Question 1

If the data for the variables being tested are not normally distributed, the statistical test:

Accepted Answer

Many statistical tests assume normal distribution of data. When this assumption is not met, the results can still be interpreted but are considered approximations. Non-parametric tests or transformations may be used as alternatives.

Question 2

A correlation is the statistical test for:

Accepted Answer

A correlation is specifically used to test the linear relationship between two continuous variables, assessing how closely related they are in terms of their variation together.

Question 3

A spurious correlation is:

Accepted Answer

A spurious correlation is one in which a third variable influences the nature of the relationship between the two variables tested.

Question 4

Researchers compute the coefficient of determination to:

Accepted Answer

The coefficient of determination, often denoted as R^2, measures the proportion of the variance in the dependent variable that is predictable from the independent variable(s). It essentially tells us how much of the variance in our outcome can be explained by the model, which is what choice B is referring to.

Question 5

A correlation matrix is used to:

Accepted Answer

A correlation matrix is a table showing correlation coefficients between variables, displaying how each variable is correlated with every other variable.

Question 6

Correlations can also be computed for variables based on ordinal or nominal data. These tests include:

Accepted Answer

Point biserial correlation is used for datasets where one variable is dichotomous (nominal) and the other is continuous, while Spearman correlation coefficient is used for ordinal data or non-parametric data, making both A and B correct. Coefficient of determination is related to Pearson's correlation coefficient for linear relationships between two continuous variables, not specifically for ordinal or nominal data.

Question 7

Regression is superior to correlation because it:

Accepted Answer

Regression is superior to correlation because it is more flexible, can predict some variables by knowing others, can be used with more than two variables, and can test for the influence of more than one independent variable. These features make regression a powerful tool for analysis and prediction in various fields, offering insights that correlation alone cannot provide.

Question 8

Exogenous variables are:

Accepted Answer

Exogenous variables are used in structural equation modeling (SEM) to represent variables that are not caused by another variable in the model, thus acting as independent variables. They are distinct from endogenous variables, which are influenced by other variables within the model.

Question 9

Tests for relationship are most commonly computed for variables of continuous level data.

Accepted Answer

Tests for relationships, such as correlation and regression analysis, are most commonly computed for variables of continuous level data because these tests require numerical input that can vary and have an infinite number of values within a given range. Continuous data allows for more precise and meaningful analysis of the relationship between variables.

Question 10

Statistical tests for relationship are limited to two variables.

Accepted Answer

Statistical tests for relationships can involve two or more variables, allowing for the analysis of complex relationships and interactions among multiple variables.

Question 11

Researchers use inferential statistics to determine if the relationship observed in the data is stronger than the relationship that might occur due to chance.

Accepted Answer

Inferential statistics allow researchers to make inferences about a population based on a sample, including determining if observed relationships in the data are statistically significant and not likely due to random chance.

Question 12

Statistical tests were designed to test for relationships and differences on normally distributed variables.

Accepted Answer

Statistical tests, especially parametric tests, were originally designed to test for relationships and differences among variables that are assumed to be normally distributed, as the normal distribution is a fundamental assumption for the application of many of these tests.

Question 13

The significance level is usually set at .05 as the criterion for making the decision to accept the research hypothesis.

Accepted Answer

The significance level is typically set at .05, which means that there is a 5% chance of rejecting the null hypothesis when it is actually true. This level of significance is commonly used in research as it provides a balance between the risk of making a Type I error (rejecting the null hypothesis when it is true) and the risk of making a Type II error (failing to reject the null hypothesis when it is false).

Question 14

Regardless of how the research hypothesis is stated, there is a complementary null hypothesis.

Accepted Answer

For every research hypothesis, there is a null hypothesis that states there is no significant effect or difference, serving as the complement to the research hypothesis.

Question 15

To use a correlation as a statistical test, each participant must have provided measurements on two separate variables.

Accepted Answer

For a correlation analysis to be conducted, each participant must have measurements on two variables to assess the relationship between them.

Question 16

Correlation tests for curvilinear relationships.

Accepted Answer

Correlation tests, specifically Pearson's correlation, are designed to measure the strength and direction of a linear relationship between two continuous variables, not curvilinear relationships.

Question 17

A significant r value would cause the researcher to retain the null hypothesis.

Accepted Answer

A significant r value (correlation coefficient) typically indicates a strong relationship between variables, leading researchers to reject the null hypothesis, not retain it.

Question 18

Regression is of particular advantage to communication researchers because it allows researchers to study variables that cannot be experimentally manipulated.

Accepted Answer

Regression analysis enables researchers to examine relationships between variables, including those that are not suitable for experimental manipulation due to ethical, practical, or theoretical reasons, thus offering a valuable tool for communication studies.

Question 19

Because there is no theoretical limit to the number of predictor variables tested in multiple regression, it is common for researchers to include 10 or more.

Accepted Answer

The answer of Because there is no theoretical limit to...

Question 20

Multiple regression allows the researcher to determine the relative importance of each variable to the regression relationship.

Accepted Answer

Multiple regression analysis enables the researcher to assess the individual contribution of each independent variable in the model to the dependent variable, while controlling for the effects of other variables. This allows for the determination of the relative importance of each variable in explaining the variation in the dependent variable.

Quiz 11: Testing for Relationships