Stats tests but better explained Flashcards
stats tests
(test for differences)
Nominal data for a repeated measures study:
Chi square
(non-parametric)
Stats tests
Test of Correlation
Nominal Data
No
Stats tests
Test of Differences
Ordinal data for a Independent measures design:
Mann Whitney U
(Non-parametric)
Stats tests
Test for Differences
Ordinal data for Repeated measures design:
Wilcoxon T
(Non-parametric)
Stats tests
Test of Correlation
Ordinal data
Spearman’s Rho
(Non-parametric)
Stats test
Test of Correlation
Interval/ Ratio data:
Pearson’s R
(Parametric)
Stats test
Level of Significance + how to read Critical Value table
in psych
ALWAYS to p<0.05
as this means its 95% likley its significant
and 5% chance the result was due to chance
the location of p<0.05 will be different depending on whether the study has a one or two tailed hypothesis
so always check this
once the correct p<0.05 collum has been identified
the row can be located by counting from the top downwards
the number of participents in the study
once the row has been located
cross between the identifed row and collum to obtain the Critical Value
now you have the observed value from the stats test and the critical value from the table
you can identify whether the test was significant
if the Observed Value is Higher/lower (depending on the test) than the Critical Value
figuring out whether it should be higher or lower can be seen be locating p<0.01 which will be either higher or lower than p<0.05
if it is higher then higher is significant and vice versa
Stats Test
Practical + stat test for each topic
Cog
Lab study to test effects of location on recall
Quant
Man Whitney U Wilcoxon T
Learning
Observation of prosocial behaviour gender diff
Quant + Qual
Chi square Thematic Analysis
Bio
Correlation of sleep and aggression
Quant
Spearman’s Rho
Social
Questionaire, Gender diff in Attitudes of Obedience and Authority figs
Quant + Qual
Measures of Central tendency, Dispersion
Standard deviation
Stats test
Levels of Measurment
Nominal data
Nominal data is categorised data
which is mutually exclusive
As the data can’t be in both frequency talies in those categories
e.g. Students who go to King eds or Dudley
Freq cant be in both categorys
so mutualy exclusive
but also still categorys and freq
Stats test
Levels of Measurment
Ordinal data
Ordinal data is when data is ranked to make it ordinal
e g best to worst
but you don’t know anything about the intervals between these rankings
it doesn’t say how close the ranks are
for example
ranking your knowledge of language level
all you know is that Advanced > intermediate
but have no scale to how much it is more so
dont know how close the ranks are
Stats test
Levels of Measurment
Interval data
Interval data
is data ranked in equal intervals
E.G.
temperature in Celsius
there is no true zero in this type of data
meaning therefore there can be - values
e.g. -5°C
but unlike ordinal data
we know what the intervals between the ranks are
e.g we know 5°C is higher than 1°C
and we know that it is higher by 4°C
the intervals between ranks are always equal
1C 2C 3C
not 1C 2C 2.5C 3C
Stats test
Levels of Measurment
Ratio data
Ratio data
is data that is ranked in equal intervals
similar to interval data
however is a true zero in this version for example
age
height
weight
or temperature in kelvins
as cannot be - values in these
we know intervals between ranks
+ intervals are the same length
