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EPPP C1: Statistics and Research Design > Statistics: Inferential Stats Concepts and Terms > Flashcards

Statistics: Inferential Stats Concepts and Terms Flashcards

1
Q

Inferential Statistics: overview

A

Descriptive stats = summarize data

Inferential Stats = make inferences about a population based on sample drawn from a population

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2
Q

Central Limit Theorem

A

Distribution approaches a normal curve as sample size increases

The mean of the sampling distribution = pop mean

SD of distribution = Standard Error of the Mean

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3
Q

Type I Error (α)

A

Rejection of a true null hypothesis

Research erroneously shows significant effects

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4
Q

Type II Error (β)

A

Retain a false null hypothesis

Research misses actual significant effects

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5
Q

Power (1-β)

A

Likelihood of rejecting false null hypothesis

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6
Q

Parametric v Nonparametric Tests:

Measurement Scales

A

Parametric Tests: Interval or Ratio Scales

Non-Parametric Tests: Nominal or Ordinal Scales

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7
Q

Parametric v Nonparametric Tests:

Commonalities and Differences

A

Both assume random selection and independent observations

Parametric tests (e.g. t-test, ANOVA) evaluate hypotheses about population means, variances, or other parameters.

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8
Q

Parametric Tests:

Assumptions

A

Normal Distribution

Homoscedasticity

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9
Q

Homoscedasticity

A

Assumption that variances of populations that groups represent are relatively equal

[For studies with more than one group]

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10
Q

One-way ANOVA vs Factorial ANOVA vs MANOVA

A

One-way ANOVA: ONE IV, ONE DV

Factorial ANOVA, two-way = 2 IV’s, three-way = 3 IVs

MANOVA: used whenever there is more than one dv
(MULTIvariate analysis)

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11
Q

Effect Size:

What is it?

Name two types

A

Measure of the practical or clinical significance of statistically significant results

Cohen’s d

Eta squared (η²)

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12
Q

Cohen’s d

A

Effect size in terms of SD (d = 1.0 = 1SD change)

Small effect size = 0.2
medium effect size = 0.5
large effect size = 0.8

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13
Q

Eta squared (η²)

A

Effect size in terms of variance accounted for by treatment

*Variance = σ², so think squared greek letter = variance

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14
Q

Bivariate correlation assumptions

A

Linearity

Unrestricted range of scores on both variables

Homoscedasticity

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15
Q

Bivariate correlation “language” (X, Y)

A

X = predictor variable

Y = criterion variable

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16
Q

Simple Regression Analysis

A

Allows predictions to be made with:

One predictor (X) 
One criterion (Y)
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17
Q

F ratio calculation

A

MSB/MSW

Mean square between divided by mean square within

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18
Q

F ratio range

A

F is always greater than +1

Larger F ratio = increased likelihood of stat significance

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19
Q

Statistical Power definition

A

Degree to which a statistical test is likely to reject a false null hypothesis (1-β)

Reject false null = show statistical significance

20
Q

Ways to Increase Statistical Power

A

Increase alpha from .01 to .05

Increase sample size

Increase the effects of the IV

Minimize error

Use one-tailed test when appropriate

Use parametric test

21
Q

Effects of increasing alpha from .01 to .05

A

Greater likelihood of rejecting null hypothesis

*Greater likelihood Type I error

22
Q

Effects of decreasing alpha from .05 to .01

A

Decreased statistical power

However, increased confidence that statistically significant results are correct

23
Q

Nonparametric tests and data distribution

A

Nonparametric only evaluates hypotheses about Shape of distribution

NOT distribution’s mean, variance, or other parameter

24
Q

Two factors that determine critical value for statistical significance

A

alpha (e.g. .05)

degrees of freedom

25
Q

Regression analysis: assumptions

A

Linear relationship between X and Y

regression line = “line of best fit”

26
Q

Regression Analysis: coefficient range

A

-1.0 to +1.0

It’s a correlational technique

27
Q

Multiple regression

A

two or more continuous or discrete predictors (X)

one criterion (Y)

28
Q

Multicollinearity

A

High correlation between two or more predictors

Makes it difficult to interpret regression coefficients
if correlated, how to know which X accounts for change in Y?

29
Q

Forward Stepwise Regression

A

One predictor is added in each subsequent analysis

30
Q

Backward Stepwise Regression

A

Analysis begins with all predictors

One predictor is eliminated in each subsequent analysis

31
Q

When to use Multiple Regression instead of ANOVA

A

when groups are unequal in size

when IV’s are measured on a continuous scale

32
Q

Multiple Regression: factors that cause most Shrinkage

A

small original sample

large number of predictors

*result of cross-validation

33
Q

Structural Equation Modeling (SEM)

A

Multivariate techniques

Evaluates the causal (predicted) influences of multiple latent factors

aka “causal modeling”

34
Q

Structural Equation Modeling: 2 techniques

A

Path Analysis

LISREL

35
Q

SEM: Path Analysis

A

Causal relationships among variables represented in path diagram

Coefficients indicate direction and strength of relationship between pairs of variables

Only recursive (one way)

36
Q

SEM: LISREL

Linear Structural Relations Analysis

A

LISREL includes:

recursive (one way) paths
nonrecursive (two way) paths

latent traits
measurement error

37
Q

Multivariate Techniques for Data Reduction

A

Factor Analysis

Cluster Analysis

38
Q

Multivariate Data reduction: Factor analysis

A

Reduces larger number of variables to a small number of factors

Factors explain inter-correlations between variables

*e.g. to develop subscales for tests

39
Q

Multivariate Data reduction: Cluster Analysis

A

Used to identify, define, confirm the nature and number of subgroups (clusters)

40
Q

ANCOVA: main use

A

Removes variability due to extraneous variable

41
Q

Interval recording/Event sampling

A

Measuring presence or absence of behavior during discrete intervals of a set period of time, or during an event

42
Q

Trend Analysis

A

Analysis of Variance

Quantitative IV

Assesses linear and nonlinear (e.g. quadratic) trends

43
Q

How to compensate for violation of homogeneity of variances

A

Decreasing alpha

Having equal-sized groups

44
Q

Best way to increase External Validity

A

Randomly select participants from target population

45
Q

Survival analysis

A

Used to evaluate the length of time to critical event

e.g. relapse, promotion

46
Q

Multiple Regression Analysis:

Weighting of predictors

A

Predictor is weighted in:

direct proportion to criterion

inverse proportion to other predictors

47
Q

Standard Error of the Mean calculation

A

σ/√N

EPPP C1: Statistics and Research Design (4 decks)

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