Variance = average of the squared differences from the mean
Standard deviation = square root of the variance
Standard error (large samples) = SD/square root of the sample
Confidence intervals = confidence that the sample intervals represent the mean of the population
Confidence interval of 95% = 95% certain the mean would fall here again & if there is no overlap
across groups = suggests a statistically significant difference (but still complete a stat test)
Central Limit Theorem = as the sample gets larger, the average begins to look more like a normal
distribution
Z score = each data point has a Z score that explains how many SDs it is away from the mean
Any normal distribution curve can have its data points turned into Z scores to allow for comparison
across different population means/SDs to examine how likely a data point is
Data point equal to the mean has a Z score of 0
1 SD away = Z score of 1, 2 SDs away = Z score of 2 etc.
13/11/2020 PY1IPR – lecture 6 t-tests
T-test - comparing 2 samples to determine if they are derived from the same population
T value - single calculated value that summarises if the 2 samples are from the same population
When conducting an experiment & using a smaller sample - has narrower & fatter tails than a normal
distribution so does not reflect the population - t distributions are adjusted for this underestimation
Difference between group means ÷ difference within groups = t value
Difference within groups AKA Standard error of the difference (variability) or measurement error & is
affected by SD & N
Smaller the t value – more likely the independent groups were from the same sample
The t value - measure of difference expressed in variability
E.g. t value of 2.5 means the difference between the groups is 2.5X higher than within the groups
Having more variance between groups than within groups = significant
T value as similar to the Z score as a standard unit
, The effect size
Measure of the magnitude of the difference between groups
SPSS output does not include effect size as there are many different formulas
Cohen’s d = difference between two groups weighted by the pooled SD of the samples
Repeated measures T-test
T value & df calculated differently when same Ps take part in both levels of the IV
Can detect small effects on the IV
T value calculated within Ps = makes error term (t value denominator) smaller so increasing power of
test vs independent groups T-test including individual differences
25/11/2020 PY1IPR – lecture 8 correlations
Covariance (+ve or -ve) AKA how much the covariables change together
Ps scoring above/below the mean in both variables make a +/- contribution to the covariance
Ps scoring above & below the mean in each variable make a negative contribution to the covariance
26/01/2021 PY1IPR – Lecture 3 qualitative research
Positivism epistemology – true knowledge can only be gained via objective science
Interpretivism epistemology –only understand something in the context of its perception
Objectivism ontology – phenomena exists outside of social factors
Constructivism ontology – phenomena are continuously created by perceptions of social actors
Inductive approach – starts with the data & works up to generate a theory
Deductive approach – starts from the theory & tests hypotheses
Analysis techniques – content analysis, (categorising) thematic analysis, (topical) discourse analysis,
(linguistic) grounded theory (keep collecting theme till a theory is derived) & interpretative
phenomenology analysis (understanding the experiences of a sub group)
03/02/2021 PY1IPR – Lecture 4 content analysis
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