CORRELATIONAL RESEARCH

EDU 702 RESEARCH METHODOLOGY CORRELATIONAL RESEARCH MARLINA BT ZUBAIRI NORLIN BT ABD GHAFAR FARADILLAH BT MD RAMLI ZURIANA BT SAARI

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 Problem Selection Data Analysis and Interpretation Sample Data Collection Instrument Design and Procedures

1. Problem selection • Based on experience or theory • 3 types of problems : • Is variable X related to variable Y? • How well does variable P predict variable C? • 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 relationexample of a positive r: GPA and scores on SAT r= -1, perfect negative relationexample of a negative r: drinking in college and GPA r= 0, no relationexample 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:

Correlation example See scatter plot of data

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

CORRELATIONAL RESEARCH