CORRELATIONALRESEARCH

MARLINA BT ZUBAIRINORLIN BT ABD GHAFAR

FARADILLAH BT MD RAMLIZURIANA BT SAARI

EDU 702 RESEARCH METHODOLOGY

Definition

To identify the relationships between two or more variables

Relationship the range of score on one variable is associated with the range of score of the other variable

When to use

As a first step prior to experimentation When experiments cannot be conducted (e.g. for ethical reason) Data collected through : - observations - surveys and questionnaire - archived information

Characteristics

Variables cannot be manipulated

Cannot prove a causal relationship

Only examine the possibilities that one variable might cause something to happen

Purpose

Help us to understand related events, conditions and behaviours : explanatory studies

To make predictions of how one variable might predict another : prediction studies

Variables used : i) predictor variable ii)criterion variable

The procedure

1. Problem selection

Based on experience or theory 3 types of problems :1. Is variable X related to variable Y?2. How well does variable P predict variable

C?3. What are the relationships among a large

no. of variables, and what predictions can be made that are based on them?

2. Sample

? appropriate population

< 30 = inaccurate estimate of the degree of the relationship

> 30 = provide meaningful results.

3. Instruments

Choose appropriate instruments Must yield quantitative data. Administrating instruments – e.g.: test,

questionaires etc. Observation Must show evidence of validity

4. Data collection

Explanatory study – short time needed to collect data on both variables

Prediction study – longer time needed to measure the criterion variables compared to prediction variables.

5. Data analysis and interpretation

Correlation coefficient is produced when variables are correlated.

In decimals between 0.00 and +1.00 or -1.00.

Data analysis If closer to +1.00 or -1.00 = stronger

relationship If + sign = high scores on both variables. If – sign = high on one v but low on the other. If at / near 0.00 = no relationship exists

Data analysis

r scores range from -1 to +1

r= +1, perfect positive relation example of a positive r: GPA and scores on SAT

r= -1, perfect negative relation example of a negative r: drinking in college and GPA r= 0, no relation example of a near zero r: hair length and GPA

Examples of topic Example - Health psychologist is interested in

testing the claim that people with more friends tend to be healthier.

Example - Health psychologist described surveys two groups of people: hospital patients being treated for chronic diseases and healthy community members.

Correlation example High Self-esteem (A) and GPA (B)

Is (A) related to (B)? Or is it the other way around? Or, are there other factors that cause both (A) and (B)?

Raw Data:

Name Self Esteem Score GPATim 42 3.8Bart 10 1.4

Kelsey 15 2.5Kim 22 3.1

Correlation example

See scatter plot of dataSelf-esteem and GPA data

0

0.5

1

1.5

2

2.5

3

3.5

4

0 5 10 15 20 25 30 35 40 45

self-esteem

GP

A Series1

Correlation example Two independent conducted studies found that

there is no causal relationship between these two factors. They are correlated because both of them are correlated to some other factors: intelligence and family social status.

**Correlations do NOT tell us that one variable CAUSES the other variable

Conclusion Strengths

– Can study a broad range of variables– Can look at multiple variables at one time– Large samples are easily obtained

Weaknesses– Relationships established are associational, not

causal– Individuals not studied in-depth– Potential problems with reliability and validity of self-

report measures