General Linear Model

Question 1

interpretation of the basic General Linear Model

Answer 1

• y(i) = score on the DV for subject i
• x(i) = intervention (0 or 1) used for subject i
• b0 = the intercept = the value of y when x is 0 -> the average [DV] for [IV=0] -> average depression at posttest of the sitting group.
• b1 = the slope = the change in y when x goes up by 1 -> the difference in average [DV] between [IV=1] and [IV=0].
• error(i) = the difference between the observed and then expected DV score for subject i

Question 2

3 tests that are based on the General Linear Model

Answer 2

• regression analysis
• t-test
• one-way ANOVA
• factorial ANOVA

Question 3

wat test je met een regressieanalyse

Answer 3

test whether x can predict y, if y is a continuous variable

Question 4

hoe doe je een regressieanalyse in SPSS

Answer 4

• Analyze -> regression -> linear -> place DV in dependent -> place IV in independent(s).
• Statistics -> estimates | Plots -> histogram and normal probability plot | Save -> standardized residuals

Question 5

waar kijk je naar & wat interpreteer je bij regressie analyse

Answer 5

-> When F is significant x significantly predicts y.

kijken naar: ANOVA
* [IV] [significantly predicts/does not significantly predict] [DV], F([df], [df error]) = [F], p = [Sig.].
* On average, the patients in the [control group] report [interpretation of the DV] at [measurement moment], t([df]) = [t (Constant)], p = [Sig.(constant)].

kijken naar: Coefficients
* On average, [dependent variable] at [measurement moment] are [lower/higher] ([b1] points) for patients in the [condition 1] than in the [condition 2], t([df]) = [t(b1)], p = [sig.(b1)].
*Treatment significantly predicts social anxiety
complaints at posttest, F(1, 138) = 7.78, p = .006.

*On average, patients in the waiting list group
report social anxiety complaints at posttest,
t(138) = 70.14, p < .001.
*On average, social anxiety complaints at
posttest are lower (4.17 points) for patients in
the mCGT group than in the waiting list group,
t(138) = -2.79, p = .006.

Question 6

waar kijk je naar voor de df

Answer 6

df 1 = regression
df 2 = residual

Question 7

wanneer gebruik je een regressieanalyse

Answer 7

continuous DV

Question 8

twee t tests en wanneer gebruik je ze

Answer 8

• paired samples t-test: when the conditions contain the same test-subjects
• independent t-test when they contain different subjects

Question 9

hoe doe je independent t-test in SPSS

Answer 9

Analyze -> compare means -> independent-sample t test.

Question 10

waar kijken & wat rapporteren bij t-test

Answer 10

-> When t is significant, the means are significantly different.

kijken naar group statistics:
* On average, the [condition] group had a [higher/lower] score on [dependent variable] (M = [Mean], SE = [Std. Deviation]) than the [control condition] (M = [Mean], SE = [Std. Deviation]).

kijken naar independent samples test:
* This difference ([mean difference]) was [not significant/significant], t([df]) = [t], p = [Sig.].

• On average, the mCGT group had lower
  scores on social anxiety (M = 64.47, SE = 1.19) than the waiting list group (M = 68.64, SE = 0.94).
• This difference (4.17) was significant, t(138) =
  2.79, p = .006.

Question 11

dependent t-test in SPSS

Answer 11

Analyze -> compare means -> paired-samples t test

Question 12

t-test =

Answer 12

the difference between the means of the groups (X1 – X2) divided by the standard
error (SE). After calculating the value, we look it up under the null-hypothesis (H0)
distribution. If the t-value is in the critical area of the distribution, you conclude that
there are significant differences between the groups.