The chi-square test of independence
-tests for significant relationships between two variables organized in a bivariate table
-makes no assumption about the shape of the population distribution
-requires that no cell have an expected frequency less than 5
-obtains probabilities based on the
The chi-square test of independence--
-tests the research hypothesis that the two variables are related in the population
-tests the null hypothesis that the two variables are
independent, i.e., have no relationship in the population
-is one of the most widely used tests in the social science
You can say that two variables are statistically independent when:
-there is no association between the two variables
-the percentage distributions of the dependent variable within each category of the independent variable are identical in a bivariate table
-an individual's score on one variable is independent of his/her
When the null hypothesis in the chi square test for independence is true, there will be
little difference between the observed frequencies and the expected frequencies
You can use the chi-square test of independence when:
-the independent variable is nominal and the dependent variable is nominal
-the independent variable is nominal and the dependent variable is ordinal
-the independent variable is ordinal and the dependent variable is ordinal
The limitations of a chi-square test of independence are:
-the chi-square test of independence does NOT give us much information about the strength of the relationship
- the chi-square test of independence is sensitive to both large and small sample sizes
-the chi-square test of independence is sensitive to smal
In the chi-square test of independence, the null hypothesis and the research hypothesis
always contradict each other
Which pattern of cell frequencies in a 2x2 table would indicate that the variables are independent?
all cell frequencies are exactly the same
In one-way ANOVA, "one-way" means that:
there is one independent variable
ANOVA is appropriate for situations in which
we are comparing more than two samples
Analysis of variance assumes that
-independent random samples are used You Answered
-the dependent variable is interval level
-the distribution of the dependent variable is normal
-the population variances of the groups defined the independent variable are equal
The logic of analysis of variance is the same as the logic of
the independent samples t-test
In the ANOVA test, what assumption must be made with regard to the population variances?
they are equal
In the ANOVA test, what assumption must be made about the shape of the population distribution of the dependent variable?
the population distribution is normal in shape
One limitation of ANOVA is that, when the null hypothesis is rejected, the test
does not tell us which sample mean(s) is/are different
The null hypothesis for ANOVA is
the population means are equal
If we reject the null hypothesis in a test using analysis of variance, we are concluding that
the populations from which our samples come are different
Which of these situations could be analyzed with a one-way analysis of variance?
a comparison of four different age groups with regard to mean hours of watching television per day
A researcher is analyzing regional differences in family size. She has information on number of children for samples of families from four regions. Which of the following would be an appropriate statistical test?
one-way ANOVA
If fear of walking alone at night (answered yes or no) is not related to gender, we would expect
the same percentage of males and females to be fearful
A student wanted to test whether there was a difference in the mean daily hours of study for students living in four dormitories. She selected a random sample of 50 students from each of the four dormitories. What is the null hypothesis for this situation
the mean daily hours of study is the same for each dormitory
Samples of Republicans and Democrats have been tested for their level of support for welfare reform and the null hypothesis has been rejected. What may we conclude?
the difference is significant, there are differences between the parties on this issue
A researcher is comparing random samples of white, black, and Hispanic Americans for differences in income, family size, and years of education. Which of the following tests of significance would be useful for all three dependent variables? AAA
One way ANOVAAA