PSYC 2001 Lecture Notes - Lecture 20: Longitudinal Study, Dependent And Independent Variables, Scatter Plot
10 Dec 2017
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PSYC2001 Lec 20
Re-Cap Non-Experimental Design
§ Correlational
§ Two continuous variables
§ Both measured at interval or ratio type (NOT categorical or ordinal)
§ Scatterplot – visually = positive, negative or zero
§ Statistical - is the trend/association different from zero and is the difference
bigger than chance?
§Correlation is represented by r
Issues that Impact Correlations
§ Outliers
§ Extreme scores or combinations of scores
§ Pull esults toads the
§ More problematic with small samples
§ Ex. Someone making 10,000$ a year at 35, vs 19
§ Restriction of range
§ Lack of variability in responses
§ Results from a correlation can be incorrect or misleading when the measure has
restriction of range issues.
§ Ceiling effect: Mainly high scores
§ Floor effect: Mainly low scores
Recap—Experimental Designs
§ We discussed our simplest experimental design
§ 2 variables
§ 1 independent (grouping variable)
§ 1 dependent variable (outcome)
§ “tatistial uestio is ae the eas aeages between the groups different
more than can be expected by chance?
§ When only 2 groups = t-test
§ When 3 or more groups = ANOVA
Inferential Statistics
Statistical Tests
§ Provide a statistical result (r, t, F) and a p-value
§ Remember:
§ We are conducting analyses on our sample
§ Our question is about the population….
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§ This is called inference (using data from the sample to infer something
about the population) and thus the term Inferential Statistics
§ The p-value is the probability of getting the result by chance.
§ When p < .05 (less than), small chance that we got result by hae…. Therefore,
olude sigifiat i.e., we found something)
§ Whe p ≥ .5 geate tha o eual to, thee is a high (reasonable) chance that
the result is by chance (fluke)
§ …. Theefoe, olude o-sigifiat (i.e., we found nothing)
§ The p-value is (very) connected with the size of the sample
§ Type I error
§ We conclude there is an effect in our population (i.e. significant result) when
there is NOT
§ Type II error
§ We conclude there is NO effect in our population (i.e. non-significant result)
when there is
Importance
§ Statistical significance
§ Effect size
§ Importance
§just because a finding is significant/non-significant does not mean it is or is not
important.
§ Fo eaple… fidig that the commitment level to a treatment for anxiety results in an
improvement
§ Result r = .04, p = .02, this size of a change is unlikely to be noticeable in a
peso’s life
§ Eaple … fidig that a ediatio does ot hage the life epeta fo a rare
type of cancer
§ Result r = .26, p = .32, non-significant – but there is an effect (not enough people
to hae sigifiat…. “aig lies is ipotat
Really Bad Experiments
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find more resources at oneclass.com
Document Summary
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