Stats

Question 1

Counterbalancing

Answer 1

Reduce Carryover effects

Latin Square

Question 2

Practice/Testing Effects

Answer 2

Solomon Four-Group Design

Question 3

Internal Validity

Answer 3

intervention causes changes in DV

Question 4

Construct Validity

Answer 4

other factors in the intervention not intended to cause changes do

Question 5

External Validity

Answer 5

interfere with generalizability

Question 6

Reactivity

Answer 6

Hawthorne Effect
bx change b/c observed
External Validity

Question 7

Rosenthal Effect

Answer 7

Construct Validity;

self-fulfilling prophecy

Question 8

Statistical Conclusion Validity

Answer 8

low power (small sample size, poor interventions), unreliable measures, procedure variability, subject heterogeneity

Question 9

Mean and SD Math

Answer 9

add or subtract from scores (Mean changes, SD does not)

multiple or divide (both change)

Question 10

Z-Score

Answer 10

Z = (score - mean)/SD

Question 11

Standard Error of the Mean

Answer 11

average amount of deviation between sample means

SDpop/square root of N

can be used to see if a treatment mean was far enough away to suggest it was the treatment that cause the difference and not just sampling error

Question 12

Type I Error

Answer 12

reject null incorrectly
found a difference when there isn’t really one

Alpha = probability of Type I Error
direct with Power
indirect with Beta

Question 13

Type II Error

Answer 13

accept null incorrectly
found no difference but there really is one

Beta = probability of Type II Error

Question 14

Power

Answer 14

reject null correctly
found a difference and there is one

Power = 1 - Beta

1. Large sample size
2. Magnitude of intervention is large
3. Random error is small
4. Parametric Stats
5. One-Tailed Test

Question 15

F ratios

Answer 15

1. 0 no significance

2. 0 + significant

Question 16

Trend Analysis

Answer 16

analyzes non-linear data

obtained from quantitative IV

Question 17

Nominal Data Tests

Answer 17

Test of Difference:
Chi Square
McNemar - correlated

Correlation/Association:
Phi

Question 18

Ordinal Data Tests

Answer 18

Test of Difference:
1 Ind Gp - Kolmogorov
2 Ind Gp - Mann-Whitney, Median, K-S
3 Ind Gp - Kruskall Wallis

2 Cor Gp - Wilcoxon
3 Cor Gp - Friedman

Correlation/Association:
Spearman or Kendall rank correlation

Question 19

Interval/Ratio Data Tests

Answer 19

Tests of Difference:
1 Ind Gp - t-test single sample
2 Ind Gp - t-test ind samples
3 Ind Gp - 1-way ANOVA

2 Cor Gp - t-test matched samples
3 Cor Gp - 1-way repeated measures ANOVA

2 IVs:
both Ind - 2-way or factorial ANOVA

1 Ind 1 Cor - mixed or split-plot ANOVA

2 Cor - repeated measures factorial ANOVA

Correlation/Association:
Pearson R

Question 20

Coefficient of Determination

Answer 20

correlation coefficient squared

amount of variability in Y that is shared with, explained by, or accounted for by X

Question 21

Assumptions of Bivariates

Answer 21

1. Linear Relationship
2. Homoscedasticity
3. Unrestricted Range

Question 22

Eta

Answer 22

correlation b/w X & Y is curvilinear

Question 23

Correlation Tests

Answer 23

X and Y interval/ratio - Pearson r
X and Y ordinal - Spearman’s Rho or Kendall’s Tau
X and Y true dichotomy - Phi
X and Y artificial dichotomy - Tetrachoric

X i/r Y true dichotomy -Point-Biserial
X i/r Y artificial - Biserial

XY Curviliniar - Eta

Question 24

Types of Correlations

Answer 24

Zero-Order - no 3rd

Partial/First Order - 3rd removed from both

Part/Semipartial - 3rd removed from one

Question 25

Moderator Variable

Answer 25

affects the strength of the relationship strength changes based on moderator

Question 26

Mediator Variable

Answer 26

explains why there is a relationship; when controlled for there is no relationship

Question 27

Multivariate Tests

Answer 27

several predictors/IV (X) and 1+ criterions/DV (Y)

Question 28

Multiple R

Answer 28

2+ Xs and 1 Y Y is interval/ratio 1X is interval/ratio

Question 29

Multiple Regression

Answer 29

predict Y based on Xs best when low correlation b/w X's and mod to high b/w each X and Y compensatory technique

Question 30

Multicollinearity

Answer 30

predictors (X's) are highly correlated thus redundant

Question 31

Stepwise vs. Hierarchiacal

Answer 31

Stepwise - all computer, fwd or bkwd (add/take away until change) - find fewest X's Hierarchical - person chooses adding order based on theory

Question 32

Canonical R and Analysis

Answer 32

2+ of each variable Analysis - prediction test

Question 33

Discriminant Function Analysis

Answer 33

2x/1y but y is nominal

Question 34

Loglinear (logit) Analysis

Answer 34

2x/1y all nominal

Question 35

Correlational Causality

Answer 35

Path Analysis and Structural Equation Modeling (LISREL)

Question 36

Tests of Structure

Answer 36

which variables in a set fit best together or form independent subtests Factor Analysis Cluster Analysis

Question 37

Increase Reliability

Answer 37

- more items - homogeneity of items - unrestricted range - less ability to guess

Question 38

Reliability Types

Answer 38

1. Test-Retest 2. Parallel Forms 3. Internal Consistency Split-Half Kuder-Richardson and Cronbach's Coefficient Alpha 4. Interrater (Kappa)

Question 39

Spearman-Brown Formula

Answer 39

used to predict how much more reliability if more questions (used for split-half)

Question 40

Validity

Answer 40

Content - (content area) Criterion - infer, predict, estimate outcome Concurrent Predictive Construct - trait or construct (Convergent [monotrait, heteromethod] and Divergent/Discriminant [heterotrait, monomethod] validity)

Question 41

Incremental Validity

Answer 41

amount of improvement when using a predictor test best when mod base rate and low selection ratio