stat tests and research methods

Question 1

The t-distribution:

Answer 1

The t-test takes into account both he expected mean and a measure of the standard error of the mean based on the sample.

Question 2

one sample design

Answer 2

Benefits=
- can be used to compare group data with known variables
Disadvantage:
we may not always no population values and we may want to compare two groups, or to investigate the change of behaviour over time.

Question 3

IMD

Answer 3

Independent measures design:
- We have two groups and the values come from different people (ie. Each person provides one measure in one group only) group 1 mean =4.75 and group b mean= 9

Benefits: the measurements are independent, we don’t have to worry about learning effects due to the repeated exposure

Disadvantages=
- people in the different groups might be quite different in various ways: personality, motivation etc. we need large sample sizes to average out these effects, or we need to counterbalance factors that we know may influence the results
- We cannot study the behavioral over time.

Question 4

RMD

Answer 4

here is a single group which provides data for both conditions, ie. The value s from each condition come from the same people.
Advantages:
- we don’t have to think about the differences in baseline factors such as personality because there will alwaus affect both conditions equally
- We can study changes in behaviour over time
- We can usually test fewer people
Disadvantages:
- Measurements are not independent we need to calculate the variance differently
- People know the treatment after the first condition and cant be naieve in the second round. This might not work for every experiment
- We need to carefully counterbalance the conditions to avoid unwanted order effects.

Question 5

comparing means

Answer 5

One sample t-tests
- We have one group with values coming from different people. This is compared to a single value.

Question 6

Non directional hypothesis:

Answer 6

• A lot of the time in psych, you will be interested in whether there is any difference between the two things being measured, for this we would use a non directional hypothesis.
  
  • In our sample t-test example thios would be something like H0= sample drawn from populatioon. And-
  • It was hypothesied that there would be a significant difference in MEP’s between performing a mental imagery task and at baseline.

Question 7

directional hypothesis

Answer 7

• For a directional hypothesis, we would use a one tailored test, H1= our sample is drawn from the population., it was hypothesised that MEPS would be sig higher when performing a m=MIT compared to baseline.
  
  • Use a one tale test can be in pos and neg test.
  • Important to first evaLUATE WHETHER THEORETICALLY AN EFFECT IN THE OTHER DIRECTION MAY BE likley, if not use a two tale-tailored test.
    When looking at efficacy of psych treatment is also important to know whether our treatment

Question 8

independent measures t-test

Answer 8

• Two different groups participating in different conditions, and we want to know if there is a change with MEPs. Group A imagines throwing the ball Group B imagine it lying on the floor.
  T-test to test Whether there is a difference between two means that is significant looking for the difference between the two means minus the population statistics (normal ttest)

Question 9

pooled variance

Answer 9

average of the two sample variances, this only works if we have unequal sample sizes, if they are unequal use different approach.

Question 10

degrees of freedom

Answer 10

need to add up the sample size for each group and then subtract two. Two groups estimating the mean for each group, and in both of the groups one value will be constrained and cant vary.

Question 11

to tell the difference between groups.,

Answer 11

If we had hypothesized any difference between the groups, that would bean we are interested in both directions possibly changing so we look in the 0.5. empirical t was smaller than the critical t this time which means no significant result.

Question 12

paired samples t-test

Answer 12

• One group of participants but two measurements from condition A and condition B.
  
  • Interested in examining the difference between two conditions.
    Calculate difference between each participant two scores, and we the calculate sums of squares by using the mean difference. D subscript= for paired samples we are interested in DIFFERENCE of scores not the raw scores.

Question 13

effect size measures

Answer 13

• We also need to work out how significant the effect is.
  
  • Standard error (sm)= standard deviation/ sample size (n)
  • Larger value of estimated standard error in the denominator will create a smaller value for D, this means a large sample variance will make it less likley to obtain a significant effect
  • Larger value of estimated standard error in the denominator will produce a smaller value for t
    This means a large sample size variance sz will make it less likley to obtain a significant effect, also think about how larger variances make it more difficult to see consistent patterns or trends in data.

Question 14

effect size, cohen’s d.

Answer 14

• Cohens d gives an estimate of the effect size this is independent of the sample size: d is the mean difference divided by the standard deviation.
  
  • We can compare these estimated d values, d= 1.325 and d=6.732 to conventional cut-off values
  • D= 0.2 small effect (mean difference around 0.2 standard deviations
  • D=0.5 medium effect (mean difference around 0.5 standard d)
    D=0.8 large effect (mean difference around 0.8 standard deviation)

Question 15

effect size- r2

Answer 15

• Another way to calculate an effect size is to estimate the percentage of variation explained by the treatmentr2= t2/ t2+ df
  
  • We simply use out t-statistic and the df to calculate r2
    r=.01= small effect
    r2= .09= medium
    r2= 0.25= large effect

Question 16

confidence intervals

Answer 16

A confidence interval, a range of values centred around a sample statisitc, the logic behind this is that the sample statistic such as the sample mean should be relatively near to the corresponding population parameter.’
We could set a 95% confidence interval around the sample mean to be sure the population mean was contained in this interval 95% of the time.

Question 17

assumptions before running t-test

Answer 17

• Observations need to be independent, people must not influence other peoples values and other peoples scores.
  
  • The population from which the sample is drawn must be normal (however, this assumption can be violated for larger sample sizes as t-tests are quite robust)
    If comparing two populations (INMT) the samples must have equal variance, if the variances are not homogenous, calculating the pooled variance becomes a problem)