Final STATS NOTES Flashcards
Aim of a systematic review?
Summarise the findings of ALL studies that address a very specific research question and meet clear inclusion criteria (e.g. must be RCT, >1 mth). And, if possible, quantify the average effect or finding.
Identify gaps or under-researched areas in the literature (e.g. there maybe a shortage of trials in a particular age group, ethnicity, or sport).
Types of systematic reviews?
A qualitative synthesis of the studies: these are quite descriptive. They report the number or proportion of studies that report a statistically significant difference – p<0.05 (systematic review)
A quantitative synthesis of the studies. An ‘average’ intervention effect is calculated from all of the intervention effects from the included studies (systematic review and/with meta-analysis)
Description of a Meta Analysis?
The general aim of a meta-analysis is to more powerfully estimate the ‘true’ intervention effect size as opposed to a less precise effect size derived in a single study.
To produce an ‘average’ intervention effect the units need to be same across all studies, or standardised:
[e.g. Diff of Means divided by Pooled SD].
If there is high heterogeneity (wide range) of effect sizes (e.g. some zero effect, some very positive) then producing a single summary estimate is not appropriate (low I2 value = low heterogeneity = effects were similar across studies). This is a score on the forest plot as well (0% is very low heterogeneity,
Notes about a forest plot for a meta analysis?
Is in chronological order In terms of publication date - done in this order as pilot studies normally show more effects than in real world
Square indicates relative risk of each study. Relative risk shows the percentage increase or decrease the study had
The wings represent the 95%CI, if wings cross the line of ‘no effect’ (relative risk ratio=1) then that study did not find a statistically significant effect.
Relative risk found by (the percentage of those who met the criteria who had the intervention) / the percentage of those who met the criteria who didn’t have the intervention)
Bigger studies will have a greater weighting when determining the ‘average intervention effect’. Instead of each having equal weighting represented by a larger rectangle
The kite/diamond shape at the bottom represents the ‘weighted average’ intervention effect and 95%CI. (the amount 1 represents how much % it increases it by) - and if it crosses the line then it is not significantly different (the size of the diamond is its confidence intervals) - can read of the p value (if lower than 0.05) to check significance as well
What differences could come up if you did a quantitative synthesis/ meta-analysis, compared to a qualitative synthesis?
If looked at studies qualitatively, could end up saying all the studies aren’t significant, but when you look at them qualitatively as a collective they actually make the result significantly
Standard criteria for assessing quality/ risk of bias in forest plots?
e.g. Newcastle-Ottawa risk of bias scale:
0-3 = very high risk
4-6 = high risk
7-9 = low risk
Or can do a Meta regression for high heterogeneity
How to test if you data is normally distributed?
Look to see if curve is bell curved - not finite
Skewness (how the curve leans) should be between -1 and 1
Kurtois = (the peakedness or flatness of a distribution) , Kurtois should be between -1 and 2
Can still go ahead and analyse if values are just outside of boundaries
Is under descriptive statistics in SPSS, you look at the statistic values in the skewness and kurtois
Independent samples t tests?
Independent groups: Different people in each sample mean
Looks at differences between 2 different groups
Dependent / paired samples t test?
One group the sample people in each sample mean (except twin studies)
Look at differences within the same group in different conditions/ time-points (before/after)
Type 1 error:
You reject the null hypothesis - when the null hypothesis is true (you say there is a difference when there isn’t)
Due to random sampling
Type 1 error:
You reject the null hypothesis - when the null hypothesis is true (you say there is a difference when there isn’t)
Due to random sampling
Type 2 error:
You fail to reject the null hypothesis when the null hypothesis is false (you say there isn’t a difference when there is)
Due to random sampling
Easy way to remember is boy who cried wolf did a type 1 error then a type 2
Assumptions about the data that need to be satisfied for the results of a t-test to be reliable?
Data is continuous and is normally distributed (eg.height weight, strength, glucose)
Both groups are a random sample of a population (if testing whether males are taller than females, don’t take female basketball players and male jockeys)
Homogenity of variance - both groups have similar variances or standard deviations (can visualise this if the distribution curve is far more spread out than the other) - if the assumption is violated in SPSS it offers a an alternative corrected result
What do ANOVAS do?
Compare 3 or more means
Type of ANOVA depends on:
The number of independent variables, 1 IV = one-way, 2 IVs = two-way and so on
The type of independent variables:
Is it independent samples = groups
Or repeated measures = conditions or time-points
Theory of independent sample ANOVA and how to work out F ratio?
The F-ratio = Between group variance / Within group variance
Between group variance = distance between group means (of the outcome variable) and the overall sample mean. (This is due to differences in the independent variable or experimental manipulation)
Within group variance = Distance between each participant and the mean of the group they belong to
