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6030 - Introduction to Educational Research > Chapter 9 > Flashcards

Flashcards in Chapter 9 Deck (33)
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1

Inferential Statistics

Statistical procedures designed to determine if differences and relationships found in sample data are sufficiently large that they can be assumed to be true at the population level

2

Degree of Uncertainty

All inferences from samples to populations involve a degree of uncertainty. The amount of uncertainty is determined by a number of factors, including sampling error and measurement error.

3

Measurement Error

This is differences in estimates of a population parameter based upon different testings of the same sample using the same instrument. These differences are the result of unreliability in our measurement instrument.

4

Sampling Error

This is the differences in estimates of a population parameter based upon different samples drawn from the same population. If the samples were randomly drawn and all of the same size, the amount of error (at a certain probability level) can be calculated.

5

Null Hypothesis

There is no difference between the groups (or the difference is in the unpredicted direction)

6

Alternative Hypothesis

There is a difference between the groups (or the difference is in the predicted direction)

7

level of significance

also known as a level

8

If the _____________ would have generated the sample statistic less often by chance than the a level, the researcher rejects the ______ and affirms the alternative.

null hypothesis, null

9

Type I Error

Rejecting the null when it is in fact true

10

Type II Error

Failing to reject the null when it is in fact false

11

Difference between group means

larger differences make it easier to achieve significance, all other things being equal

12

Sample size (N)

larger sample sizes reduce sampling error and make it easier to achieve significance, all other things being equal

13

Reliability of the instruments

more reliable instruments reduce measurement error, and make it easier to achieve significance, all other things being equal

14

Confidence Intervals

An interval within which we believe the true population parameter falls, at a given level of confidence (usually 95% or 99%)

15

Effect Size

Statistical significance answers the question, “Are the results likely to be due to sampling error?”

16

Sample size does not play a role in ______________.

effect size

17

Cohen’s d =

Mean One – Mean Two/Standard Deviation (Either Pooled or for Control)

18

Coefficient of Determination =

r squared (for correlations)

19

Eta Squared =

Sum of Squared Deviations Between/Sum of Squared Deviations Total

20

Parametric Statistics

Inferential statistical tests that make a number of assumptions about the data (normality, equality of group variances, and interval/ratio level of measurement)

21

Non-Parametric Statistics

Inferential statistical tests that make relatively few assumption about the data

22

Parametric statistics

are more “powerful” than non-parametric statistics; that is, they are more likely to reject the null hypothesis when it is in fact false

23

The t-test

Tests for differences between the means of two groups.

24

Independent samples t-test

Used to test differences in means of two different groups of subjects

25

Paired dependent samples t-test

Used to test differences in means of two groups of subjects who are either the same or matched on one or more variables

26

T-test for correlations

Tests whether a sample correlation is large enough to indicate a relationship different from zero in the population

27

Simple Analysis of Variance (One-Way ANOVA)

Tests for differences in the means of three or more groups representing levels of a single IV factor

28

Factorial Analysis of Variance (N-way ANOVA)

Allows testing of multiple IVs, each of which may have many levels.

Allows determination of the significance of each IV

Allows determination of the significance of changes in an IV as a function of changes in other IVs

29

Univariate

techniques have a single DV

30

Multivariate techniques

have multiple DVs
account for the correlations among the DVs (which would not happen if you ran multiple univariate tests)