Chi-square analysis enables researchers to test for statistical significance between the frequency distributions of two or more nominally scaled variables in a cross-tabulation table to determine if there is any association between the variables. True False

Which of the following statements about the Chi-square analysis is true? Categorical data from questions about sex, education, or other nominal variables cannot be tested with this statistic. The expected frequencies in Chi-square cannot be calculated theoretically. The larger the Chi-square, the less likely it is that the two variables that are measured are related. When the differences between observed and expected frequencies are large, one must accept the null hypothesis. Chi-square analysis compares the observed frequencies of responses with the expected frequencies.

A necessary assumption that is made when conducting a chi-squared analysis is: (select the one false statement)Group of answer choicesat least 80% of the observed frequencies are greater than or equal to 5all expected frequencies are greater than or equal to 1the sample size is large enough to approximate a Binomial random variable by a normal random variablethe sample data consist of frequency counts for different categories of a tablea subject can only fit into one category of the table (i.e. they belong with only one cell of the table)

What type of data is commonly analyzed using a chi-square test?*1 pointContinouous dataRatio dataOrdinal dataNominal or categorical data

What does a chi-square test compare?Question 4Answera.the frequency of incorrect responses for each variableb.correct and incorrect reaction timesc.the observed and total frequencies for each individual participantd.the observed and expected frequencies for each cell

What does a large Chi-Square statistic indicate in a Chi-Square test?Select one:a. A strong association between variablesb. An error in the calculationc. No association between variablesd. A weak association between variables