stats review

Question 1

levels of measurement (in order lowest to highest)

Answer 1

nominal, ordinal, interval ratio

Question 2

nominal

Answer 2

numbers or letters assigned to the object serve as labels for identification or classification _→ used to categorize data into categories (lowest)(qualitative)
Basic comparisons: identity
Examples: male/female, user/non user, occupations, uniform numbers
Measures of average: mode

Question 3

ordinal

Answer 3

arranges objects or alternatives according to their magnitude in an ordered relationship. Ordinal only indicates relative size differences between objects. Puts variables into natural order → rating, ranking (qualitative)
Basic comparisons: order
Examples: brand preferences, social class, hardness of minerals, quality of lumber
Measures of average: median, mode

Question 4

interval

Answer 4

arranges objects according to their magnitudes and also distinguishes in units of equal magnitude (quantitative)
Basic comparisons: order
Examples: temperature, grade point average, brand attitude
Measures of average: mean, median, mode

Question 5

ratio

Answer 5

has absolute rather than relative quantities, and an absolute zero where there is the absence of an attitude → periods of time, $ spent, # of items purchased (highest) (quantitative)
Basic comparisons: comparison of absolute magnitudes
Examples: sold, # of purchases, age, income
Measures of average: mean, median, mode

Question 6

central tendency

Answer 6

mean, median, mode, range, standard deviation, variance, z-score

Question 6

mean

Answer 6

the average. Sum of numbers/amount of numbers

Question 7

median

Answer 7

the middle score

Question 8

mode

Answer 8

the most frequent score

Question 9

range

Answer 9

max score – min score. Distance between the smallest and largest values in the set

Question 10

standard deviation

Answer 10

the square root of the largest values in the set of the variation
Deviation scores: the differences between each observation value and the mean → Di = Xi - mean
Average deviation: (not super informative)
Mean squared deviation
★sample standard variation

Question 11

variance

Answer 11

given is squared units → population σ squared, sample s squared
Sample variance

Question 12

z-score

Answer 12

how much a single score is from the mean (in SD units)

Question 13

Variability

Question 14

pearson correlation

Answer 14

is a numeric measure of the strength of the linear association between two continuous variables
- interval level data
-We can also use the pearson r for one dichotomous and one continuous

Question 15

spearman correlation

Answer 15

-coefficient degree of association between two ordinal variables
-ordinal level data

Question 16

t-test

Answer 16

IV: Categorical (2 levels/groups) DV: interval or ratio
-interval level data

Question 17

null hypothesis

Answer 17

no difference between the 2 variables

Question 18

alternate hypothesis

Answer 18

some difference between the 2 variables

Question 19

degrees of freedom

Question 20

type 1 error

Answer 20

-when you falsely reject null hypothesis
-You rejected the null hypothesis.
BUT when other researchers replicated your study, they found no difference between the groups (no significance).
-E.g. False positive covid test result (you don’t actually have covid –
null; but test says you are)

Question 21

type 2 error

Answer 21

-when you falsely don’t reject the null hypothesis
You did not get stat. significance and therefore don’t reject the null.
Other researchers replicate your study and find that there is
significance.
-typically happens due to small sample/small effect sizes
-Bigger sample = powerful telescope
-E.g. False negative covid test result. (you have covid, but test says you
don’t)

Question 22

one way ANOVA

Answer 22

-ANOVA stands for Analysis of Variance.
-When you have one categorical IV with 2 or more levels/groups
-When you have interval/ratio DV
-“One-way” means “one independent variable”
-It’s to compare the means for 3 groups or more.
IV: categorical variable (manipulation) (3 levels or more)
DV: either interval or ratio level variable
-need at least interval level data, ratio also works
-reduces the likelihood of type 1 error

Question 23

chi-squared test

Answer 23

IV: Categorical DV: Categorical
-looking at frequencies

Question 24

factorial ANOVA

Answer 24

When IV is categorical, you can run several tests:
* IV (2 categories: cat vs dog person) & DV (interval/ratio:
extraversion scores) 🡪 t-test
* IV (3 categories: coffee vs tea vs water) & DV (interval/ratio:
exam score) 🡪 1 way ANOVA
* But what if you have 2 IVs?
* If you want to see whether another variable also affects the DV?
Both IVs: Categorical
DV: Interval or ratio
-need at least interval level data, ratio also works

Question 25

main effects

Answer 25

-whether each IV induces any differences in DV (on their own)
-If you have 2 IV, you could have 2 Main Effects.
-2 main effect hypotheses.
-Main effect 1: How ad length (IV1) affects ad recall (DV)
-Main effect 2: How medium (IV2) affects ad recall (DV)

Question 26

interaction effects

Answer 26

-whether both IVs induces any differences in DV (together)
-You will have an interaction effect between IV1 x IV2
-1 Interaction is possible, so 1 interaction hypothesis.
-How does the combined effect of ad length and medium (IV2 *IV2) affect ad recall (DV)