Interpreting results help

#1
Hi, currently doing my quantitative undergraduate dissertation and using SPSS/statistics is not my strong point. I have just performed an independent t-test to analyse if having a medical condition affects the degree of change in health anxiety between 2018-2021. When looking at the means, there is a slight increase showing it does affect health anxiety, however the t test found the pattern to be not significant. How can I comment on this and how would you finish the write up? Cohen's d showed small effect size.
 

Karabiner

TS Contributor
#2
You report that in your sample a small increase of mean health anxiety was observed;
you can express this in terms of Cohen's d, if you wish (mind that d isn't an effect
size here, but only a characterization of the sample, using a common effect size
measure; true "effect size" refers to populations), or maybe it makes sense to express
this in terms of the original scale (something like "it changed just 0.5 points on a
20-point-scale"). And then you state that "the observed mean change was not
statistically significant (p=...)".

HTH

Karabiner
 
#3
Thanks so much for your help. I'm struggling how to interpret my findings. For example, one of my correlation statements in my write up is

(The variables are change in health anxiety, and resilience)

'There was weak, positive correlation between the two variables, r(47) = .142, n = 49, p = .33; however the relationship was not significant (p = .332)'

As well as speaking about the effect size, how would I interpret what I have found if the relationship was weak and not significant?
 

Karabiner

TS Contributor
#4
o variables, r(47) = .142, n = 49, p = .33; however the relationship was not significant (p = .332)'

As well as speaking about the effect size, how would I interpret what I have found if the relationship was weak and not significant?
I don't know why "however" is used here. Or why you state the same fact twice (p=.33 and p=.332; why the difference, by the way?).

You have NOT FOUND a weak relationship between the variables. What you have at hand is just some sample sample
data, and these show an association larger or smaller than the true association, that in the population . Now you use
your sample to infer statements about a population. The inference is: may I assume that H0 is true (H0: "there is
absolutely no association in the population (r=0), and the r=0.14 coefficient in my sample is completely due to
chance), or may I reject that H0. The emprical evidence based on r=0.14 & n=49 was not enough to reject H0,.

With kind regards

Karabiner
 
#5
Thankyou so much. I've removed the however I don't know why I included that either! My work is only undergraduate level so we have to describe the relationship between the variables.