Week Seven - Correlation & Regression

Question 1

What is Correlational Research?

Answer 1

A form of nonexperimental research in which VARIABLES ARE MEASURED NOT MANIPULATED.

Question 2

What are the two goals of Correlational Research?

Answer 2

Describe
Predict
CAN NOT explain

Question 3

Internal Validity is what in regards to correlational research and WHY?

Answer 3

Low, due to data collection methods and their lack of control. Ability to make claims is low.

Question 4

External Validity is what in regards to correlational research and WHY?

Answer 4

High, due to o strong control.

Question 5

When is Correlational Research used? (5)

Answer 5

Manipulation of independent variable is not possible

Manipulation of independent variable is not feasible

Manipulation of independent variable is not ethical

Establishing reliability and validity

Exploratory research

Question 6

What variables can Correlational Research include?

Answer 6

Correlational research can involve continuous variables and/or categorical variables.

Question 7

What does Pearson’s Correlation do?

Answer 7

Quantifies strength of correlation and association between two continuous variables

Question 8

Range of Pearson’s r

Answer 8

From −1.00 (Strongest Possible Negative Relationship), Through 0 (No Relationship), to +1.00 (Strongest Possible Positive Relationship).

±.50-1.00 = Strong 
±.30-.49 = Moderate 
±.10-.29 = Weak
0-±.10= Trivial

Question 9

What is the correlation coefficient?

Answer 9

Range = -1.00 to +1.00
Magnitude indicates strength of relationship
Sign indicates direction of relationship
Pearson’s product moment correlation (Pearson’s r).

Question 10

What does a Scatterplot represent?

Answer 10

Visual presentation of the relationship between two variables.
Two measurements for each individual are represented by a single point.

Question 11

How should we interpret a strong correlation?

Answer 11

Does not mean there is a causal relationship
Third variable
Causality may be reversed
Might reflect influence of outliers

Question 12

How should we interpret a weak correlation?

Answer 12

Does not mean there isn’t a causal relationship
Might reflect truncated range
Might reflect curvilinear relationship
Might reflect outliers.

Question 13

What are the 4 assumptions for Pearson’s r? What do they mean?

Answer 13

Linearity
If there is a relationship it must be linear (straight line), not curved.

Interval or Ratio data
The data for the Pearson’s r correlation must be interval or ratio level data.
If data are ordinal, use Kendall’s tau or Spearman’s rho

Univariate normality
Both variables should be normally distributed.
No outliers.

Bi-variate normal
Distribution of variables in combination should be normal.

Question 14

How to calculate a confidence interval for Pearson’s r

Answer 14

r has to be converted to z-scores to calculate a CI

Question 15

Writing up correlation, what does it look like?

Answer 15

r(N = sample size) = .r stat, p =, 95% CI [lower, upper].

Plain Englisht to describe relationship i.e., strength
include stats
explain what findings mean

Question 16

What is Regression?

Answer 16

Attempts to determine the strength and character of the relationship between one dependent variable (usually denoted by Y) and a series of other variables (known as independent variables)

Question 17

What does regression allow?

Answer 17

Allows prediction of one variable, from one or more measured variables.

Question 18

What is Simple Regression?

Answer 18

Mathematical procedure that determines the straight line that best ‘fits’ the data.

Question 19

What does Simple Regression include?

Answer 19

One predictor variable and one outcome variable.

Minimizes the distance between the outcome variable (Y) the predicted value of the regression line for each predictor value (X).

Question 20

What is the Intercept (SR)?

Answer 20

Value of outcome variable (Y) when value of the predictor variable (X) is 0

• Not always meaningful or interpretable.
• Centring predictor makes the intercept equal to the mean of the outcome variable.

Question 21

What is the B-Coefficient (SR)?

Answer 21

How much the outcome variable (Y) changes for each unit change of the predictor variable (X).

Question 22

What is R-squared? What does it indicate?

Answer 22

How much of the variance in the outcome variable (Y) is accounted for by predictor variable (X).

R-squared indicates how much information the predictor variable provides about the value of the outcome variable

Question 23

What does the SR stat formula look like?

Answer 23

F(df1, df2) = , p = , R = , R 2 =.

Question 24

How can we check for Univariate Normality?

Answer 24

Univariate normality can be tested through inspection of statistics such as skewness, but a simple and powerful approach is to is to visually inspect figures of the data using histograms, box plots, or other graphical methods such as stem-and-leaf plots.

Question 25

How do we check for Bivariate normality and outliers?

Answer 25

A simple method to check this requirement is to plot the variables against one another in a scatter plot.