STATS WAVIER

0.0(0)
studied byStudied by 0 people
learnLearn
examPractice Test
spaced repetitionSpaced Repetition
heart puzzleMatch
flashcardsFlashcards
Card Sorting

1/46

encourage image

There's no tags or description

Looks like no tags are added yet.

Study Analytics
Name
Mastery
Learn
Test
Matching
Spaced

No study sessions yet.

47 Terms

1
New cards

Independent Variable

Thing you change or group people by

2
New cards

Dependent Variable

Outcome you’re measuring

3
New cards

Types/Levels of Measurement

  • Nominal

  • Ordinal

  • Interval

  • Ratio

4
New cards

Nominal

names and categories (no order)

examples: nike, adidas

5
New cards

Ordinal

ordered but uneven spacing

examples: rankings 1st, 2nd, and 3rd

6
New cards

Interval

equal gaps and no true 0

examples: temperature in F

7
New cards

Ratio

interval and true 0

examples: time spent and income

8
New cards

Descriptive stats

mean, median, mode

9
New cards

Mean

average

10
New cards

Median

middle value

11
New cards

Mode

most frequently seen value

12
New cards

Standard Deviation

how far data is spread

13
New cards

Normal Curve

bell shaped and most scores fall near average

14
New cards

Chi Square Test

use when you have 2 categorical variables

15
New cards

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

16
New cards

Paired T-Test

same group at 2 time points

(pre/post tests)

17
New cards

Independent Samples T-Test

2 separate groups

18
New cards

T-Test SPSS output

  • means for each group/time

  • sig (2 tailed) → if p<.05 difference is significant

19
New cards

ANOVA

analysis of variance

3 or more groups

20
New cards

ANOVA SPSS output

  • look for F value

  • sig (p<.05 = groups are different)

21
New cards

Pearsons Correlation

tests relationships between 2 scale variables to determine strength and association

22
New cards

r=0

no correlation

23
New cards

r>0

positive correlation

24
New cards

r<0

negative correlation

25
New cards

Regression

predict outcome using 1 or more variables

26
New cards

Linear regression

1 predictor

27
New cards

Multiple regression

2+ predictors

28
New cards

Regression SPSS output

R2 = % of variation explained

B= slope (direction and strength)

sig = if predictor matters

29
New cards

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?

30
New cards

Good Use for stats

  • support fair policy (access and justice)

  • identify health disparities

31
New cards

Harmful use for stats

  • cherrypicking data

  • ignore underrepresented groups

  • misrepresent who benefits

32
New cards

Chi Square IV and DV

IV = nominal categories

DV= nominal categories

33
New cards

Independent T-Test IV and DV

IV= nominal (2 group)

DV= scale (interval or ratio)

34
New cards

Paired T-Test IV and DV

IV= time 1 vs time 2 (same group)

DV= scale (interval or ratio)

35
New cards

ANOVA IV and DV

IV= nominal (3+ groups)

DV= scale (interval or ratio)

36
New cards

Correlation IV and DV

BOTH= scale (interval or ratio)

37
New cards

Regression IV and DV

BOTH= scale (interval or ratio)

38
New cards

EPSEM

equal probability of selection method (same chance of being selected)

39
New cards

descriptive stats

summarize, organize, and describe

40
New cards

descriptive stats categorical

nominal and ordinal → counts, percentages, mode

41
New cards

descriptive stats continuous

interval and ratio → mean, median, mode, SD, min/max, range, variance

42
New cards

measures of association

are 2 variables related/how strong?

43
New cards

Cross tabulation

table shows relationship between 2 or more categorical variables

44
New cards

Linear Regression

predict 1 outcome 1 variable

45
New cards

Multiple regression

predict 1 outcome using multiple variables

46
New cards

How to use regression equation to predict DV

simple linear y=b0 + b1X

47
New cards

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)