Tests 666 Ctw

Front 1

Elevated F Score:

Back 1

65+ = random responses, deliberate malingering, eccentric or all t, all f

(tells validity)

Front 2

Low/High L Score:

Back 2

Low: frank or insightful, or exaggerate badness
High: Denial, lying, or lack of insight

tells you if valid.

Front 3

K Scale: (correction) High/Low

Back 3

High K: defensive or fake good, associated with poor prognosis

Low K: frank, self-critical, fake bad.
"Suppressor variable" corresponds

with education, SES, defensiveness

Front 4

? scale: High

Back 4

high ? scale suggests readng problems, indecisive, distractible, rebellious, defensive.

Front 5

True Experimental vs. Quasi Experimental:

Back 5

Experimental you are controlling assignment of subjects to tx groups, QUASI-EXPERIMENTAL you are not. (ie, cluster sampling?)

Front 6

Random Assignment vs. Random Selection

Back 6

Random assignment: allows you to be sure IV caused DV, random selection allows you to generalize this outward. Random assignment is true experimental.

Front 7

5 Ways to control extraneous (confounding) variables:

Back 7

Confoundign variable: irrelevant to purpose of study, but has an effect (ie, level of depression)
1. Random assignment to tx group
2. Hold it constant- (ie, use all mildly depressed patients)
3. Match subjects on the variable (ie, one mild in each group, one moderate in each group)
4. "Blocking" build it in- lump and match, rather than individual match
5. Statistical Control (ANCOVA)

Front 8

ANCOVA:

Back 8

Allows you to remove variability of DV due to the confounding variable.
Equalizes all subjects on the variable, good in quasi-experimental.

Comparing a bunch of samples, want to get out confound (ie, looking at school lunches on health, but confound is how much exercise)

(an-CONFOUND-ova)

Front 9

Interrater Reliability

Back 9

Kappa Statistic

Front 10

Split-Half Reliability

Back 10

Use: Spearman-Brown Prophecy (once youve done part)

Front 11

Internal Consistency Reliability:

Back 11

Use Coefficient alpha, or if dichotomous, use Kuder-Richardson 20 (KR20)

(internal consistency reliability not good on speed tests: speed is the opposite of power)

Front 12

Stability

Back 12

AKA Test-Retest Reliability

Front 13

Standard Error of Estimate:

Back 13

Think Regression Line

Front 14

Standard Error of Measurement:

Back 14

To construct Confidence Interval around a score.

Front 15

Convergent & Discriminant Validity

Back 15

Convergent: Does it match other
Forms of construct validity

ideas of measurement- ie, other constructs of "depression", ie, is it not confusing depression with anxiety?

Divergent: Does it NOT match other things that are NOT depression

USE: MTMM Matrix

Front 16

MTMH Matrices

Back 16

For Convergent/discriminant

Front 17

Monotrait Monomethod

Back 17

Reliability Coefficients

Front 18

Monotrait-Heteromethod

Back 18

Correlation between different measures of same trait: (ie, the bdi and the phq-9) so high = convergent

Front 19

Hetertrait-Monomethod

Back 19

Correlation between different traits and the measure- ie, if low it has discriminant validity

Front 20

Heterotrait-Heteromethod

Back 20

correlation between different traits that have been measured by differed methods- also show discriminant validity if small.

Front 21

Factor Analysis: Why?

Back 21

Find minimum number of common factors to account for intercorrelations in set of tests.
To find construct validity, convergent and discriminant validity.

Front 22

Factor Analysis: How

Back 22

1. Administer a bunch of tests
2. Correlate scores on each test with every other one to create a matrix ("R")
3. Using magic, convert correlation matrix to factor matrix
4. Simplify the factors by "rotating" them"
5. Interpret and name the factors

Front 23

Factor loadings

Back 23

The correlation coefficients on a factor matrix - ie, the correlation coefficient of test A and the factor. (ie, test A's measure of assertiveness)

Front 24

Communality:

Back 24

The "common variance" in factor matrix: ie, the column after the tests and the two factors that shows the common variability of the two factors. ie, 64% variance is explained by combo of factor I and II.

Front 25

orthogonal rotation:

Back 25

Resulting factors (in factor analysis matrix) are uncorrelated. Used if you think it WONT be related.

Front 26

Oblique rotation

Back 26

Resulting factors ARE correlated, and attributes are NOT independent. Used if you think it will be related.

Front 27

Hetero vs. homoscedacity

Back 27

How much regression line clustering starts to change shape- ie, does it start to widen/ turn into a "v" or tornado shape?

Front 28

Reactivity / Reactive arrangements

Back 28

When participants change behavior because they are being observed- figure it out due to demand characteristics.

Front 29

Regression line: (correlation)

Back 29

Where the clustering happens, and you get a correlation between -1 and 1. (then you take that number and square it to get %variability accounted for by the IV.)

Front 30

Counterbalanced design:

Back 30

different subjects get different IVs in different order to to get rid of order and carryover effects.

Front 31

Factorial Design:

Back 31

2+ IVs. Analyze each IV and their interaction. (ie, cbt, medication and their combined power, or mild, moderate and medication)

Front 32

Aurocorrelation

Back 32

A confound- screws you up because people will correlate pre and post test on measurements, and increasing type 1 errors.

Front 33

Ordinal Scale:

Back 33

Ranks, numbers, more/less, likert scale, but dont tell us HOW MUC difference between. (ie, how different is a 10 and 8 from a 2 and 4 on likert)
Central tendency is mode or median

Front 34

Interval:

Back 34

Like ordinal, but intervals are set- ie, a WISC has same amount of difference between 80 and 85 as between 115 and 120. So can be added/subtracted.
Standardized scores.
Central tendency is M, M, or M.

Front 35

Ratio Scale

Back 35

Has an absolute 0: ie, 100$ is twice 50$. (reaction time, # correct on test, frequency of a behavior). Can be multiplied/divided.