x2 contingency table, each subject will fall into one of four possible outcomes, eventually displaying a ___ of scores within the four categories

frequency / count

if you compute a negative X^2-value, there was an error in ___

computation all X^2-values are positive numerals

after you've computed the X^2-value, the next step is to test for statistical significance; your null hypothesis is: Ho: X^2 = 0.00 and your alternative hypothesis is Ha: X^2 ___ 0.00

> (greater than 0.00)

the degrees of freedom for simple chi-square is always ___

1

if your computed X^2-value is equals or exceeds the critical X^2-value, you ___ the null hypothesis and conclude ___

reject, there is a relationship between the two variables

if your computed X^2-value is less than the critical X^2-value, you ___ the null hypothesis and conclude ___

accept, there is NO relationship between the two variables

if you have rejected the null hypothesis for a simple chi-square, you must go another step and compute the ___

phi coefficient when this is squared and read as a percentage, phi^2 shows the magnitude / strength of the relationship between two variables