PY2501.6 ~ {Assignment; Data Screening and normalisation}
when we have our results what do we assume
data is valid and participants have read every item on our sutvey and thouggth about their answer + responded to experimental stimuli as requested + given appropriate responses with few errors
what indictators do we look at to know our data is valid
how long participants spen on the experiment
how accurate are their responses
validity
rthe extent to which a measurement, test, or study accurately measures or reflects the conecept is intended to assess. it is crucial because it determines whether the conclusions drawn from the study are sound and meaningful
why does validity matters
accuarte measurement - ensures results reflect the true relationships in the data
replicability - strengthens credibility
policy and practice - ensures decisions are based on reliable evidence
exclusion criteria
specific charateristics that disqualify individuals from participating in a study. used to ensure that study pop is homogennous and results are cofounded by factors that could skew the data
key purposes of exclusion criteria
enhance safety
improve internal validity
ensure homogenous group
compliance with ethical guidlines
coomon types of exlusion criteria
[severe] health + psych / cog conditions
age limitations
medication + substance use
pregnancy
inability to follow protocol
getting rid of outliers
multiply IQR by 1.5 add / subtract that to the median and get rid of everything above that score
2 indicators of participant non-engagement for experimental tasks
suspiciously short RTs
accuracy around choice levels [if there are 2 responses theres a 50% chance they get the answer right]
RT outliers
Whelan (2008) suggests that RTs of less than 200ms sre unlikely to be valid [less than this means a participant wouldn't have time to process the stimulus + give response]
what is an outlier generally
unusually large or small scores [data points that differ significantly from the rest of the dataset, can distort stats analysis] is 1.5 x IQR above + below the median
pivot tables
found in excel, they help you quickly organize and manipulate large datasets, making it easier to draw insights or perform complex calculations without altering the original data
transformation of variables
applying mathematical functions to data to make data more suitable for analysis, improve model performance, or meet specific assumptions required by statistical techniques
why do we transform variables
handling outliers + normal distribution + stanilizing variance + linearising variance + improving interpretability
transforming experimental data
need to make the same transformation to all data in each condition or group so they're comparable on terms of diff
what kind if transformations to use when
depends on how data is skewed
square/cube root + logarithmic (if there are no negative numbers) bht you can add a
constant to make them positive power
box cox transformation
jamovi doesnt do boxcox
can normalise data more efficiently than root, log or power transformations (you can only use positive lambda values)
what is an outlier generally
unusually large or small scores [data points that differ significantly from the rest of the dataset, can disstort stats analysis] is 1.5 x IQR above + below the median
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