module 5

Question 1

data fishiness assumptions

Answer 1

• assumption of normality
• assumption of homogeneity of variance
• independence of observation

Question 2

assumption of normality general definition

Answer 2

scores on the dependent variable within each group are assumed to be sampled from a normal distribution

Question 3

NHST for evaluating normality general definition

Answer 3

• tests if sample distribution is sig different from normal distribution (same mean and SD)

Question 4

what tests are used for NHST tests for assumption of normality

Answer 4

• shapiro wilkes test
• kolomogorov smirnov test

Question 5

skew and kurtosis definition and cut offs

Answer 5

• skew: asymmetry of distribution (0=normal) for descriptive approach >2
• kurtosis: measure of how heavy/light distribution tails are (heavy=high kurtosis/many outliers, light=low kurtosis/no outliers) for descriptive approach >7
• for both, 1.96 or above is non normal

Question 6

limitations of stat tests of normality

Answer 6

• big difference needed for small samples, small difference for large sample
• non-normality is less of a concern in small samples
• doesnt take type of non normality into account

Question 7

descriptive approach for evaluating normality definition

Answer 7

• looks at descriptives and or graphic displays to quantify magnitude and nature of non-normality

Question 8

____ kurtosis is more problematic than ____ kurtosis in t tests, ANOVAs, correlations, and regressions

Answer 8

positive, negative

Question 9

which approach makes more sense for normality testing; NHST or descriptives

Answer 9

descriptives bc it combines threshold of values and qq plots

Question 10

thin vs fat tails for normality distributions

Answer 10

• thin: fewer extreme observations than normal distributions
• fat: more extreme observations than normal distributions

Question 11

if data is normal, scatterplot should resemble a _____

Answer 11

straight line (as opposed to cloud shape)

Question 12

if the middle of the scatterplot line is straight and the ends flatten, it _____

Answer 12

indicates thin tails and is not problematic

Question 13

if the middle of the scatterplot line is straight and the ends have a steep slope, it _____

Answer 13

indicates fat tails and is problematic

Question 14

assumption of homogeneity of variance definition

Answer 14

variance of scores on dependent variable with in each group (condition) are the same across all groups (conditions)

Question 15

evaluating homo of variance; NHST approach definition

Answer 15

• tests if variances in groups are significantly dif from one another

Question 16

evaluating homo of variance; descriptive approach

Answer 16

• looks only at imperfection
• looks at descriptive stats and or graphic displays to quantify magnitude of differential variances (largest vs smallest SD)
• looks at threshold ratio of largest to smallest variances

Question 17

tests for homo of variance

Answer 17

• levenes tests
• hertley variance ratio test or f-max tests

Question 18

limitations of NHST approach for homo of variance

Answer 18

• role of sample size (dif in variance is less concern for small and more concern for larger sample sizes)
• insensitive to dif in variance in small and sensitive to big
• dif in variance is a magnitude problem

Question 19

if variances are equal, scatterplot should resemble a straight line with a slope of ___ and the intercept is ____ whereas when the variances are not equal, scatterplot will not cluster around the line and will be different from __

Answer 19

1, the difference between means,1

Question 20

independence of observation definition

Answer 20

• each observation (between subjects) or set of observations (repeated measures) from the dataset is independent of all other observations/sets
• ex of independance= roommates/partners

Question 21

positive associations inflate ___ and negative associations inflate ___

Answer 21

alpha, beta

Question 22

evaluating independence of observation

Answer 22

• examine structural properties of data to see if basis exists for questioning validity of assumption
• if no evident basis, its okay to carry on
• thresholds are up for debate
• if basis exists, independence can be assessed by computing interclass correlation for the part of data that is assumed to have lack of independence
• if correlation is very small (<0.10), its fine to use t test/ANOVA

Question 23

address violation for normality

Answer 23

• use alt stat procedures that dont need normality
• evaluate level of measurement assumptions
• identity and remove outliers
• transform data to normalize distribution

Question 24

address violations of homo of variance

Answer 24

• use alt procedures that dont need normality
• evaluate level of measurement assumptions
• identity and remove outliers

Question 25

addressing violations of independence of observations

Answer 25

- alt stat procedures - ex multi level modeling (MLM) or hierarchal linear modeling (HLM)

Question 26

outliers definition

Answer 26

- extreme values that differ largely from other other observations in dataset and suggest theyre drawn from another population

Question 27

examples of common outliers

Answer 27

- data entry/encoding error (less common now, no longer manual data entry) - response latency data (longer response time due to distortion of error, due to distraction etc) - open ended estimate data

Question 28

problems with outliers

Answer 28

- responsible often for violations of homo variance/normality - conceptual validity - disproportionate influence on stat results

Question 29

identifying outliers

Answer 29

- impossible values in frequency tables/histogram - steep tails in normal qq plots - standardized residuals for observations - studentized deleted residuals

Question 30

standardized residuals for observations

Answer 30

- index of deviation from the mean - follows z distribution - normal distributed N=100, 1 value should be >2.6 - normal distributed N=1000, 4 values should be >3.0 - general threshold of 4 or 5 is suggested

Question 31

studentized deleted residuals

Answer 31

- index of deviation from mean (not including target observation in mean and SD calculation) - follows t distribution of df=n-2 - sample of 100, value of >3.6 = outlier - sample of 1000, value of >4.07 = outlier

Question 32

response to outlier

Answer 32

- correct or treat impossible values as missing data - possible but highly discrepant values can be trimmed or capped to most extreme value/specified values - highly discrepant values are treated as missing

Question 33

philosophical issues w outliers

Answer 33

- minimalist perspective: never touch the data, strong rational needed for deletion/alteration of data (due to potential abuse) - maximalist perspective: routine altering/deleting of values, outliers violate assumptions, hard to interpret, must set clear rules/procedures to avoid abuse - intermediate perspective: justifiable w/ clear rules/procedures and high thresholds for outliers

Question 34

levels of measurement

Answer 34

- nominal: # assignment is abt group membership/categorical (ex nationality) - ordinal: # assignment is abt rank order on scale but is not reflective of mag of dif (ex favs, difference between top 1-2 and 4-5 may be different) - interval: # assignment is abt rank order and mag of dif but no ratio (ex C degrees scale, 0 for freezing, 100 for boiling) - ratio: # assignment is abt rank order, mag and ratio dif (ex mass, length)

Question 35

what level of measurement has an absolute meaning ful zero (0) point

Answer 35

ratio

Question 36

before conducting analysis (t test/ANOVA) and descriptive stats, its only meaningful independent variable has at least _______ properties

Answer 36

interval