Question 1

A Phi coefficient is used for
A) rank level data.
B) continuous level data.
C) artificial dichotomous level data.
D) true dichotomous level data.

Accepted Answer

A Phi coefficient is used for true dichotomous level data, where there are only two possible response options for each variable. It measures the degree of association between two dichotomous variables.

Question 2

The criterion variable in a prediction study is another name for the
A) the predictor variable.
B) the variable that is predicted.
C) the regression variable.
D) the independent variable.

Accepted Answer

The criterion variable is the variable that is being predicted, therefore the best choice is option B. The predictor variable is the independent variable that is used to make the prediction, so option A is not correct. The regression variable is another way to refer to the predictor variable, so option C is not correct. Option D is not correct because the independent variable is not the same as the criterion variable.

Question 3

General guidelines for interpreting correlations, in a group study, as presented in your text indicate that a correlation of r=-.82 is considered
A) un-interpretable.
B) low.
C) average.
D) strong.

Accepted Answer

According to the general guidelines for interpreting correlations in a group study, a correlation of r=-.82 is considered a strong correlation.

Question 4

Sam concluded in his research report that as class size increased teaching efficacy also increased. Of the following which is a correlation coefficient would best support Sam's claim.
A) -.82
B) .09
C) .24
D) .68

Accepted Answer

A correlation coefficient ranges between -1 and 1, with -1 indicating a perfect negative correlation, 1 indicating a perfect positive correlation, and 0 indicating no correlation. A correlation coefficient of .68 suggests a strong positive correlation, which supports Sam's claim that teaching efficacy increases as class size increases. Choice A (-.82) suggests a strong negative correlation, which would contradict Sam's claim, while choices B (.09) and C (.24) suggest weak positive correlations, which may not provide enough evidence to support Sam's claim.

Question 5

Attentuation refers to
A) stating there is not a relationship between two variables when there is one
B) lowered accuracy in a prediction equation when it is applied to a new sample.
C) combining many variables in order to predict the most amount of variance.
D) effects of unreliable instruments underestimating the correlation.

Accepted Answer

Attenuation refers to the underestimation of a true correlation due to unreliable measures or instruments. Option A refers to suppression, not attenuation. Option B describes the issue of overfitting rather than attenuating. Option C refers to multiple regression, which is a different concept.

Question 6

Gretchen wanted to see if given a sample of teachers' that graduated from colleges and Universities, GPA in college was related to starting salary as a teacher. Which correlation coefficient should Gretchen calculate?
A) Pearson r
B) eta
C) Kendall's tau
D) Spearman rho

Accepted Answer

Pearson r (or Pearson's correlation coefficient) is the most appropriate choice for analyzing the relationship between two continuous variables, such as GPA and starting salary. It measures the strength and direction of the linear relationship between two variables. Both Eta and Kendall's tau are measures of association, but they are more appropriate for ordinal or categorical data, respectively. Spearman rho is also a measure of association, but it is more appropriate when the relationship between two variables is non-linear or when the data is ordinal.

Question 7

Joni teaches a preschool class. She has eight students and ranks them based upon math and reading ability. She would like to see if there is a relationship between how well her students are ranked in math and in reading. Which of the following correlations should she use?
A) Kendall's tau
B) Phi coefficient
C) Biserial
D) eta

Accepted Answer

Kendall's tau is a non-parametric measure of association that is appropriate for finding a relationship between two ranked variables. As the teacher has ranked her students based upon their math and reading abilities, Kendall's tau would be a suitable correlation coefficient to use in this scenario. Phi coefficient and eta are measures of association for nominal variables, and the biserial correlation is used to measure the relationship between a nominal and continuous variable.

Question 8

Testing the statistical significance of a correlation requires knowledge of
A) mean, sample size, level of significance.
B) mean and level of significance.
C) level of significance and sample size.
D) sample size and mean.

Accepted Answer

Testing the statistical significance of a correlation requires knowledge of the level of significance and sample size to determine if the observed correlation is likely due to chance.

Question 9

Data collection for types of correlation studies is generally
A) longer than most qualitative approaches.
B) shorter for prediction studies than for correlation studies.
C) longer than most types of quantitative research.
D) shorter than most types of quantitative research.

Accepted Answer

Data collection for types of correlation studies is generally shorter than most types of quantitative research. This is because the focus is on identifying relationships between variables rather than gathering a large amount of data.

Question 10

The most commonly used correlation is the
A) Pearson r.
B) eta.
C) Kendall's tau.
D) Spearman's rho.

Accepted Answer

Pearson r is the most commonly used correlation as it measures the linear relationship between two continuous variables. It is also widely used due to its simplicity and ease of interpretation.

Question 11

George suspects that there is a curvilinear relationship in his data set. Which of the following correlation coefficient should George calculate?
A) Pearson
B) eta
C) Kendall's tau
D) Spearman rho

Accepted Answer

The eta correlation coefficient is used to measure the strength of a curvilinear relationship between two variables.

Question 12

Regarding sampling, one strategy to facilitate obtaining accurate results if there will be subgroup correlations would be to select a
A) stratified sample.
B) cluster sample.
C) random sample.
D) purposive sample.

Accepted Answer

A stratified sample involves dividing the population into subgroups based on specific criteria, such as age or gender, and then selecting a sample from each subgroup in proportion to its representation in the population. This can help ensure that each subgroup is adequately represented in the sample, which can facilitate obtaining accurate results for subgroup correlations. Cluster sampling involves selecting a sample of groups (clusters) rather than individuals, and may not be as effective for accurately representing subgroups. Random sampling involves selecting individuals at random from the population and may not ensure adequate representation of subgroups. Purposive sampling involves selecting individuals based on specific criteria, which may introduce bias and not ensure adequate representation of subgroups.

Question 13

Shrinkage refers to
A) lowered accuracy in a prediction equation when it is applied to a new sample.
B) combining many variables in order to predict the most amount of variance.
C) effects of unreliable instruments underestimating the correlation.
D) a measure of the error found in a regression equation.

Accepted Answer

Shrinkage refers to the phenomenon where the coefficients of a predictive model are "shrunk" towards a central value, usually zero. This is done in order to avoid overfitting of the model to the training data, which can lead to reduced accuracy when the model is applied to new data. Therefore, option A is the correct choice.

Question 14

Errors in instrument reliability
A) do not effect interrelatedness of variables.
B) decrease the number of significant relationships found.
C) increase the number of significant relationships found.
D) may either increase of decrease the number of relationships between variables.

Accepted Answer

Errors in instrument reliability may either increase or decrease the number of relationships between variables. This is because unreliable instruments may not accurately measure the intended variable, leading to inaccurate or inconsistent data. As a result, some relationships between variables may appear stronger or weaker than they actually are, while other relationships may be overlooked altogether.

Question 15

A Pearson r correlation is best used for data that is
A) only ordinal level.
B) both ordinal and interval level.
C) only interval level.
D) both interval and ratio.

Accepted Answer

Pearson r correlation is best used for data that is either interval or ratio level. This is because both of these levels of data involve continuous numerical measurements, allowing for meaningful calculations of correlation coefficients. Data at the ordinal level, while also ordered, does not have a consistent unit of measurement and therefore may not be as suitable for Pearson r correlation.

Question 16

General guidelines for interpreting correlations in a group study as presented in your text indicate that a correlation of r=30 is considered
A) not interpretable.
B) low and not important.
C) average but meaningful.
D) high and predictive.

Accepted Answer

According to the guidelines presented in the text, a correlation of r=30 is considered low and not important. However, it's worth noting that this answer is technically incorrect as r values must fall between -1 and +1, and there is no r=30 correlation coefficient.

Question 17

In her prediction study of high school mathematics experiences and University calculus performance, Kirby reported that number of courses taken was correlated r=.42 while the grades in high school mathematics courses was correlated r=.25 with university grades. Given her findings, Kirby can conclude which of the following?
A) High school grades are a better predictor than are number of courses taken.
B) Both high school grades and number of courses taken are strong predictors.
C) Both high school grades and number of courses taken are weak predictors.
D) Number of courses taken are a better predictor than are high school grades.

Accepted Answer

The correlation coefficient (r) indicates the strength and direction of a linear relationship between two variables. A higher absolute value of r indicates a stronger relationship. Since r=.42 for the number of courses taken is higher than r=.25 for grades, the number of courses taken is a better predictor of university grades than high school grades.

Question 18

The correlation that Kim found in her study between math self-efficacy and enrollment in advanced mathematics was r=.25. Her correlation can be described as
A) low.
B) meaningful.
C) strong.
D) predictive.

Accepted Answer

A correlation of r=.25 is considered low, indicating a weak relationship between math self-efficacy and enrollment in advanced mathematics.

Question 19

In a prediction study of low achieving students academic motivation, Hillary was surprised that achievement scores were not a significant predictor of motivation. Hillary's analysis is most likely is displaying
A) shrinkage.
B) attenuation.
C) standard variance.
D) restriction of range.

Accepted Answer

Restriction of range occurs when the sample's range of scores is limited, which reduces the correlation coefficient between variables. In this case, the low achieving students have a restricted range of achievement scores, which may explain why achievement was not a significant predictor of motivation.

Question 20

Generally, when more variables are added to a prediction equation
A) more variance in the criterion variable is accounted for.
B) more variance in the predictor variables are accounted for.
C) less variance in the criterion variable is accounted for.
D) less variance in the predictor variable is accounted for.

Accepted Answer

Adding more variables to a prediction equation increases the amount of variance in the criterion variable that can be explained by the predictor variables. This is because the additional variables provide more information and potential sources of variation that can be taken into account when making predictions.

Quiz 8: Correlational Research and Resources

A Phi coefficient is used for

The criterion variable in a prediction study is another name for the

General guidelines for interpreting correlations, in a group study, as presented in your text indicate that a correlation of r=-.82 is considered

Sam concluded in his research report that as class size increased teaching efficacy also increased. Of the following which is a correlation coefficient would best support Sam's claim.

Attentuation refers to

Gretchen wanted to see if given a sample of teachers' that graduated from colleges and Universities, GPA in college was related to starting salary as a teacher. Which correlation coefficient should Gretchen calculate?

Joni teaches a preschool class. She has eight students and ranks them based upon math and reading ability. She would like to see if there is a relationship between how well her students are ranked in math and in reading. Which of the following correlations should she use?

Testing the statistical significance of a correlation requires knowledge of

Data collection for types of correlation studies is generally

The most commonly used correlation is the

George suspects that there is a curvilinear relationship in his data set. Which of the following correlation coefficient should George calculate?

Regarding sampling, one strategy to facilitate obtaining accurate results if there will be subgroup correlations would be to select a

Shrinkage refers to

Errors in instrument reliability

A Pearson r correlation is best used for data that is

General guidelines for interpreting correlations in a group study as presented in your text indicate that a correlation of r=30 is considered

The correlation that Kim found in her study between math self-efficacy and enrollment in advanced mathematics was r=.25. Her correlation can be described as

In a prediction study of low achieving students academic motivation, Hillary was surprised that achievement scores were not a significant predictor of motivation. Hillary's analysis is most likely is displaying

Generally, when more variables are added to a prediction equation