Which statement is not true about both the chi-square and McNemar test?Question 12Answera.Both tests are used on frequency data (or counts)b.The data in separate cells of the contingency table must come from different participants (independent sample)c.The test statistic is based on comparing the observed frequency of an event, with the expected frequency of the event if the null hypothesis is trued.They are non-parametric tests
Question
Which statement is not true about both the chi-square and McNemar test?Question 12Answera.Both tests are used on frequency data (or counts)b.The data in separate cells of the contingency table must come from different participants (independent sample)c.The test statistic is based on comparing the observed frequency of an event, with the expected frequency of the event if the null hypothesis is trued.They are non-parametric tests
Solution
The statement that is not true about both the chi-square and McNemar test is:
b. The data in separate cells of the contingency table must come from different participants (independent sample)
This is because while the chi-square test requires independent observations, the McNemar test is specifically designed for dependent or paired samples. The McNemar test is often used in case-control studies, where each case is paired with a control. The same participants are measured twice, under different conditions, and the test is used to determine whether the condition affects the outcome.
Similar Questions
What is the purpose of the McNemar test?Question 1Answera.It is similar to chi-square but the McNemar test allows us to test independent samples instead of repeated measuresb.It is used for post-hoc analysis after a chi-square testc.It corrects for small samples sizes to give a more accurate result for the chi-square analysis when cell size is less than 5d.It compares the relative frequency of two alternative outcomes within the same sample e.g. before and after treatment
3. The Chi Square test is applicable for a given data, if*
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.
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
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
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