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Independent Variable
Thing you change or group people by
Dependent Variable
Outcome you’re measuring
Types/Levels of Measurement
Nominal
Ordinal
Interval
Ratio
Nominal
names and categories (no order)
examples: nike, adidas
Ordinal
ordered but uneven spacing
examples: rankings 1st, 2nd, and 3rd
Interval
equal gaps and no true 0
examples: temperature in F
Ratio
interval and true 0
examples: time spent and income
Descriptive stats
mean, median, mode
Mean
average
Median
middle value
Mode
most frequently seen value
Standard Deviation
how far data is spread
Normal Curve
bell shaped and most scores fall near average
Chi Square Test
use when you have 2 categorical variables
Chi Square Test SPSS output
look at p value
if p<.05 variables are significantly related
use cramers v to measure strength 0 to 1
Paired T-Test
same group at 2 time points
(pre/post tests)
Independent Samples T-Test
2 separate groups
T-Test SPSS output
means for each group/time
sig (2 tailed) → if p<.05 difference is significant
ANOVA
analysis of variance
3 or more groups
ANOVA SPSS output
look for F value
sig (p<.05 = groups are different)
Pearsons Correlation
tests relationships between 2 scale variables to determine strength and association
r=0
no correlation
r>0
positive correlation
r<0
negative correlation
Regression
predict outcome using 1 or more variables
Linear regression
1 predictor
Multiple regression
2+ predictors
Regression SPSS output
R2 = % of variation explained
B= slope (direction and strength)
sig = if predictor matters
Ethical Questions
Are authors being transparent
Who benefits from data
Who is harmed by the data
Could date be manipulated to support one group and erase another?
Good Use for stats
support fair policy (access and justice)
identify health disparities
Harmful use for stats
cherrypicking data
ignore underrepresented groups
misrepresent who benefits
Chi Square IV and DV
IV = nominal categories
DV= nominal categories
Independent T-Test IV and DV
IV= nominal (2 group)
DV= scale (interval or ratio)
Paired T-Test IV and DV
IV= time 1 vs time 2 (same group)
DV= scale (interval or ratio)
ANOVA IV and DV
IV= nominal (3+ groups)
DV= scale (interval or ratio)
Correlation IV and DV
BOTH= scale (interval or ratio)
Regression IV and DV
BOTH= scale (interval or ratio)
EPSEM
equal probability of selection method (same chance of being selected)
descriptive stats
summarize, organize, and describe
descriptive stats categorical
nominal and ordinal → counts, percentages, mode
descriptive stats continuous
interval and ratio → mean, median, mode, SD, min/max, range, variance
measures of association
are 2 variables related/how strong?
Cross tabulation
table shows relationship between 2 or more categorical variables
Linear Regression
predict 1 outcome 1 variable
Multiple regression
predict 1 outcome using multiple variables
How to use regression equation to predict DV
simple linear y=b0 + b1X
Cramers V/Phi
Categorical
use - both variables nominal (2 or more categories)
Phi → use both variables binary (2 options each) (ranges 0 no association to 1 perfect association)