Correlational Research Strategy

Strengths an Weaknesses

Strengths

foundation for further directional research

Nonintrusive

High external validity

Weaknesses

No Information on causality

low internal validity

Third Variable Problem

Directionality Problem

Goals

Differences to

describe relationship bewteen two are more variables

no attempt to

manipulate

control

interfere

Data presentation

Experimental research

Differential research

focuses on difference between two groups

demonstrates cause-and-effect relationship

Data in Scatter Plot

Relationship

relationship characteristics

Direction

positive relationship

negative relationship

r < 1

r > 1

Form

Consistency(Strength)

linear

monotonic

straight line in scatter plot

Pearson Correlation

Spearman correlation

one-directional trend

image

image

amount of increase need not to be constantly at the same size

variable Y indreases in a consistently predictable amount compared to X

image

Data in list

Non-Numerical Relationships

one numerical & one boolean value

sign of correlation become meaningless

Use the non numerical variable to organize the scores into separate groups: the data would consist of a group of scores for one boolean value, and a group of scores for the second boolean value

Both Non-Numerical Scores from Nominal Scales

Chi Square Test

image

Coefficient Determination

measures percentage of variability in one variable that is determined/predicted

Statistical Significance

relationship found in sample is unlikely random

statistical significant =/= correlation is large

with a large sample it is possible for a correlation of r=0.1 to be statistically significant

Usage of Correlational Research

Prediction

criterion variable

correlational research allows researchers to use knowledge in variable to explan (predict)the second variable

predictor variable

variable being explained

Regression

Goal

find equation that produces most accurate predictions of Y (criterion variable) for each value of X (predictor variable)

simple & well defined

complex and unknown

variable predicting the second one

Reliability

Validity

evaluates the consitency/stability of two measurements

Test-Retest-Reliability

Relationship between an original set of measurements and a follow up set of measurements

Concurrent Validity

The valditiy of the test can be established by demonstrating that the scores from one test are strongly related to the scores from an established test

evaluates the extent to which the measurement actually measures what it claims to measure

Multiple Regression

for more than 2 variables

Note:
Predictor variables only predict, not explain a relationship

used to examine relationship between two specific variables while controlling the influence of other potentially confounding variables

adding predictor variables one at a time shows their individual influence