ERM resit paper component one

Part 1. 

In Part 1 of the assignment, please choose the best response for each question. In each
question, only one response is correct. Use the grid at the end of this section to record your
responses. Each correct answer is worth one point. This section is worth 10 points.
1. When conducting paired sample t-test the following should be conducted to assess
that the data are normally distributed.
a. The Shapiro-Wilik test using the data from both conditions
b. The Shapiro-Wilik test using the difference scores found by subtracting the scores
from both conditions.
c. Levene’s test
d. Mauchly’s test 

Qn2. 
A researcher wanted to study muscle reactivity after three different forms of massage.
She recruited 30 participants and randomly allocated them to one of the following
conditions: effleurage massage, kneading or trigger point massage. After the massage,
the participants were asked to catch a ball. The researcher recorded muscle response
using electromyography. The researcher hypothesised that muscle response would be
more rapid after the trigger point massage. Which statistical analysis should she use?
a. A between subjects one-way ANOVA
b. A t-test
c. A repeated measures one-way ANOVA
d. A 3 x 2 mixed ANOVA

Qn3.
A researcher devised a two-week intervention to help children with poor reading
skills gain new vocabulary. After two weeks, the researcher compared the new
vocabulary learnt by the intervention group to the number of new words learnt by a
control group of children. The researcher hypothesised that the intervention group 2
would learn more vocabulary words than the control group. Look at the output below
and choose the correct way to report the result.  
a. t(11.46) = 2.80, p = .008
b. t(11.46) = 2.80, p = .02
c. t(18) = 2.80 p = .006
d. t(18) = 4.78 p = .04

Qn4. 
A researcher runs a multiple regression and finds that two of her predictor variables
correlate with each other with an r of 1. This is an example of…
a. Singularity
b. Linearity
c. The presence of an outlier
d. Multicollinearity 

Qn5.
A researcher wanted to test whether gender is identified more rapidly depending on
the emotion shown on a face. Participants viewed 50 rapidly presented faces and were
asked to press a button as quickly as possible indicating if the face was male or
female. Participants were randomly allocated to one of three conditions (neutral
expression faces, happy expression faces or angry expression faces). The researcher
hypothesised that detection of gender would be more rapid in the angry condition.
Look at the output from SPSS and use the answer that best represents the results. 

a. Gender in the angry condition was identified significantly more rapidly than in both
the happy and neutral conditions.
b. Gender in the angry condition was identified significantly more rapidly than in the
neutral condition and gender in the happy condition was identified significantly more
rapidly than in the neutral condition.
c. Gender in the happy condition was identified significantly more rapidly than in both
the angry and neutral conditions.
d. Gender in the neutral condition was identified significantly more rapidly than in both
the angry and happy conditions.

Qn6.
Which of the following assumptions would you need to test before you interpret the
results of a one-way repeated measures ANOVA with 3 experimental conditions?
Select one:
a. Sphericity
b. Both sphericity and homogeneity of variance
c. Homogeneity of variance
d. Neither sphericity nor homogeneity of variance

Qn7. 
A researcher investigated whether a measure of general anxiety and scores on a maths
test would predict students enjoyability ratings of their statistics module. They ran a
multiple regression analysis and gained the following output.

Which statement best sums up what the research has found here?
Select one:
a. The model explains 66% of the variance in the data and is a significant predictor
enjoyability
b. The model explains 66% of the variance in the data and is not a significant predictor
of enjoyability
c. The model explains 11% of the variance in the data and is not a significant predictor
of enjoyability.
d. The model explains 11% of the variance in the data and is a significant predictor of
enjoyability