Summary for block 1.2 at Erasmus university (). I'm enrolled in international psychology, however the sources and study materials are the same in both psychology courses. Hence, these summaries may also be useful for Dutch students. The summaries are based on at least 2 of the required reading mate...
Summary #4
Correlational research
- Correlational
= demonstrate the existence of a relationship between 2 or more variables, by simply
measuring 2 different variables for an individual search for patterns/relations
Correlation doesn’t imply causation.
- Experimental
= determine cause and effect, by manipulating one variable and measure the dependent
variable. All other variables are controlled. if dependent variable indeed is variable
causal relationship
- Differential
= demonstrate difference between groups. Two variables 2 groups. Obtain scores for
different groups compare scores
Correlational research scores are presented in a scatter plot.
X/Y axis plot these two variables (horizontal X & vertical Y) taken together draw
data points
Measuring relationships
1. Direction
= positive correlation relationship x and y in same direction = x grows y grows
negative correlation relationship x and y in opposite direction = as x goes up, y goes down
2. form
linear data points cluster around a straight line, increase is consistently predictable.
monotonic the amount of increase/decrease need not be constantly the same number
3. consistency
a correlation coefficient of 1 (or -1) is a strong/the strongest consistency (still not causation)
a correlation coefficient of 0 is no consistency between to variables (no correlation)
non-numerical data
1 score = numerical
1 score = non-numerical
option 1.
use non-numerical variable as groups differential research
Give numerical score per group
Compare using
- independent measures test
- analysis of variance
option 2
calculate correlation by giving the non-numerical variables numbers
e.g.; female= 1 & male= 2
2 scores p.p. you can calculate consistency!
This is a point-biseral correlation
, 2 variables that are numerical
organize data in matrix (rows/columns) fill in frequency of combination in individuals
If both variables have 2 categories use coding
Female 1 succeed 1
Male 2 fail 2
Turn into coded data phi-coefficient
Prediction
Correlations provide basic information that can be used to make predictions.
Even though it cannot be derived from correlational research which of the variables are
causes/effects.
Regression occurs:
Statistical process:
predictor variable: X(1st)
Criterion variable: Y (2nd = explained/predicted)
Purpose
= gain better understanding of the unknown variable by demonstrated that it’s related to an
established/known variable
So; known variable is often predictor
Unknown variable is often criterion
Reliability/validity
Reliability
= consistency/stability of measurement
Validity
= is the variable that is supposed/claimed to be measured, actually measured?
Correlation research is often done to evaluate theories
Interpreting a correlation coefficient (r)
r2 coefficient of determination
= how much of the variability of variable Y is predicted by variable X
Guidelines for interpreting strength of a correlation
coefficient of determination strength
0.10 small
0.30 medium
0.50 large
A correlation found in a relatively large sample group likely to be significant (unlikely to
be caused by another variable)
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