Me bz..stil struggling with my SPSS thingy..
Just a short note as a reminder:
- always do TEST OF NORMALITY to check ur data distribution..normal or not normally distributed..this is important, it will help u to determine the type of test..either parametric test or non-parametric test..there's a few item to look at..Shapiro-Wilk (for less than 100 sample) or Kolmogorov-Smirnov (for more than 100 sample), not to forget Q-Q Normal Plot and Boxplot.
- For datas which are normally distributed...
- compare mean with reference/known value - simple student's T-test
- compare means between 2(only) independent group/sample ie: gender (male & female), diet (vege & non vege) - Independent T-test
- compare means between 2 dependent group/sample ie: before and 2 repetitive after value/readings/figure/results- Dependent T-test
- compare means between 3 or more group/sample ie: age range (10-15, 16-20, 21-25, 26-30..total 4 groups) - ANOVA (Analysis of variance) <--there are many types of ANOVA depending on the number of factors involved, one way or 2 way etc..gotta do more reading on this!! and Post-hoc test is needed if there is significant value..just to differentiate which groups/sample is actually giving significant result and which doesn't. Quite a number of post-hoc test..choose one that is suitable..regarding ur cases/sample.
- useful links for ANOVA:
WHAT IF THE DATA IS NOT NORMALLY DISTRIBUTED??
That would be even easier!! We opt for non-parametric test..instead of comparing means..we compare median instead..so do your descriptive analysis before hand..
- for 2 different/independent group/sample - Mann Whitney Test
- for more than 2 group/sample - Kruskal Wallis Test
- for 2 dependent group/sample - Wilcoxon Test
How about correlation??
Normal distribution - Pearson Correlation
Not normally distributed - Spearman Correlation
P/S: Parametric tests are way better than the non-parametric..so if ur data is not normally distributed...just log(log/Ln) them...