## Research Methods: Lecture 5

##### Created by:

MariahT1  on August 6, 2012

##### Description:

Correlational research and multiple regression

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# Research Methods: Lecture 5

 Correlational Research definitionThe independent variable is not manipulated by the experimenter (just measured). Research relies on finding natural variation in the variables.
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#### Definitions

Correlational Research definition The independent variable is not manipulated by the experimenter (just measured). Research relies on finding natural variation in the variables.
Important difference between correlational research and experimental research In correlational research, the independent variables normally correlate with each other.
In experimental research the independent variables do not with each other or with background variables.
(Within-subjects, same backgrounds mean they can't correlate and between-subjects, random assignment means doesn't correlate)
Positives with correlational research - Good EXTERNAL VALIDITY (as normally studies human behaviour in its natural environment)
- Cheap and easy to do
Negatives with correlational research - Major problems with INTERNAL VALIDITY as cannot infer causation. (Variables often correlate with each other, something that experimental research gets around).
Collecting data for correlational research - Questionnaires
- Official stats
- Observation
- Surveys (psychological scales)
Correlation coefficients - r (Pearson correlation coefficient)
- p (Spearman rank)
Range from -1 to 1 with 0 being no correlation. Perfect relationships imply that no other variable is important for predicting an effect.
Variance accounted for Given by r^2 and can range from 0 to 1. (When r=1 we have accounted for all of the variance)
Simple linear regression (Least squares). Involves finding the lines that expresses the relationship between the two variables whilst producing the smallest squared error.
Y'= A + BX
where Y is the dependent variable and X is the independent variable
Standardized linear regression The dependent (Y) and independent (X) variables in the linear regression line can be standardized to give y= (beta)X. This means that the variables have had their MEANS SUBTRACTED from them and have been DIVIDED BY THEIR STANDARD DEVIATION.
Beta= r for one variable.
Idea of multiple regression A relatively simple linear regression equation with one independent variable turns into a more complicated equation with more than one independent variable (needed for everyday-life complexities).
Y= A + B1X1 + B2X2... BnXn
y= (beta)1X1 + (beta)nXn
Describe the Y variable as regressed on the independent variable (X)
Two reasons for doing multiple regression 1.) Generally get better prediction if you use more independent variables.
2.) Increases internal validity as deals with the 3rd variable problem and therefore can talk of causation. It is therefore controlling for extraneous variables statistically (instead of random assignment).
Beta weights are... Measures of what the correlations would be if all the other variables in the equation were held constant. E.g. estimate of result if everone had same education level.

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