Ch. 12: The Correlational Research Strategy

Question 1

the correlational research strategy

Answer 1

involves measuring two or more variables to obtain a set of scores (usually two) for each individual. The measurements are then examined to identify any patterns of relationship that exist between the variables and to measure the strength of the relationship

Question 2

goal of the correlational research strategy

Answer 2

to establish that a relationship exists between variables and to describe the nature of that relationship

Question 3

data used for the correlational research strategy

Answer 3

Data usually consist of two or more measurements (one for each of the variables being examined)

Question 4

individual

Answer 4

refers to a single source, not necessarily a single person

Question 5

correlational vs. experimental research

Answer 5

• Does not involve manipulating, controlling, or interfering with variables
• This would be experimental research

Question 6

differences between correlational and differential research

Answer 6

• Correlational research views the data as two scores for each individual and looks for patterns within the pairs of scores to determine whether a relationship exists, while differential design establishes the existence of a relationship by demonstrating a difference between groups
• Correlational studies focus on the relationship between the two variables while differential studies focus on the difference between the groups

Question 7

labelling pairs of scores in the correlational design

Answer 7

When the data consist of numerical values, the scores in each pair are traditionally identified as X and Y

Question 8

scatter plot

Answer 8

a graph that presents the two scores for each individual

Question 9

how do scatter plots represent data?

Answer 9

Represents each individual with a single point

Question 10

correlation coefficient

Answer 10

a numerical value that measures and describes the relationship between two variables

Question 11

what does the correlation coefficient describe

Answer 11

the direction, form, and consistency of the relationship

Question 12

positive relationship

Answer 12

the two variables change in the same direction

Question 13

negative relationship

Answer 13

the two variables change in opposite directions

Question 14

linear relationship

Answer 14

the data points tend to cluster around a straight line

Question 15

pearson correlation

Answer 15

used to describe and measure linear relationships when both variables are numerical scores from interval or ratio scales

Question 16

monotonic relationship

Answer 16

a relationship that is consistently one-directional, either consistently positive or consistently negative

Question 17

spearman correlation

Answer 17

used to measure and describe monotonic relationships when both variables are ranks from an ordinal score or have been transformed into ranks

Question 18

interpreting the consistency of a relationship

Answer 18

• Typically measured by the numerical value obtained for a correlation coefficient
• A coefficient of +1 or -1 indicates a perfectly consistent relationship and a value of 0 indicates no relationship

Question 19

evaluating relationships for one numerical and one non-numerical score

Answer 19

• Use the non-numerical variable to organize the scores in separate groups. Then, compare the groups using an independent measures t-test (for two groups) or ANOVA (for more than two groups)
• If the non-numerical variable consists of exactly two categories, you can calculate a correlation called a point-biserial correlation

Question 20

evaluating relationships for two non-numerical scores

Answer 20

• Evaluate the relationship by organizing the data in a matrix, with the categories of one variable forming the rows and the categories of the second variable forming the columns. Then, use a chi-square hypothesis test
• If non-numerical variables consist of two categories, they can be numerically coded as 0 and 1 and then a Pearson correlation can be computer for the data

Question 21

phi-coefficient

Answer 21

a Pearson correlation computed from coded data

Question 22

interpreting the phi coefficient

Answer 22

Its numerical value is meaningful but its sign and the concept of a linear relationship are not

Question 23

how can you evaluate the strength of a relationship?

Answer 23

by computing the coefficient of determination

Question 24

coefficient of determination

Answer 24

the squared value of a correlation that measures the percentage of variability in one variable that is determined by its relationship with the other variable

Question 25

how is the coefficient of determination represented?

Answer 25

by r²

Question 26

guidelines for interpreting the coefficient of determination

Answer 26

small: r= 0.1 or r²= 0.01
medium: r= 0.3 or r²= 0.09
large: r= 0.5 or r²= 0.25

Question 27

significant relationship

Answer 27

a correlation found in the sample data is very unlikely to have been produced by random variation

Question 28

sample size and significance

Answer 28

As the sample size increases, it becomes increasingly more likely that the sample correlation accurately represents the real relationship that exists in the population

Question 29

3 applications of the correlational strategy

Answer 29

• prediction
• reliability and validity
• evaluating theories

Question 30

prediction and the correlational strategy

Answer 30

• In a correlational study used for prediction, one variable is identified as the predictor variable and the other as the criterion variable (the value being explained or predicted)
• The predictor variable is usually the x value while the criterion variable is the y value

Question 31

regression

Answer 31

the statistical process of using one variable to predict another

Question 32

reliability and validity and the correlational strategy

Answer 32

both are commonly defined by relationships that are established using the correlational research design

Question 33

how is test-retest reliability determined?

Answer 33

by the relationship between an original set of measurements and a follow-up set of measurements

Question 34

how is concurrent validity determined?

Answer 34

by demonstrating that the scores from the test are strongly related to scores from established tests

Question 35

strengths of the correlational research design

Answer 35

• describes relationships between variables
• nonintrusive (studies natural behaviours)
• high external validity
• allows researchers to investigate variables that would be impossible or unethical to manipulate

Question 36

weakenesses of the correlational research strategy

Answer 36

• cannot assess causality
• third-variable problem
• directionality problem
• low internal validity

Question 37

common use of the correlational research strategy

Answer 37

• Often used for preliminary work in an area that has not received a lot of research attention
• It can describe relationships between variables that might suggest further investigation using the experimental strategy

Question 38

two limitations in explanations from results of a correlational study

Answer 38

• the third-variable problem
• the directionality problem

Question 39

the third-variable problem

Answer 39

it is possible that a third (unidentified) variable is controlling the two variables and is responsible for producing the observed relation

Question 40

the directionality problem

Answer 40

correlational studies do not determine which variable is the cause and which variable is the effect

Question 41

multiple regression

Answer 41

a statistical technique for studying multivariable relationships

Question 42

functions of multiple regression

Answer 42

• Allows researchers to examine the relationship between two variables while controlling the influence of the other, potentially confounding variables
• By adding predictor variables one at a time into the regression analysis, it is possible to see how each new variable adds to the prediction after the influence of the earlier predictors has already been considered