Advanced and Multivariate Statistical Methods, Eighth Edition, Craig A. Mertler, Rachel A.
Vannatta, & Kristina N. LaVenia, Routledge, 2025.
Chapter 1: Introduction to Multivariate Statistics
Test Items: True-False Format
Instructions: Mark the statements “T” for true, “F” for false, or “?” for don’t know.
1. The use of multivariate statistical techniques has become more commonplace largely due
to the increasingly complex nature of research designs and related research questions.
T*
F
2. A study appropriate for multivariate statistical analysis is typically defined as one with
several dependent variables (DVs).
T*
F
3. The basic distinction between experimental and nonexperimental research designs is
whether the levels of the independent variable(s) have been manipulated by the
researcher.
T*
F
4. In nonexperimental research (e.g., descriptive, correlational, survey, or causal-
comparative designs), the researcher has no control over the levels of the independent
variables (IVs).
T*
F
5. In an experimental research study, if the researcher finds a statistically significant
difference between two or more of the groups representing different treatment conditions,
she or he can have some confidence in attributing causality to the IV.
T*
F
6. Nonexperimental research studies also enable a researcher to conclude that the IV and
DV are related and infer causality.
T
F*
7. In experimental studies, IVs may also be referred to as criterion or outcome variables.
T
F*
8. In experimental studies, DVs are sometimes referred to as the predictor or causal
variables.
T
F*
9. Univariate statistics refers to analyses where there is only one IV and one DV.
T
F*
10. Bivariate statistics refers to analyses that involve two variables where one is identified as
an IV and the other is identified as a DV.
, T
F*
11. Quantitative variables are also referred to as continuous or interval variables.
T*
F
12. Categorical variables consist of separate, indivisible categories.
T*
F
13. Categorical variables may also be referred to as nominal, ordinal, discrete, or qualitative.
T*
F
14. A dichotomous variable is one that has only two possible levels or categories.
T*
F
15. Age is a quantitative variable, but one could recode the values so that it would be
transformed into a dichotomous variable.
T*
F
16. When conducting a multivariate analysis, the best recommendation is to obtain the
solution with the largest number of variables.
T
F*
17. The mathematical calculations involved in multivariate statistical analyses are performed
only on a correlation matrix.
T
F*
18. Orthogonality is perfect association between variables.
T
F*
19. Orthogonality is not a desirable quality for multivariate statistical analyses.
T
F*
20. Having a data set with orthogonal variables is not the ideal situation.
T
F*
21. When variables are correlated, they have overlapping, or shared, variance.
T*
F
22. Using a standard analysis approach, the overlapping portion of variance is included in the
overall summary statistics of the relationship of the set of IVS to the DV, but that portion
is not assigned to either of the IVs as part of their individual contribution.
T*
F
23. The sequential analysis requires the researcher to prioritize the entry of IVs into the
equation or solution.
T*
, F
24. One of the major difficulties in using multivariate statistical analyses is that it is
sometimes nearly impossible to get a firm statistical answer to your research questions.
T*
F
25. The first step in nearly any data analysis situation is to describe or summarize the data
collected on a set of participants that constitute the sample of interest.
T*
F
Test Items: Multiple-Choice Format
Instructions: Circle the letter of the best answer. If you do not know the best answer, you may put
a question mark to the left of the answers instead of circling a letter.
26. Measures of central tendency include:
a. Mean, median, and mode.*
b. Only mean and median.
c. Range.
d. Quartile deviation.
27. Measures of variability include:
a. Range, quartile deviation, and standard deviation.*
b. Only standard deviation and variance.
c. Range, standard deviation, and mean.
d. Standard deviation, mean, and variance.
28. The two most common measures of relative position are:
a. Mean and standard deviation.
b. Percentile ranks and standard scores.*
c. z-scores and T-scores.
d. Spearman rho and Pearson r.
29. Two most commonly used measures of relationship are:
a. Chi-square and T-test.
b. Spearman rho and Pearson r.*
c. Mean and standard deviation.
d. Range and percentile ranks.
30. A Type I error has been committed by the researchers if:
a. The null hypothesis is true, and the researcher concludes that it is true.
b. The null hypothesis is false, and the researcher concludes that it is false.
c. The null hypothesis is false, and the researcher concludes that it is true.
d. The null hypothesis is true and the researcher concludes that it is false.*
31. Inferential statistics deal with collecting and analyzing information from samples in order
to:
a. Draw conclusions, or inferences, about the larger population.*
b. Prove that the sample is a perfect replica of the larger population.
c. Prove that the null hypothesis is incorrect.
d. Predict that the only differences that exist are chance differences that do not represent
random sampling error.
, Chapter 2: A Guide to Multivariate Techniques
Test Items: True-False Format
Instructions: Mark the statements “T” for true, “F” for false, or “?” for don’t know.
1. The primary factor that determines the statistical test students should use is the number of
independent and dependent variables.
T
F*
2. When investigating the relationship between two or more quantitative variables, chi-
square is the appropriate test.
T
F*
3. The Pearson correlation coefficient measures the association between two quantitative
variables, distinguishing between the independent and dependent variables.
T
F*
4. Multiple regression is used when there are several dependent variables and one
independent quantitative variable.
T
F*
5. When testing for the significance of group differences, the number of IVs, the number of
DVs, and the number of categories in the DV determine the appropriate test.
T
F*
6. The most basic statistical test that measures group difference is the T-test.
T*
F
7. One-way analysis of variance (ANOVA) only determines the significance of group
differences and does not identify which groups are significantly different.
T*
F
8. One-way analysis of covariance (ANCOVA) is similar to ANOVA but additionally
controls for a variable that may influence the DV.
T*
F
9. Factorial analysis of variance (factorial ANOVA) extends ANOVA to research scenarios
with two or more IVs that are categorical.
T*
F
10. Factorial analysis of variance (factorial ANCOVA) examines group differences in a single
quantitative dependent variable based upon two or more categorical independent
variables, while controlling for a covariate that may influence the DV.
T*
F
Vannatta, & Kristina N. LaVenia, Routledge, 2025.
Chapter 1: Introduction to Multivariate Statistics
Test Items: True-False Format
Instructions: Mark the statements “T” for true, “F” for false, or “?” for don’t know.
1. The use of multivariate statistical techniques has become more commonplace largely due
to the increasingly complex nature of research designs and related research questions.
T*
F
2. A study appropriate for multivariate statistical analysis is typically defined as one with
several dependent variables (DVs).
T*
F
3. The basic distinction between experimental and nonexperimental research designs is
whether the levels of the independent variable(s) have been manipulated by the
researcher.
T*
F
4. In nonexperimental research (e.g., descriptive, correlational, survey, or causal-
comparative designs), the researcher has no control over the levels of the independent
variables (IVs).
T*
F
5. In an experimental research study, if the researcher finds a statistically significant
difference between two or more of the groups representing different treatment conditions,
she or he can have some confidence in attributing causality to the IV.
T*
F
6. Nonexperimental research studies also enable a researcher to conclude that the IV and
DV are related and infer causality.
T
F*
7. In experimental studies, IVs may also be referred to as criterion or outcome variables.
T
F*
8. In experimental studies, DVs are sometimes referred to as the predictor or causal
variables.
T
F*
9. Univariate statistics refers to analyses where there is only one IV and one DV.
T
F*
10. Bivariate statistics refers to analyses that involve two variables where one is identified as
an IV and the other is identified as a DV.
, T
F*
11. Quantitative variables are also referred to as continuous or interval variables.
T*
F
12. Categorical variables consist of separate, indivisible categories.
T*
F
13. Categorical variables may also be referred to as nominal, ordinal, discrete, or qualitative.
T*
F
14. A dichotomous variable is one that has only two possible levels or categories.
T*
F
15. Age is a quantitative variable, but one could recode the values so that it would be
transformed into a dichotomous variable.
T*
F
16. When conducting a multivariate analysis, the best recommendation is to obtain the
solution with the largest number of variables.
T
F*
17. The mathematical calculations involved in multivariate statistical analyses are performed
only on a correlation matrix.
T
F*
18. Orthogonality is perfect association between variables.
T
F*
19. Orthogonality is not a desirable quality for multivariate statistical analyses.
T
F*
20. Having a data set with orthogonal variables is not the ideal situation.
T
F*
21. When variables are correlated, they have overlapping, or shared, variance.
T*
F
22. Using a standard analysis approach, the overlapping portion of variance is included in the
overall summary statistics of the relationship of the set of IVS to the DV, but that portion
is not assigned to either of the IVs as part of their individual contribution.
T*
F
23. The sequential analysis requires the researcher to prioritize the entry of IVs into the
equation or solution.
T*
, F
24. One of the major difficulties in using multivariate statistical analyses is that it is
sometimes nearly impossible to get a firm statistical answer to your research questions.
T*
F
25. The first step in nearly any data analysis situation is to describe or summarize the data
collected on a set of participants that constitute the sample of interest.
T*
F
Test Items: Multiple-Choice Format
Instructions: Circle the letter of the best answer. If you do not know the best answer, you may put
a question mark to the left of the answers instead of circling a letter.
26. Measures of central tendency include:
a. Mean, median, and mode.*
b. Only mean and median.
c. Range.
d. Quartile deviation.
27. Measures of variability include:
a. Range, quartile deviation, and standard deviation.*
b. Only standard deviation and variance.
c. Range, standard deviation, and mean.
d. Standard deviation, mean, and variance.
28. The two most common measures of relative position are:
a. Mean and standard deviation.
b. Percentile ranks and standard scores.*
c. z-scores and T-scores.
d. Spearman rho and Pearson r.
29. Two most commonly used measures of relationship are:
a. Chi-square and T-test.
b. Spearman rho and Pearson r.*
c. Mean and standard deviation.
d. Range and percentile ranks.
30. A Type I error has been committed by the researchers if:
a. The null hypothesis is true, and the researcher concludes that it is true.
b. The null hypothesis is false, and the researcher concludes that it is false.
c. The null hypothesis is false, and the researcher concludes that it is true.
d. The null hypothesis is true and the researcher concludes that it is false.*
31. Inferential statistics deal with collecting and analyzing information from samples in order
to:
a. Draw conclusions, or inferences, about the larger population.*
b. Prove that the sample is a perfect replica of the larger population.
c. Prove that the null hypothesis is incorrect.
d. Predict that the only differences that exist are chance differences that do not represent
random sampling error.
, Chapter 2: A Guide to Multivariate Techniques
Test Items: True-False Format
Instructions: Mark the statements “T” for true, “F” for false, or “?” for don’t know.
1. The primary factor that determines the statistical test students should use is the number of
independent and dependent variables.
T
F*
2. When investigating the relationship between two or more quantitative variables, chi-
square is the appropriate test.
T
F*
3. The Pearson correlation coefficient measures the association between two quantitative
variables, distinguishing between the independent and dependent variables.
T
F*
4. Multiple regression is used when there are several dependent variables and one
independent quantitative variable.
T
F*
5. When testing for the significance of group differences, the number of IVs, the number of
DVs, and the number of categories in the DV determine the appropriate test.
T
F*
6. The most basic statistical test that measures group difference is the T-test.
T*
F
7. One-way analysis of variance (ANOVA) only determines the significance of group
differences and does not identify which groups are significantly different.
T*
F
8. One-way analysis of covariance (ANCOVA) is similar to ANOVA but additionally
controls for a variable that may influence the DV.
T*
F
9. Factorial analysis of variance (factorial ANOVA) extends ANOVA to research scenarios
with two or more IVs that are categorical.
T*
F
10. Factorial analysis of variance (factorial ANCOVA) examines group differences in a single
quantitative dependent variable based upon two or more categorical independent
variables, while controlling for a covariate that may influence the DV.
T*
F
