multiple regression

Question 1

aims of MR

Answer 1

to assess the whether multiple IVs can predict the outcome (DV)

Question 2

difference between unstandardised and standardised regression coefficients

Answer 2

unstandardised coeffieicents show for every 1 unit increase in one of your variables the other increases by the B value

standardised coefficients show this change in standard deviations as shown by beta weights

Question 3

r2

Answer 3

the correlation coefficient squared

Question 4

what does r2 tell us

Answer 4

the proportion of the variance in the y scores explained by the line

Question 5

f=

Answer 5

variance in y (DV) explain by x (IV)/total variance

which is shown by a percentage - r2

Question 6

regression equation

Answer 6

y=a+bx

a= constant
b=slope
y=DV

Question 7

regression line - what do you have to assume?

Answer 7

to work out the equation of the regression line have to assume that the X scores are correct and ones that do not fit on the line are due to random error

Question 8

regression coefficient

Answer 8

shows the estimated amount of change that occurs in the DV for one unit change in the IV with the effect of all other ICs in the equation partialled out

Question 9

dichotomous variables

Answer 9

B value shoes how much a shift from conservative (0) to labour (1) / Lib den (2) changes the DV e.g. intention to attend a ralley.

Question 10

entry options

Answer 10

direct
hierachical
stepwise

Question 11

direct entry

Answer 11

the analyses tests a single model
all IVs entered simultaneously

assesses the joint and individual effects of a fixed set of predictors

Question 12

to what extent can drinking motives and the demographic variables of age and gender explain alcohol consumption

Answer 12

direct entry

Question 13

to what extend can drinking motives explain variance in alcohol consumptions over and above that explained by the demographic variables of gender and age

Answer 13

hierachical entry

entered in two blocks:

1) gender and age
2) Drinking motives: coping, enhancement,social, conformity

Question 14

will a given entry of a IV significantly increase the R2?

Answer 14

stepwise entry

Question 15

hierachial entry

Answer 15

assesses the effect of one set of predictors OVER and ABOVE that of another
IVs are entered in USER-DETERMINED order
based on THEORY
IVs can be entered in GROUPS or INDIVIDUALLY

Question 16

stepwise entry

Answer 16

find an efficient model with the minimum numbers of significant predictors
(SPSS determines the order of entry)
IVs are entered or discard step by step according to statisitical criterion i.e if they significantly increase the R2

Question 17

at each stage of hierachical

Answer 17

the increase in r2 is examined when a new IV is added

can asses the model as a whole and the unique contribution of each IV added so far

Question 18

typically enter hirachically

Answer 18

in groups e.g. demographics

when put in more demographic variables and psychological variables how much additional variance is explained

Question 19

types of stepwise regression entry

Answer 19

• forward selection
• backward selection
• forward and backward combined

Question 20

forward selection

Answer 20

add the IV with the highest correlation with the DV (if significant)
calculate regression
assesses increase of R2 each IV would add the largest amount of explained variance (providing it’s significant)
and then calculates the regression again adding the next best IV
the procedure terminate when no addition IV can significantly explain any of the remaining variance

Question 21

backward selection

Answer 21

starts with all the IVS
SPSS runs the regression
assess the contribution of each IV on the R2 and removes the ones with the least contribution providing it’s non significant
then runs the regression again and does the same

Question 22

forward and back combined

Answer 22

starts with all the IVs with no specific predictors in the model
if they meet statistical criteria for entry then they get added
but also deleted the ones that don’t significantly contribute to the R2 variance

Question 23

reporting a stepwise

Answer 23

a stepwise MR was performed with the DV ….of and the CANDIDATE IV of ….. for entry into the model

number of IVS entered
and number of IVS removed

together these accounted for % of the variance in the DV R2= f(df1, df2) p=

Question 24

stepwise regression entry is useful when

Answer 24

it doesn’t matter which IVs are included and which are not-
only important which account for the most amount of variance

the objective is to create a prediction equation with the minimum number of significant predictors

when doing something very applied e.g. predicting long term health status/
how much people earn on the basis of their childhood

Question 25

criticisms of stepwise regression entry

Answer 25

risky procedure
it surrenders decision making to statistics
the meaning and interpretation of the variables are not relevant in the analysis
capitalises on chance relationships

an important predictor may be excluded because of it’s correlation with another IV

may lead to suboptimal solution in term s of variance explained (there may exist a different combination that explains more variance)

as psychologists we want to test a theory and not just blindly see which IVs account for the most variance