YOU WERE LOOKING FOR: Chi Square Test Multiple Choice Questions And Answers
For example, in some clinical trials the outcome is a classification such as hypertensive, pre-hypertensive or normotensive. We could use the same classification in an observational study such as the Framingham Heart Study to compare men and women...
We will consider chi-square tests here with one, two and more than two independent comparison groups. Learning Objectives After completing this module, the student will be able to: Perform chi-square tests by hand Appropriately interpret results of...
The observed frequencies are those observed in the sample and the expected frequencies are computed as described below. The test above statistic formula above is appropriate for large samples, defined as expected frequencies of at least 5 in each of the response categories. These expected frequencies are determined by allocating the sample to the response categories according to the distribution specified in H0.
This is done by multiplying the observed sample size n by the proportions specified in the null hypothesis p 10 , p 20 , To ensure that the sample size is appropriate for the use of the test statistic above, we need to ensure that the following: min np10 , n p20 , As the name indicates, the idea is to assess whether the pattern or distribution of responses in the sample "fits" a specified population external or historical distribution.
In the next example we illustrate the test. As we work through the example, we provide additional details related to the use of this new test statistic. Example: A University conducted a survey of its recent graduates to collect demographic and health information for future planning purposes as well as to assess students' satisfaction with their undergraduate experiences. The survey revealed that a substantial proportion of students were not engaging in regular exercise, many felt their nutrition was poor and a substantial number were smoking. The next year the University launched a health promotion campaign on campus in an attempt to increase health behaviors among undergraduates. The program included modules on exercise, nutrition and smoking cessation. To evaluate the impact of the program, the University again surveyed graduates and asked the same questions. The survey was completed by graduates and the following data were collected on the exercise question:.
Chapter 4: Multiple Choice Questions Try the multiple choice questions below to test your knowledge of this Chapter. Once you have completed the test, click on 'Submit Answers' to get your results. This activity contains 15 questions. By which other name is the chi-square goodness of fit test known? What type of data do you need for a chi-square test? How many variables do you need to run a one-sample chi-square analysis? What is the purpose of a goodness-of-fit test? How many cases need to appear in one category for chi-square? How can you deal with low expected values? In which section of the SPSS output will you find the chi-square analysis? What does a significant result in a chi-square test imply? What symbol is used to represent chi-square? By which other name is a chi-square contingency table analysis known? Chi-square test for independence assesses which of the following? Which tests could be used if your expected cases were fewer than 5?
Which row would you normally consult to find your chi-square results in a Chi-square test for independence all assumptions being met? What is an effect size? Answer choices in this exercise appear in a different order each time the page is loaded.
Chapter 7: Multiple Choice Questions Try the multiple choice questions below to test your knowledge of this Chapter. Once you have completed the test, click on 'Submit Answers' to get your results. This activity contains 20 questions. What are the two types of variance which can occur in your data? What would you use to determine whether significant differences were observed between all levels of your independent variable? What does partial eta squared tell us? What type of MANOVA would be used with more than one dependent variable and one independent variable with only two dichotomous levels? What type of MANOVA would be used with more than one dependent variable and one independent variable with more than two levels?
What type of MANOVA would be used with more than one dependent variable and more than one independent variable which all have more than two levels? Answer choices in this exercise appear in a different order each time the page is loaded.
They make several statements. Which of these statements most relates to the effect size of Drug A vs. Drug B. A what is the probability of observing our sample data by random chance, if some assumptions are true about the population? B does one of our variables of interest directly cause a change in another variable? C to what situations or populations can we generalize our sample data?
D None of the above Answer: what is the probability of observing our sample data by random chance, if some assumptions are true about the population? A sample statistic; population parameter B sample statistic; sample statistic C population parameter; sample statistic D population parameter; population parameter View Answer Answer: sample statistic; population parameter 20 Two dog owners are interested in whether their shared dog tends to run to a specific one of them first when they come home together.
They walk through the door at the same time and record which person the dog runs to first. They repeat this procedure many times. What test should they use to ask if the dog runs to one of them more often than we would expect if the dog was choosing randomly between the owners? A binomial test for a single proportion B one-sample t-test for a single mean C z-test for a difference in two proportions D two-sample t-test for a difference in means.
A random sample of former student-athletes are picked from each of the 16 colleges that are members of the Big East conference. Students are surveyed about whether or not they feel they received a quality education while participating in varsity athletics. Which of the following is the most appropriate test to determine whether there is a difference among these schools as to the student-athlete perception of having received a quality education.
A chi-square goodness-of-fit test for a uniform distribution B. A chi-square test of independence C. A chi-square test of homogeneity of proportions D. A multiple-sample z-test of proportions E. A multiple-population z-test of proportions 5. Anthropologists come upon a previously unknown civilization living on a remote island. Is there sufficient evidence to conclude that the distribution of blood types found among the island population differs from that which occurs in the general population?
The data prove that blood type distribution on the island is different from that of the general population. The data prove that blood type distribution on the island is not different from that of the general population. Is there a relationship between education level and sports interest? A study cross-classified randomly selected adults in three categories of education level not a high school graduate, high school graduate, and college graduate and five categories of major sports interest baseball, basketball, football, hockey, and tennis. Is there evidence of a relationship between education level and sports interest? The data prove there is a relationship between education level and sports interest.
The evidence points to a cause-and-effect relationship between education and sports interest. The P-value is greater than 0. A disc jockey wants to determine whether middle school students and high school students have similar music tastes. A chi-square test of homogeneity of proportions is performed, and the resulting P-value is below 0. Which of the following is a proper conclusion? There is sufficient evidence that for all three music choices the proportion of middle school students who prefer each choice is equal to the corresponding proportion of high school students. There is sufficient evidence that the proportion of middle school students who prefer hip-hop is different from the proportion of high school students who prefer hip-hop.
There is sufficient evidence that for all three music choices the proportion of middle school students who prefer each choice is different from the corresponding proportion of high school students. There is sufficient evidence that for at least one of the three music choices the proportion of middle school students who prefer that choice is equal to the corresponding proportion of high school students. There is sufficient evidence that for at least one of the three music choices the proportion of middle school students who prefer that choice is different from the corresponding proportion of high school students. A geneticist claims that four species of fruit flies should appear in the ratio Suppose that a sample of flies contained , , , and flies of each species, respectively. Is there sufficient evidence to reject the geneticist's claim? The data prove the geneticist's claim.
The data prove the geneticist's claim is false. The data do not give sufficient evidence to reject the geneticist's claim. The data give sufficient evidence to reject the geneticist's claim. The evidence from this data is inconclusive. A food biologist surveys people at an ice cream parlor, noting their taste preferences and cross-classifying against the presence or absence of a particular marker in a saliva swab test. Is there sufficient evidence of a relationship between taste preference and the marker presence? Random samples of 25 students are chosen from each high school class level, students are asked whether or not they are satisfied with the school cafeteria food, and the results are summarized in the following table: Is there sufficient evidence of a difference in cafeteria food satisfaction among the class levels? The data prove that there is a difference in cafeteria food satisfaction among the class levels. There is sufficient evidence of a linear relationship between food satisfaction and class level.
Can dress size be predicted from a woman's height? In a random sample of 20 female high school students, dress size versus height cm gives the following regression results: Is there statistical evidence of a linear relationship between dress size and height? No, because r2, the coefficient of determination, is too small. No, because 0. Yes, because by any reasonable observation, taller women tend to have larger dress sizes. Yes, because the computer printout does give a regression equation.
To study the relationship between calories kcal and fat g in pizza, slices of 14 randomly selected major brand pizzas are chemically analyzed. Variability in calories among slices B. Variability in fat among slices C. Variability in the y-intercept of the regression line E. Variability in the residuals Which of the following must be true? The slope of the regression line is 0. The slope of the regression line is A scatterplot of the data would show a linear pattern. A residual plot would show no pattern. The correlation coefficient is negative. For the Chicago Bulls season, a linear association study yields the following computer output: Which of the following is an appropriate test statistic for testing the null hypothesis that the slope of the regression line is greater than 0?
It is a nonparametric test. This test is also known as: Chi-Square Test of Association. This test utilizes a contingency table to analyze the data. A contingency table also known as a cross-tabulation, crosstab, or two-way table is an arrangement in which data is classified according to two categorical variables. The categories for one variable appear in the rows, and the categories for the other variable appear in columns. Each variable must have two or more categories. Each cell reflects the total count of cases for a specific pair of categories. There are several tests that go by the name "chi-square test" in addition to the Chi-Square Test of Independence. Look for context clues in the data and research question to make sure what form of the chi-square test is being used. Common Uses The Chi-Square Test of Independence is commonly used to test the following: Statistical independence or association between two or more categorical variables.
The Chi-Square Test of Independence can only compare categorical variables. It cannot make comparisons between continuous variables or between categorical and continuous variables. Additionally, the Chi-Square Test of Independence only assesses associations between categorical variables, and can not provide any inferences about causation. If your categorical variables represent "pre-test" and "post-test" observations, then the chi-square test of independence is not appropriate. This is because the assumption of the independence of observations is violated. In this situation, McNemar's Test is appropriate. Data Requirements Your data must meet the following requirements: Two categorical variables. Two or more categories groups for each variable. Independence of observations. There is no relationship between the subjects in each group. The categorical variables are not "paired" in any way e.
Relatively large sample size. Expected frequencies for each cell are at least 1. Data Set-Up There are two different ways in which your data may be set up initially. The format of the data will determine how to proceed with running the Chi-Square Test of Independence. At minimum, your data should include two categorical variables represented in columns that will be used in the analysis. The categorical variables must include at least two groups. Your data may be formatted in either of the following ways: If you have the raw data each row is a subject : Cases represent subjects, and each subject appears once in the dataset. That is, each row represents an observation from a unique subject. The dataset contains at least two nominal categorical variables string or numeric. The categorical variables used in the test must have two or more categories.
If you have frequencies each row is a combination of factors : An example of using the chi-square test for this type of data can be found in the Weighting Cases tutorial. Cases represent the combinations of categories for the variables. Each row in the dataset represents a distinct combination of the categories. The value in the "frequency" column for a given row is the number of unique subjects with that combination of categories. You should have three variables: one representing each category, and a third representing the number of occurrences of that particular combination of factors. Before running the test, you must activate Weight Cases, and set the frequency variable as the weight.
Recall that the Crosstabs procedure creates a contingency table or two-way table, which summarizes the distribution of two categorical variables. A Row s : One or more variables to use in the rows of the crosstab s. You must enter at least one Row variable. B Column s : One or more variables to use in the columns of the crosstab s.
You must enter at least one Column variable. Also note that if you specify one row variable and two or more column variables, SPSS will print crosstabs for each pairing of the row variable with the column variables. The same is true if you have one column variable and two or more row variables, or if you have multiple row and column variables. A chi-square test will be produced for each table. Additionally, if you include a layer variable, chi-square tests will be run for each pair of row and column variables within each level of the layer variable. C Layer: An optional "stratification" variable. If you have turned on the chi-square test results and have specified a layer variable, SPSS will subset the data with respect to the categories of the layer variable, then run chi-square tests between the row and column variables.
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