Correlation A Research

8/2/2019 Correlation A Research

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Research Designs

Correlational

By Mike Rippy


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Correlational Research

Designs

Correlational studies may be used to

A. Show relationships between two

variables there by showing a cause andeffect relationship

B. show predictions of a future event or

outcome from a variable


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Types of Correlation studies

1. Observational Research e.g. class

attendance and grades

2.Survey Research e.g. living togetherand divorce rate

3. Archival Research e.g.violence and

economics


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Advantages of the

correlational method

1. It allows the researcher to analyze

the relationship among a large number

of variables

2. Correlation coefficients can provide

for the degree and direction of

relationships


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Planning a Relationship Study

Purpose to identify the cause and effects ofimportant phenomena

Method

1. Define the problem 2. Review existing literature 3. Select participants who can have

measurable variables-reasonablyhomogeneous

4. Collect data-test, questionnaires,interviews, &etc.

5. Analysis of data


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What do correlations

measure?

Correlations measure the association, or co-

variation of two or more dependent variables.

Example: Why are some students

aggressive?

Hypothesis: Aggression is learned from

modeling

Test: Look for associations betweenaggressive behavior and


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Interpreting Correlations

Scattergram- a pictorial representationof correlations between two variables

Use of a scattergramAn x and y axes are produced

perpendicular to each otherResults of correlates are plotted

The relationship of these plots areinterpreted


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continued

The amount of correlation is expressed as r= The r scores can range from 1 to 1 If r= 1 there is said to be perfect correlation

with the other variableAn r score of 0 shows no relationship If r= -1 there is a lack of relationship between

the two variablesAnything between 1 and 1 shows a varying

degrees of relationships


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Continued

The expression r squared = the percent ofvariation accounted for between the relationsbetween two variables like x and y this is

called the explained variance Example: correlation between G.P.A. scores

and A.C.T. if r=.6 then r squared =.36 so theper cent of accuracy is 36% in predicting

A.C.T. scores from the person G.P.A.A complete interpretation would include

attempts to explain nonsignificant results


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Other measures of interest in

Correlational Studies

R is multiple correlation (0 to 1)

(b) is regression weight which is a

multiplier added to a predictor variableto maximize predictive value

B is beta weight which is used in a

multiple regression equation toestablish the equation in a standard

score form


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Correlation and Causality

If there is no association between twovariables, then there is no causal connection

Correlation does not always prove causation

a third variable may have the causal relationexample: Women surveyed during pregnancythat smoked correlated with arrest of theirsons 34 years later. Is a third variable the

cause. Other variables- socioeconomicstatus, age, fathers or mothers criminalhistory, Parents psychiatric problems


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Use of causal-comparative

approach

However, when comparing two

variables sometimes inference may be

made that one causes the other.

Only an experiment can provide a

definitive conclusion of a cause and

effect relationship.


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Limitations of Relationship

Studies

Researcher tend to break down

complex patterns into two simple

components.

Researcher identify complex

components that interest them but could

probably be achieved in many different

ways.


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Prediction Studies

A variable whose value is being used topredict is known as the predictorvariable

A variable whose value is beingpredicted is the criterion variable.

The aim of prediction studies is to

forecast academic and vocationalsuccess.


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Ways to fix problems of

correlational Design

Add more variables to the model

Replicate design

Convert question to the experimentaldesign


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Types of Information provided

in a prediction study

The extent to which a criterion pattern

can be predicted

Data for developing a theory fordetermining criterion patterns

Evidence about predicting the validity of

a test


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Basic Design of Prediction

Studies

The problem-reflect the type of information

you are trying to predict

Selection of research participants- draw from

population most pertinent to your study Data collection-predictor variables must be

measured before criterion patterns occur

Data Analysis- correlate each predictorvariable with the criterion


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Definitions useful in Prediction

Studies

Bivariate correlational statistics- express the

magnitude of relationships between two

variables

Multiple regression- uses scores on two ormore predictor variables to predict

performance of criterion variables. The

purpose is to determine which variables can

be combined to form the best prediction of

each criterion variable.


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Multiple Regression Facts

Too large of a sample may cause faulty

data to occur

15 to 54 people should be sampled pervariable used.


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Statistical Factors in

Prediction Research

Prediction research in useful forpractical purposes

Definitions- selection ratio- proportion of

the available candidates that must beselected

Base rate- percentage of candidates

who would be selected without aselection process


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Statistical Factors in

Prediction Research cont.

Taylor-Russell Tables- a combination of three

factors; predictive validity, selection ratio, and

base rate (If these three factors are present

the researcher should be able to predict theproportion of candidates that will be

successful)

Shrinkage- The tendency for predictive

validity to decrease when research is

repeated


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Techniques used to analyze

Bivariates

Product-Moment Correlation- Used

when both variables are expressed as

continuous scores

Correlation Ratio- Used to detect

nonlinear relationships


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Part and Partial Correlation

This is an application employed to rule

out the influence of one or more

variables upon the criterion in order to

clarify the role of the other variables.


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Multivariate correlational

Statistics

These are used when examining the

interrelationship of three or more

variables.


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Correlation Coefficient

It measures the magnitude of the relationship

between a criterion variable and some

combination of predictor variables

Correlation coefficient of determination equalsR squared. This expresses the amount of

variance that can be explained by a predictor

variable of a combination of predictor

variables


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Correlation Coefficient

Determinates cont.

R can range from 0.00 to 1.00. The

larger R is the better the prediction of

the criterion variable.

There is more statistical significance if

the R squared value is significantly

different from zero.


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Canonical Correlations

Is when there is a combination of

several predictor variables used to

predict a combination of several

criterion variables


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Path Analysis

Is a method of measuring the validity of

theories about causal relationships

between two for more variables that

have been studied in a correlational

research design


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Steps of Path Anaylsis

Formulate a hypothesis that causally link thevariables of interest

Select or develop measures of the variables

that are specified by the hypothesis Compute statistics that show the strength of

relationship between each pair of variablesthat are causally linked in the hypothesis

Interpret to determine if they support thetheory


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Correlation Matrix

Is an arrangement of row ad columns

that make it easy to see how measured

variables in a set correlate with other

variables in the set


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Structural Equation Modeling

Is a method of multivariate analysis that

test causal relationships between

variables and supplies more reliable

and valid measures than path analysis

It is also called LISREL which stands for

Analysis of Linear Structural

Relationships


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Differential Analysis

This is subgroup analysis in relationship

studies

This application is used when theresearcher believes that correlated

variables might be influenced by a

particular factor. Then subjects from the

sample are selected who have thischaracteristic

M d t V i bl i


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Moderator Variables in a

prediction Study

There are times when a certain test is

more valid in predicting a subgroups

behavior. The variable that is used in

this instance is called a moderator

variable

Correlation A Research

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