Psyc 344 6-8

Front 1

correlational research

Back 1

independent variable is not manipulated by the experimenter. Instead research relies on finding natural variation in the independent variable or dependent variables. Not defined by whether you work with correlation coefficients.

Front 2

one reason for doing experimental design

Back 2

to stop variables correlating

Front 3

collecting data

Back 3

questionnaires, archives and official statistics, observations. Psychological scales are often used

Front 4

analyzing the data:
correlation coefficients

Back 4

Spearman rank correlation coefficient (Ρ (Greek Rho)) & the Pearson coefficient (r)

Front 5

key concept of correlation

Back 5

variance accounted for (r²) sometimes written as a %

Front 6

linear regression

Back 6

the line that produces the smallest squared error
Y= A+BX
Y= dependent variable
X= independent variable
A= a constant
B= a constant (slope of line)
Y'= predicted value

Front 7

standardized formula
linear regression

Back 7

means subtracted from them and then divided by their standard deviation.(written in small letters y and x)
y = βx β=r

Front 8

multiple regression

Back 8

better prediction more than 1 independent variable (Xi)
Y/Y'= A+B1X1 + B2X2 + ... +BnXn

Front 9

multiple regression
Standardised

Back 9

y/y' = β1x1 + β2x2+ ... + βnxn

Front 10

main reasons to use multiple regression

Back 10

separate out the effect of the different independent variables so tell which are causally important. attempt to help internal validity (third variable) statistically. used in field setting where you have less experimental control.

Front 11

beta weights

Back 11

measures of what the correlations would be if all the other variables in the equation were held constant

Front 12

assumptions of multiple regression

Back 12

1)independence
2)linearity
3)normality
4)nature of independent variables
5)identify key variables

Front 13

assumptions of multiple regression
1)independence

Back 13

observations/cases are independent of eachother

Front 14

assumptions of multiple regression
2)linearity

Back 14

the relationship between the dependent variable and each of the independent variables should actually be linear than than peaked.

Front 15

assumptions of multiple regression
3)normality

Back 15

dependent variable should be normally distributed

Front 16

assumptions of multiple regression
4)nature of the independent variables

Back 16

multiple regression is used when independent variables are continuous or they are are a mixture of categorical and continuous variables. (categorical 1,0 code etc how you code, makes a diff)

Front 17

assumptions of multiple regression
5)identify key variables

Back 17

in general beta weights will change every time you put in new independent variables or take out old ones. If you miss some out then you will not be measuring the true real world beta weights etc.

Front 18

causal modelling

Back 18

correlation does not = causation. some correlation can only go one way (time)

Front 19

guidelines for causal modelling

Back 19

1.time
2.A doesn't have to measured before B
3.causing-> caused not caused-> causing

Front 20

multiple regression and causal modelling

Back 20

helps us distinguish between causal scenarios. by signif beta weights

Front 21

path analysis

Back 21

one variable can be dependent and independent

Front 22

moderation

Back 22

e.g. contraceptive sex
|
=
----->pregnancy