Question 1

When variables are correlated, they have overlapping, or shared, variance.

Accepted Answer

When variables are correlated, it means there is a relationship between them, indicating that changes in one variable are associated with changes in another. This relationship implies that they share variance, as part of the variation in each variable can be explained by the variation in the other.

Question 2

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.

Accepted Answer

In standard analysis approaches like multiple regression, the overlapping variance among independent variables (IVs) is accounted for in the model's overall variance explained in the dependent variable (DV), but it is not attributed to any specific IV, to avoid double counting the shared variance.

Question 3

The sequential analysis requires the researcher to prioritize the entry of IVs into the equation or solution.

Accepted Answer

Sequential analysis involves the researcher deciding the order in which independent variables (IVs) are entered into the analysis, which can affect the interpretation of their relative importance or effect.

Question 4

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.

Accepted Answer

Multivariate statistical analyses can be complex due to the involvement of multiple variables and their interactions, making it challenging to derive clear, straightforward statistical conclusions for specific research questions.

Question 5

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.

Accepted Answer

Describing or summarizing the collected data is essential as it provides an initial understanding of the dataset's characteristics, trends, and potential anomalies, which is crucial for any further analysis or statistical testing.

Question 6

Measures of central tendency include:
A) Mean, median, and mode.
B) Only mean and median.
C) Range.
D) Quartile deviation.

Accepted Answer

Mean, median, and mode are all measures of central tendency, which describe the center of a data set.

Question 7

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.

Accepted Answer

Measures of variability describe the spread or dispersion of a data set. Range, quartile deviation, and standard deviation are all measures of variability, capturing different aspects of dispersion.

Question 8

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.

Accepted Answer

Percentile ranks and standard scores are the two most common measures of relative position. Percentile ranks indicate the percentage of scores that fall below a given score, while standard scores indicate the number of standard deviations a score is above or below the mean.

Question 9

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.

Accepted Answer

Spearman rho and Pearson r are the two most commonly used measures of relationship. Spearman rho is used for ordinal data or non-linear relationships, while Pearson r is used for linear relationships between variables on interval or ratio scales.

Question 10

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.

Accepted Answer

Inferential statistics are used to draw conclusions or make inferences about a larger population based on data collected from a sample of that population. This involves estimating population parameters and testing hypotheses to make probabilistic statements about the population.

Quiz 1: Introduction to Multivariate Statistics

When variables are correlated, they have overlapping, or shared, variance.

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.

The sequential analysis requires the researcher to prioritize the entry of IVs into the equation or solution.

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.

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.

Measures of central tendency include:

Measures of variability include:

The two most common measures of relative position are:

Two most commonly used measures of relationship are:

Inferential statistics deal with collecting and analyzing information from samples in order to: