⭐Correlation Research ⭐

Data Analysis 🚩 :

Data analysis & interpretation

:single variable prediction equation

multiple regression equation ( Y= a+bx1 + cX2+cX3)

prediction studies

correlation coefficient

near <1 = positive direction

near 0 = variables unrelated

near > 1 = negative direction

/ -0.5 = useless

0.9 = very reliable

0.8 = moderate reliability

0.7 = low reliability

0.4 = bad reliability

if 2 variables highly related = use either one score to predict the score of other

criterion = variable to be predicted

conducted to test if variables are good predictors of a criterion

can use >1 variable to make predictions

combination of many well correlated variable to a criterion = better than a single one of them

Design & Procedure 🏴

  1. determine if causal connection between variables exits
  1. suggest experimental studies

obtain 2 scores from each member of sample

1 score for each variables of interest

correlate pairs score

result = correlation coefficient

Participation & Instrument 🏁

Selection

minimum is 30

develop valid measure of variable

Data collection steps

  1. identify variables
  1. select sample
  1. apply instrument
  1. collect data

data collection

  1. obtain predator variables before criterion variables
  1. may lose participants in long run
  1. when strength of predictor variable is defined, test on 1 or > new group

Problem & Solution 🔥

Discover variables that are related to complex one

test hypothesis

select variables

logical relationship (from theory or experience)

have meaningful result